{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  \n",
    "import pandas as pd  \n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"https://github.com/masterfloss/data/blob/main/socialmedia.xlsx?raw=true\")\n",
    "X=df[['Linux']]**(1/2)\n",
    "X1=df[['Linux']]\n",
    "Y=df[['Facebook']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#linear_regressor = LinearRegression()  \n",
    "linear_regressor = LinearRegression(fit_intercept = False)  \n",
    "linear_regressor.fit(X, Y)  \n",
    "Y_pred = linear_regressor.predict(X) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(X1, Y)\n",
    "plt.plot(X1, Y_pred, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"data.xlsx\")\n",
    "X=df[['Chrome']]**(1/2)\n",
    "X1=df[['Chrome']]\n",
    "Y=df[['Facebook']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_regressor = LinearRegression(fit_intercept = False)  \n",
    "linear_regressor.fit(X, Y)  \n",
    "Y_pred = linear_regressor.predict(X) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(X1, Y)\n",
    "plt.plot(X1, Y_pred, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df = pd.read_excel(\"data.xlsx\")\n",
    "X=df[['Twitter']]**(1/2)\n",
    "X1=df[['Twitter']]\n",
    "Y=df[['Facebook']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_regressor = LinearRegression(fit_intercept = False)  \n",
    "linear_regressor.fit(X, Y)  \n",
    "Y_pred = linear_regressor.predict(X) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(X1, Y)\n",
    "plt.plot(X1, Y_pred, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "XX=df[['Twitter']]**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "XX['Linux']=df[['Linux']]**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "XX['Chrome']=df[['Chrome']]**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "# VIF dataframe \n",
    "vif_data = pd.DataFrame() \n",
    "vif_data[\"feature\"] = XX.columns \n",
    "\n",
    "# calculating VIF for each feature \n",
    "vif_data[\"VIF\"] = [variance_inflation_factor(XX.values, i) for i in range(len(XX.columns))] \n",
    "  \n",
    "print(vif_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_regressor = LinearRegression()  \n",
    "linear_regressor.fit(X, Y)  \n",
    "Y_pred = linear_regressor.predict(X) "
   ]
  }
 ],
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