Building Machine Learning Models in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Machine Learning Linear Regression and Mean Absolute Error Models in Python\n",
    "\n",
    "\n",
    "In the modeling stage of the machine learning process, our goal is to choose and apply the appropriate machine learning approach that works with the data we have and solves the problem that we intend to solve. If our objective is to build a model that predicts a numeric or continuous value, then our problem is known as a regression problem. One of the most common models used in solving regression problems is **Linear Regression**. By the end of the tutorial, you will have learned:\n",
    "\n",
    "+ how to collect, explore and prepare data\n",
    "+ how to build and evaluate a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "H4h5CV-p3fsD"
   },
   "source": [
    "## Step 1: Read (GET) the data from the csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZBtXnome3fr0",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>rentals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46.716528</td>\n",
       "      <td>0.815969</td>\n",
       "      <td>13.669663</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.350239</td>\n",
       "      <td>0.800497</td>\n",
       "      <td>15.199782</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.212394</td>\n",
       "      <td>0.592097</td>\n",
       "      <td>13.247558</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34.520000</td>\n",
       "      <td>0.623196</td>\n",
       "      <td>11.687963</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.800562</td>\n",
       "      <td>0.624643</td>\n",
       "      <td>13.148281</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temperature  humidity  windspeed  rentals\n",
       "0    46.716528  0.815969  13.669663      985\n",
       "1    48.350239  0.800497  15.199782      801\n",
       "2    34.212394  0.592097  13.247558     1349\n",
       "3    34.520000  0.623196  11.687963     1562\n",
       "4    36.800562  0.624643  13.148281     1600"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "bikes = pd.read_csv(\"bikes.csv\")\n",
    "bikes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: List out all information about the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   temperature  731 non-null    float64\n",
      " 1   humidity     731 non-null    float64\n",
      " 2   windspeed    731 non-null    float64\n",
      " 3   rentals      731 non-null    int64  \n",
      "dtypes: float64(3), int64(1)\n",
      "memory usage: 23.0 KB\n"
     ]
    }
   ],
   "source": [
    "bikes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>rentals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59.509553</td>\n",
       "      <td>0.486937</td>\n",
       "      <td>9.238886</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.486114</td>\n",
       "      <td>0.185415</td>\n",
       "      <td>3.379815</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>22.602432</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.932208</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>46.117264</td>\n",
       "      <td>0.353548</td>\n",
       "      <td>6.863568</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>59.758972</td>\n",
       "      <td>0.502227</td>\n",
       "      <td>9.503508</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>73.048236</td>\n",
       "      <td>0.624671</td>\n",
       "      <td>11.814559</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.497028</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>21.126627</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temperature    humidity   windspeed      rentals\n",
       "count   731.000000  731.000000  731.000000   731.000000\n",
       "mean     59.509553    0.486937    9.238886  4504.348837\n",
       "std      15.486114    0.185415    3.379815  1937.211452\n",
       "min      22.602432    0.000000    0.932208    22.000000\n",
       "25%      46.117264    0.353548    6.863568  3152.000000\n",
       "50%      59.758972    0.502227    9.503508  4548.000000\n",
       "75%      73.048236    0.624671   11.814559  5956.000000\n",
       "max      90.497028    0.972500   21.126627  8714.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='temperature', ylabel='rentals'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "bikes.plot(kind = 'scatter', x = 'temperature', y = 'rentals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='humidity', ylabel='rentals'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes.plot(kind = 'scatter', x = 'humidity', y = 'rentals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='windspeed', ylabel='rentals'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes.plot(kind = 'scatter', x = 'windspeed', y = 'rentals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: List out the specific data we want to see/use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rentals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>2114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>1796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     rentals\n",
       "0        985\n",
       "1        801\n",
       "2       1349\n",
       "3       1562\n",
       "4       1600\n",
       "..       ...\n",
       "726     2114\n",
       "727     3095\n",
       "728     1341\n",
       "729     1796\n",
       "730     2729\n",
       "\n",
       "[731 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = 'rentals'\n",
    "y = bikes[[response]]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46.716528</td>\n",
       "      <td>0.815969</td>\n",
       "      <td>13.669663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.350239</td>\n",
       "      <td>0.800497</td>\n",
       "      <td>15.199782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.212394</td>\n",
       "      <td>0.592097</td>\n",
       "      <td>13.247558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34.520000</td>\n",
       "      <td>0.623196</td>\n",
       "      <td>11.687963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.800562</td>\n",
       "      <td>0.624643</td>\n",
       "      <td>13.148281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>39.102528</td>\n",
       "      <td>0.482493</td>\n",
       "      <td>10.801229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>39.031972</td>\n",
       "      <td>0.480433</td>\n",
       "      <td>8.996301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>39.031972</td>\n",
       "      <td>0.717730</td>\n",
       "      <td>11.829425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>39.243472</td>\n",
       "      <td>0.523039</td>\n",
       "      <td>12.805314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>35.859472</td>\n",
       "      <td>0.494808</td>\n",
       "      <td>9.346850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     temperature  humidity  windspeed\n",
       "0      46.716528  0.815969  13.669663\n",
       "1      48.350239  0.800497  15.199782\n",
       "2      34.212394  0.592097  13.247558\n",
       "3      34.520000  0.623196  11.687963\n",
       "4      36.800562  0.624643  13.148281\n",
       "..           ...       ...        ...\n",
       "726    39.102528  0.482493  10.801229\n",
       "727    39.031972  0.480433   8.996301\n",
       "728    39.031972  0.717730  11.829425\n",
       "729    39.243472  0.523039  12.805314\n",
       "730    35.859472  0.494808   9.346850\n",
       "\n",
       "[731 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors = list(bikes.columns)\n",
    "predictors.remove(response)\n",
    "x = bikes[predictors]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state = 1234) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Build the Linear Regression Model to Calculate Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression().fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3800.68469948])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   80.35314543, -4665.73867387,  -196.21650368]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model coefficients correspond to the order in which the independent variables are listed in the training data. This means that the equation for the fitted regression line can be written as:\n",
    "\n",
    "$y = 3800.68 + 80.35 \\times temperature - 4665.74 \\times humidity - 196.22 \\times windspeed$\n",
    "\n",
    "With the linear regression equation, we can estimate what our model will predict given any weather condition. For example, given a temperature of $72^{\\circ}F$, $22\\%$ humidity and windspeed of $5$ miles per hour, our model would predict:\n",
    "\n",
    "$7,578 \\text{ bikes} \\approx 3800.68 + 80.35 \\times 72 - 4665.74 \\times .22 - 196.22 \\times 5$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Evaluate the Model via Z-Score and find the Mean Absolute Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9820623857913312"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "194.31620720519683"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "mean_absolute_error(y_test, y_pred)"
   ]
  }
 ],
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