A4-Simple Linear Regression-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6347c465",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b66039ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('D:\\Data Science material\\Assignments\\Simple Linear Regression\\Salary_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b772af35",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary\n",
       "0               1.1   39343.0\n",
       "1               1.3   46205.0\n",
       "2               1.5   37731.0\n",
       "3               2.0   43525.0\n",
       "4               2.2   39891.0\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36bbc52e",
   "metadata": {},
   "source": [
    "# EDA and Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f8df1ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   YearsExperience  30 non-null     float64\n",
      " 1   Salary           30 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 608.0 bytes\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4b652e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='YearsExperience', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "sns.distplot(dataset['YearsExperience'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c36e141a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Salary', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "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": [
    "sns.distplot(dataset['Salary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d73ade7",
   "metadata": {},
   "source": [
    "# Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3619be94",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YearsExperience</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>0.978242</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 YearsExperience    Salary\n",
       "YearsExperience         1.000000  0.978242\n",
       "Salary                  0.978242  1.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a9ccffe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='YearsExperience', ylabel='Salary'>"
      ]
     },
     "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": [
    "sns.regplot(x=dataset['YearsExperience'], y=dataset['Salary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7ed4e1",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "989ae2a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = smf.ols(\"Salary~YearsExperience\", data = dataset).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5d06f0f",
   "metadata": {},
   "source": [
    "# Model Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d42aa4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept          25792.200199\n",
       "YearsExperience     9449.962321\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Finding Cefficient Parameters\n",
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2d915c94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Intercept          11.346940\n",
       " YearsExperience    24.950094\n",
       " dtype: float64,\n",
       " Intercept          5.511950e-12\n",
       " YearsExperience    1.143068e-20\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Pvalues and tvalues\n",
    "model.tvalues , model.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a0a4445c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9569566641435086, 0.9554194021486339)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Rsquared values\n",
    "model.rsquared , model.rsquared_adj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddac258f",
   "metadata": {},
   "source": [
    "# Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee9ec951",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Manual prediction for say 3 Years Experience\n",
    "Salary = (25792.200199) + (9449.962321)*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4baf00dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Automatic Prediction for say 3 & 5 Years Experience\n",
    "new_data = pd.Series([3,5])\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "68a7d7a5",
   "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>YearsExperience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience\n",
       "0                3\n",
       "1                5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pred = pd.DataFrame(new_data, columns=['YearsExperience'])\n",
    "data_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1a6adcbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    54142.087163\n",
       "1    73042.011806\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(data_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70d9f013",
   "metadata": {},
   "outputs": [],
   "source": [
    "#EDA and Data Visualization, Feature Engineering, Correlation Analysis, \n",
    "#Model Building, Model Testing and Model Predictions using simple linear regression."
   ]
  }
 ],
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