Multiple Linear regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv('50_Startups.csv')\n",
    "X = dataset.iloc[:, :-1]\n",
    "y = dataset.iloc[:, 4]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 5)\n"
     ]
    }
   ],
   "source": [
    "print(dataset.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R&D Spend          float64\n",
      "Administration     float64\n",
      "Marketing Spend    float64\n",
      "State               object\n",
      "Profit             float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(dataset.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R&D Spend          0\n",
       "Administration     0\n",
       "Marketing Spend    0\n",
       "State              0\n",
       "Profit             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing values\n",
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
      "0   165349.20       136897.80        471784.10    New York  192261.83\n",
      "1   162597.70       151377.59        443898.53  California  191792.06\n",
      "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
      "3   144372.41       118671.85        383199.62    New York  182901.99\n",
      "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
      "5   131876.90        99814.71        362861.36    New York  156991.12\n",
      "6   134615.46       147198.87        127716.82  California  156122.51\n",
      "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
      "8   120542.52       148718.95        311613.29    New York  152211.77\n",
      "9   123334.88       108679.17        304981.62  California  149759.96\n",
      "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
      "11  100671.96        91790.61        249744.55  California  144259.40\n",
      "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
      "13   91992.39       135495.07        252664.93  California  134307.35\n",
      "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
      "15  114523.61       122616.84        261776.23    New York  129917.04\n",
      "16   78013.11       121597.55        264346.06  California  126992.93\n",
      "17   94657.16       145077.58        282574.31    New York  125370.37\n",
      "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
      "19   86419.70       153514.11             0.00    New York  122776.86\n"
     ]
    }
   ],
   "source": [
    "# head\n",
    "print(dataset.head(20))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           R&D Spend  Administration  Marketing Spend         Profit\n",
      "count      50.000000       50.000000        50.000000      50.000000\n",
      "mean    73721.615600   121344.639600    211025.097800  112012.639200\n",
      "std     45902.256482    28017.802755    122290.310726   40306.180338\n",
      "min         0.000000    51283.140000         0.000000   14681.400000\n",
      "25%     39936.370000   103730.875000    129300.132500   90138.902500\n",
      "50%     73051.080000   122699.795000    212716.240000  107978.190000\n",
      "75%    101602.800000   144842.180000    299469.085000  139765.977500\n",
      "max    165349.200000   182645.560000    471784.100000  192261.830000\n"
     ]
    }
   ],
   "source": [
    "print(dataset.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New York      17\n",
      "California    17\n",
      "Florida       16\n",
      "Name: State, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset['State'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New York      0.34\n",
      "California    0.34\n",
      "Florida       0.32\n",
      "Name: State, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# for Percentages\n",
    "print(dataset['State'].value_counts()/dataset.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograms\n",
    "dataset.hist(figsize=(10,10))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "scatter_matrix(dataset)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "plt.boxplot(dataset['Profit'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "plt.boxplot(dataset['R&D Spend'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oznbprFtu/dxi1cGqGlTVYGpqaqmhS5Iu0EouN/1z4LtVNVdV/xf4MvCLwFXt8hPANuD5tjwLbAdo668E5sfrC/r06pKkVbKSkHgW2JXkze3ewo3Ad4CvAR9obaaBh9rysfaatv6rVVWtvrfNfroG2Al8A3gC2NlmS13G6Ob2sRWMV5K0TBuXbrK4qvp6kgeBbwKvAN8CDgL/BTia5NOtdm/rci/wpSQzjM4g9rbtPJ3kAUYB8wrwkar6MUCSjwLHGc2cOlRVT086XknS8mX0YX79GAwGNRwO13oY0jmSsN7+3rR+JDlZVYOFdb9xLUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqSuFYVEkquSPJjkvyf5syT/KMnmJCeSnGrPm1rbJLk7yUySbye5bmw70639qSTTY/XrkzzZ+tydJCsZryRpeVZ6JvE54L9W1c8C/wD4M+AO4JGq2gk80l4D3ALsbI/9wD0ASTYDB4D3ADcAB84GS2uzf6zf7hWOV5K0DBOHRJIrgH8C3AtQVS9X1YvAHuBwa3YYuLUt7wGO1MjjwFVJ3gbcDJyoqvmqOgOcAHa3dVdU1WNVVcCRsW1JklbBSs4kfgqYA/5zkm8l+WKSnwDeWlUvALTnq1v7rcBzY/1nW+189dlF6udIsj/JMMlwbm5uBYckSRq3kpDYCFwH3FNV/xD43/ztpaXFLHY/oSaon1usOlhVg6oaTE1NnX/UkqQLtpKQmAVmq+rr7fWDjELje+1SEe359Fj77WP9twHPL1HftkhdkrRKJg6Jqvob4Lkk72ylG4HvAMeAszOUpoGH2vIx4LY2y2kX8FK7HHUcuCnJpnbD+ibgeFv3gyS72qym28a2JUlaBRtX2P83gN9PchnwDPBhRsHzQJJ9wLPAB1vbrwDvB2aAH7a2VNV8kk8BT7R2n6yq+bZ8O3Af8Cbg4faQJK2SjCYOrR+DwaCGw+FaD0M6RxLW29+b1o8kJ6tqsLDuN64lSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVLXSr9MJ71hTfLvTSbp43crtJYMCWlCvnnrjcDLTZKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1rbv/TJdkDvirtR6HtIgtwPfXehBSx9+vqqmFxXUXEtJrVZLhYv8eUnot83KTJKnLkJAkdRkS0uo5uNYDkJbLexKSpC7PJCRJXYaEJKnLkJAusSSHkpxO8tRaj0VaLkNCuvTuA3av9SCkSRgS0iVWVY8C82s9DmkShoQkqcuQkCR1GRKSpC5DQpLUZUhIl1iS+4HHgHcmmU2yb63HJF0of5ZDktTlmYQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSer6//OXqEJwv8A8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(dataset['Administration'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.boxplot(dataset['Marketing Spend'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outliers handleing\n",
    "# Here we will  cap the outlier for above 95th percentile to 95th percentile and  below 5th percentile to 5th percentile\n",
    "\n",
    "#dataset.loc[dataset['CreditScore']>dataset['CreditScore'].quantile(0.95),['CreditScore']]=dataset['CreditScore'].quantile(0.95)\n",
    "\n",
    "#dataset.loc[dataset['salary']>dataset['salary'].quantile(0.95),['salary']]=dataset['salary'].quantile(0.95)\n",
    "dataset.loc[dataset['Profit']<dataset['Profit'].quantile(0.05),['Profit']]=dataset['Profit'].quantile(0.05)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.boxplot(dataset['Profit'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation matrix\n",
    "cols=['Profit','Marketing Spend','Administration','R&D Spend']\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(dataset.corr(),cmap='cool', vmin=-1, vmax=1, interpolation='none')\n",
    "fig.colorbar(cax)\n",
    "ticks = np.arange(0,4,1)\n",
    "ax.set_xticks(ticks)\n",
    "ax.set_yticks(ticks)\n",
    "ax.set_xticklabels(cols)\n",
    "ax.set_yticklabels(cols)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.241955</td>\n",
       "      <td>0.724248</td>\n",
       "      <td>0.980966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administration</th>\n",
       "      <td>0.241955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>0.195216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing Spend</th>\n",
       "      <td>0.724248</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.748498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>0.980966</td>\n",
       "      <td>0.195216</td>\n",
       "      <td>0.748498</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 R&D Spend  Administration  Marketing Spend    Profit\n",
       "R&D Spend         1.000000        0.241955         0.724248  0.980966\n",
       "Administration    0.241955        1.000000        -0.032154  0.195216\n",
       "Marketing Spend   0.724248       -0.032154         1.000000  0.748498\n",
       "Profit            0.980966        0.195216         0.748498  1.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x720a358>"
      ]
     },
     "execution_count": 20,
     "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": [
    "\n",
    "#Categorical-Continuous combination\n",
    "dataset.boxplot(column='Profit', by= 'State')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert the column into categorical columns\n",
    "\n",
    "states=pd.get_dummies(X['State'],drop_first=True)\n",
    "# Drop the state coulmn\n",
    "X=X.drop('State',axis=1)\n",
    "\n",
    "# concat the dummy variables\n",
    "X=pd.concat([X,states],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Florida</th>\n",
       "      <th>New York</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend  Florida  New York\n",
       "0   165349.20       136897.80        471784.10        0         1\n",
       "1   162597.70       151377.59        443898.53        0         0\n",
       "2   153441.51       101145.55        407934.54        1         0\n",
       "3   144372.41       118671.85        383199.62        0         1\n",
       "4   142107.34        91391.77        366168.42        1         0\n",
       "5   131876.90        99814.71        362861.36        0         1\n",
       "6   134615.46       147198.87        127716.82        0         0\n",
       "7   130298.13       145530.06        323876.68        1         0\n",
       "8   120542.52       148718.95        311613.29        0         1\n",
       "9   123334.88       108679.17        304981.62        0         0\n",
       "10  101913.08       110594.11        229160.95        1         0\n",
       "11  100671.96        91790.61        249744.55        0         0\n",
       "12   93863.75       127320.38        249839.44        1         0\n",
       "13   91992.39       135495.07        252664.93        0         0\n",
       "14  119943.24       156547.42        256512.92        1         0\n",
       "15  114523.61       122616.84        261776.23        0         1\n",
       "16   78013.11       121597.55        264346.06        0         0\n",
       "17   94657.16       145077.58        282574.31        0         1\n",
       "18   91749.16       114175.79        294919.57        1         0\n",
       "19   86419.70       153514.11             0.00        0         1\n",
       "20   76253.86       113867.30        298664.47        0         0\n",
       "21   78389.47       153773.43        299737.29        0         1\n",
       "22   73994.56       122782.75        303319.26        1         0\n",
       "23   67532.53       105751.03        304768.73        1         0\n",
       "24   77044.01        99281.34        140574.81        0         1\n",
       "25   64664.71       139553.16        137962.62        0         0\n",
       "26   75328.87       144135.98        134050.07        1         0\n",
       "27   72107.60       127864.55        353183.81        0         1\n",
       "28   66051.52       182645.56        118148.20        1         0\n",
       "29   65605.48       153032.06        107138.38        0         1\n",
       "30   61994.48       115641.28         91131.24        1         0\n",
       "31   61136.38       152701.92         88218.23        0         1\n",
       "32   63408.86       129219.61         46085.25        0         0\n",
       "33   55493.95       103057.49        214634.81        1         0\n",
       "34   46426.07       157693.92        210797.67        0         0\n",
       "35   46014.02        85047.44        205517.64        0         1\n",
       "36   28663.76       127056.21        201126.82        1         0\n",
       "37   44069.95        51283.14        197029.42        0         0\n",
       "38   20229.59        65947.93        185265.10        0         1\n",
       "39   38558.51        82982.09        174999.30        0         0\n",
       "40   28754.33       118546.05        172795.67        0         0\n",
       "41   27892.92        84710.77        164470.71        1         0\n",
       "42   23640.93        96189.63        148001.11        0         0\n",
       "43   15505.73       127382.30         35534.17        0         1\n",
       "44   22177.74       154806.14         28334.72        0         0\n",
       "45    1000.23       124153.04          1903.93        0         1\n",
       "46    1315.46       115816.21        297114.46        1         0\n",
       "47       0.00       135426.92             0.00        0         0\n",
       "48     542.05        51743.15             0.00        0         1\n",
       "49       0.00       116983.80         45173.06        0         0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48930.16638970106\n",
      "[ 7.50600463e-01  1.70739263e-02  3.16812707e-02 -2.52333274e+03\n",
      " -1.46451263e+02]\n"
     ]
    }
   ],
   "source": [
    "# Fitting Simple Linear Regression to the Training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "print(regressor.intercept_)\n",
    "print(regressor.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <td>0.750600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administration</th>\n",
       "      <td>0.017074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing Spend</th>\n",
       "      <td>0.031681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>-2523.332740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>-146.451263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Coefficient\n",
       "R&D Spend           0.750600\n",
       "Administration      0.017074\n",
       "Marketing Spend     0.031681\n",
       "Florida         -2523.332740\n",
       "New York         -146.451263"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(regressor.coef_,X.columns,columns=['Coefficient'])\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9685285132685564\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "regressor.coef_\n",
    "\n",
    "r2 = r2_score(y_train,regressor.predict(X_train))\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9383795704565936\n"
     ]
    }
   ],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "score=r2_score(y_test,y_pred)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#import seaborn as sns\n",
    "\n",
    "#sns.distplot((y_test-y_pred),bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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