T2EX2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Input/housing.txt', header = None)\n",
    "X = df.iloc[:,:-1].values\n",
    "Y = df.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "Y_train_pred = model.predict(X_train)\n",
    "Y_test_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: [:.2f]\n",
      "0.6082849862044333\n"
     ]
    }
   ],
   "source": [
    "accuracy = model.score(X_test, Y_test)\n",
    "print(\"Model Accuracy: [:.2f]\".format(accuracy))\n",
    "print(accuracy)"
   ]
  }
 ],
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