SIMPLE_LINEAR_SALARY.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SIMPLE_LINEAR_REGRESSION_SALARY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Important Libaraies\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.graphics.regressionplots import influence_plot"
   ]
  },
  {
   "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>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",
       "  </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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing dataset Salary_Data.csv\n",
    "data = pd.read_csv('F://RStudio//excelR//assignments//assignment 4//Salary_Data.csv')\n",
    "data.head()"
   ]
  },
  {
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Correlation\n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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='YearsExperience', ylabel='Density'>"
      ]
     },
     "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": [
    "# Density Plot\n",
    "sns.distplot(data['YearsExperience'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='YearsExperience', ylabel='Salary'>"
      ]
     },
     "execution_count": 7,
     "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": [
    "model = smf.ols(\"Salary~YearsExperience\",data=data).fit()\n",
    "sns.regplot(x=\"YearsExperience\",y=\"Salary\",data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept          25792.200199\n",
       "YearsExperience     9449.962321\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***B0 = 25792.20***     ***B1 = 9449.96***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.tvalues,model.pvalues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***p-value < 0.05, so we can proceed and predict with our assumption.***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Manual Check :***\n",
    "Let's check for YearsExpereince = 5 Yrs.\n",
    "* **Salary** = B0 + B1 * YearsExperience*\n",
    "***Salary** = 25792.20 + 9449.96 * 5\n",
    ">     = 73041.99 Rs/- = 73042 Rs/-\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    73042.011806\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new = pd.Series([5])\n",
    "data_prediction = pd.DataFrame(data_new,columns = ['YearsExperience'])\n",
    "model.predict(data_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9569566641435086"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}