diff --git a/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb b/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb
index e2eca4841..72448bc45 100644
--- a/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb
+++ b/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb
@@ -2905,7 +2905,7 @@
     "\n",
     "Let's figure it out.\n",
     "\n",
-    "First, we'll import the `car-sales-extended.csv` dataset."
+    "First, we'll import the [`car-sales-extended.csv`](https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/data/car-sales-extended.csv) dataset."
    ]
   },
   {
@@ -3059,7 +3059,8 @@
    ],
    "source": [
     "# Import car-sales-extended.csv\n",
-    "car_sales = pd.read_csv(\"../data/car-sales-extended.csv\")\n",
+    "# car_sales = pd.read_csv(\"../data/car-sales-extended.csv\") # load data from local directory \n",
+    "car_sales = pd.read_csv(\"https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales-extended.csv\") # load data directly from raw URL (source: https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/data/car-sales-extended.csv)\n",
     "car_sales"
    ]
   },
@@ -3136,14 +3137,14 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/c4/qj4gdk190td18bqvjjh0p3p00000gn/T/ipykernel_30502/1044518071.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Try to predict with random forest on price column (doesn't work)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensemble\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1148\u001b[0m                 skip_parameter_validation=(\n\u001b[1;32m   1149\u001b[0m                     \u001b[0mprefer_skip_nested_validation\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1150\u001b[0m                 )\n\u001b[1;32m   1151\u001b[0m             ):\n\u001b[0;32m-> 1152\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    344\u001b[0m         \"\"\"\n\u001b[1;32m    345\u001b[0m         \u001b[0;31m# Validate or convert input data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sparse multilabel-indicator for y is not supported.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 348\u001b[0;31m         X, y = self._validate_data(\n\u001b[0m\u001b[1;32m    349\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m         )\n\u001b[1;32m    351\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    618\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"estimator\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcheck_y_params\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m                     \u001b[0mcheck_y_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdefault_check_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    621\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 622\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    623\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcheck_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ensure_2d\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1142\u001b[0m         raise ValueError(\n\u001b[1;32m   1143\u001b[0m             \u001b[0;34mf\"{estimator_name} requires y to be passed, but the target y is None\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1144\u001b[0m         )\n\u001b[1;32m   1145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1146\u001b[0;31m     X = check_array(\n\u001b[0m\u001b[1;32m   1147\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1148\u001b[0m         \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m         \u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    912\u001b[0m                         )\n\u001b[1;32m    913\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_with_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m                 raise ValueError(\n\u001b[1;32m    918\u001b[0m                     \u001b[0;34m\"Complex data not supported\\n{}\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m                 ) from complex_warning\n",
-      "\u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/_array_api.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[1;32m    376\u001b[0m         \u001b[0;31m# Use NumPy API to support order\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 380\u001b[0;31m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    381\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m         \u001b[0;31m# At this point array is a NumPy ndarray. We convert it to an array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m         \u001b[0;31m# container that is consistent with the input's namespace.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   2082\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2083\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2084\u001b[0;31m         \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2085\u001b[0m         if (\n\u001b[1;32m   2086\u001b[0m             \u001b[0mastype_is_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2087\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/var/folders/c4/qj4gdk190td18bqvjjh0p3p00000gn/T/ipykernel_23180/1044518071.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Try to predict with random forest on price column (doesn't work)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensemble\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1469\u001b[0m                 skip_parameter_validation=(\n\u001b[1;32m   1470\u001b[0m                     \u001b[0mprefer_skip_nested_validation\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1471\u001b[0m                 )\n\u001b[1;32m   1472\u001b[0m             ):\n\u001b[0;32m-> 1473\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    359\u001b[0m         \u001b[0;31m# Validate or convert input data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sparse multilabel-indicator for y is not supported.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m         X, y = self._validate_data(\n\u001b[0m\u001b[1;32m    364\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m             \u001b[0mmulti_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    646\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"estimator\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcheck_y_params\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m                     \u001b[0mcheck_y_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdefault_check_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    648\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    649\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 650\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    651\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    652\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    653\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcheck_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ensure_2d\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1297\u001b[0m         raise ValueError(\n\u001b[1;32m   1298\u001b[0m             \u001b[0;34mf\"{estimator_name} requires y to be passed, but the target y is None\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1299\u001b[0m         )\n\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1301\u001b[0;31m     X = check_array(\n\u001b[0m\u001b[1;32m   1302\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1303\u001b[0m         \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\u001b[0m         \u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m   1009\u001b[0m                         )\n\u001b[1;32m   1010\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1011\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_with_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1014\u001b[0m                 raise ValueError(\n\u001b[1;32m   1015\u001b[0m                     \u001b[0;34m\"Complex data not supported\\n{}\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m                 ) from complex_warning\n",
+      "\u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/utils/_array_api.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(array, dtype, order, copy, xp, device)\u001b[0m\n\u001b[1;32m    747\u001b[0m         \u001b[0;31m# Use NumPy API to support order\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    750\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 751\u001b[0;31m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    752\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m         \u001b[0;31m# At this point array is a NumPy ndarray. We convert it to an array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    754\u001b[0m         \u001b[0;31m# container that is consistent with the input's namespace.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, dtype, copy)\u001b[0m\n\u001b[1;32m   2149\u001b[0m     def __array__(\n\u001b[1;32m   2150\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool_t\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2151\u001b[0m     ) -> np.ndarray:\n\u001b[1;32m   2152\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2153\u001b[0;31m         \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2154\u001b[0m         if (\n\u001b[1;32m   2155\u001b[0m             \u001b[0mastype_is_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2156\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Honda'"
      ]
     }
@@ -3161,13 +3162,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Oops... this doesn't work, we'll have to convert the non-numerical features into numbers first.\n",
+    "Oh no! We get a another `ValueError` (some of data is in string format rather than numerical format).\n",
+    "\n",
+    "```\n",
+    "ValueError: could not convert string to float: 'Honda'\n",
+    "```\n",
+    "\n",
+    "Machine learning models prefer to work with numbers than text. \n",
+    "\n",
+    "So we'll have to convert the non-numerical features into numbers first.\n",
     "\n",
     "The process of turning categorical features into numbers is often referred to as **encoding**.\n",
     "\n",
     "Scikit-Learn has a fantastic in-depth guide on [*Encoding categorical features*](https://scikit-learn.org/stable/modules/preprocessing.html#encoding-categorical-features).\n",
     "\n",
-    "But let's look at one of the most straightforward ways to turn categorical features into numbers, [one-hot encoding](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html).\n",
+    "But let's look at one of the most straightforward ways to turn categorical features into numbers, one-hot encoding.\n",
     "\n",
     "In machine learning, [one-hot encoding](https://en.wikipedia.org/wiki/One-hot#Machine_learning_and_statistics) gives a value of `1` to the target value and a value of `0` to the other values.\n",
     "\n",
@@ -4123,12 +4132,12 @@
     "\n",
     "> **Note:** Dealing with missing values differs from problem to problem, meaning there's no 100% best way to fill missing values across datasets and problem types. It will often take careful experimentation and practice to figure out the best way to deal with missing values in your own datasets.\n",
     "\n",
-    "To practice dealing with missing values, let's import a version of the `car_sales` dataset with several missing values."
+    "To practice dealing with missing values, let's import a version of the `car_sales` dataset with several missing values (namely [`car-sales-extended-missing-data.csv`](https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/data/car-sales-extended-missing-data.csv))."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -4201,91 +4210,82 @@
        "      <td>14043.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
+       "      <th>5</th>\n",
+       "      <td>Honda</td>\n",
+       "      <td>Red</td>\n",
+       "      <td>42652.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>23883.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>995</th>\n",
+       "      <th>6</th>\n",
        "      <td>Toyota</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>35820.0</td>\n",
+       "      <td>Blue</td>\n",
+       "      <td>163453.0</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>32042.0</td>\n",
+       "      <td>8473.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>996</th>\n",
-       "      <td>NaN</td>\n",
+       "      <th>7</th>\n",
+       "      <td>Honda</td>\n",
        "      <td>White</td>\n",
-       "      <td>155144.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5716.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>997</th>\n",
-       "      <td>Nissan</td>\n",
-       "      <td>Blue</td>\n",
-       "      <td>66604.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>31570.0</td>\n",
+       "      <td>20306.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>998</th>\n",
-       "      <td>Honda</td>\n",
+       "      <th>8</th>\n",
+       "      <td>NaN</td>\n",
        "      <td>White</td>\n",
-       "      <td>215883.0</td>\n",
+       "      <td>130538.0</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>4001.0</td>\n",
+       "      <td>9374.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>999</th>\n",
-       "      <td>Toyota</td>\n",
+       "      <th>9</th>\n",
+       "      <td>Honda</td>\n",
        "      <td>Blue</td>\n",
-       "      <td>248360.0</td>\n",
+       "      <td>51029.0</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>12732.0</td>\n",
+       "      <td>26683.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1000 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "       Make Colour  Odometer (KM)  Doors    Price\n",
-       "0     Honda  White        35431.0    4.0  15323.0\n",
-       "1       BMW   Blue       192714.0    5.0  19943.0\n",
-       "2     Honda  White        84714.0    4.0  28343.0\n",
-       "3    Toyota  White       154365.0    4.0  13434.0\n",
-       "4    Nissan   Blue       181577.0    3.0  14043.0\n",
-       "..      ...    ...            ...    ...      ...\n",
-       "995  Toyota  Black        35820.0    4.0  32042.0\n",
-       "996     NaN  White       155144.0    3.0   5716.0\n",
-       "997  Nissan   Blue        66604.0    4.0  31570.0\n",
-       "998   Honda  White       215883.0    4.0   4001.0\n",
-       "999  Toyota   Blue       248360.0    4.0  12732.0\n",
-       "\n",
-       "[1000 rows x 5 columns]"
+       "     Make Colour  Odometer (KM)  Doors    Price\n",
+       "0   Honda  White        35431.0    4.0  15323.0\n",
+       "1     BMW   Blue       192714.0    5.0  19943.0\n",
+       "2   Honda  White        84714.0    4.0  28343.0\n",
+       "3  Toyota  White       154365.0    4.0  13434.0\n",
+       "4  Nissan   Blue       181577.0    3.0  14043.0\n",
+       "5   Honda    Red        42652.0    4.0  23883.0\n",
+       "6  Toyota   Blue       163453.0    4.0   8473.0\n",
+       "7   Honda  White            NaN    4.0  20306.0\n",
+       "8     NaN  White       130538.0    4.0   9374.0\n",
+       "9   Honda   Blue        51029.0    4.0  26683.0"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Import car sales dataframe with missing values\n",
-    "car_sales_missing = pd.read_csv(\"../data/car-sales-extended-missing-data.csv\")\n",
-    "car_sales_missing"
+    "# car_sales_missing = pd.read_csv(\"../data/car-sales-extended-missing-data.csv\") # load from local directory\n",
+    "car_sales_missing = pd.read_csv(\"https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales-extended-missing-data.csv\") # read directly from URL (source: https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/data/car-sales-extended-missing-data.csv)\n",
+    "car_sales_missing.head(10)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you're dataset is large, it's likely you aren't going to go through it sample by sample to find the missing values.\n",
+    "Notice the `NaN` value in row 7 for the `Odometer (KM)` column, that means pandas has detected a missing value there.\n",
+    "\n",
+    "However, if you're dataset is large, it's likely you aren't going to go through it sample by sample to find the missing values.\n",
     "\n",
     "Luckily, pandas has a method called [`pd.DataFrame.isna()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isna.html) which is able to detect missing values.\n",
     "\n",
@@ -4294,7 +4294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -4308,7 +4308,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4329,7 +4329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -4353,7 +4353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -4379,7 +4379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -4400,7 +4400,7 @@
        "        0.00000e+00, 2.48360e+05]])"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4434,25 +4434,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "ename": "ValueError",
-     "evalue": "Input X contains NaN.\nRandomForestRegressor does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values",
+     "evalue": "Input y contains NaN.",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m/Users/daniel/code/zero-to-mastery-ml/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb Cell 96\u001b[0m line \u001b[0;36m8\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/daniel/code/zero-to-mastery-ml/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb#Y164sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39m# Fit and score a model\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/daniel/code/zero-to-mastery-ml/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb#Y164sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m model \u001b[39m=\u001b[39m RandomForestRegressor()\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/daniel/code/zero-to-mastery-ml/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb#Y164sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m model\u001b[39m.\u001b[39;49mfit(X_train, y_train)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/daniel/code/zero-to-mastery-ml/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb#Y164sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m model\u001b[39m.\u001b[39mscore(X_test, y_test)\n",
-      "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py:1152\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1145\u001b[0m     estimator\u001b[39m.\u001b[39m_validate_params()\n\u001b[1;32m   1147\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[1;32m   1148\u001b[0m     skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[1;32m   1149\u001b[0m         prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1150\u001b[0m     )\n\u001b[1;32m   1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0m     \u001b[39mreturn\u001b[39;00m fit_method(estimator, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
-      "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:348\u001b[0m, in \u001b[0;36mBaseForest.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    346\u001b[0m \u001b[39mif\u001b[39;00m issparse(y):\n\u001b[1;32m    347\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39msparse multilabel-indicator for y is not supported.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 348\u001b[0m X, y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[1;32m    349\u001b[0m     X, y, multi_output\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, accept_sparse\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcsc\u001b[39;49m\u001b[39m\"\u001b[39;49m, dtype\u001b[39m=\u001b[39;49mDTYPE\n\u001b[1;32m    350\u001b[0m )\n\u001b[1;32m    351\u001b[0m \u001b[39mif\u001b[39;00m sample_weight \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    352\u001b[0m     sample_weight \u001b[39m=\u001b[39m _check_sample_weight(sample_weight, X)\n",
-      "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py:622\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    620\u001b[0m         y \u001b[39m=\u001b[39m check_array(y, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_y_params)\n\u001b[1;32m    621\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 622\u001b[0m         X, y \u001b[39m=\u001b[39m check_X_y(X, y, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcheck_params)\n\u001b[1;32m    623\u001b[0m     out \u001b[39m=\u001b[39m X, y\n\u001b[1;32m    625\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m check_params\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mensure_2d\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mTrue\u001b[39;00m):\n",
-      "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:1146\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1141\u001b[0m         estimator_name \u001b[39m=\u001b[39m _check_estimator_name(estimator)\n\u001b[1;32m   1142\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m   1143\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mestimator_name\u001b[39m}\u001b[39;00m\u001b[39m requires y to be passed, but the target y is None\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1144\u001b[0m     )\n\u001b[0;32m-> 1146\u001b[0m X \u001b[39m=\u001b[39m check_array(\n\u001b[1;32m   1147\u001b[0m     X,\n\u001b[1;32m   1148\u001b[0m     accept_sparse\u001b[39m=\u001b[39;49maccept_sparse,\n\u001b[1;32m   1149\u001b[0m     accept_large_sparse\u001b[39m=\u001b[39;49maccept_large_sparse,\n\u001b[1;32m   1150\u001b[0m     dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[1;32m   1151\u001b[0m     order\u001b[39m=\u001b[39;49morder,\n\u001b[1;32m   1152\u001b[0m     copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[1;32m   1153\u001b[0m     force_all_finite\u001b[39m=\u001b[39;49mforce_all_finite,\n\u001b[1;32m   1154\u001b[0m     ensure_2d\u001b[39m=\u001b[39;49mensure_2d,\n\u001b[1;32m   1155\u001b[0m     allow_nd\u001b[39m=\u001b[39;49mallow_nd,\n\u001b[1;32m   1156\u001b[0m     ensure_min_samples\u001b[39m=\u001b[39;49mensure_min_samples,\n\u001b[1;32m   1157\u001b[0m     ensure_min_features\u001b[39m=\u001b[39;49mensure_min_features,\n\u001b[1;32m   1158\u001b[0m     estimator\u001b[39m=\u001b[39;49mestimator,\n\u001b[1;32m   1159\u001b[0m     input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mX\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m   1160\u001b[0m )\n\u001b[1;32m   1162\u001b[0m y \u001b[39m=\u001b[39m _check_y(y, multi_output\u001b[39m=\u001b[39mmulti_output, y_numeric\u001b[39m=\u001b[39my_numeric, estimator\u001b[39m=\u001b[39mestimator)\n\u001b[1;32m   1164\u001b[0m check_consistent_length(X, y)\n",
-      "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:957\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    951\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    952\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mFound array with dim \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m. \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m expected <= 2.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    953\u001b[0m             \u001b[39m%\u001b[39m (array\u001b[39m.\u001b[39mndim, estimator_name)\n\u001b[1;32m    954\u001b[0m         )\n\u001b[1;32m    956\u001b[0m     \u001b[39mif\u001b[39;00m force_all_finite:\n\u001b[0;32m--> 957\u001b[0m         _assert_all_finite(\n\u001b[1;32m    958\u001b[0m             array,\n\u001b[1;32m    959\u001b[0m             input_name\u001b[39m=\u001b[39;49minput_name,\n\u001b[1;32m    960\u001b[0m             estimator_name\u001b[39m=\u001b[39;49mestimator_name,\n\u001b[1;32m    961\u001b[0m             allow_nan\u001b[39m=\u001b[39;49mforce_all_finite \u001b[39m==\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39mallow-nan\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    962\u001b[0m         )\n\u001b[1;32m    964\u001b[0m \u001b[39mif\u001b[39;00m ensure_min_samples \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m    965\u001b[0m     n_samples \u001b[39m=\u001b[39m _num_samples(array)\n",
-      "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:122\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[39mif\u001b[39;00m first_pass_isfinite:\n\u001b[1;32m    120\u001b[0m     \u001b[39mreturn\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m _assert_all_finite_element_wise(\n\u001b[1;32m    123\u001b[0m     X,\n\u001b[1;32m    124\u001b[0m     xp\u001b[39m=\u001b[39;49mxp,\n\u001b[1;32m    125\u001b[0m     allow_nan\u001b[39m=\u001b[39;49mallow_nan,\n\u001b[1;32m    126\u001b[0m     msg_dtype\u001b[39m=\u001b[39;49mmsg_dtype,\n\u001b[1;32m    127\u001b[0m     estimator_name\u001b[39m=\u001b[39;49mestimator_name,\n\u001b[1;32m    128\u001b[0m     input_name\u001b[39m=\u001b[39;49minput_name,\n\u001b[1;32m    129\u001b[0m )\n",
-      "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:171\u001b[0m, in \u001b[0;36m_assert_all_finite_element_wise\u001b[0;34m(X, xp, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[39mif\u001b[39;00m estimator_name \u001b[39mand\u001b[39;00m input_name \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mX\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mand\u001b[39;00m has_nan_error:\n\u001b[1;32m    155\u001b[0m     \u001b[39m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m    156\u001b[0m     \u001b[39m# scikit-learn.\u001b[39;00m\n\u001b[1;32m    157\u001b[0m     msg_err \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m (\n\u001b[1;32m    158\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{\u001b[39;00mestimator_name\u001b[39m}\u001b[39;00m\u001b[39m does not accept missing values\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    159\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    169\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m#estimators-that-handle-nan-values\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    170\u001b[0m     )\n\u001b[0;32m--> 171\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(msg_err)\n",
-      "\u001b[0;31mValueError\u001b[0m: Input X contains NaN.\nRandomForestRegressor does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values"
+      "Cell \u001b[0;32mIn[52], line 8\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Fit and score a model\u001b[39;00m\n\u001b[1;32m      7\u001b[0m model \u001b[38;5;241m=\u001b[39m RandomForestRegressor()\n\u001b[0;32m----> 8\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m model\u001b[38;5;241m.\u001b[39mscore(X_test, y_test)\n",
+      "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1471\u001b[0m     )\n\u001b[1;32m   1472\u001b[0m ):\n\u001b[0;32m-> 1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/ensemble/_forest.py:363\u001b[0m, in \u001b[0;36mBaseForest.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(y):\n\u001b[1;32m    361\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msparse multilabel-indicator for y is not supported.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    364\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    365\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    366\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    367\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    368\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[38;5;66;03m# _compute_missing_values_in_feature_mask checks if X has missing values and\u001b[39;00m\n\u001b[1;32m    372\u001b[0m \u001b[38;5;66;03m# will raise an error if the underlying tree base estimator can't handle missing\u001b[39;00m\n\u001b[1;32m    373\u001b[0m \u001b[38;5;66;03m# values. Only the criterion is required to determine if the tree supports\u001b[39;00m\n\u001b[1;32m    374\u001b[0m \u001b[38;5;66;03m# missing values.\u001b[39;00m\n\u001b[1;32m    375\u001b[0m estimator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimator)(criterion\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcriterion)\n",
+      "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/base.py:650\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    648\u001b[0m         y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[1;32m    649\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 650\u001b[0m         X, y \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_X_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    651\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m    653\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n",
+      "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/utils/validation.py:1318\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1297\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1298\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires y to be passed, but the target y is None\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1299\u001b[0m     )\n\u001b[1;32m   1301\u001b[0m X \u001b[38;5;241m=\u001b[39m check_array(\n\u001b[1;32m   1302\u001b[0m     X,\n\u001b[1;32m   1303\u001b[0m     accept_sparse\u001b[38;5;241m=\u001b[39maccept_sparse,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1315\u001b[0m     input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1316\u001b[0m )\n\u001b[0;32m-> 1318\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_check_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmulti_output\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_numeric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my_numeric\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1320\u001b[0m check_consistent_length(X, y)\n\u001b[1;32m   1322\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m X, y\n",
+      "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/utils/validation.py:1328\u001b[0m, in \u001b[0;36m_check_y\u001b[0;34m(y, multi_output, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1326\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Isolated part of check_X_y dedicated to y validation\"\"\"\u001b[39;00m\n\u001b[1;32m   1327\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m multi_output:\n\u001b[0;32m-> 1328\u001b[0m     y \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1329\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1330\u001b[0m \u001b[43m        \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1331\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1332\u001b[0m \u001b[43m        \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1333\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1334\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43my\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1335\u001b[0m \u001b[43m        \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1336\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1337\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1338\u001b[0m     estimator_name \u001b[38;5;241m=\u001b[39m _check_estimator_name(estimator)\n",
+      "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/utils/validation.py:1064\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m   1058\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1059\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1060\u001b[0m         \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[1;32m   1061\u001b[0m     )\n\u001b[1;32m   1063\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[0;32m-> 1064\u001b[0m     \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1065\u001b[0m \u001b[43m        \u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1066\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1067\u001b[0m \u001b[43m        \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1068\u001b[0m \u001b[43m        \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1069\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1071\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy:\n\u001b[1;32m   1072\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m _is_numpy_namespace(xp):\n\u001b[1;32m   1073\u001b[0m         \u001b[38;5;66;03m# only make a copy if `array` and `array_orig` may share memory`\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/utils/validation.py:123\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    120\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m first_pass_isfinite:\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m--> 123\u001b[0m \u001b[43m_assert_all_finite_element_wise\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    124\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    125\u001b[0m \u001b[43m    \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    126\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nan\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    127\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmsg_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmsg_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    128\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    129\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    130\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/ai/lib/python3.11/site-packages/sklearn/utils/validation.py:172\u001b[0m, in \u001b[0;36m_assert_all_finite_element_wise\u001b[0;34m(X, xp, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    155\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[1;32m    156\u001b[0m     \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m    157\u001b[0m     \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[1;32m    158\u001b[0m     msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    159\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    160\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    171\u001b[0m     )\n\u001b[0;32m--> 172\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n",
+      "\u001b[0;31mValueError\u001b[0m: Input y contains NaN."
      ]
     }
    ],
@@ -4472,9 +4473,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ahh... dam! Looks like the model we're trying to use doesn't work with missing values.\n",
+    "Ahh... dam! Another `ValueError` (our input data contains missing values).\n",
+    "\n",
+    "`ValueError: Input y contains NaN.`\n",
     "\n",
-    "When we try to fit it on a dataset with missing samples, Scikit-Learn produces the error:\n",
+    "Looks like the model we're trying to use doesn't work with missing values.\n",
+    "\n",
+    "When we try to fit it on a dataset with missing samples, Scikit-Learn produces an error similar to:\n",
     "\n",
     "`ValueError: Input X contains NaN. RandomForestRegressor does not accept missing values encoded as NaN natively...`\n",
     "\n",
@@ -4504,7 +4509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -4518,7 +4523,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4562,6 +4567,7 @@
     "| `Odometer (KM)` | mean of `Odometer (KM)` | \n",
     "| `Price` (target) | NA, remove samples missing `Price` |\n",
     "\n",
+    "\n",
     "> **Note:** The practice of filling missing data with given or calculated values is called [**imputation**](https://scikit-learn.org/stable/modules/impute.html). And it's important to remember there's no perfect way to fill missing data (unless it's with data that should've actually been there in the first place). The methods we're using are only one of many. The techniques you use will depend heavily on your dataset. A good place to look would be searching for \"data imputation techniques\".\n",
     "\n",
     "Let's start with the `Make` column.\n",
@@ -4571,12 +4577,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Fill the missing values in the Make column\n",
-    "car_sales_missing[\"Make\"].fillna(value=\"missing\", inplace=True)"
+    "# Note: In previous versions of pandas, inplace=True was possible, however this will be changed in a future version, can use reassignment instead.\n",
+    "# car_sales_missing[\"Make\"].fillna(value=\"missing\", inplace=True)\n",
+    "\n",
+    "car_sales_missing[\"Make\"] = car_sales_missing[\"Make\"].fillna(value=\"missing\")"
    ]
   },
   {
@@ -4588,12 +4597,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Note: In previous versions of pandas, inplace=True was possible, however this will be changed in a future version, can use reassignment instead.\n",
+    "# car_sales_missing[\"Colour\"].fillna(value=\"missing\", inplace=True)\n",
+    "\n",
     "# Fill the Colour column\n",
-    "car_sales_missing[\"Colour\"].fillna(value=\"missing\", inplace=True)"
+    "car_sales_missing[\"Colour\"] = car_sales_missing[\"Colour\"].fillna(value=\"missing\")"
    ]
   },
   {
@@ -4605,7 +4617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -4619,7 +4631,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4639,7 +4651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -4652,7 +4664,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4664,12 +4676,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Fill the Doors column with the most common value\n",
-    "car_sales_missing[\"Doors\"].fillna(value=4, inplace=True)"
+    "car_sales_missing[\"Doors\"] = car_sales_missing[\"Doors\"].fillna(value=4)"
    ]
   },
   {
@@ -4681,12 +4693,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Fill the Odometer (KM) column\n",
-    "car_sales_missing[\"Odometer (KM)\"].fillna(value=car_sales_missing[\"Odometer (KM)\"].mean(), inplace=True)"
+    "# Old: car_sales_missing[\"Odometer (KM)\"].fillna(value=car_sales_missing[\"Odometer (KM)\"].mean(), inplace=True)\n",
+    "\n",
+    "car_sales_missing[\"Odometer (KM)\"] = car_sales_missing[\"Odometer (KM)\"].fillna(value=car_sales_missing[\"Odometer (KM)\"].mean())"
    ]
   },
   {
@@ -4698,7 +4712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -4712,7 +4726,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4730,12 +4744,14 @@
     "\n",
     "Finally, we can remove the rows which are missing the target value `Price`.\n",
     "\n",
-    "> **Note:** Another option would be to impute the `Price` value with the mean or median or some other calculated value (such as by using similar cars to estimate the price), however, to keep things simple and prevent introducing too many fake labels to the data, we'll remove the samples missing a `Price` value. "
+    "> **Note:** Another option would be to impute the `Price` value with the mean or median or some other calculated value (such as by using similar cars to estimate the price), however, to keep things simple and prevent introducing too many fake labels to the data, we'll remove the samples missing a `Price` value. \n",
+    "\n",
+    "We can remove rows with missing values in place from a pandas DataFrame with the [`pandas.DataFrame.dropna(inplace=True)`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html) method."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4752,7 +4768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -4766,7 +4782,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4785,7 +4801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -4794,7 +4810,7 @@
        "950"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4823,7 +4839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -4852,7 +4868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -4873,7 +4889,7 @@
        "        0.00000e+00, 2.48360e+05]])"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4898,7 +4914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -4907,7 +4923,7 @@
        "0.22011714008302485"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4955,7 +4971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -4969,7 +4985,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4987,7 +5003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -5001,14 +5017,15 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Reimport the DataFrame\n",
-    "car_sales_missing = pd.read_csv(\"../data/car-sales-extended-missing-data.csv\")\n",
+    "# Reimport the DataFrame (so that all the missing values are back)\n",
+    "# car_sales_missing = pd.read_csv(\"../data/car-sales-extended-missing-data.csv\") # read from local directory\n",
+    "car_sales_missing = pd.read_csv(\"https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales-extended-missing-data.csv\") # read directly from URL (source: https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/data/car-sales-extended-missing-data.csv)\n",
     "car_sales_missing.isna().sum()"
    ]
   },
@@ -5021,7 +5038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5038,7 +5055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -5052,7 +5069,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5072,7 +5089,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5106,7 +5123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5137,7 +5154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5181,7 +5198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5220,7 +5237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -5235,7 +5252,7 @@
        "       ['Honda', 'missing', 4.0, 150582.0]], dtype=object)"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5262,7 +5279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -5275,7 +5292,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5301,7 +5318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -5314,7 +5331,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5333,7 +5350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -5347,7 +5364,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5372,7 +5389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -5451,7 +5468,7 @@
        "4   Honda   Blue   4.0      219217.0"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5471,7 +5488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -5486,7 +5503,7 @@
        "       [0.0, 1.0, 0.0, ..., 1.0, 0.0, 150582.0]], dtype=object)"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5529,7 +5546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -5538,7 +5555,7 @@
        "0.21229043336119102"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5623,7 +5640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5642,7 +5659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -5758,7 +5775,7 @@
        "4    -122.25   3.422  "
       ]
      },
-     "execution_count": 81,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5771,7 +5788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -5780,7 +5797,7 @@
        "20640"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5817,16 +5834,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.5758549611440122"
+       "0.5758549611440125"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5879,7 +5896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -5888,7 +5905,7 @@
        "0.8059809073051385"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5941,12 +5958,12 @@
     "\n",
     "Say you were trying to predict whether or not a patient had heart disease based on their medical records.\n",
     "\n",
-    "The dataset in `../data/heart-disease.csv` contains data for just that problem."
+    "The dataset in `../data/heart-disease.csv` (or at [`heart-disease.csv`](https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/data/heart-disease.csv)) contains data for just that problem."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
@@ -6092,19 +6109,20 @@
        "4   0     2       1  "
       ]
      },
-     "execution_count": 85,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "heart_disease = pd.read_csv(\"../data/heart-disease.csv\")\n",
+    "# heart_disease = pd.read_csv(\"../data/heart-disease.csv\") # load from local directory\n",
+    "heart_disease = pd.read_csv(\"https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/heart-disease.csv\") # load directly from URL (source: https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/data/heart-disease.csv)\n",
     "heart_disease.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
@@ -6113,7 +6131,7 @@
        "303"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6144,7 +6162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -6153,7 +6171,7 @@
        "0.8688524590163934"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6206,7 +6224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
@@ -6215,7 +6233,7 @@
        "0.8524590163934426"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6334,7 +6352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -6343,7 +6361,7 @@
        "0.8524590163934426"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6385,7 +6403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
@@ -6525,7 +6543,7 @@
        "4   0     2  "
       ]
      },
-     "execution_count": 90,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6543,7 +6561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
@@ -6557,7 +6575,7 @@
        "Name: target, dtype: int64"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6607,7 +6625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
@@ -6618,7 +6636,7 @@
        "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6641,16 +6659,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.8524590163934426"
+       "np.float64(0.8524590163934426)"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6672,7 +6690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
@@ -6681,7 +6699,7 @@
        "0.8524590163934426"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6700,7 +6718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
@@ -6713,7 +6731,7 @@
        "       [0.18, 0.82]])"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6732,7 +6750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
@@ -6741,7 +6759,7 @@
        "array([0, 1, 1, 0, 1])"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6762,7 +6780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [
     {
@@ -6771,7 +6789,7 @@
        "array([[0.89, 0.11]])"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6792,7 +6810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
@@ -6801,7 +6819,7 @@
        "array([0])"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6826,7 +6844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6860,16 +6878,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.3270458119670544"
+       "np.float64(0.3270458119670544)"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6923,7 +6941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6956,7 +6974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -6965,7 +6983,7 @@
        "0.8524590163934426"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7001,7 +7019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7032,7 +7050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
@@ -7041,7 +7059,7 @@
        "0.8059809073051385"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7085,7 +7103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7123,7 +7141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -7132,7 +7150,7 @@
        "0.8524590163934426"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7144,7 +7162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
@@ -7153,7 +7171,7 @@
        "array([0.81967213, 0.86885246, 0.81967213, 0.78333333, 0.76666667])"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7211,7 +7229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
@@ -7220,7 +7238,7 @@
        "array([0.83606557, 0.8852459 , 0.7704918 , 0.8       , 0.8       ])"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7241,16 +7259,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.8524590163934426, 0.8248087431693989)"
+       "(0.8524590163934426, np.float64(0.8248087431693989))"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7284,7 +7302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
@@ -7293,7 +7311,7 @@
        "array([0.78688525, 0.86885246, 0.80327869, 0.78333333, 0.76666667])"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7337,7 +7355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
@@ -7346,7 +7364,7 @@
        "0.8524590163934426"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7382,7 +7400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
@@ -7422,7 +7440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
@@ -7435,7 +7453,7 @@
        "       0.65517241, 0.72413793, 0.72413793, 0.82758621, 1.        ])"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7469,12 +7487,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -7522,16 +7540,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9304956896551724"
+       "np.float64(0.9304956896551724)"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7556,12 +7574,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -7590,12 +7608,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -7612,16 +7630,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.0"
+       "np.float64(1.0)"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7653,7 +7671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
@@ -7663,7 +7681,7 @@
        "       [ 4, 28]])"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7687,7 +7705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
@@ -7742,7 +7760,7 @@
        "1                 4  28"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7775,12 +7793,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAG0CAYAAAA1hY5rAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA27klEQVR4nO3de3RU1fn/8c/kNkkgCSRIQjRA0HAThBiQi1agQCgKQmkLiFpUvFAUjFzlS5FoJRHaQhQKovVLUixf9FcFtVUkXgApohDAykUsGiBcYrBGQi7kMnN+f0TGjgHNZCYZZs77tdZZy7PP2WeeQRbPPHvvc47FMAxDAADAbwV4OwAAANC4SPYAAPg5kj0AAH6OZA8AgJ8j2QMA4OdI9gAA+DmSPQAAfo5kDwCAnyPZAwDg50j2AAD4OZI9AACNYOvWrRo5cqTi4+NlsVi0YcOGOuccPHhQt9xyi6KiohQREaG+ffvq2LFjjuOVlZWaOnWqWrVqpWbNmumWW27R8ePHXY4lyJ0v4m12u10nT55URESELBaLt8MBALjIMAydPXtW8fHxCghovPrz3Llzqqqqcvs6ISEhCg0Nrde5ZWVl6tGjh+666y794he/qHP8888/1w033KBJkybpscceU1RUlA4ePOh0/bS0NL3++utat26dYmJiNGPGDI0YMUJ5eXkKDAysf+CGDysoKDAksbGxsbH5+FZQUNBouaKiosKIax3okTjj4uKMiooKl2OQZKxfv96pbdy4ccbtt99+0T7ffPONERwcbKxbt87RduLECSMgIMDYuHGjS5/v05V9RESEJOmRdwYotLlPfxXgojb3jfB2CECjqTGqtU3/cPx73hiqqqpUWGTT0bz2ioxo+OhByVm72qUc0VdffaXIyEhHu9VqldVqdeladrtd//jHPzR79mwNGzZMe/bsUWJioubOnavRo0dLkvLy8lRdXa3U1FRHv/j4eHXr1k3bt2/XsGHD6v15Pp0hzw/dhzYPItnDbwVZgr0dAtC4DDXJVGzzCIuaRzT8c+yq7ZuQkODUvmDBAqWnp7t0raKiIpWWlurJJ5/UE088oUWLFmnjxo0aM2aM3nvvPQ0YMECFhYUKCQlRy5YtnfrGxsaqsLDQpc8jQwIATMFm2GUz3OsvSQUFBXUqe1fZ7bXXGjVqlB5++GFJUs+ePbV9+3Y988wzGjBgwEX7Gobh8o8jVuMDAEzBLsPtTZIiIyOdtoYk+1atWikoKEhdu3Z1au/SpYtjNX5cXJyqqqpUXFzsdE5RUZFiY2Nd+jySPQAATSwkJES9e/fWoUOHnNo/++wztWvXTpKUkpKi4OBg5ebmOo6fOnVK+/btU//+/V36PIbxAQCmYJdddjf7u6K0tFSHDx927Ofn52vv3r2Kjo5W27ZtNWvWLI0bN0433nijBg0apI0bN+r111/X5s2bJUlRUVGaNGmSZsyYoZiYGEVHR2vmzJnq3r27hgwZ4lIsJHsAgCnYDEM2o+GT9q723bVrlwYNGuTYnz59uiRp4sSJys7O1s9//nM988wzyszM1LRp09SpUye9/PLLuuGGGxx9li5dqqCgII0dO1YVFRUaPHiwsrOzXbvHXpLl2/v/fFJJSYmioqKU/uFgVuPDb73dPfLHTwJ8VI1Rrc3GBp05c8Zp0Zsnnc8VBZ9e7vatdwmdTzRqrI2FDAkAMIX/XmTX0P6+imQPADAFuwzZTJrsWY0PAICfo7IHAJgCw/gAAPi5pl6NfylhGB8AAD9HZQ8AMAX7t5s7/X0VyR4AYAo2N1fju9PX20j2AABTsBly8613noulqTFnDwCAn6OyBwCYAnP2AAD4ObssssniVn9fxTA+AAB+jsoeAGAKdqN2c6e/ryLZAwBMwebmML47fb2NYXwAAPwclT0AwBTMXNmT7AEApmA3LLIbbqzGd6OvtzGMDwCAn6OyBwCYAsP4AAD4OZsCZHNjQNvmwViaGskeAGAKhptz9gZz9gAA4FJFZQ8AMAXm7AEA8HM2I0A2w405ex9+XC7D+AAA+DkqewCAKdhlkd2NGtcu3y3tSfYAAFMw85w9w/gAAPg5KnsAgCm4v0CPYXwAAC5ptXP2brwIh2F8AABwqaKyBwCYgt3NZ+OzGh8AgEscc/YAAPg5uwJMe589c/YAAPg5kj0AwBRshsXtzRVbt27VyJEjFR8fL4vFog0bNlz03Pvvv18Wi0VZWVlO7ZWVlZo6dapatWqlZs2a6ZZbbtHx48dd/u4kewCAKdi+XaDnzuaKsrIy9ejRQ8uXL//B8zZs2KAPP/xQ8fHxdY6lpaVp/fr1WrdunbZt26bS0lKNGDFCNpvNpViYswcAoBEMHz5cw4cP/8FzTpw4oQcffFBvvfWWbr75ZqdjZ86c0fPPP681a9ZoyJAhkqQXXnhBCQkJevvttzVs2LB6x0JlDwAwBbsR4Pbm0Xjsdt1xxx2aNWuWrr766jrH8/LyVF1drdTUVEdbfHy8unXrpu3bt7v0WVT2AABTaMhQvHP/2tX4JSUlTu1Wq1VWq9Xl6y1atEhBQUGaNm3aBY8XFhYqJCRELVu2dGqPjY1VYWGhS59FZQ8AgAsSEhIUFRXl2DIzM12+Rl5enp566illZ2fLYnFt4Z9hGC73obIHAJiCXXJ5Rf33+0tSQUGBIiMjHe0Nqerff/99FRUVqW3bto42m82mGTNmKCsrS0eOHFFcXJyqqqpUXFzsVN0XFRWpf//+Ln0elT0AwBTOP1THnU2SIiMjnbaGJPs77rhD//rXv7R3717HFh8fr1mzZumtt96SJKWkpCg4OFi5ubmOfqdOndK+fftcTvZU9gAANILS0lIdPnzYsZ+fn6+9e/cqOjpabdu2VUxMjNP5wcHBiouLU6dOnSRJUVFRmjRpkmbMmKGYmBhFR0dr5syZ6t69u2N1fn2R7AEApuD+s/Fd67tr1y4NGjTIsT99+nRJ0sSJE5WdnV2vayxdulRBQUEaO3asKioqNHjwYGVnZyswMNClWEj2AABTaOr32Q8cOFCGCy/POXLkSJ220NBQLVu2TMuWLXPps7+PZA8AMIWmruwvJb4bOQAAqBcqewCAKbj/UB3frY9J9gAAU7AbFtnduc/ejb7e5rs/UwAAQL1Q2QMATMHu5jC+3YfrY5I9AMAU3H1znaffeteUfDdyAABQL1T2AABTsMkimxsP1XGnr7eR7AEApsAwPgAA8FtU9gAAU7DJvaF4m+dCaXIkewCAKZh5GJ9kDwAwBV6EAwAA/BaVPQDAFAw332dvcOsdAACXNobxAQCA36KyBwCYgplfcUuyBwCYgs3Nt96509fbfDdyAABQL1T2AABTYBgfAAA/Z1eA7G4MaLvT19t8N3IAAFAvVPYAAFOwGRbZ3BiKd6evt5HsAQCmwJw9AAB+znDzrXcGT9ADAACXKip7AIAp2GSRzY2X2bjT19tI9gAAU7Ab7s272w0PBtPEGMYHAMDPUdlDxbsCdXR1iEoOBKjqdICueapCrQfXOI5//qcQfbkxSOcKAxQQLEV2tenKaZWKusbuOOfgY1Z9/UGQKk9bFBhuKKqnTUkPV6lZB/uFPhK4pNw+/ZTumPGlU9vXRUG6NbmblyJCY7C7uUDPnb7e5vXIV6xYocTERIWGhiolJUXvv/++t0MyHVuF1LyTTZ3/p/KCx5u1t6vT/1Sq7ytl6vWXcoXG27X7vnBVff3dcFhEV7u6PnFO/V4rU/KqCsmwaPd9YTJsTfUtAPcc+TRU43te7dgmD+7s7ZDgYXZZ3N58lVcr+xdffFFpaWlasWKFrr/+eq1atUrDhw/XgQMH1LZtW2+GZiqtfmJTq59cPCvH3VzzX3uGOs6u1MlXQlT6WYCi+9b2u+JX1Y4zwi43dOXUSn34i2aqOGFReFsfnuiCadhsUvHpYG+HATQKr1b2S5Ys0aRJk3TPPfeoS5cuysrKUkJCglauXOnNsPAD7NXSif8XrKAIQ807XXiI3lYundwQrLAr7AptQ6KHb7g8sUpr8/Yp54MDmrviiOLaXnikC77r/BP03Nl8ldcq+6qqKuXl5emRRx5xak9NTdX27du9FBUu5vTmQO2bFSbbOcl6maHkZ8sV0tI5kResC9bhP1plq7AoPNGm5GfLFUChBB/w6Z5m+v1DYTr+hVUtL6vRrdMKtfTVf+u+n3bW2WKWNvkL5uy94KuvvpLNZlNsbKxTe2xsrAoLCy/Yp7KyUiUlJU4bmkb0dTb1eblMvV8oV8z1NfpkZpiq/uP8K7fNzdXq87cypWSXK7ydoU9mhslGcQQfsOu9SG17o4WOfBqmPe9HaP6vO0iShv7qay9HBl+2detWjRw5UvHx8bJYLNqwYYPjWHV1tebMmaPu3burWbNmio+P169//WudPHnS6RqVlZWaOnWqWrVqpWbNmumWW27R8ePHXY7F6z9TLBbnhGEYRp228zIzMxUVFeXYEhISmiJESAoMl8LbGorqYVfX31XKEiideMW5bA+KkMLbGWrZy6ZrllaoLD9Ap9+hKoLvqawI1JFPQ3V5Ir9W/YldFsfz8Ru0ubhAr6ysTD169NDy5cvrHCsvL9fu3bs1f/587d69W6+88oo+++wz3XLLLU7npaWlaf369Vq3bp22bdum0tJSjRgxQjaba6ufvfYvcatWrRQYGFinii8qKqpT7Z83d+5cTZ8+3bFfUlJCwvcWQ7JXeeAc4BIUHGJXQlKl9n3Y3NuhwIMMN1fUGy72HT58uIYPH37BY1FRUcrNzXVqW7Zsma677jodO3ZMbdu21ZkzZ/T8889rzZo1GjJkiCTphRdeUEJCgt5++20NGzas3rF4LdmHhIQoJSVFubm5+vnPf+5oz83N1ahRoy7Yx2q1ymq1NlWIplFTLlUc+26Qp+KERWc/DVBwlKHgKEP5z4boskE1CrnMUPU3Fh1fF6zKLy2KHVa7Sr+8wKIvNwYrpn+NQqINnfvSoqP/G6JAq35wlT9wqbh3/gntyI1S0YlgtWhVowkPfanw5jbl/r9ob4cGD7rU33p35swZWSwWtWjRQpKUl5en6upqpaamOs6Jj49Xt27dtH37dt9I9pI0ffp03XHHHerVq5f69eunZ599VseOHdPkyZO9GZbplOwL1O67wx37/14cKklqM6panR89p7L8AJ16LUxVxRYFtzAU2c2mlJxyNb+qdjV+oFX6ZnegCtYEq7rEopCY2qH8Xi+UKSSG1fi49LVqU625fzqiyGibzvwnSJ/uDlfayI4qOhHi7dBwCfr+ejFPFKLnzp3TI488ogkTJigyMlKSVFhYqJCQELVs2dLp3B9a23YxXk3248aN03/+8x89/vjjOnXqlLp166Y33nhD7dq182ZYphN9nU1D9p296PEeT537wf7W1oaSV1Z4OiygyWROae/tENAEPLUa//vTxwsWLFB6enqDr1tdXa3x48fLbrdrxYoVP3r+D61tuxivr56aMmWKpkyZ4u0wAAB+zlPD+AUFBY7qW5JbVX11dbXGjh2r/Px8vfvuu07XjYuLU1VVlYqLi52q+6KiIvXv39+lz/H6anwAAHxJZGSk09bQZH8+0f/73//W22+/rZiYGKfjKSkpCg4OdlrId+rUKe3bt8/lZO/1yh4AgKbg7vPtXe1bWlqqw4cPO/bz8/O1d+9eRUdHKz4+Xr/85S+1e/du/f3vf5fNZnPMw0dHRyskJERRUVGaNGmSZsyYoZiYGEVHR2vmzJnq3r27Y3V+fZHsAQCm0NSr8Xft2qVBgwY59s/fOj5x4kSlp6frtddekyT17NnTqd97772ngQMHSpKWLl2qoKAgjR07VhUVFRo8eLCys7MVGBjoUiwkewAAGsHAgQNlGBe/I+mHjp0XGhqqZcuWadmyZW7FQrIHAJjCpX6ffWMi2QMATMHMyZ7V+AAA+DkqewCAKZi5sifZAwBMwZDrt899v7+vItkDAEzBzJU9c/YAAPg5KnsAgCmYubIn2QMATMHMyZ5hfAAA/ByVPQDAFMxc2ZPsAQCmYBgWGW4kbHf6ehvD+AAA+DkqewCAKTT1++wvJSR7AIApmHnOnmF8AAD8HJU9AMAUzLxAj2QPADAFMw/jk+wBAKZg5sqeOXsAAPwclT0AwBQMN4fxfbmyJ9kDAEzBkGQY7vX3VQzjAwDg56jsAQCmYJdFFp6gBwCA/2I1PgAA8FtU9gAAU7AbFll4qA4AAP7LMNxcje/Dy/EZxgcAwM9R2QMATMHMC/RI9gAAUyDZAwDg58y8QI85ewAA/ByVPQDAFMy8Gp9kDwAwhdpk786cvQeDaWIM4wMA4OdI9gAAUzi/Gt+dzRVbt27VyJEjFR8fL4vFog0bNnwvHkPp6emKj49XWFiYBg4cqP379zudU1lZqalTp6pVq1Zq1qyZbrnlFh0/ftzl706yBwCYguGBzRVlZWXq0aOHli9ffsHjixcv1pIlS7R8+XLt3LlTcXFxGjp0qM6ePes4Jy0tTevXr9e6deu0bds2lZaWasSIEbLZbC7Fwpw9AACNYPjw4Ro+fPgFjxmGoaysLM2bN09jxoyRJOXk5Cg2NlZr167V/fffrzNnzuj555/XmjVrNGTIEEnSCy+8oISEBL399tsaNmxYvWOhsgcAmEJTD+P/kPz8fBUWFio1NdXRZrVaNWDAAG3fvl2SlJeXp+rqaqdz4uPj1a1bN8c59UVlDwAwh4aMxX+/v6SSkhKnZqvVKqvV6tKlCgsLJUmxsbFO7bGxsTp69KjjnJCQELVs2bLOOef71xeVPQDAHNyt6r+t7BMSEhQVFeXYMjMzGxySxeI8WmAYRp22Ol+jHud8H5U9AAAuKCgoUGRkpGPf1apekuLi4iTVVu9t2rRxtBcVFTmq/bi4OFVVVam4uNipui8qKlL//v1d+jwqewCAKZx/gp47myRFRkY6bQ1J9omJiYqLi1Nubq6jraqqSlu2bHEk8pSUFAUHBzudc+rUKe3bt8/lZE9lDwAwhaZ+611paakOHz7s2M/Pz9fevXsVHR2ttm3bKi0tTRkZGUpKSlJSUpIyMjIUHh6uCRMmSJKioqI0adIkzZgxQzExMYqOjtbMmTPVvXt3x+r8+iLZAwDQCHbt2qVBgwY59qdPny5JmjhxorKzszV79mxVVFRoypQpKi4uVp8+fbRp0yZFREQ4+ixdulRBQUEaO3asKioqNHjwYGVnZyswMNClWCyG4btP+y0pKVFUVJTSPxys0Ob8boF/ert75I+fBPioGqNam40NOnPmjNM8uCedzxXtn5+vgPDQBl/HXn5ORyb9rlFjbSxkSACAKZj5rXcs0AMAwM9R2QMAzMFDD9XxRfVK9k8//XS9Lzht2rQGBwMAQGNp6tX4l5J6JfulS5fW62IWi4VkDwDAJaZeyT4/P7+x4wAAoPH58FC8Oxq8QK+qqkqHDh1STU2NJ+MBAKBRXEpvvWtqLif78vJyTZo0SeHh4br66qt17NgxSbVz9U8++aTHAwQAwCMMD2w+yuVkP3fuXH388cfavHmzQkO/ezjBkCFD9OKLL3o0OAAA4D6Xb73bsGGDXnzxRfXt29fpFXtdu3bV559/7tHgAADwHMu3mzv9fZPLyf706dNq3bp1nfaysjKX368LAECTMfF99i4P4/fu3Vv/+Mc/HPvnE/xzzz2nfv36eS4yAADgES5X9pmZmfrZz36mAwcOqKamRk899ZT279+vDz74QFu2bGmMGAEAcB+Vff31799f//znP1VeXq4rr7xSmzZtUmxsrD744AOlpKQ0RowAALjPsLi/+agGPRu/e/fuysnJ8XQsAACgETQo2dtsNq1fv14HDx6UxWJRly5dNGrUKAUF8V4dAMClycyvuHU5O+/bt0+jRo1SYWGhOnXqJEn67LPPdNlll+m1115T9+7dPR4kAABuY86+/u655x5dffXVOn78uHbv3q3du3eroKBA11xzje67777GiBEAALjB5cr+448/1q5du9SyZUtHW8uWLbVw4UL17t3bo8EBAOAx7i6y8+EFei5X9p06ddKXX35Zp72oqEhXXXWVR4ICAMDTLIb7m6+qV2VfUlLi+O+MjAxNmzZN6enp6tu3ryRpx44devzxx7Vo0aLGiRIAAHeZeM6+Xsm+RYsWTo/CNQxDY8eOdbQZ3y5RHDlypGw2WyOECQAAGqpeyf69995r7DgAAGhcJp6zr1eyHzBgQGPHAQBA42IY33Xl5eU6duyYqqqqnNqvueYat4MCAACe06BX3N5111168803L3icOXsAwCXJxJW9y7fepaWlqbi4WDt27FBYWJg2btyonJwcJSUl6bXXXmuMGAEAcJ/hgc1HuVzZv/vuu3r11VfVu3dvBQQEqF27dho6dKgiIyOVmZmpm2++uTHiBAAADeRyZV9WVqbWrVtLkqKjo3X69GlJtW/C2717t2ejAwDAU0z8itsGPUHv0KFDkqSePXtq1apVOnHihJ555hm1adPG4wECAOAJPEHPBWlpaTp16pQkacGCBRo2bJj++te/KiQkRNnZ2Z6ODwAAuMnlZH/bbbc5/js5OVlHjhzRp59+qrZt26pVq1YeDQ4AAI8x8Wr8Bt9nf154eLiuvfZaT8QCAAAaQb2S/fTp0+t9wSVLljQ4GAAAGotF7s27++7yvHom+z179tTrYv/9shwAAHBp8IsX4Wzu01xBlmBvhwE0irdO1u/HNuCLSs7a1bJjE32YiV+E4/KtdwAA+KQmfoJeTU2Nfvvb3yoxMVFhYWHq0KGDHn/8cdnt9u9CMgylp6crPj5eYWFhGjhwoPbv3+/mF62LZA8AQCNYtGiRnnnmGS1fvlwHDx7U4sWL9fvf/17Lli1znLN48WItWbJEy5cv186dOxUXF6ehQ4fq7NmzHo2FZA8AMIcmruw/+OADjRo1SjfffLPat2+vX/7yl0pNTdWuXbtqwzEMZWVlad68eRozZoy6deumnJwclZeXa+3atR74wt8h2QMATKGpn6B3ww036J133tFnn30mSfr444+1bds23XTTTZKk/Px8FRYWKjU11dHHarVqwIAB2r59u8e+t+SB++wBADCTkpISp32r1Sqr1VrnvDlz5ujMmTPq3LmzAgMDZbPZtHDhQt16662SpMLCQklSbGysU7/Y2FgdPXrUozE3qLJfs2aNrr/+esXHxzsCysrK0quvvurR4AAA8BgPDeMnJCQoKirKsWVmZl7w41588UW98MILWrt2rXbv3q2cnBz94Q9/UE5OjtN5379t3TAMj9/K7nKyX7lypaZPn66bbrpJ33zzjWw2mySpRYsWysrK8mhwAAB4jIeSfUFBgc6cOePY5s6de8GPmzVrlh555BGNHz9e3bt31x133KGHH37Y8eMgLi5O0ncV/nlFRUV1qn13uZzsly1bpueee07z5s1TYGCgo71Xr1765JNPPBocAACXmsjISKftQkP4klReXq6AAOc0GxgY6Lj1LjExUXFxccrNzXUcr6qq0pYtW9S/f3+PxuzynH1+fr6Sk5PrtFutVpWVlXkkKAAAPM3d19S62nfkyJFauHCh2rZtq6uvvlp79uzRkiVLdPfdd9dez2JRWlqaMjIylJSUpKSkJGVkZCg8PFwTJkxoeKAX4HKyT0xM1N69e9WuXTun9jfffFNdu3b1WGAAAHhUEz9Bb9myZZo/f76mTJmioqIixcfH6/7779ejjz7qOGf27NmqqKjQlClTVFxcrD59+mjTpk2KiIhoeJwX4HKynzVrlh544AGdO3dOhmHoo48+0v/93/8pMzNTf/7znz0aHAAAHtPEr7iNiIhQVlbWD65ns1gsSk9PV3p6uhuB/TiXk/1dd92lmpoazZ49W+Xl5ZowYYIuv/xyPfXUUxo/fnxjxAgAANzQoPvs7733Xt1777366quvZLfb1bp1a0/HBQCARzX1nP2lxK2H6rRq1cpTcQAA0LiaeBj/UtKgBXo/dLP/F1984VZAAADAs1xO9mlpaU771dXV2rNnjzZu3KhZs2Z5Ki4AADzLzWF8U1X2Dz300AXb//SnPzne5AMAwCXHxMP4Hnvr3fDhw/Xyyy976nIAAMBDPPbWu7/97W+Kjo721OUAAPAsE1f2Lif75ORkpwV6hmGosLBQp0+f1ooVKzwaHAAAnsKtdy4YPXq0035AQIAuu+wyDRw4UJ07d/ZUXAAAwENcSvY1NTVq3769hg0b5ng1HwAAuLS5tEAvKChIv/nNb1RZWdlY8QAA0Dg89D57X+Tyavw+ffpoz549jRELAACN5vycvTubr3J5zn7KlCmaMWOGjh8/rpSUFDVr1szp+DXXXOOx4AAAgPvqnezvvvtuZWVlady4cZKkadOmOY5ZLBYZhiGLxSKbzeb5KAEA8AQfrs7dUe9kn5OToyeffFL5+fmNGQ8AAI2D++x/nGHUfst27do1WjAAAMDzXJqz/6G33QEAcCnjoTr11LFjxx9N+F9//bVbAQEA0CgYxq+fxx57TFFRUY0VCwAAaAQuJfvx48erdevWjRULAACNhmH8emC+HgDg00w8jF/vJ+idX40PAAB8S70re7vd3phxAADQuExc2bv8uFwAAHwRc/YAAPg7E1f2Lr/1DgAA+BYqewCAOZi4sifZAwBMwcxz9gzjAwDg56jsAQDmwDA+AAD+jWF8AADgt6jsAQDmwDA+AAB+zsTJnmF8AAD8HMkeAGAKFg9srjpx4oRuv/12xcTEKDw8XD179lReXp7juGEYSk9PV3x8vMLCwjRw4EDt37+/4V/yIkj2AABzMDywuaC4uFjXX3+9goOD9eabb+rAgQP64x//qBYtWjjOWbx4sZYsWaLly5dr586diouL09ChQ3X27Fn3vuv3MGcPADCFpr71btGiRUpISNDq1asdbe3bt3f8t2EYysrK0rx58zRmzBhJUk5OjmJjY7V27Vrdf//9DQ/2e6jsAQBoBK+99pp69eqlX/3qV2rdurWSk5P13HPPOY7n5+ersLBQqampjjar1aoBAwZo+/btHo2FZA8AMAcPDeOXlJQ4bZWVlRf8uC+++EIrV65UUlKS3nrrLU2ePFnTpk3TX/7yF0lSYWGhJCk2NtapX2xsrOOYp5DsAQDm4YH5+oSEBEVFRTm2zMzMC36U3W7Xtddeq4yMDCUnJ+v+++/Xvffeq5UrVzqdZ7E4L/0zDKNOm7uYswcAwAUFBQWKjIx07Fut1gue16ZNG3Xt2tWprUuXLnr55ZclSXFxcZJqK/w2bdo4zikqKqpT7buLyh4AYArnF+i5s0lSZGSk03axZH/99dfr0KFDTm2fffaZ2rVrJ0lKTExUXFyccnNzHcerqqq0ZcsW9e/f36PfncoeAGAOTfwEvYcfflj9+/dXRkaGxo4dq48++kjPPvusnn32WUm1w/dpaWnKyMhQUlKSkpKSlJGRofDwcE2YMMGNQOsi2QMA0Ah69+6t9evXa+7cuXr88ceVmJiorKws3XbbbY5zZs+erYqKCk2ZMkXFxcXq06ePNm3apIiICI/GQrIHAJiCN15xO2LECI0YMeLi17RYlJ6ervT09IYHVg8kewCAOfAiHAAA4K+o7AEApuCNYfxLBckeAGAOJh7GJ9kDAMzBxMmeOXsAAPwclT0AwBSYswcAwN8xjA8AAPwVlT0AwBQshiGL0fDy3J2+3kayBwCYA8P4AADAX1HZAwBMgdX4AAD4O4bxAQCAv6KyBwCYAsP4AAD4OxMP45PsAQCmYObKnjl7AAD8HJU9AMAcGMYHAMD/+fJQvDsYxgcAwM9R2QMAzMEwajd3+vsokj0AwBRYjQ8AAPwWlT0AwBxYjQ8AgH+z2Gs3d/r7Kobx4bJxD36pt05+rMmPnfB2KEC9fLKjmR79daJuTb5aw+J7avubUXXOOfZvqxZMTNTPO3XX6KTuemhEkoqOBzuOv/FCjGb94ir9vGN3DYvvqdIzgU35FQC3kOzhko49ynXT7V/ri/2h3g4FqLdz5QHqcHWFHlh4/ILHTx4J0fTRSUq46px+/7fDWvn2IU1I+1Ihod+N256rCFCvgSUaP/XLpgobnmZ4YPNRXh3G37p1q37/+98rLy9Pp06d0vr16zV69GhvhoQfEBpu05zlR5U16wrd+hD/4MF39P7pWfX+6dmLHs9+so2u+2mJ7pl/ytHWpl2V0zlj7j0tSfp4e/PGCRKNjtX4XlJWVqYePXpo+fLl3gwD9fRgxgl99E6k9rwf4e1QAI+x26WP3onU5R0q9T+3dtDY7ldr2s1JFxzqh487f5+9O5uP8mplP3z4cA0fPtybIaCeBowq1lXdKzT1piRvhwJ41DdfBamiLFAvLm+tO+cUatK8U9r1XoQev6e9Fv/tsK7pV+btEAG3+dRq/MrKSlVWVjr2S0pKvBiNeVwWX6XfPH5S/3NrB1VXsswD/sX4doV1v2ElGnNf7VD9ld0qdGBXM/3jL61I9n7EzMP4PpXsMzMz9dhjj3k7DNO56poKtbysRss3fuZoCwySuvct0y13faUR7a+R3W7xYoRAw0VG2xQYZKhdx3NO7QlJ57T/o2ZeigqNgvvsfcPcuXM1ffp0x35JSYkSEhK8GJE57H2/ue4b1NGpbcbSAhUcDtVLf7qMRA+fFhxiqGOPch3/3OrUfuILq1pfUe2lqADP8qlkb7VaZbVaf/xEeFRFWaCOHgpzajtXHqCzxXXbgUtRRVmATuZ/929HYUGIPt8XpogWNWp9RbV+NaVIGZPbqVvfUvXoX6pd70VqR26Ufv+3w44+XxcFqbgoWCfzQyRJ+Z+GKryZXZddXqXIlrYm/05wnZmH8ZmABeD3Pvs4XFNSO2lKaidJ0qr0yzUltZP+8oc2kqTrh5/RtCeP6/+tiNXkwZ21cW2M5j+Xr259vpuv/8dfWmlKaidlzWorSZr58yRNSe2kHZtYte8zvLgaPzMzUxaLRWlpaf8VjqH09HTFx8crLCxMAwcO1P79+z3wRevyamVfWlqqw4e/++Wcn5+vvXv3Kjo6Wm3btvViZPgxs395lbdDAOqtR/9SvXVy7w+eM+zWrzXs1q8vevyOmYW6Y2ahhyODGezcuVPPPvusrrnmGqf2xYsXa8mSJcrOzlbHjh31xBNPaOjQoTp06JAiIjx7i7NXK/tdu3YpOTlZycnJkqTp06crOTlZjz76qDfDAgD4ofPD+O5sriotLdVtt92m5557Ti1btnS0G4ahrKwszZs3T2PGjFG3bt2Uk5Oj8vJyrV271oPfupZXk/3AgQNlGEadLTs725thAQD8kRcel/vAAw/o5ptv1pAhQ5za8/PzVVhYqNTUVEeb1WrVgAEDtH37dtc/6Ef41AI9AAC87fvPeLnY4vF169Zp9+7d2rlzZ51jhYW1U0KxsbFO7bGxsTp69KgHo63FAj0AgCl4ahg/ISFBUVFRji0zM7POZxUUFOihhx7SCy+8oNDQi784zGJxvnXZMIw6bZ5AZQ8AMAe7Ubu501+1iTwyMtLRfKGqPi8vT0VFRUpJSXG02Ww2bd26VcuXL9ehQ4ck1Vb4bdq0cZxTVFRUp9r3BJI9AMAcPPQEvcjISKdkfyGDBw/WJ5984tR21113qXPnzpozZ446dOiguLg45ebmOhapV1VVacuWLVq0aJEbQV4YyR4AAA+LiIhQt27dnNqaNWummJgYR3taWpoyMjKUlJSkpKQkZWRkKDw8XBMmTPB4PCR7AIApWOTmE/Q8Fkmt2bNnq6KiQlOmTFFxcbH69OmjTZs2efwee4lkDwAwC3ffSe/m++w3b97stG+xWJSenq709HS3rlsfrMYHAMDPUdkDAEzBzC/CIdkDAMzBxO+zZxgfAAA/R2UPADAFi2HI4sYiO3f6ehvJHgBgDvZvN3f6+yiG8QEA8HNU9gAAU2AYHwAAf2fi1fgkewCAOXj5CXrexJw9AAB+jsoeAGAKPEEPAAB/xzA+AADwV1T2AABTsNhrN3f6+yqSPQDAHBjGBwAA/orKHgBgDjxUBwAA/2bmx+UyjA8AgJ+jsgcAmIOJF+iR7AEA5mDIvXfS+26uJ9kDAMyBOXsAAOC3qOwBAOZgyM05e49F0uRI9gAAczDxAj2G8QEA8HNU9gAAc7BLsrjZ30eR7AEApsBqfAAA4Leo7AEA5mDiBXokewCAOZg42TOMDwCAn6OyBwCYg4kre5I9AMAcuPUOAAD/xq13AADAb5HsAQDmcH7O3p3NBZmZmerdu7ciIiLUunVrjR49WocOHfpeSIbS09MVHx+vsLAwDRw4UPv37/fkt5ZEsgcAmIXdcH9zwZYtW/TAAw9ox44dys3NVU1NjVJTU1VWVuY4Z/HixVqyZImWL1+unTt3Ki4uTkOHDtXZs2c9+tWZswcAoBFs3LjRaX/16tVq3bq18vLydOONN8owDGVlZWnevHkaM2aMJCknJ0exsbFau3at7r//fo/FQmUPADAHDw3jl5SUOG2VlZX1+vgzZ85IkqKjoyVJ+fn5KiwsVGpqquMcq9WqAQMGaPv27R796iR7AIBJuJvoa5N9QkKCoqKiHFtmZuaPf7JhaPr06brhhhvUrVs3SVJhYaEkKTY21unc2NhYxzFPYRgfAAAXFBQUKDIy0rFvtVp/tM+DDz6of/3rX9q2bVudYxaL883/hmHUaXMXyR4AYA4eeoJeZGSkU7L/MVOnTtVrr72mrVu36oorrnC0x8XFSaqt8Nu0aeNoLyoqqlPtu4thfACAOTTxanzDMPTggw/qlVde0bvvvqvExESn44mJiYqLi1Nubq6jraqqSlu2bFH//v098pXPo7IHAKARPPDAA1q7dq1effVVRUREOObho6KiFBYWJovForS0NGVkZCgpKUlJSUnKyMhQeHi4JkyY4NFYSPYAAHMw7LWbO/1dsHLlSknSwIEDndpXr16tO++8U5I0e/ZsVVRUaMqUKSouLlafPn20adMmRURENDzOCyDZAwDMoYnfemfU43yLxaL09HSlp6c3MKj6IdkDAMzB/t3tcw3v75tYoAcAgJ+jsgcAmEMTD+NfSkj2AABzMORmsvdYJE2OYXwAAPwclT0AwBwYxgcAwM/Z7ZLcuM/e7kZfL2MYHwAAP0dlDwAwB4bxAQDwcyZO9gzjAwDg56jsAQDmYOLH5ZLsAQCmYBh2GW689c6dvt5GsgcAmINhuFedM2cPAAAuVVT2AABzMNycs/fhyp5kDwAwB7tdsrgx7+7Dc/YM4wMA4Oeo7AEA5sAwPgAA/s2w22W4MYzvy7feMYwPAICfo7IHAJgDw/gAAPg5uyFZzJnsGcYHAMDPUdkDAMzBMCS5c5+971b2JHsAgCkYdkOGG8P4BskeAIBLnGGXe5U9t94BAIBLFJU9AMAUGMYHAMDfmXgY36eT/flfWTWqdus5CcClrOSs7/4DA/yYktLav99NUTW7mytqVO25YJqYTyf7s2fPSpK26Q0vRwI0npYdvR0B0PjOnj2rqKioRrl2SEiI4uLitK3Q/VwRFxenkJAQD0TVtCyGD09C2O12nTx5UhEREbJYLN4OxxRKSkqUkJCggoICRUZGejscwKP4+930DMPQ2bNnFR8fr4CAxlszfu7cOVVVVbl9nZCQEIWGhnogoqbl05V9QECArrjiCm+HYUqRkZH8Ywi/xd/vptVYFf1/Cw0N9ckk7SncegcAgJ8j2QMA4OdI9nCJ1WrVggULZLVavR0K4HH8/Ya/8ukFegAA4MdR2QMA4OdI9gAA+DmSPQAAfo5kj3pbsWKFEhMTFRoaqpSUFL3//vveDgnwiK1bt2rkyJGKj4+XxWLRhg0bvB0S4FEke9TLiy++qLS0NM2bN0979uzRT37yEw0fPlzHjh3zdmiA28rKytSjRw8tX77c26EAjYLV+KiXPn366Nprr9XKlSsdbV26dNHo0aOVmZnpxcgAz7JYLFq/fr1Gjx7t7VAAj6Gyx4+qqqpSXl6eUlNTndpTU1O1fft2L0UFAKgvkj1+1FdffSWbzabY2Fin9tjYWBUWFnopKgBAfZHsUW/ff7OgYRi8bRAAfADJHj+qVatWCgwMrFPFFxUV1an2AQCXHpI9flRISIhSUlKUm5vr1J6bm6v+/ft7KSoAQH359Pvs0XSmT5+uO+64Q7169VK/fv307LPP6tixY5o8ebK3QwPcVlpaqsOHDzv28/PztXfvXkVHR6tt27ZejAzwDG69Q72tWLFCixcv1qlTp9StWzctXbpUN954o7fDAty2efNmDRo0qE77xIkTlZ2d3fQBAR5GsgcAwM8xZw8AgJ8j2QMA4OdI9gAA+DmSPQAAfo5kDwCAnyPZAwDg50j2AAD4OZI9AAB+jmQPuCk9PV09e/Z07N95550aPXp0k8dx5MgRWSwW7d2796LntG/fXllZWfW+ZnZ2tlq0aOF2bBaLRRs2bHD7OgAahmQPv3TnnXfKYrHIYrEoODhYHTp00MyZM1VWVtbon/3UU0/V+xGr9UnQAOAuXoQDv/Wzn/1Mq1evVnV1td5//33dc889Kisr08qVK+ucW11dreDgYI98blRUlEeuAwCeQmUPv2W1WhUXF6eEhARNmDBBt912m2Mo+fzQ+//+7/+qQ4cOslqtMgxDZ86c0X333afWrVsrMjJSP/3pT/Xxxx87XffJJ59UbGysIiIiNGnSJJ07d87p+PeH8e12uxYtWqSrrrpKVqtVbdu21cKFCyVJiYmJkqTk5GRZLBYNHDjQ0W/16tXq0qWLQkND1blzZ61YscLpcz766CMlJycrNDRUvXr10p49e1z+M1qyZIm6d++uZs2aKSEhQVOmTFFpaWmd8zZs2KCOHTsqNDRUQ4cOVUFBgdPx119/XSkpKQoNDVWHDh302GOPqaamxuV4ADQOkj1MIywsTNXV1Y79w4cP66WXXtLLL7/sGEa/+eabVVhYqDfeeEN5eXm69tprNXjwYH399deSpJdeekkLFizQwoULtWvXLrVp06ZOEv6+uXPnatGiRZo/f74OHDigtWvXKjY2VlJtwpakt99+W6dOndIrr7wiSXruuec0b948LVy4UAcPHlRGRobmz5+vnJwcSVJZWZlGjBihTp06KS8vT+np6Zo5c6bLfyYBAQF6+umntW/fPuXk5Ojdd9/V7Nmznc4pLy/XwoULlZOTo3/+858qKSnR+PHjHcffeust3X777Zo2bZoOHDigVatWKTs72/GDBsAlwAD80MSJE41Ro0Y59j/88EMjJibGGDt2rGEYhrFgwQIjODjYKCoqcpzzzjvvGJGRkca5c+ecrnXllVcaq1atMgzDMPr162dMnjzZ6XifPn2MHj16XPCzS0pKDKvVajz33HMXjDM/P9+QZOzZs8epPSEhwVi7dq1T2+9+9zujX79+hmEYxqpVq4zo6GijrKzMcXzlypUXvNZ/a9eunbF06dKLHn/ppZeMmJgYx/7q1asNScaOHTscbQcPHjQkGR9++KFhGIbxk5/8xMjIyHC6zpo1a4w2bdo49iUZ69evv+jnAmhczNnDb/39739X8+bNVVNTo+rqao0aNUrLli1zHG/Xrp0uu+wyx35eXp5KS0sVExPjdJ2Kigp9/vnnkqSDBw9q8uTJTsf79eun995774IxHDx4UJWVlRo8eHC94z59+rQKCgo0adIk3XvvvY72mpoax3qAgwcPqkePHgoPD3eKw1XvvfeeMjIydODAAZWUlKimpkbnzp1TWVmZmjVrJkkKCgpSr169HH06d+6sFi1a6ODBg7ruuuuUl5ennTt3OlXyNptN586dU3l5uVOMALyDZA+/NWjQIK1cuVLBwcGKj4+vswDvfDI7z263q02bNtq8eXOdazX09rOwsDCX+9jtdkm1Q/l9+vRxOhYYGChJMgyjQfH8t6NHj+qmm27S5MmT9bvf/U7R0dHatm2bJk2a5DTdIdXeOvd959vsdrsee+wxjRkzps45oaGhbscJwH0ke/itZs2a6aqrrqr3+ddee60KCwsVFBSk9u3bX/CcLl26aMeOHfr1r3/taNuxY8dFr5mUlKSwsDC98847uueee+ocDwkJkVRbCZ8XGxuryy+/XF988YVuu+22C163a9euWrNmjSoqKhw/KH4ojgvZtWuXampq9Mc//lEBAbXLd1566aU659XU1GjXrl267rrrJEmHDh3SN998o86dO0uq/XM7dOiQS3/WAJoWyR741pAhQ9SvXz+NHj1aixYtUqdOnXTy5Em98cYbGj16tHr16qWHHnpIEydOVK9evXTDDTfor3/9q/bv368OHTpc8JqhoaGaM2eOZs+erZCQEF1//fU6ffq09u/fr0mTJql169YKCwvTxo0bdcUVVyg0NFRRUVFKT0/XtGnTFBkZqeHDh6uyslK7du1ScXGxpk+frgkTJmjevHmaNGmSfvvb3+rIkSP6wx/+4NL3vfLKK1VTU6Nly5Zp5MiR+uc//6lnnnmmznnBwcGaOnWqnn76aQUHB+vBBx9U3759Hcn/0Ucf1YgRI5SQkKBf/epXCggI0L/+9S998skneuKJJ1z/HwHA41iND3zLYrHojTfe0I033qi7775bHTt21Pjx43XkyBHH6vlx48bp0Ucf1Zw5c5SSkqKjR4/qN7/5zQ9ed/78+ZoxY4YeffRRdenSRePGjVNRUZGk2vnwp59+WqtWrVJ8fLxGjRolSbrnnnv05z//WdnZ2erevbsGDBig7Oxsx616zZs31+uvv64DBw4oOTlZ8+bN06JFi1z6vj179tSSJUu0aNEidevWTX/961+VmZlZ57zw8HDNmTNHEyZMUL9+/RQWFqZ169Y5jg8bNkx///vflZubq969e6tv375asmSJ2rVr51I8ABqPxfDE5B8AALhkUdkDAODnSPYAAPg5kj0AAH6OZA8AgJ8j2QMA4OdI9gAA+DmSPQAAfo5kDwCAnyPZAwDg50j2AAD4OZI9AAB+jmQPAICf+/+gX/ygu3ylIwAAAABJRU5ErkJggg==",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -7797,12 +7815,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -7834,7 +7852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
@@ -7890,7 +7908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
@@ -7966,7 +7984,7 @@
        "support    9999.00000  1.0    0.9999  10000.000000   10000.00000"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8020,7 +8038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -8053,7 +8071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
@@ -8062,7 +8080,7 @@
        "0.8059809073051385"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8083,7 +8101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
@@ -8092,7 +8110,7 @@
        "0.0"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8115,7 +8133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
@@ -8124,7 +8142,7 @@
        "1.0"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8151,16 +8169,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.3270458119670544"
+       "np.float64(0.3270458119670544)"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8187,7 +8205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -8293,7 +8311,7 @@
        "[4128 rows x 2 columns]"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8316,12 +8334,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -8351,16 +8369,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.2542443610174998"
+       "np.float64(0.2542443610174998)"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8407,7 +8425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -8431,7 +8449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
@@ -8440,7 +8458,7 @@
        "array([0.81967213, 0.90163934, 0.83606557, 0.78333333, 0.78333333])"
       ]
      },
-     "execution_count": 134,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8462,7 +8480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
@@ -8487,7 +8505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -8515,7 +8533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
@@ -8541,7 +8559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
@@ -8567,7 +8585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [
     {
@@ -8595,7 +8613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -8614,23 +8632,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The default is `\"r2\"`."
+    "The default is `\"r2\"`.\n",
+    "\n",
+    "> **Note:** We can time how long a single cell of code takes to run using the [`%%time` magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-time)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The cross-validated R^2 score is: 0.65\n"
+      "The cross-validated R^2 score is: 0.65\n",
+      "CPU times: user 40.5 s, sys: 286 ms, total: 40.8 s\n",
+      "Wall time: 41.6 s\n"
      ]
     }
    ],
    "source": [
+    "%%time \n",
     "np.random.seed(42)\n",
     "cv_r2 = cross_val_score(model, X, y, cv=5, scoring=\"r2\")\n",
     "print(f\"The cross-validated R^2 score is: {np.mean(cv_r2):.2f}\")"
@@ -8640,23 +8663,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "But we can use `\"neg_mean_absolute_error\"` for MAE (mean absolute error)."
+    "But we can use `\"neg_mean_absolute_error\"` for MAE (mean absolute error).\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The cross-validated MAE score is: -0.47\n"
+      "The cross-validated MAE score is: -0.47\n",
+      "CPU times: user 40.4 s, sys: 246 ms, total: 40.7 s\n",
+      "Wall time: 41.6 s\n"
      ]
     }
    ],
    "source": [
+    "%%time\n",
     "np.random.seed(42)\n",
     "cv_mae = cross_val_score(model, X, y, cv=5, scoring=\"neg_mean_absolute_error\")\n",
     "print(f\"The cross-validated MAE score is: {np.mean(cv_mae):.2f}\")"
@@ -8678,7 +8705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 159,
    "metadata": {},
    "outputs": [
     {
@@ -8730,7 +8757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 160,
    "metadata": {},
    "outputs": [
     {
@@ -8787,7 +8814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 161,
    "metadata": {},
    "outputs": [
     {
@@ -8875,7 +8902,7 @@
     "* Could we improve our data? This could mean filling in misisng values or finding a better encoding (turning data into numbers) strategy.\n",
     "\n",
     "From a model perspective asks:\n",
-    "* Is there a better model we could use? If you've started out with a simple model, could you use a more complex one? (we saw an example of this when looking at the [Scikit-Learn machine learning map](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html), ensemble methods are generally considered more complex models)\n",
+    "* Is there a better model we could use? If you've started out with a simple model, could you use a more complex one? (we saw an example of this when looking at the [Scikit-Learn machine learning map](https://scikit-learn.org/stable/machine_learning_map.html), ensemble methods are generally considered more complex models)\n",
     "* Could we improve the current model? If the model you're using performs well straight out of the box, can the hyperparameters be tuned to make it even better?\n",
     "\n",
     "> **Note:** Patterns in data are also often referred to as data parameters. The difference between *parameters* and *hyperparameters* is a machine learning model seeks to find parameters in data on its own, where as, hyperparameters are settings on a model which a person (you) can adjust.\n",
@@ -8893,7 +8920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -8913,7 +8940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 163,
    "metadata": {},
    "outputs": [
     {
@@ -8931,6 +8958,7 @@
        " 'min_samples_leaf': 1,\n",
        " 'min_samples_split': 2,\n",
        " 'min_weight_fraction_leaf': 0.0,\n",
+       " 'monotonic_cst': None,\n",
        " 'n_estimators': 100,\n",
        " 'n_jobs': None,\n",
        " 'oob_score': False,\n",
@@ -8939,7 +8967,7 @@
        " 'warm_start': False}"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 163,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9024,7 +9052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [
     {
@@ -9042,6 +9070,7 @@
        " 'min_samples_leaf': 1,\n",
        " 'min_samples_split': 2,\n",
        " 'min_weight_fraction_leaf': 0.0,\n",
+       " 'monotonic_cst': None,\n",
        " 'n_estimators': 100,\n",
        " 'n_jobs': None,\n",
        " 'oob_score': False,\n",
@@ -9050,7 +9079,7 @@
        " 'warm_start': False}"
       ]
      },
-     "execution_count": 148,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9081,7 +9110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9119,7 +9148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [
     {
@@ -9135,10 +9164,13 @@
     {
      "data": {
       "text/plain": [
-       "{'accuracy': 0.8, 'precision': 0.78, 'recall': 0.88, 'f1': 0.82}"
+       "{'accuracy': 0.8,\n",
+       " 'precision': np.float64(0.78),\n",
+       " 'recall': np.float64(0.88),\n",
+       " 'f1': np.float64(0.82)}"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 166,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9152,7 +9184,8 @@
     "np.random.seed(42)\n",
     "\n",
     "# Read in the data\n",
-    "heart_disease = pd.read_csv(\"../data/heart-disease.csv\")\n",
+    "# heart_disease = pd.read_csv(\"../data/heart-disease.csv\") # load in from local directory\n",
+    "heart_disease = pd.read_csv(\"https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/heart-disease.csv\") # load directly from URL (source: https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/data/heart-disease.csv)\n",
     "\n",
     "# Split into X (features) & y (labels)\n",
     "X = heart_disease.drop(\"target\", axis=1)\n",
@@ -9177,7 +9210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 167,
    "metadata": {},
    "outputs": [
     {
@@ -9208,7 +9241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [
     {
@@ -9278,7 +9311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9313,7 +9346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [
     {
@@ -9355,7 +9388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
@@ -9363,16 +9396,16 @@
      "output_type": "stream",
      "text": [
       "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time=   0.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time=   0.3s\n",
       "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
@@ -9383,21 +9416,21 @@
       "[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=8, n_estimators=100; total time=   0.1s\n",
       "[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=8, n_estimators=100; total time=   0.1s\n",
       "[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=8, n_estimators=100; total time=   0.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
       "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
       "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
-      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=1200; total time=   1.0s\n",
-      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=1200; total time=   1.0s\n",
-      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=1200; total time=   1.0s\n",
-      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=1200; total time=   1.0s\n",
-      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=1200; total time=   1.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=1200; total time=   0.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=1200; total time=   0.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=1200; total time=   0.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=1200; total time=   0.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=4, n_estimators=1200; total time=   0.7s\n",
       "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
@@ -9413,36 +9446,36 @@
       "[CV] END max_depth=5, max_features=log2, min_samples_leaf=8, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=5, max_features=log2, min_samples_leaf=8, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=5, max_features=log2, min_samples_leaf=8, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=1200; total time=   1.0s\n",
-      "[CV] END max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=1200; total time=   1.0s\n",
-      "[CV] END max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=1200; total time=   1.0s\n",
-      "[CV] END max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=1200; total time=   1.0s\n",
-      "[CV] END max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=1200; total time=   1.0s\n",
-      "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=1200; total time=   0.7s\n",
+      "[CV] END max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=1200; total time=   0.7s\n",
+      "[CV] END max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=1200; total time=   0.8s\n",
+      "[CV] END max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=1200; total time=   0.8s\n",
+      "[CV] END max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=1200; total time=   0.7s\n",
+      "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
       "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time=   1.1s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time=   1.0s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time=   1.0s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time=   1.0s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time=   1.1s\n",
-      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time=   0.8s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time=   0.7s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time=   0.8s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time=   0.7s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time=   0.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time=   0.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time=   0.6s\n",
       "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time=   0.0s\n",
@@ -9453,41 +9486,41 @@
       "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.1s\n",
       "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.1s\n",
       "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.1s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=8, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=8, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=8, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=8, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=8, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time=   0.5s\n",
-      "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time=   0.5s\n",
-      "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=8, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=8, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=8, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=8, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=8, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
       "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
       "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=8, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=8, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=8, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=8, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=8, n_estimators=10; total time=   0.0s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
       "[CV] END max_depth=None, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=None, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=None, max_features=None, min_samples_leaf=1, min_samples_split=8, n_estimators=10; total time=   0.0s\n",
@@ -9498,10 +9531,10 @@
       "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=100; total time=   0.1s\n",
       "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=100; total time=   0.1s\n",
       "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=100; total time=   0.1s\n",
-      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.4s\n",
-      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.3s\n",
       "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time=   0.4s\n",
       "[CV] END max_depth=None, max_features=None, min_samples_leaf=8, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=None, max_features=None, min_samples_leaf=8, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
@@ -9513,7 +9546,7 @@
       "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=10; total time=   0.0s\n",
       "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=10; total time=   0.0s\n",
-      "[INFO] Total time taken for 30 random combinations of hyperparameters: 49.50 seconds.\n"
+      "[INFO] Total time taken for 30 random combinations of hyperparameters: 36.02 seconds.\n"
      ]
     }
    ],
@@ -9561,7 +9594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 174,
    "metadata": {},
    "outputs": [
     {
@@ -9574,7 +9607,7 @@
        " 'max_depth': 30}"
       ]
      },
-     "execution_count": 156,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9593,7 +9626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 175,
    "metadata": {},
    "outputs": [
     {
@@ -9643,7 +9676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 176,
    "metadata": {},
    "outputs": [
     {
@@ -9656,7 +9689,7 @@
        " 'min_samples_leaf': [1, 2, 4, 8]}"
       ]
      },
-     "execution_count": 158,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9693,7 +9726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 177,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9714,7 +9747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 178,
    "metadata": {},
    "outputs": [
     {
@@ -9745,7 +9778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 179,
    "metadata": {},
    "outputs": [
     {
@@ -9754,125 +9787,125 @@
      "text": [
       "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n",
       "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
       "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.7s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.7s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s\n",
       "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s\n",
       "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.1s\n",
       "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.2s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.9s\n",
-      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.8s\n"
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   0.7s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time=   0.1s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.6s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.5s\n",
+      "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time=   0.5s\n"
      ]
     }
    ],
@@ -9910,14 +9943,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 181,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[INFO] The total running time for running GridSearchCV was 61.45 seconds.\n"
+      "[INFO] The total running time for running GridSearchCV was 41.95 seconds.\n"
      ]
     }
    ],
@@ -9936,7 +9969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 182,
    "metadata": {},
    "outputs": [
     {
@@ -9949,7 +9982,7 @@
        " 'n_estimators': 200}"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 182,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9968,7 +10001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 183,
    "metadata": {},
    "outputs": [
     {
@@ -9984,10 +10017,13 @@
     {
      "data": {
       "text/plain": [
-       "{'accuracy': 0.89, 'precision': 0.88, 'recall': 0.91, 'f1': 0.89}"
+       "{'accuracy': 0.89,\n",
+       " 'precision': np.float64(0.88),\n",
+       " 'recall': np.float64(0.91),\n",
+       " 'f1': np.float64(0.89)}"
       ]
      },
-     "execution_count": 164,
+     "execution_count": 183,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10010,12 +10046,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 184,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]
@@ -10074,7 +10110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 185,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10094,7 +10130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 186,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10111,7 +10147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 187,
    "metadata": {},
    "outputs": [
     {
@@ -10127,10 +10163,13 @@
     {
      "data": {
       "text/plain": [
-       "{'accuracy': 0.89, 'precision': 0.88, 'recall': 0.91, 'f1': 0.89}"
+       "{'accuracy': 0.89,\n",
+       " 'precision': np.float64(0.88),\n",
+       " 'recall': np.float64(0.91),\n",
+       " 'f1': np.float64(0.89)}"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 187,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10151,7 +10190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [
     {
@@ -10160,7 +10199,7 @@
        "True"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10182,7 +10221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 189,
    "metadata": {},
    "outputs": [
     {
@@ -10191,7 +10230,7 @@
        "['gs_random_forest_model_1.joblib']"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10213,7 +10252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 190,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10230,7 +10269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 191,
    "metadata": {},
    "outputs": [
     {
@@ -10246,10 +10285,13 @@
     {
      "data": {
       "text/plain": [
-       "{'accuracy': 0.89, 'precision': 0.88, 'recall': 0.91, 'f1': 0.89}"
+       "{'accuracy': 0.89,\n",
+       " 'precision': np.float64(0.88),\n",
+       " 'recall': np.float64(0.91),\n",
+       " 'f1': np.float64(0.89)}"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 191,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10270,7 +10312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 192,
    "metadata": {},
    "outputs": [
     {
@@ -10279,7 +10321,7 @@
        "True"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10329,12 +10371,12 @@
     "\n",
     "Good news is, `Pipeline` can help us clean it up.\n",
     "\n",
-    "Let's remind ourselves what the data looks like."
+    "Let's remind ourselves what our [`car-sales-extended-missing-data.csv`](https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/data/car-sales-extended-missing-data.csv) looks like in DataFrame form."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 193,
    "metadata": {},
    "outputs": [
     {
@@ -10419,19 +10461,20 @@
        "4  Nissan   Blue       181577.0    3.0  14043.0"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 193,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data = pd.read_csv(\"../data/car-sales-extended-missing-data.csv\")\n",
+    "# data = pd.read_csv(\"../data/car-sales-extended-missing-data.csv\") # load from local directory\n",
+    "data = pd.read_csv(\"https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales-extended-missing-data.csv\") # load directly from URL\n",
     "data.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [
     {
@@ -10445,7 +10488,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 194,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10456,7 +10499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 195,
    "metadata": {},
    "outputs": [
     {
@@ -10470,7 +10513,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 195,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10501,7 +10544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 199,
    "metadata": {},
    "outputs": [
     {
@@ -10510,7 +10553,7 @@
        "0.22188417408787875"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 199,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10532,7 +10575,7 @@
     "np.random.seed(42)\n",
     "\n",
     "# Import data and drop the rows with missing labels\n",
-    "data = pd.read_csv(\"../data/car-sales-extended-missing-data.csv\")\n",
+    "data = pd.read_csv(\"https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales-extended-missing-data.csv\")\n",
     "data.dropna(subset=[\"Price\"], inplace=True)\n",
     "\n",
     "# Define different features and transformer pipelines\n",
@@ -10594,7 +10637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 200,
    "metadata": {},
    "outputs": [
     {
@@ -10612,16 +10655,16 @@
       "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
       "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
       "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.9s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.9s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.8s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.9s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.9s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.9s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.9s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.9s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.9s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.9s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.5s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.5s\n",
       "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
       "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
       "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
@@ -10632,16 +10675,16 @@
       "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
       "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
       "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.8s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.8s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.8s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.8s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.9s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.8s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.8s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.9s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.8s\n",
-      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.8s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.5s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.5s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.5s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.5s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.6s\n",
+      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.6s\n",
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
@@ -10652,16 +10695,16 @@
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.8s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.7s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.7s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.7s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.7s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.6s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.5s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.5s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.5s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.6s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.6s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.6s\n",
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.7s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.8s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.8s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.8s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.8s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.5s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.6s\n",
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
@@ -10672,22 +10715,426 @@
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
       "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.8s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.8s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.8s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.7s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.7s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.7s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.7s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.7s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.7s\n",
-      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.7s\n"
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.5s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.6s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.6s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.5s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   0.5s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.6s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.5s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.5s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.5s\n",
+      "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   0.5s\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
        "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
        "                                        ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
        "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
@@ -10715,7 +11162,7 @@
        "                         &#x27;model__n_estimators&#x27;: [100, 1000],\n",
        "                         &#x27;preprocessor__num__imputer__strategy&#x27;: [&#x27;mean&#x27;,\n",
        "                                                                  &#x27;median&#x27;]},\n",
-       "             verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
+       "             verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
        "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
        "                                        ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
        "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
@@ -10743,7 +11190,7 @@
        "                         &#x27;model__n_estimators&#x27;: [100, 1000],\n",
        "                         &#x27;preprocessor__num__imputer__strategy&#x27;: [&#x27;mean&#x27;,\n",
        "                                                                  &#x27;median&#x27;]},\n",
-       "             verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+       "             verbose=2)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
        "                 ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
        "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
        "                                                                   SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
@@ -10760,7 +11207,9 @@
        "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
        "                                                                   SimpleImputer())]),\n",
        "                                                  [&#x27;Odometer (KM)&#x27;])])),\n",
-       "                (&#x27;model&#x27;, RandomForestRegressor(n_jobs=-1))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
+       "                (&#x27;model&#x27;,\n",
+       "                 RandomForestRegressor(max_depth=5, max_features=&#x27;sqrt&#x27;,\n",
+       "                                       n_jobs=-1))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
        "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
        "                                                  SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
        "                                                                strategy=&#x27;constant&#x27;)),\n",
@@ -10774,7 +11223,7 @@
        "                                 [&#x27;Doors&#x27;]),\n",
        "                                (&#x27;num&#x27;,\n",
        "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer())]),\n",
-       "                                 [&#x27;Odometer (KM)&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Make&#x27;, &#x27;Colour&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">door</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Doors&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=4, strategy=&#x27;constant&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Odometer (KM)&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_jobs=-1)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
+       "                                 [&#x27;Odometer (KM)&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Make&#x27;, &#x27;Colour&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">door</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Doors&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(fill_value=4, strategy=&#x27;constant&#x27;)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Odometer (KM)&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=5, max_features=&#x27;sqrt&#x27;, n_jobs=-1)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
       ],
       "text/plain": [
        "GridSearchCV(cv=5,\n",
@@ -10808,12 +11257,14 @@
        "             verbose=2)"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 200,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "%%time\n",
+    "\n",
     "# Using grid search with pipeline\n",
     "pipe_grid = {\n",
     "    \"preprocessor__num__imputer__strategy\": [\"mean\", \"median\"], # note the double underscore after each prefix \"preprocessor__\"\n",
@@ -10836,7 +11287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 201,
    "metadata": {},
    "outputs": [
     {
@@ -10845,7 +11296,7 @@
        "0.2848784564026805"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 201,
      "metadata": {},
      "output_type": "execute_result"
     }