Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Linear Regression\n",
    "\n",
    "Let suppose, you own an ice cream business and you would like to get an idea of your future revenue based on the outside air temperature. A simple Linear Regression model can help you figure that out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 2)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"IceCream.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>24.566884</td>\n",
       "      <td>534.799028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>26.005191</td>\n",
       "      <td>625.190122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>27.790554</td>\n",
       "      <td>660.632289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>20.595335</td>\n",
       "      <td>487.706960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11.503498</td>\n",
       "      <td>316.240194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature     Revenue\n",
       "0    24.566884  534.799028\n",
       "1    26.005191  625.190122\n",
       "2    27.790554  660.632289\n",
       "3    20.595335  487.706960\n",
       "4    11.503498  316.240194"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 2 columns):\n",
      "Temperature    500 non-null float64\n",
      "Revenue        500 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 7.9 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>22.232225</td>\n",
       "      <td>521.570777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>8.096388</td>\n",
       "      <td>175.404751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>17.122258</td>\n",
       "      <td>405.558681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>22.392791</td>\n",
       "      <td>529.368565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>27.740674</td>\n",
       "      <td>642.257922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Temperature      Revenue\n",
       "count   500.000000   500.000000\n",
       "mean     22.232225   521.570777\n",
       "std       8.096388   175.404751\n",
       "min       0.000000    10.000000\n",
       "25%      17.122258   405.558681\n",
       "50%      22.392791   529.368565\n",
       "75%      27.740674   642.257922\n",
       "max      45.000000  1000.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['Temperature']]\n",
    "y = df[['Revenue']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient (m):  [[21.37822239]]\n",
      "Y-intercept/Constant (b):  [46.91550105]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression(fit_intercept =True)\n",
    "regressor.fit(X_train, y_train)\n",
    "print('Coefficient (m): ', regressor.coef_)\n",
    "print('Y-intercept/Constant (b): ', regressor.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict\n",
    "y_predict = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot train results\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.scatter(X_train, y_train, color = 'blue')\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'red')\n",
    "plt.ylabel('Revenue')\n",
    "plt.xlabel('Temperature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot test results\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.scatter(X_test, y_test, color = 'red')\n",
    "plt.plot(X_test, regressor.predict(X_test), color = 'green')\n",
    "plt.ylabel('Revenue')\n",
    "plt.xlabel('Temperature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>30.427792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>254</td>\n",
       "      <td>28.335363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>283</td>\n",
       "      <td>28.865589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>445</td>\n",
       "      <td>18.908489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>461</td>\n",
       "      <td>28.900192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11.503498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>318</td>\n",
       "      <td>28.298690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>331</td>\n",
       "      <td>29.241752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>245</td>\n",
       "      <td>14.263541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>14.352514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Temperature\n",
       "90     30.427792\n",
       "254    28.335363\n",
       "283    28.865589\n",
       "445    18.908489\n",
       "461    28.900192\n",
       "..           ...\n",
       "4      11.503498\n",
       "318    28.298690\n",
       "331    29.241752\n",
       "245    14.263541\n",
       "5      14.352514\n",
       "\n",
       "[150 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[292.83983955]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict_num = regressor.predict([[11.503498]])\n",
    "y_predict_num"
   ]
  }
 ],
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