Session-3/EvaluatingVariousMLModelforEnergyMeter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ENERGY METER USING ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fileName = \"Energy Meter.csv\"\n",
    "names = ['Voltage', 'Current', 'Power', 'class']\n",
    "dataset = read_csv(fileName, names=names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarize Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 4)\n",
      "    Voltage  Current  Power   class\n",
      "0    0.0000      0.0    0.0  NoLoad\n",
      "1    0.0000      0.0    0.0  NoLoad\n",
      "2    0.0000      0.0    0.0  NoLoad\n",
      "3    0.0000      0.0    0.0  NoLoad\n",
      "4    0.0000      0.0    0.0  NoLoad\n",
      "5    0.0000      0.0    0.0  NoLoad\n",
      "6    0.0000      0.0    0.0  NoLoad\n",
      "7    0.3833      0.0    0.0  NoLoad\n",
      "8    0.0000      0.0    0.0  NoLoad\n",
      "9    0.0000      0.0    0.0  NoLoad\n",
      "10   0.0000      0.0    0.0  NoLoad\n",
      "11   0.0000      0.0    0.0  NoLoad\n",
      "12   0.0000      0.0    0.0  NoLoad\n",
      "13   0.0000      0.0    0.0  NoLoad\n",
      "14   0.0000      0.0    0.0  NoLoad\n",
      "15   0.0000      0.0    0.0  NoLoad\n",
      "16   0.0000      0.0    0.0  NoLoad\n",
      "17   0.0000      0.0    0.0  NoLoad\n",
      "18   0.0000      0.0    0.0  NoLoad\n",
      "19   0.0000      0.0    0.0  NoLoad\n",
      "          Voltage     Current       Power\n",
      "count  400.000000  400.000000  400.000000\n",
      "mean   160.657320    0.317734   68.065053\n",
      "std     93.125536    0.320430   68.941682\n",
      "min      0.000000    0.000000    0.000000\n",
      "25%     79.151450    0.076849    0.000000\n",
      "50%    213.114800    0.176985   38.430426\n",
      "75%    215.031300    0.398764  101.438042\n",
      "max    304.340200    1.227349  264.859337\n",
      "class\n",
      "Medium    100\n",
      "NoLoad    100\n",
      "Normal    100\n",
      "Risk      100\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset.shape)\n",
    "print(dataset.head(20))\n",
    "print(dataset.describe())\n",
    "print(dataset.groupby('class').size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.plot(kind='bar',subplots=True,layout=(2,2))\n",
    "pyplot.title('BAR PLOT')\n",
    "pyplot.show()\n",
    "\n",
    "dataset.hist()\n",
    "pyplot.title('HISTOGRAM PLOT')\n",
    "pyplot.show()\n",
    "\n",
    "scatter_matrix(dataset)\n",
    "pyplot.title('SCATTER PLOT')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating various ML Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6 ML Algorithm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = dataset.values\n",
    "X = array[:,0:3]\n",
    "y = array[:,3]\n",
    "X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC(gamma='auto')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.996875 (0.009375)\n",
      "LDA: 0.996875 (0.009375)\n",
      "KNN: 0.993750 (0.018750)\n",
      "CART: 0.996875 (0.009375)\n",
      "NB: 0.990625 (0.020010)\n",
      "SVM: 0.990625 (0.020010)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEICAYAAABWJCMKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYh0lEQVR4nO3de7xdZWHm8d/TUGCoSEIIqCQQRiMlE242xuoIXlALlBFBRxOxRIZLcQC59CIiH4HOFMHWUqo4GaZQpcpNIQ5OuQ6OgC0IQQKTEC7hJjGgQcBAwyUnPv1jvZHF5pycfTiXHfI+38/nfM5e633ftd5372Q/a71r73Vkm4iIqM9v9boDERHRGwmAiIhKJQAiIiqVAIiIqFQCICKiUgmAiIhKJQBiVEn6hqT/PkrbPkjStesof6+kZaOx79c6SSdJ+vte9yN6KwEQI0LSDyU9JWmTsdqn7W/b/lCrD5b0lrHavxqflbRI0r9KWibpO5J2Hqs+vFq2T7d9WK/7Eb2VAIhhkzQV2AMw8OEx2udGY7GfQZwNHAt8FtgSeCvwPeAPe9mpwawnz12sBxIAMRIOBm4BvgHMXVdFSX8u6TFJyyUd1j5ql7SFpAskrZD0iKSTJf1WKfu0pH+WdJakJ4FTy7oflfIbyy7ulPSspE+09vknkn5R9ntIa/03JH1d0lWlzT9LeoOkvy1nM/dI2n2AcUwDjgLm2P6B7RdsrypnJWcMcTxPS3pQ0rvK+kdLf+d29HWepOskPSPpBknbt8rPLu1WSrpd0h6tslMlfVfStyStBD5d1n2rlG9ayn5Z+nKbpG1K2ZskXSHpSUlLJR3esd1LyxifkbRY0sx1vf6xfkkAxEg4GPh2+fmDtW8enSTtDZwAfAB4C/CejipfBbYA/n0pOxg4pFX+DuBBYGvgL9sNbe9ZHu5q+3W2LynLbyjb3BY4FDhH0oRW048DJwNbAS8ANwM/KcvfBf5mgDHvBSyzfesA5d2O5y5gInAhcDHwdprn5lPA1yS9rlX/IOC/lb4tpHm+17oN2I3mTORC4DuSNm2V71/GM76jHTShvQUwpfTlSOC5UnYRsAx4E/Ax4HRJe7Xafrj0ezxwBfC1dTwfsZ5JAMSwSHo3sD1wqe3bgQeATw5Q/ePAP9hebHsVcFprO+OATwCft/2M7YeBrwB/1Gq/3PZXbffZfo7urAb+wvZq21cCzwI7tsrn277d9vPAfOB52xfYXgNcAvR7BkDzRvnYQDvtcjwP2f6H1r6mlL6+YPta4EWaMFjrn2zfaPsF4AvAOyVNAbD9Ldu/LM/NV4BNOsZ5s+3v2f51P8/d6jKet9heU56PlWXb7wY+Z/t52wuBv+8Yw49sX1nG8I/ArgM9J7H+SQDEcM0FrrX9RFm+kIGngd4EPNpabj/eCtgYeKS17hGaI/f+6nfrl7b7WsurgPZR9c9bj5/rZ7ld92XbBd64jv12M57OfWF7Xfv/zfhtPws8SfOcrp3mWiLpV5Kepjmi36q/tv34R+Aa4OIyNfdlSb9dtv2k7WfWMYbHW49XAZvmGsNrRwIgXjVJ/47mqP49kh6X9DhwPLCrpP6OBB8DJreWp7QeP0FzJLp9a912wM9ay+vTrWuvByavY867m/EM1W+erzI1tCWwvMz3f47mtZhgezzwK0CttgM+d+Xs6DTb04F3AfvRTFctB7aUtPkIjiHWIwmAGI6PAGuA6TTzz7sBOwE30byBdLoUOETSTpI2A764tqBMIVwK/KWkzcsFzhOAbw2hPz+nmW8fdbbvB74OXKTm+wYbl4upsyWdOELj6bSvpHdL2pjmWsCPbT8KbA70ASuAjSR9EXh9txuV9D5JO5dpq5U0wbWmbPtfgC+Vse1Ccx2l8xpCvEYlAGI45tLM6f/U9uNrf2guBB7UORVg+yrg74D/ByylueAKzcVXgGOAf6W50Psjmumk84fQn1OBb5ZPsnz8VY5pKD5LM9ZzgKdprn8cAHy/lA93PJ0uBE6hmfr5PZqLwtBM31wF3EczRfM8Q5suewPNBeKVwBLgBl4KqjnAVJqzgfnAKbavG8YYYj2i/EGY6BVJOwGLgE065umjg6Rv0Hzq6ORe9yU2HDkDiDEl6YAyXTIBOBP4ft78I3ojARBj7Y9p5qofoLl+8JnedieiXpkCioioVM4AIiIq9Zr6wsZWW23lqVOn9robERGvKbfffvsTtid1rn9NBcDUqVNZsGBBr7sREfGaIumR/tZnCigiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIq1VUASNpb0r2Slko6sZ/yCZLmS7pL0q2SZrTKjpW0SNJiSce11u8m6RZJCyUtkDRrZIYUERHdGDQAJI0DzgH2AaYDcyRN76h2ErDQ9i7AwcDZpe0M4HBgFrArsJ+kaaXNl4HTbO8GfLEsR0TEGOnmDGAWsNT2g7ZfBC4G9u+oMx24HsD2PcBUSdsAOwG32F5luw+4ATigtDHw+vJ4C2D5sEYSERFD0k0AbAs82lpeVta13QkcCFCmcrYHJgOLgD0lTZS0GbAvMKW0OQ74K0mPAn8NfP7VDiIiIoaumwBQP+vcsXwGMEHSQuAY4A6gz/YS4EzgOuBqmqDoK20+AxxvewpwPHBevzuXjijXCBasWLGii+5GREQ3ugmAZbx01A7Nkf3Lpmtsr7R9SJnPPxiYBDxUys6z/TbbewJPAveXZnOBy8vj79BMNb2C7XNtz7Q9c9KkSV0OKyIiBtNNANwGTJO0g6SNgdnAFe0KksaXMoDDgBttryxlW5ff29FME11U6i0H3lMev5+XgiEiIsbARoNVsN0n6WjgGmAccL7txZKOLOXzaC72XiBpDXA3cGhrE5dJmgisBo6y/VRZfzhwtqSNgOeBI0ZqUBERMTjZndP566+ZM2d6wYIFve5GRMRriqTbbc/sXJ9vAkdEVCoBEBFRqQRARESlEgAREZVKAEREVCoBEBFRqQRARESlEgAREZVKAEREVCoBEBFRqQRARESlEgAREZVKAEREVCoBEBFRqQRARESlEgAREZVKAEREVCoBEBFRqQRARESlEgAREZVKAEREVCoBEBFRqY163YGxcprU6y5wij0q2+312EZrXBuqXr9ekNcsGjkDiIioVAIgIqJSCYCIiEolACIiKpUAiIioVAIgIqJSCYCIiEolACIiKpUAiIioVAIgIqJSCYCIiEolACIiKpUAiIioVAIgIqJSCYCIiEolACIiKpUAiIioVFcBIGlvSfdKWirpxH7KJ0iaL+kuSbdKmtEqO1bSIkmLJR3X0e6Yst3Fkr48/OFERES3Bv2TkJLGAecAHwSWAbdJusL23a1qJwELbR8g6XdL/b1KEBwOzAJeBK6W9E+275f0PmB/YBfbL0jaemSHFhER69LNGcAsYKntB22/CFxM88bdNh24HsD2PcBUSdsAOwG32F5luw+4ATigtPkMcIbtF0q7Xwx7NBER0bVuAmBb4NHW8rKyru1O4EAASbOA7YHJwCJgT0kTJW0G7AtMKW3eCuwh6ceSbpD09v52LukISQskLVixYkW344qIiEF0EwDqZ507ls8AJkhaCBwD3AH02V4CnAlcB1xNExR9pc1GwATg94E/Ay6V9Ip92T7X9kzbMydNmtRFdyMiohuDXgOgOeKf0lqeDCxvV7C9EjgEoLyJP1R+sH0ecF4pO71sb+12L7dt4FZJvwa2AnKYHxExBro5A7gNmCZpB0kbA7OBK9oVJI0vZQCHATeWUGDtxV1J29FME11U6n0PeH8peyuwMfDE8IYTERHdGvQMwHafpKOBa4BxwPm2F0s6spTPo7nYe4GkNcDdwKGtTVwmaSKwGjjK9lNl/fnA+ZIW0XxCaG45G4iIiDHQzRQQtq8EruxYN6/1+GZg2gBt9xhg/YvAp7ruaUREjKh8EzgiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIqlQCIiKhUAiAiolIJgIiISiUAIiIq1VUASNpb0r2Slko6sZ/yCZLmS7pL0q2SZrTKjpW0SNJiScf10/ZPJVnSVsMbSkREDMWgASBpHHAOsA8wHZgjaXpHtZOAhbZ3AQ4Gzi5tZwCHA7OAXYH9JE1rbXsK8EHgp8MfSkREDEU3ZwCzgKW2H7T9InAxsH9HnenA9QC27wGmStoG2Am4xfYq233ADcABrXZnAX8OeHjDiIiIoeomALYFHm0tLyvr2u4EDgSQNAvYHpgMLAL2lDRR0mbAvsCUUu/DwM9s37munUs6QtICSQtWrFjRRXcjIqIb3QSA+lnXecR+BjBB0kLgGOAOoM/2EuBM4Drgapqg6Cth8AXgi4Pt3Pa5tmfanjlp0qQuuhsREd3YqIs6yyhH7cVkYHm7gu2VwCEAkgQ8VH6wfR5wXik7vWzvzcAOwJ1NdSYDP5E0y/bjwxhPRER0qZsAuA2YJmkH4GfAbOCT7QqSxgOryjWCw4AbSyggaWvbv5C0Hc000TttPwVs3Wr/MDDT9hMjMKaIiOjCoAFgu0/S0cA1wDjgfNuLJR1ZyufRXOy9QNIa4G7g0NYmLpM0EVgNHFXe/CMiose6OQPA9pXAlR3r5rUe3wxM62xXyvboYvtTu+lHRESMnK4CIKIXTlN/nz8YO6c4n06ODVtuBRERUakEQEREpRIAERGVSgBERFQqARARUakEQEREpRIAERGVSgBERFQqARARUakEQEREpRIAERGVSgBERFQqARARUakEQEREpRIAERGVSgBERFQqARARUakEQEREpRIAERGVSgBERFQqARARUakEQEREpRIAERGVSgBERFQqARARUakEQEREpRIAERGVSgBERFQqARARUakEQEREpRIAERGVSgBERFQqARARUakEQEREpRIAERGVSgBERFQqARARUakEQEREpboKAEl7S7pX0lJJJ/ZTPkHSfEl3SbpV0oxW2bGSFklaLOm41vq/knRPaTNf0viRGVJERHRj0ACQNA44B9gHmA7MkTS9o9pJwELbuwAHA2eXtjOAw4FZwK7AfpKmlTbXATNKm/uAzw9/OBER0a1uzgBmAUttP2j7ReBiYP+OOtOB6wFs3wNMlbQNsBNwi+1VtvuAG4ADSr1ryzqAW4DJwx5NRER0rZsA2BZ4tLW8rKxruxM4EEDSLGB7mjf0RcCekiZK2gzYF5jSzz7+C3BVfzuXdISkBZIWrFixoovuRkREN7oJAPWzzh3LZwATJC0EjgHuAPpsLwHOpJnuuZomKPraDSV9oaz7dn87t32u7Zm2Z06aNKmL7kZERDc26qLOMl5+1D4ZWN6uYHslcAiAJAEPlR9snwecV8pOL9ujLM8F9gP2st0ZKhERMYq6OQO4DZgmaQdJGwOzgSvaFSSNL2UAhwE3llBA0tbl93Y000QXleW9gc8BH7a9aiQGExER3Rv0DMB2n6SjgWuAccD5thdLOrKUz6O52HuBpDXA3cChrU1cJmkisBo4yvZTZf3XgE2A65qTBm6xfeQIjSsiIgbRzRQQtq8EruxYN6/1+GZgWme7UrbHAOvf0n03IyJipOWbwBERlUoARERUKgEQEVGpBEBERKUSABERlUoARERUKgEQEVGpBEBERKUSABERlUoARERUKgEQEVGpBEBERKUSABERlUoARERUKgEQEVGpBEBERKUSABERlUoARERUKgEQEVGpBEBERKUSABERlUoARERUKgEQEVGpBEBERKUSABERlUoARERUKgEQEVGpBEBERKU26nUHImLDcZrU0/2fYo/Kdns9LhidseUMICKiUgmAiIhKJQAiIiqVAIiIqFQCICKiUgmAiIhKJQAiIiqVAIiIqFQCICKiUgmAiIhKJQAiIirVVQBI2lvSvZKWSjqxn/IJkuZLukvSrZJmtMqOlbRI0mJJx7XWbynpOkn3l98TRmZIERHRjUEDQNI44BxgH2A6MEfS9I5qJwELbe8CHAycXdrOAA4HZgG7AvtJmlbanAhcb3sacH1ZjoiIMdLNGcAsYKntB22/CFwM7N9RZzrNmzi27wGmStoG2Am4xfYq233ADcABpc3+wDfL428CHxnWSCIiYkjkQW4xKuljwN62DyvLfwS8w/bRrTqnA5vaPkHSLOBfgHcAq4D/DbwTeI4mJBbYPkbS07bHt7bxlO1XTANJOgI4oizuCNz7qkc7PFsBT/Ro36NtQx3bhjou2HDHlnGNju1tT+pc2c3fA+jvRtidqXEGcLakhcD/B+4A+mwvkXQmcB3wLHAn0DeUXts+Fzh3KG1Gg6QFtmf2uh+jYUMd24Y6Lthwx5Zxja1uAmAZMKW1PBlY3q5geyVwCIAkAQ+VH2yfB5xXyk4v2wP4uaQ32n5M0huBXwxjHBERMUTdXAO4DZgmaQdJGwOzgSvaFSSNL2UAhwE3llBA0tbl93bAgcBFpd4VwNzyeC7NVFFERIyRQc8AbPdJOhq4BhgHnG97saQjS/k8mou9F0haA9wNHNraxGWSJgKrgaNsP1XWnwFcKulQ4KfAfx6pQY2Snk9DjaINdWwb6rhgwx1bxjWGBr0IHBERG6Z8EzgiolIJgIiISiUA+iHp2X7WnSrpZ5IWSrpb0pxe9G2ouhjL/ZIu7/x2t6TdJVnSH4xdb7vTHpOkfcsYtivjWrX2gwf91LWkr7SW/1TSqWPW8XWQ9AZJF0t6oPz7ulLSW0vZ8ZKel7RFq/57Jf1K0h2S7pH015J2Lq/pQklPSnqoPP6/vRtZ/9b1WnT8+7xH0v+QtN6+V0n6QrnVzV2lz1dJ+lJHnd0kLSmPH5Z0U0f5QkmLxrLfkAAYqrNs70bzLeb/Kem3e92hYTjL9m7lVhyXAD+Q1P6iyBzgR+X3eknSXsBXab6o+NOy+gngTwZo8gJwoKStxqJ/3SofnZ4P/ND2m21Pp7m9yjalyhyaT+Md0NH0Jtu7A7sD+wGvL6/pbjSfsvuzsvyBMRnI0Az2Wqz9vzYd2Bl4z5j1bAgkvZPmuX9buRXOB2g+4PKJjqqzgQtby5tLmlK2sdNY9LU/CYBXwfb9NN9y3iBuYGf7EuBa4JPwmzekjwGfBj4kadPe9a5/kvYA/hfwh7YfaBWdD3xC0pb9NOuj+TTG8WPQxaF4H7C6fKIOANsLbd8k6c3A64CTGSCMbT8HLAS2HYvOjpBuX4uNgU2Bpwap1ytvBJ6w/QKA7Sds3wA8LekdrXofp7mNzlqX8lJIzOGlj8ePqQTAqyDpbcD9tjekL6/9BPjd8vg/Ag+VN9YfAvv2qlMD2ITmeyMfKfeeanuWJgSOHaDtOcBB7emU9cAM4PYByta+OdwE7Nie3lpLzZ10pwE3jloPR8e6Xovjy50FHgPus71wbLvWtWuBKZLuk/R1SWvPVC6iOepH0u8DvywHjmt9l+Z7UQD/Cfj+WHW4LQEwNMdLuhf4MXBqj/sy0tq3/JjDS0crF7P+TQOtprnf1KEDlP8dMFfS6zsLyhcULwA+O3rdG1GzgYtt/xq4nJd/X2YPSXcBjwP/x/bjvejgqzXIa7F2Cmhr4HckzR7TznXJ9rPA79Hcr2wFcImkT9P8v/lYuXYxm1ce4T8JPFXGtYRmRmHMJQCG5izbO9Kcul2wPk6NDMPuwBI1t//+KPBFSQ/TzLHvI2nzXnauw69pTqnfLumkzkLbT9PMt/7XAdr/LU14/M6o9XBoFtO8ibyMpF1ojuyvK6/FbF4exjeVeeedgc9I2m0M+jrS1vla2F4NXA3sOZadGgrba2z/0PYpwNHAR20/CjxMc+3iozRTPp0uoTkL6sn0DyQAXhXblwMLeOlWFq9pkj4KfIjmH+IHgDttT7E91fb2wGWsZ7frtr2K5uLbQeXb5J3+Bvhj+vm2u+0naf5DDnQGMdZ+AGwi6fC1KyS9nebvapxaXoeptt8EbCtp+3Zj2/cBXwI+N5adHgmDvRbletS7gAf6K+81STvqpb9xArAb8Eh5fBFwFvCA7WWvaNxc+P8yzV0WeiIB0L/NJC1r/ZzQT52/AE5Ynz+eVgw0luPXfgwU+BTwftsraI4w53ds4zLKBeL1SXnz2Bs4WdL+HWVP0IxjkwGaf4XmFr095+br+AcAHywfA11MM8X4Xl75WsynzC13mAfsKWmHUezqaOnvtVh7DWARTYh/fcx71Z3XAd8sH929i+ZTS6eWsu8A/4GXX/z9DdvP2D6z/J2VnsitICIiKrW+H71GRMQoSQBERFQqARARUakEQEREpRIAERGVSgBERFQqARARUal/A1onWib1N2TnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = []\n",
    "names = []\n",
    "res = []\n",
    "for name, model in models:\n",
    "    kfold = StratifiedKFold(n_splits=10, random_state=None)\n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    res.append(cv_results.mean())\n",
    "    print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))\n",
    "\n",
    "pyplot.ylim(.990, .999)\n",
    "pyplot.bar(names, res, color ='maroon', width = 0.6)\n",
    "\n",
    "pyplot.title('Algorithm Comparison')\n",
    "pyplot.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}