2.Cat_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "url_path = \"https://raw.githubusercontent.com/AJV009/Intro_to_Data_Science_Regression/master/startups.csv\"\n",
    "\n",
    "df = pd.read_csv(url_path)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pandas get_dummies function encodes catogarical data to 1 & 0\n",
    "df = pd.get_dummies(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.copy()\n",
    "# Copy label column to y\n",
    "y = X[\"Profit\"]\n",
    "# Drop Salary Column from df because its a Label\n",
    "X.drop([\"Profit\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# modified r2_square scoring\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(\"Testted Values: \",y_test.values)\n",
    "print(\"Predicted Values: \",y_pred.round(2))\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "r2s = r2_score(y_test.values, y_pred.round(2))\n",
    "print(\"R^2 (coefficient of determination) regression score: \",r2s)"
   ]
  }
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