correction for towards data science.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is for this https://towardsdatascience.com/introduction-to-machine-learning-algorithms-linear-regression-14c4e325882a article "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression \n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('train.csv')\n",
    "df_test = pd.read_csv('test.csv')\n",
    "\n",
    "df_train.dropna(inplace=True)\n",
    "\n",
    "x_train = df_train['x']\n",
    "y_train = df_train['y']\n",
    "x_test = df_test['x']\n",
    "y_test = df_test['y']\n",
    "\n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)\n",
    "x_test = np.array(x_test)\n",
    "y_test = np.array(y_test)\n",
    "\n",
    "x_train = x_train.reshape(-1,1)\n",
    "x_test = x_test.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9889654279220864\n"
     ]
    }
   ],
   "source": [
    "clf = LinearRegression(normalize=True)\n",
    "clf.fit(x_train,y_train)\n",
    "y_pred = clf.predict(x_test)\n",
    "print(r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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