Assignments/Home_Work_Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Homework Linear Regression\n",
    "\n",
    "- The dataset you are working is Boston Housing Price\n",
    "\n",
    "- The Boston_Housing_Prices dataset is a famous dataset usually used for learning or benchmarking regression techniques. This dataset contains 14 variables, one of which (MEDV, median value of home) we'll use as the target. \n",
    "\n",
    "- For this assignment, your task is to:\n",
    "\n",
    "1- Read in the dataset using sklearn's load_boston() function (found in sklearn.datasets) \n",
    "\n",
    "2- Perform some basic exploratory data analysis to get a feel for the data. Graph some stuff! \n",
    "\n",
    "3- Create a correlation heatmap to check to see how highly correlated our predictor variables (features) are (Remember, if our predictors are highly correlated, this is bad.)\n",
    "\n",
    "4- Train the model based on the train set. Use 75 percent of the data for train part. Use 25 percent of the data for test part.\n",
    "\n",
    "Hint: `from sklearn.model_selection import train_test_split`\n",
    "\n",
    "Hint: `X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)`\n",
    "\n",
    "5 -Make predictions on your test set (X_test) and see how well it compares to the actual targets (y_test) from the test set.\n",
    "\n",
    "6- Compute, Mean-Square-Error (MSE) and R Squared score of your Model \n",
    "\n",
    "Hint: `from sklearn.metrics import r2_score`\n",
    "\n",
    "Hint: `from sklearn.metrics import mean_squared_error`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference for Columns:\n",
    "* CRIM: Per capita crime rate by town\n",
    "* ZN: Proportion of residential land zoned for lots over 25,000 sq. ft\n",
    "* INDUS: Proportion of non-retail business acres per town\n",
    "* CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
    "* NOX: Nitric oxide concentration (parts per 10 million)\n",
    "* RM: Average number of rooms per dwelling\n",
    "* AGE: Proportion of owner-occupied units built prior to 1940\n",
    "* DIS: Weighted distances to five Boston employment centers\n",
    "* RAD: Index of accessibility to radial highways\n",
    "* TAX: Full-value property tax rate per 10,000(dollars)\n",
    "* PTRATIO: Pupil-teacher ratio by town\n",
    "* B: 1000(Bk — 0.63)², where Bk is the proportion of [people of African American descent] by town\n",
    "* LSTAT: Percentage of lower status of the population\n",
    "* MEDV: Median value of owner-occupied homes in 1000s (dollars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Read in the dataset using sklearn's load_boston() function (found in sklearn.datasets) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.3200e-03 1.8000e+01 2.3100e+00 ... 1.5300e+01 3.9690e+02 4.9800e+00]\n",
      " [2.7310e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9690e+02 9.1400e+00]\n",
      " [2.7290e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9283e+02 4.0300e+00]\n",
      " ...\n",
      " [6.0760e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 5.6400e+00]\n",
      " [1.0959e-01 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9345e+02 6.4800e+00]\n",
      " [4.7410e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 7.8800e+00]]\n",
      "(506, 13)\n",
      "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
      "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
      "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
      "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
      "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
      "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
      "\n",
      "   PTRATIO       B  LSTAT  PRICE  \n",
      "0     15.3  396.90   4.98   24.0  \n",
      "1     17.8  396.90   9.14   21.6  \n",
      "2     17.8  392.83   4.03   34.7  \n",
      "3     18.7  394.63   2.94   33.4  \n",
      "4     18.7  396.90   5.33   36.2  \n"
     ]
    }
   ],
   "source": [
    "# import the load_boston() function from sklearn.datasets\n",
    "from sklearn.datasets import load_boston\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# load the data\n",
    "boston = load_boston()\n",
    "\n",
    "print(boston.data)\n",
    "print(boston.data.shape)\n",
    "\n",
    "bos = pd.DataFrame(boston.data)\n",
    "bos.columns = boston.feature_names\n",
    "bos['PRICE'] = boston.target\n",
    "\n",
    "print(bos.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "PRICE      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for null values\n",
    "# if there are null values, remove them with .dropna()\n",
    "bos.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Perform some basic exploratory data analysis to get a feel for the data. Graph some stuff! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    506.000000\n",
       "mean      22.532806\n",
       "std        9.197104\n",
       "min        5.000000\n",
       "25%       17.025000\n",
       "50%       21.200000\n",
       "75%       25.000000\n",
       "max       50.000000\n",
       "Name: PRICE, dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos['PRICE'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAYjUlEQVR4nO3dfZRlVXnn8e9PkIA072iHadBGIRkdWxmo+BIS7QZlUFAYl2SpRIEw05ksX5iIjugYiRIUx4VvK44RgwEzaIv4AiM6SpCScUbRbl5sEB0IEORlwIwgNBK1wzN/nFOnq5qq6ktX3Xur634/a91179nn1D1P7VW3nrv3PnufVBWSJAE8btgBSJIWDpOCJKljUpAkdUwKkqSOSUGS1Nl+2AHMxd57713Lly8fdhhz8tBDD7HzzjsPO4wFw/rYxLqYyvqYai71sW7dun+sqidOt2+bTgrLly9n7dq1ww5jTsbHx1m5cuWww1gwrI9NrIuprI+p5lIfSf5hpn12H0mSOiYFSVLHpCBJ6vQtKST5VJJ7k1w/qWzPJJclual93qMtT5KPJrk5yQ+SHNyvuCRJM+tnS+E84MjNyk4DLq+qA4HL222AlwAHto/VwMf7GJckaQZ9SwpVdSXws82KjwHOb1+fDxw7qfzT1fgusHuSffoVmyRpeoMeU1haVXcDtM9PasuXAT+ZdNwdbZkkaYAWyjyFTFM27ZreSVbTdDGxdOlSxsfH+xhW/23YsGGb/x3mk/WxiXUxlfUxVb/qY9BJ4Z4k+1TV3W330L1t+R3AfpOO2xe4a7o3qKpzgHMAxsbGalufzOKEnKmsj02si6msj6n6VR+DTgqXACcAZ7XPF08qf0OSNcBzgZ9PdDNp8Vl+2qUz7jt1xUZOnGX/5m4766j5CElSq29JIclngZXA3knuAE6nSQYXJjkZuB04rj38q8BLgZuBXwAn9SsuSdLM+pYUqurVM+w6fJpjC3h9v2KRJPXGGc2SpI5JQZLUMSlIkjomBUlSx6QgSeqYFCRJHZOCJKljUpAkdUwKkqTOQlklVdoqs62j9Fi5jpJkS0GSNIlJQZLUMSlIkjomBUlSx6QgSeqYFCRJHZOCJKljUpAkdUwKkqSOSUGS1DEpSJI6JgVJUsekIEnqmBQkSR2TgiSpY1KQJHVMCpKkjklBktQxKUiSOiYFSVLHpCBJ6pgUJEkdk4IkqTOUpJDkT5PckOT6JJ9NsmOS/ZNcleSmJJ9LssMwYpOkUTbwpJBkGfAmYKyqnglsB7wKeD/woao6ELgPOHnQsUnSqBtW99H2wE5JtgeeANwNHAZc1O4/Hzh2SLFJ0shKVQ3+pMkpwJnAw8A3gFOA71bVAe3+/YCvtS2JzX92NbAaYOnSpYesWbNmYHH3w4YNG1iyZMmwwxio9Xf+fMZ9S3eCex4eYDCTrFi223BOPINR/NuYjfUx1VzqY9WqVeuqamy6fdvPKaqtkGQP4Bhgf+B+4PPAS6Y5dNpsVVXnAOcAjI2N1cqVK/sT6ICMj4+zrf8Oj9WJp106475TV2zk7PUD/7ME4LbjVw7lvDMZxb+N2VgfU/WrPobRffQi4Naq+mlV/Rr4IvC7wO5tdxLAvsBdQ4hNkkbaMJLC7cDzkjwhSYDDgR8CVwCvbI85Abh4CLFJ0kgbeFKoqqtoBpSvBta3MZwDvA14c5Kbgb2AcwcdmySNuqF03lbV6cDpmxXfAjxnCOFIklrOaJYkdUwKkqSOSUGS1DEpSJI6JgVJUsekIEnqmBQkSR2TgiSpY1KQJHW2mBSSnJJk1zTOTXJ1kiMGEZwkabB6aSn8UVU9ABwBPBE4CTirr1FJkoail6SQ9vmlwN9U1XWTyiRJi0gvSWFdkm/QJIWvJ9kFeKS/YUmShqGXVVJPBg4CbqmqXyTZi6YLSZK0yPTSUijgGcCb2u2dgR37FpEkaWh6SQr/FXg+8Op2+0HgY32LSJI0NL10Hz23qg5Ocg1AVd2XZIc+xyVJGoJeWgq/TrIdTTcSSZ6IA82StCj1khQ+CnwJeFKSM4FvA+/ta1SSpKHYYvdRVV2QZB1wOM38hGOr6sa+RyZJGrhelrl4GnBrVX0MuB54cZLd+x6ZJGngeuk++gLwz0kOAP4a2B/4TF+jkiQNRS9J4ZGq2gi8AvhIVf0psE9/w5IkDUOvVx+9Gngd8JW27PH9C0mSNCy9JIWTaCavnVlVtybZH/hv/Q1LkjQMvVx99EM2LXFBVd2KS2dL0qK0xaSQ5FbaiWuTVdVT+xKRJGloelnmYmzS6x2B44A9+xOOJGmYtjimUFX/b9Ljzqr6MHDYAGKTJA1YL91HB0/afBxNy2GXvkUkSRqaXrqPzp70eiNwG/AHfYlGkjRUvVx9tGoQgUiShq+XtY92S/LBJGvbx9lJdhtEcJKkwepl8tqnaO629gft4wHgb+Zy0iS7J7koyY+S3Jjk+Un2THJZkpva5z3mcg5J0mPXS1J4WlWdXlW3tI93A3Odo/AR4H9U1b8Eng3cCJwGXF5VBwKXt9uSpAHqJSk8nOT3JjaSHAo8vLUnTLIr8ALgXICq+lVV3Q8cA5zfHnY+cOzWnkOStHVS9ajJylMPSA6i+Se9G81Ndn4GnFhV123VCZv3Owf4IU0rYR1wCnBnVe0+6bj7qupRXUhJVgOrAZYuXXrImjVrtiaMBWPDhg0sWbJk2GEM1Po7fz7jvqU7wT1b/ZVjblYsW1hDZaP4tzEb62OqudTHqlWr1lXV2HT7tpgUugObb/hU1QNbFcWm9xkDvgscWlVXJfkIzTjFG3tJCpONjY3V2rVr5xLO0I2Pj7Ny5cphhzFQy0+7dMZ9p67YyNnre7lSev7ddtZRQznvTEbxb2M21sdUc6mPJDMmhRk/fUnePEM5AFX1wa2KBu4A7qiqq9rti2jGD+5Jsk9V3Z1kH+DerXx/9cFs/8glLR6zjSnsMunxls22t3pGc1X9X+AnSX67LTqcpivpEuCEtuwE4OKtPYckaevM2FJorzICIMmxk7fnwRuBC5LsANxCc8+GxwEXJjkZuJ1m4T1J0gD12nnb28BDr29WdS1TV1+dcPh8nkeS9Nj0ckmqJGlEzDbQvJ5NLYQDkvxgYhdQVfWsfgcnSRqs2bqPjh5YFJKkBWG2geZ/GGQgkqThc0xBktQxKUiSOjMmhSSXt8/vH1w4kqRhmm2geZ8kLwRenmQNzVVHnaq6uq+RSZIGbrak8C6aNYn2BTZf56iAw/oVlCRpOGa7+ugi4KIkf1ZVZwwwJknSkGxxmYuqOiPJy2lujAMwXlVf6W9YkqRh2OLVR0neR3MTnB+2j1PaMknSItPLgnhHAQdV1SMASc4HrgHe3s/AJEmD1+sqqbvT3IYTmttyahvgjXEkPVa9JIX3AdckuYLmstQXYCtBkhalXgaaP5tkHPgdmqTwtvbuaZKkRaan7qOqupvmdpmSpEXMtY8kSR2TgiSpM2tSSPK4JNcPKhhJ0nDNmhTauQnXJXnygOKRJA1RLwPN+wA3JPke8NBEYVW9vG9RSZKGopek8O6+RyFJWhB6mafwrSRPAQ6sqr9L8gRgu/6HJkkatF4WxPv3wEXAJ9qiZcCX+xmUJGk4erkk9fXAocADAFV1E/CkfgYlSRqOXpLCL6vqVxMbSbanufOaJGmR6SUpfCvJO4CdkrwY+Dzw3/sbliRpGHpJCqcBPwXWA38MfBV4Zz+DkiQNRy9XHz3S3ljnKppuox9Xld1HkrQIbTEpJDkK+Cvg72mWzt4/yR9X1df6HZwkabB6mbx2NrCqqm4GSPI04FLApCBJi0wvYwr3TiSE1i3AvX2KR5I0RDO2FJK8on15Q5KvAhfSjCkcB3x/ridOsh2wFrizqo5Osj+wBtgTuBp47eRLYSVJ/TdbS+Fl7WNH4B7ghcBKmiuR9piHc58C3Dhp+/3Ah6rqQOA+4OR5OIck6TGYsaVQVSf166RJ9gWOAs4E3pwkwGHAa9pDzgf+HPh4v2KQJD1atnR1adut80ZgOZOSyFyWzk5yEfA+YBfgLcCJwHer6oB2/37A16rqmdP87GpgNcDSpUsPWbNmzdaGsSBs2LCBJUuW9OW919/58768bz8t3QnueXg4516xbLfhnHgG/fzb2BZZH1PNpT5WrVq1rqrGptvXy9VHXwbOpZnF/MhWRTBJkqNpBq/XJVk5UTzNodNmq6o6BzgHYGxsrFauXDndYduM8fFx+vU7nHjapX153346dcVGzl7fy5/l/Lvt+JVDOe9M+vm3sS2yPqbqV3308un7p6r66Dye81Dg5UleSjNesSvwYWD3JNtX1UZgX+CueTynJKkHvVyS+pEkpyd5fpKDJx5be8KqentV7VtVy4FXAd+squOBK4BXtoedAFy8teeQJG2dXloKK4DX0gwET3QfVbs9n94GrEnyF8A1NF1WkqQB6iUp/Fvgqf2YM1BV48B4+/oW4DnzfQ5JUu966T66Dti934FIkoavl5bCUuBHSb4P/HKicC6XpEqSFqZeksLpfY9CkrQg9HI/hW8NIhBJ0vD1cj+FB9k0kWwH4PHAQ1W1az8DkyQNXi8thV0mbyc5Fq8SkqRFqZerj6aoqi8z/3MUJEkLQC/dR6+YtPk4YIwZ1iXS3C3fBtcrkrR49HL10csmvd4I3AYc05doJElD1cuYQt/uqyBJWlhmux3nu2b5uaqqM/oQjyRpiGZrKTw0TdnONLfJ3AswKUjSIjPb7TjPnnidZBeaeyqfBKwBzp7p5yRJ265ZxxSS7Am8GTie5r7JB1fVfYMITJI0eLONKXwAeAXNrS9XVNWGgUUlSRqK2VoKp9KsivpO4D8n3W2UQzPQ7DIX0gzmY77JqSs2cuJpl3LbWUfNQ0RSb2YbU3jMs50lSdu2XiavSSPB2eTSVqx9JElavEwKkqSOSUGS1DEpSJI6JgVJUsekIEnqmBQkSR2TgiSpY1KQJHVMCpKkjklBktQxKUiSOiYFSVLHpCBJ6pgUJEmdgSeFJPsluSLJjUluSHJKW75nksuS3NQ+7zHo2CRp1A3jJjsbgVOr6uokuwDrklwGnAhcXlVnJTkNOA142xDikxaU+b75j7f31GwG3lKoqrur6ur29YPAjcAy4Bjg/Paw84FjBx2bJI26VNXwTp4sB64EngncXlW7T9p3X1U9qgspyWpgNcDSpUsPWbNmzWCCncX6O3++1T+7dCe45+F5DGYbZ31s0q+6WLFst/l/0wHYsGEDS5YsGXYYC8Zc6mPVqlXrqmpsun1DSwpJlgDfAs6sqi8mub+XpDDZ2NhYrV27tt+hbtFcmvenrtjI2eu9VfYE62OTftXFttp9ND4+zsqVK4cdxoIxl/pIMmNSGMqnL8njgS8AF1TVF9vie5LsU1V3J9kHuHcYsUlSv8zn+NB5R+48b+812TCuPgpwLnBjVX1w0q5LgBPa1ycAFw86NkkadcNoKRwKvBZYn+TatuwdwFnAhUlOBm4HjhtCbJI00gaeFKrq20Bm2H34IGORJE3ljGZJUsekIEnqmBQkSR2TgiSp4ywhacTM57Xy2+pEOM3MloIkqWNSkCR1TAqSpI5JQZLUMSlIkjomBUlSx0tSJW01L29dfGwpSJI6I9tSmO+boUvSYmBLQZLUMSlIkjomBUlSx6QgSeqYFCRJHZOCJKljUpAkdUZ2noKkxcuZ1lvPloIkqWNSkCR1TAqSpI5JQZLUMSlIkjpefSRJsxi1FZVtKUiSOiYFSVLHpCBJ6pgUJEkdB5olLQhbGtA9dcVGThyxQd9hWFAthSRHJvlxkpuTnDbseCRp1CyYpJBkO+BjwEuAZwCvTvKM4UYlSaNlwSQF4DnAzVV1S1X9ClgDHDPkmCRppKSqhh0DAEleCRxZVf+u3X4t8NyqesNmx60GVrebvw38eKCBzr+9gX8cdhALiPWxiXUxlfUx1Vzq4ylV9cTpdiykgeZMU/aojFVV5wDn9D+cwUiytqrGhh3HQmF9bGJdTGV9TNWv+lhI3Ud3APtN2t4XuGtIsUjSSFpISeH7wIFJ9k+yA/Aq4JIhxyRJI2XBdB9V1cYkbwC+DmwHfKqqbhhyWIOwaLrC5on1sYl1MZX1MVVf6mPBDDRLkoZvIXUfSZKGzKQgSeqYFAYoyaeS3Jvk+klleya5LMlN7fMew4xxUJLsl+SKJDcmuSHJKW35qNbHjkm+l+S6tj7e3Zbvn+Sqtj4+116EMRKSbJfkmiRfabdHuS5uS7I+ybVJ1rZlffmsmBQG6zzgyM3KTgMur6oDgcvb7VGwETi1qp4OPA94fbusyajWxy+Bw6rq2cBBwJFJnge8H/hQWx/3AScPMcZBOwW4cdL2KNcFwKqqOmjS3IS+fFZMCgNUVVcCP9us+Bjg/Pb1+cCxAw1qSKrq7qq6un39IM2HfxmjWx9VVRvazce3jwIOAy5qy0emPpLsCxwF/HW7HUa0LmbRl8+KSWH4llbV3dD8owSeNOR4Bi7JcuBfA1cxwvXRdpdcC9wLXAb8PXB/VW1sD7mDJnGOgg8D/wl4pN3ei9GtC2i+IHwjybp2qR/o02dlwcxT0GhKsgT4AvAfq+qB5gvhaKqqfwYOSrI78CXg6dMdNtioBi/J0cC9VbUuycqJ4mkOXfR1McmhVXVXkicBlyX5Ub9OZEth+O5Jsg9A+3zvkOMZmCSPp0kIF1TVF9vika2PCVV1PzBOM9aye5KJL2+jsvTLocDLk9xGs1ryYTQth1GsCwCq6q72+V6aLwzPoU+fFZPC8F0CnNC+PgG4eIixDEzbR3wucGNVfXDSrlGtjye2LQSS7AS8iGac5Qrgle1hI1EfVfX2qtq3qpbTLHfzzao6nhGsC4AkOyfZZeI1cARwPX36rDijeYCSfBZYSbPk7T3A6cCXgQuBJwO3A8dV1eaD0YtOkt8D/iewnk39xu+gGVcYxfp4Fs1g4XY0X9YurKr3JHkqzbflPYFrgD+sql8OL9LBaruP3lJVR49qXbS/95faze2Bz1TVmUn2og+fFZOCJKlj95EkqWNSkCR1TAqSpI5JQZLUMSlIkjomBS04STZstn1ikr8cQhznJbm1XZny6iTPn+G4/5DkdfNwviT5ZpJdJ5UtT3LiZse9oI1nY5JXbrbvhHbVzJuSnDCp/JB2lc2bk3y0nScy40qbSY6eWKlVo8WkIM3urVV1EM0KlJ/YfGeS7avqr6rq0/NwrpcC11XVA+17/wnN7WnPSDKe5Dfb424HTgQ+s1kse9LMfXkuzYzX0yctp/xxYDVwYPuYWK13ppU2L6WZVfyEefi9tA0xKWibkuQpSS5P8oP2+clt+XmTvzVPtDaS7JPkyvbb/vVJfr8tPyLJd9pv3J9v12CazZXAAe3Pjid5b5JvAack+fMkb2n3HZDk79LcF+HqJE9ry9+a5Ptt3DN9Az+edlZqO4P13cDrgD+jSQIPAVTVbVX1AzZN+pvwb4DLqupnVXUfzaJ6R7ZLIOxaVd+pZmLSp9m0oua0K222x40DR2+hXrTImBS0EO3U/hO/tl019D2T9v0l8OmqehZwAfDRLbzXa4Cvt9/2nw1cm2Rv4J3Ai6rqYGAt8OYtvM/LaGZfT9i9ql5YVWdvdtwFwMfa+yL8LnB3kiNovp0/h+ZeCYckecE05zgUWNe+fgTYAdgVukTw4BZiXAb8ZNL2xEqiy9rXm5fD7CttrgV+fwvn1CLjKqlaiB5u/4kDzZgCMHFjkecDr2hf/y3wX7bwXt8HPtUuvvflqro2yQuBZwD/q+1a3wH4zgw//4Ek7wR+ytSbunxu8wPbb/fLqupLAFX1T235ETTr1VzTHrqEJklcudlb7Dnxj7+qHmrHKd4L/GaSZwLvqqpfzPK7zrSS6NauMHov8C96OE6LiElB27qJf24baVu+7SDqDtDc2Kj9Vn4U8LdJPkBz167LqurVPbz/W6vqomnKH5qmbKZ1vwO8r6oeNSaxmY1JHldVj7SxX5LkBzStlDHgVOCMWX7+Dpq1tSbsS9MFdEf7enL5xAqj9yTZp6runmalzR2Bh7cQsxYZu4+0rfnfNCtnQtMH/+329W3AIe3rY2juXEaSp9Cszf9JmlVZDwa+CxyaZGKM4AlJfmuugbUDxHckObZ9399oB2q/DvzRxLhFkmVp1sXf3I+Bp7bHLGljB5i4M90uWwjh68ARSfZoB5iPoOk6uxt4MMnz2oT5OjatqDnbSpu/RbMap0aILQVta95E0x30VpounZPa8k8CFyf5Hs1VNBPf5FcCb03ya2AD8Lqq+mnbJfXZJL/RHvdO4P/MQ3yvBT6R5D3Ar2lWrvxGkqcD32m7qzYAf8ij17+/tI33Zpqk9gmaFXX3orni6DUASX6HZtXMPYCXJXl3Vf2rqvpZkjNouswA3jNp1cw/oblH+E7A19oHwFnAhUlObs9x3KR4VgFvn1NtaJvjKqnSAtF233y6ql48qWw5sLKqzhtwLEtplmg+fJDn1fDZfSQtEG03zycnT14D7geuHUI4T6YZw9CIsaUgSerYUpAkdUwKkqSOSUGS1DEpSJI6JgVJUuf/A35KRmVEKjazAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bos['PRICE'].hist(bins=16)\n",
    "plt.xlabel(\"House Price ($1000)\")\n",
    "plt.ylabel(\"Number of Houses\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = bos['PRICE'], y = bos['CRIM'])\n",
    "plt.xlabel(\"House Price ($1000)\")\n",
    "plt.ylabel(\"Per Capita Crime Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Create a correlation heatmap to check to see how highly correlated our predictor variables (features) are (Remember, if our predictors are highly correlated, this is bad.)\n",
    "* Note: use website for customizing heatmaps: https://seaborn.pydata.org/tutorial/color_palettes.html\n",
    "\n",
    "## The heatmap is cut off on the top and bottom, this is an issue we discovered in DS 1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e75de90>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(16, 9))\n",
    "\n",
    "# compute the correlation matrix\n",
    "corr = bos.corr()\n",
    "\n",
    "# sns.palplot(sns.cubehelix_palette(8))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.cubehelix_palette(8, as_cmap=True)\n",
    "\n",
    "\n",
    "# Draw the heatmap with title\n",
    "ax.set_title('Heatmap of Boston Housing Features', fontsize=20)\n",
    "sns.heatmap(corr, cmap=cmap, vmax=.3, center=0, square=True, linewidths=0.5, cbar_kws={\"shrink\": .5}, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4) Train the model based on the train set. \n",
    "* Use 75 percent of the data for train part. \n",
    "* Use 25 percent of the data for test part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
      "       'PTRATIO', 'B', 'LSTAT', 'PRICE'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(bos.columns)\n",
    "bos.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "col_features = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']\n",
    "X = bos[col_features]\n",
    "y = bos.PRICE\n",
    "\n",
    "# test_size = 0.25 which is what we want to test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "379.5"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use 75% of the data for the train part: 506*0.75\n",
    "bos.shape[0] * 0.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS   RAD    TAX  \\\n",
      "245   0.19133  22.0   5.86   0.0  0.431  5.605  70.2  7.9549   7.0  330.0   \n",
      "59    0.10328  25.0   5.13   0.0  0.453  5.927  47.2  6.9320   8.0  284.0   \n",
      "276   0.10469  40.0   6.41   1.0  0.447  7.267  49.0  4.7872   4.0  254.0   \n",
      "395   8.71675   0.0  18.10   0.0  0.693  6.471  98.8  1.7257  24.0  666.0   \n",
      "416  10.83420   0.0  18.10   0.0  0.679  6.782  90.8  1.8195  24.0  666.0   \n",
      "..        ...   ...    ...   ...    ...    ...   ...     ...   ...    ...   \n",
      "323   0.28392   0.0   7.38   0.0  0.493  5.708  74.3  4.7211   5.0  287.0   \n",
      "192   0.08664  45.0   3.44   0.0  0.437  7.178  26.3  6.4798   5.0  398.0   \n",
      "117   0.15098   0.0  10.01   0.0  0.547  6.021  82.6  2.7474   6.0  432.0   \n",
      "47    0.22927   0.0   6.91   0.0  0.448  6.030  85.5  5.6894   3.0  233.0   \n",
      "172   0.13914   0.0   4.05   0.0  0.510  5.572  88.5  2.5961   5.0  296.0   \n",
      "\n",
      "     PTRATIO       B  LSTAT  \n",
      "245     19.1  389.13  18.46  \n",
      "59      19.7  396.90   9.22  \n",
      "276     17.6  389.25   6.05  \n",
      "395     20.2  391.98  17.12  \n",
      "416     20.2   21.57  25.79  \n",
      "..       ...     ...    ...  \n",
      "323     19.6  391.13  11.74  \n",
      "192     15.2  390.49   2.87  \n",
      "117     17.8  394.51  10.30  \n",
      "47      17.9  392.74  18.80  \n",
      "172     16.6  396.90  14.69  \n",
      "\n",
      "[379 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "# X_train should have 379 rows\n",
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126.5"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use 25% of the data for the test part\n",
    "bos.shape[0]*0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         CRIM    ZN  INDUS  CHAS    NOX     RM    AGE     DIS   RAD    TAX  \\\n",
      "329   0.06724   0.0   3.24   0.0  0.460  6.333   17.2  5.2146   4.0  430.0   \n",
      "371   9.23230   0.0  18.10   0.0  0.631  6.216  100.0  1.1691  24.0  666.0   \n",
      "219   0.11425   0.0  13.89   1.0  0.550  6.373   92.4  3.3633   5.0  276.0   \n",
      "403  24.80170   0.0  18.10   0.0  0.693  5.349   96.0  1.7028  24.0  666.0   \n",
      "78    0.05646   0.0  12.83   0.0  0.437  6.232   53.7  5.0141   5.0  398.0   \n",
      "..        ...   ...    ...   ...    ...    ...    ...     ...   ...    ...   \n",
      "49    0.21977   0.0   6.91   0.0  0.448  5.602   62.0  6.0877   3.0  233.0   \n",
      "498   0.23912   0.0   9.69   0.0  0.585  6.019   65.3  2.4091   6.0  391.0   \n",
      "309   0.34940   0.0   9.90   0.0  0.544  5.972   76.7  3.1025   4.0  304.0   \n",
      "124   0.09849   0.0  25.65   0.0  0.581  5.879   95.8  2.0063   2.0  188.0   \n",
      "306   0.07503  33.0   2.18   0.0  0.472  7.420   71.9  3.0992   7.0  222.0   \n",
      "\n",
      "     PTRATIO       B  LSTAT  \n",
      "329     16.9  375.21   7.34  \n",
      "371     20.2  366.15   9.53  \n",
      "219     16.4  393.74  10.50  \n",
      "403     20.2  396.90  19.77  \n",
      "78      18.7  386.40  12.34  \n",
      "..       ...     ...    ...  \n",
      "49      17.9  396.90  16.20  \n",
      "498     19.2  396.90  12.92  \n",
      "309     18.4  396.24   9.97  \n",
      "124     19.1  379.38  17.58  \n",
      "306     18.4  396.90   6.47  \n",
      "\n",
      "[127 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "# X_test should have 126 rows\n",
    "print(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5) Make predictions on your test set (X_test) and see how well it compares to the actual targets (y_test) from the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.95233283, 23.61699724, 29.20588553, 11.96070515, 21.33362042,\n",
       "       19.46954895, 20.42228421, 21.52044058, 18.98954101, 19.950983  ,\n",
       "        4.92468244, 16.09694058, 16.93599574,  5.33508402, 39.84434398,\n",
       "       32.33549843, 22.32772572, 36.54017819, 31.03300611, 23.32172503,\n",
       "       24.92086498, 24.26106474, 20.71504422, 30.45072552, 22.45009234,\n",
       "        9.87470006, 17.70324412, 17.974775  , 35.69932012, 20.7940972 ,\n",
       "       18.10554174, 17.68317865, 19.71354713, 23.79693873, 29.06528958,\n",
       "       19.23738284, 10.97815878, 24.56199978, 17.32913052, 15.20340817,\n",
       "       26.09337458, 20.87706795, 22.26187518, 15.32582693, 22.85847963,\n",
       "       25.08887173, 19.74138819, 22.70744911,  9.66708558, 24.46175926,\n",
       "       20.72654169, 17.52545047, 24.45596997, 30.10668865, 13.31250981,\n",
       "       21.52052342, 20.65642932, 15.34285652, 13.7741129 , 22.07429287,\n",
       "       17.53293957, 21.60707766, 32.91050188, 31.32796114, 17.64346364,\n",
       "       32.69909854, 18.56579207, 19.32110821, 18.81256692, 23.04351442,\n",
       "       22.81649956, 23.99800754, 30.62733254, 28.8662207 , 25.78198145,\n",
       "        5.00689605, 36.83409088, 23.80490328, 27.36965299, 19.33057146,\n",
       "       28.51439846, 19.19147448, 18.81694283, 37.94245021, 39.3441055 ,\n",
       "       23.90759744, 24.95985846, 15.70262358, 25.92060968, 16.56968829,\n",
       "       15.76303806, 12.89190083, 24.4389405 , 30.9319885 , 22.36798609,\n",
       "       20.17628607,  0.22819711, 25.26459847, 15.35369364, 17.7983399 ,\n",
       "       25.44689554, 22.46612234, 32.57911019, 22.01436262, 27.37542798,\n",
       "       23.31348039,  6.29811532, 14.75925592, 22.44945223, 29.11172812,\n",
       "       32.97160856, 12.85457141, 19.79963356, 20.60017439, 12.06928939,\n",
       "       23.38532482,  4.75243811, 19.8342911 ,  9.17555571, 44.63299931,\n",
       "       30.63144603, 12.29971299, 17.53804831, 21.50222331, 23.63281301,\n",
       "       20.28259817, 35.17973368])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6) Compute, Mean-Square-Error (MSE) and R Squared score of your Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Instantiate the model(Linear Regression) and train it\n",
    "bos_reg = LinearRegression()\n",
    "bos_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.17735289e-01  4.40174969e-02 -5.76814314e-03  2.39341594e+00\n",
      " -1.55894211e+01  3.76896770e+00 -7.03517828e-03 -1.43495641e+00\n",
      "  2.40081086e-01 -1.12972810e-02 -9.85546732e-01  8.44443453e-03\n",
      " -4.99116797e-01]\n"
     ]
    }
   ],
   "source": [
    "# coefficient\n",
    "print(bos_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.93325545711978\n"
     ]
    }
   ],
   "source": [
    "# y-intercept\n",
    "print(bos_reg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.7822450923024"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_pred = bos_reg.predict(X_test)\n",
    "\n",
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6354638433202124"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# r^2 is rated from -1 to 1, the closer to 1 the better\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "y_pred = bos_reg.predict(X_test)\n",
    "\n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply data normalization (Scaling) to improve the prediction result\n",
    "\n",
    "- Use StandardScaler and then train your Linear Regression Model. Compare the R Square score with previous one you got\n",
    "\n",
    "- Use MinMaxScaler and then train your Linear Regression Model. Compare the R Square score with previous one you got\n",
    "\n",
    "- Watch this video: https://www.youtube.com/watch?v=YKP31T5LIXQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# correct way for data normalization:\n",
    "# min_max scaling puts the values in a range (0,1)\n",
    "\n",
    "min_max_scaler = MinMaxScaler()\n",
    "\n",
    "X_train_min_max = min_max_scaler.fit_transform(X_train) \n",
    "\n",
    "X_test_min_max = min_max_scaler.transform(X_test) # for x_test_scaled you must use .transform not .fit_transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00207947 0.22       0.19794721 ... 0.69148936 0.98040748 0.47460993]\n",
      " [0.00108981 0.25       0.17118768 ... 0.75531915 1.         0.21248227]\n",
      " [0.00110566 0.4        0.2181085  ... 0.53191489 0.98071007 0.12255319]\n",
      " ...\n",
      " [0.00162594 0.         0.35007331 ... 0.55319149 0.99397347 0.24312057]\n",
      " [0.0025059  0.         0.23643695 ... 0.56382979 0.98951031 0.48425532]\n",
      " [0.00149286 0.         0.13159824 ... 0.42553191 1.         0.36765957]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train_min_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.84726112e-04 0.00000000e+00 1.01906158e-01 ... 4.57446809e-01\n",
      "  9.45307378e-01 1.59148936e-01]\n",
      " [1.03697791e-01 0.00000000e+00 6.46627566e-01 ... 8.08510638e-01\n",
      "  9.22462051e-01 2.21276596e-01]\n",
      " [1.21310718e-03 0.00000000e+00 4.92302053e-01 ... 4.04255319e-01\n",
      "  9.92031873e-01 2.48794326e-01]\n",
      " ...\n",
      " [3.85613648e-03 0.00000000e+00 3.46041056e-01 ... 6.17021277e-01\n",
      "  9.98335771e-01 2.33758865e-01]\n",
      " [1.03596858e-03 0.00000000e+00 9.23387097e-01 ... 6.91489362e-01\n",
      "  9.55822280e-01 4.49645390e-01]\n",
      " [7.72283834e-04 3.30000000e-01 6.30498534e-02 ... 6.17021277e-01\n",
      "  1.00000000e+00 1.34468085e-01]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test_min_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use MinMaxScaler and then train your Linear Regression Model. Compare the R Square score with previous one you got"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the model(Linear Regression) and train it using MinMax\n",
    "bos_reg = LinearRegression()\n",
    "\n",
    "bos_reg.fit(X_train_min_max, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.95233283, 23.61699724, 29.20588553, 11.96070515, 21.33362042,\n",
       "       19.46954895, 20.42228421, 21.52044058, 18.98954101, 19.950983  ,\n",
       "        4.92468244, 16.09694058, 16.93599574,  5.33508402, 39.84434398,\n",
       "       32.33549843, 22.32772572, 36.54017819, 31.03300611, 23.32172503,\n",
       "       24.92086498, 24.26106474, 20.71504422, 30.45072552, 22.45009234,\n",
       "        9.87470006, 17.70324412, 17.974775  , 35.69932012, 20.7940972 ,\n",
       "       18.10554174, 17.68317865, 19.71354713, 23.79693873, 29.06528958,\n",
       "       19.23738284, 10.97815878, 24.56199978, 17.32913052, 15.20340817,\n",
       "       26.09337458, 20.87706795, 22.26187518, 15.32582693, 22.85847963,\n",
       "       25.08887173, 19.74138819, 22.70744911,  9.66708558, 24.46175926,\n",
       "       20.72654169, 17.52545047, 24.45596997, 30.10668865, 13.31250981,\n",
       "       21.52052342, 20.65642932, 15.34285652, 13.7741129 , 22.07429287,\n",
       "       17.53293957, 21.60707766, 32.91050188, 31.32796114, 17.64346364,\n",
       "       32.69909854, 18.56579207, 19.32110821, 18.81256692, 23.04351442,\n",
       "       22.81649956, 23.99800754, 30.62733254, 28.8662207 , 25.78198145,\n",
       "        5.00689605, 36.83409088, 23.80490328, 27.36965299, 19.33057146,\n",
       "       28.51439846, 19.19147448, 18.81694283, 37.94245021, 39.3441055 ,\n",
       "       23.90759744, 24.95985846, 15.70262358, 25.92060968, 16.56968829,\n",
       "       15.76303806, 12.89190083, 24.4389405 , 30.9319885 , 22.36798609,\n",
       "       20.17628607,  0.22819711, 25.26459847, 15.35369364, 17.7983399 ,\n",
       "       25.44689554, 22.46612234, 32.57911019, 22.01436262, 27.37542798,\n",
       "       23.31348039,  6.29811532, 14.75925592, 22.44945223, 29.11172812,\n",
       "       32.97160856, 12.85457141, 19.79963356, 20.60017439, 12.06928939,\n",
       "       23.38532482,  4.75243811, 19.8342911 ,  9.17555571, 44.63299931,\n",
       "       30.63144603, 12.29971299, 17.53804831, 21.50222331, 23.63281301,\n",
       "       20.28259817, 35.17973368])"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = bos_reg.predict(X_test_min_max)\n",
    "\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6354638433202124"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MinMax Scaler r^2 score: \n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.7822450923024"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean square error of MinMax Scaler\n",
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use StandardScaler and then train your Linear Regression Model. Compare the R Square score with previous one you got"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "std_scaler = StandardScaler()\n",
    "\n",
    "X_train_std_scaler = std_scaler.fit_transform(X_train)\n",
    "\n",
    "X_test_std_scaler = std_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.3906002   0.42637011 -0.74491444 ...  0.27878885  0.34049624\n",
      "   0.82212111]\n",
      " [-0.40127639  0.5525335  -0.84901832 ...  0.55287698  0.42774893\n",
      "  -0.46241699]\n",
      " [-0.40110543  1.18335044 -0.66648002 ... -0.40643148  0.34184377\n",
      "  -0.90310809]\n",
      " ...\n",
      " [-0.3954927  -0.49882807 -0.15309105 ... -0.31506877  0.40091059\n",
      "  -0.31227617]\n",
      " [-0.38599992 -0.49882807 -0.59517599 ... -0.26938741  0.38103449\n",
      "   0.86938766]\n",
      " [-0.39692832 -0.49882807 -1.003035   ... -0.86324503  0.42774893\n",
      "   0.29801844]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train_std_scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.4056463  -0.49882807 -1.11854752 ... -0.72620097  0.18418254\n",
      "  -0.72377323]\n",
      " [ 0.7056307  -0.49882807  1.00060803 ...  0.78128376  0.08244389\n",
      "  -0.41932101]\n",
      " [-0.39994626 -0.49882807  0.40022816 ... -0.95460774  0.39226392\n",
      "  -0.28447232]\n",
      " ...\n",
      " [-0.37143398 -0.49882807 -0.16877794 ... -0.04098064  0.42033751\n",
      "  -0.35815253]\n",
      " [-0.40185719 -0.49882807  2.07729878 ...  0.27878885  0.23100928\n",
      "   0.69978415]\n",
      " [-0.40470175  0.8889692  -1.26971205 ... -0.04098064  0.42774893\n",
      "  -0.84471999]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test_std_scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the model(Linear Regression) and train it\n",
    "bos_reg = LinearRegression()\n",
    "\n",
    "bos_reg.fit(X_train_std_scaler, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.95233283, 23.61699724, 29.20588553, 11.96070515, 21.33362042,\n",
       "       19.46954895, 20.42228421, 21.52044058, 18.98954101, 19.950983  ,\n",
       "        4.92468244, 16.09694058, 16.93599574,  5.33508402, 39.84434398,\n",
       "       32.33549843, 22.32772572, 36.54017819, 31.03300611, 23.32172503,\n",
       "       24.92086498, 24.26106474, 20.71504422, 30.45072552, 22.45009234,\n",
       "        9.87470006, 17.70324412, 17.974775  , 35.69932012, 20.7940972 ,\n",
       "       18.10554174, 17.68317865, 19.71354713, 23.79693873, 29.06528958,\n",
       "       19.23738284, 10.97815878, 24.56199978, 17.32913052, 15.20340817,\n",
       "       26.09337458, 20.87706795, 22.26187518, 15.32582693, 22.85847963,\n",
       "       25.08887173, 19.74138819, 22.70744911,  9.66708558, 24.46175926,\n",
       "       20.72654169, 17.52545047, 24.45596997, 30.10668865, 13.31250981,\n",
       "       21.52052342, 20.65642932, 15.34285652, 13.7741129 , 22.07429287,\n",
       "       17.53293957, 21.60707766, 32.91050188, 31.32796114, 17.64346364,\n",
       "       32.69909854, 18.56579207, 19.32110821, 18.81256692, 23.04351442,\n",
       "       22.81649956, 23.99800754, 30.62733254, 28.8662207 , 25.78198145,\n",
       "        5.00689605, 36.83409088, 23.80490328, 27.36965299, 19.33057146,\n",
       "       28.51439846, 19.19147448, 18.81694283, 37.94245021, 39.3441055 ,\n",
       "       23.90759744, 24.95985846, 15.70262358, 25.92060968, 16.56968829,\n",
       "       15.76303806, 12.89190083, 24.4389405 , 30.9319885 , 22.36798609,\n",
       "       20.17628607,  0.22819711, 25.26459847, 15.35369364, 17.7983399 ,\n",
       "       25.44689554, 22.46612234, 32.57911019, 22.01436262, 27.37542798,\n",
       "       23.31348039,  6.29811532, 14.75925592, 22.44945223, 29.11172812,\n",
       "       32.97160856, 12.85457141, 19.79963356, 20.60017439, 12.06928939,\n",
       "       23.38532482,  4.75243811, 19.8342911 ,  9.17555571, 44.63299931,\n",
       "       30.63144603, 12.29971299, 17.53804831, 21.50222331, 23.63281301,\n",
       "       20.28259817, 35.17973368])"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = bos_reg.predict(X_test_std_scaler)\n",
    "\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4967900069591079"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.782245092302325"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions: \n",
    "\n",
    "### Without Scaling:\n",
    "* MSE: 29.7822450923024\n",
    "* R^2: 0.6354638433202124\n",
    "\n",
    "### MinMaxScaler:\n",
    "* MSE: 29.7822450923024\n",
    "* R^2: 0.6354638433202124\n",
    "\n",
    "### Standard Scaler:\n",
    "* MSE: 29.782245092302325\n",
    "* R^2: 0.4967900069591079"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}