{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import classification_report as summary\n",
    "\n",
    "sns.set()\n",
    "sns.set(style=\"whitegrid\")\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "plt.rcParams[\"figure.figsize\"] = (5,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "Boston = pd.read_csv(\"../../datasets/Boston.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.95004935] 34.5538408793831\n",
      "0.5441462975864797\n"
     ]
    }
   ],
   "source": [
    "X = np.array(Boston[\"lstat\"]).reshape(-1,1)\n",
    "y = Boston[\"medv\"]\n",
    "model = linear_model.LinearRegression()\n",
    "model.fit(X, y)\n",
    "print(model.coef_, model.intercept_)\n",
    "print(model.score(X,y))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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