Day 49 - 50 Days of Data Analysis .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88139ab3",
   "metadata": {},
   "source": [
    "## 2.\t\n",
    "\n",
    "#### Load the house_prediction dataset using pandas. Check for missing values, shape of the DataFrame and for duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b8141b35",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "44cb9bc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>X1 transaction date</th>\n",
       "      <th>X2 house age</th>\n",
       "      <th>X3 distance to the nearest MRT station</th>\n",
       "      <th>X4 number of convenience stores</th>\n",
       "      <th>X5 latitude</th>\n",
       "      <th>X6 longitude</th>\n",
       "      <th>Y house price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2012.917</td>\n",
       "      <td>32.0</td>\n",
       "      <td>84.87882</td>\n",
       "      <td>10</td>\n",
       "      <td>24.98298</td>\n",
       "      <td>121.54024</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012.917</td>\n",
       "      <td>19.5</td>\n",
       "      <td>306.59470</td>\n",
       "      <td>9</td>\n",
       "      <td>24.98034</td>\n",
       "      <td>121.53951</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013.583</td>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013.500</td>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2012.833</td>\n",
       "      <td>5.0</td>\n",
       "      <td>390.56840</td>\n",
       "      <td>5</td>\n",
       "      <td>24.97937</td>\n",
       "      <td>121.54245</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No  X1 transaction date  X2 house age   \n",
       "0   1             2012.917          32.0  \\\n",
       "1   2             2012.917          19.5   \n",
       "2   3             2013.583          13.3   \n",
       "3   4             2013.500          13.3   \n",
       "4   5             2012.833           5.0   \n",
       "\n",
       "   X3 distance to the nearest MRT station  X4 number of convenience stores   \n",
       "0                                84.87882                               10  \\\n",
       "1                               306.59470                                9   \n",
       "2                               561.98450                                5   \n",
       "3                               561.98450                                5   \n",
       "4                               390.56840                                5   \n",
       "\n",
       "   X5 latitude  X6 longitude  Y house price of unit area  \n",
       "0     24.98298     121.54024                        37.9  \n",
       "1     24.98034     121.53951                        42.2  \n",
       "2     24.98746     121.54391                        47.3  \n",
       "3     24.98746     121.54391                        54.8  \n",
       "4     24.97937     121.54245                        43.1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing dataset\n",
    "df = pd.read_csv(\"house_price_prediction_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "724deb58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No                                        0\n",
       "X1 transaction date                       0\n",
       "X2 house age                              0\n",
       "X3 distance to the nearest MRT station    0\n",
       "X4 number of convenience stores           0\n",
       "X5 latitude                               0\n",
       "X6 longitude                              0\n",
       "Y house price of unit area                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ee2e5593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(414, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking shape\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "211a11ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for duplicates\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4a23a26",
   "metadata": {},
   "source": [
    "## a. \n",
    "\n",
    "#### Plot a scatter plot of the \" X2 house age\" column and \"Y house price of unit area\" column. This will reveal any relationship between this variable and the target column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "26c3c780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "plt.scatter(x = df[\"X2 house age\"], \n",
    "            y = df[\"Y house price of unit area\"])\n",
    "plt.xlabel(\"House Age\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.title( \"House Age vs Price\", fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fc012e7",
   "metadata": {},
   "source": [
    "## b. \n",
    "    \n",
    "#### Using seaborn, plot a barplot of \"X4 number of convenience stores\" column and the \"Y house price of unit area\". Is there a correlation between the number of stores and the price of the house?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d36fee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(data=df, \n",
    "            x=df[\"X4 number of convenience stores\"], \n",
    "            y=df[\"Y house price of unit area\"])\n",
    "plt.title(\"House Price vs Number of Stores\", fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe634325",
   "metadata": {},
   "source": [
    "## c.\n",
    "\n",
    "#### To check the distribution of data, using Matplotlib, create a hist plot of the \"Y house price of unit area\" column(target column). What is the mean of the column? Add a mean line to the plot. Do you notice ant outliers in the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "feaa73e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean price is: 37.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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FkncluUZ3nzfO+lySa8xZ5cgkn5mZPmdsAwCATWvbondQVVdO8sokj+/ui6rqv+d1d1dVH+T2T0hyQpIcffTRB7MpYJ127Dp1bvvuE4/b4EoAYHNZaM91VR2aIVi/pLtfNTZ/vqquNc6/VpLz56x6bpLrzEwfNbZdQnef1N07u3vn9u3bpyseAAD20yKvFlJJXpDkw939zJlZr0uycvWPhyZ57ZzV35Dk7lV1tfFExruPbQAAsGktsuf6J5L8fJI7V9VZ4+PeSU5Mcreq+niSu47TqaqdVfX8JOnuLyX5nSTvGR9PH9sAAGDTWtiY6+5+R5JaY/Zd5ix/epJHzkyfnOTkxVQHAADTc4dGAACYiHANAAATEa4BAGAiwjUAAExEuAYAgIkI1wAAMBHhGgAAJiJcAwDARIRrAACYiHANAAATEa4BAGAiwjUAAExEuAYAgIkI1wAAMBHhGgAAJrJt2QUAm9eOXacuuwQA2FL0XAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgItuWXQAwrR27Tv3v17uXVwYAXCbpuQYAgIkI1wAAMBHhGgAAJiJcAwDARIRrAACYiHANAAATWdil+Krq5CT3SXJ+d99kbHt5khuNixye5MLuvvmcdXcn+WqS7ybZ0907F1UnAABMZZHXuT4lyXOTvGilobsfvPK6qv4oyVf2sv6duvsLC6sOAAAmtrBw3d1vr6od8+ZVVSV5UJI7L2r/AACw0ZY15vr2ST7f3R9fY34neWNVnVFVJ+xtQ1V1QlWdXlWnX3DBBZMXCgAA67WscH18kpfuZf7tuvuYJPdK8tiqusNaC3b3Sd29s7t3bt++feo6AQBg3TY8XFfVtiQ/leTlay3T3eeOz+cneXWSW21MdQAAcOCW0XN91yQf6e5z5s2sqsOq6iorr5PcPckHN7A+AAA4IAsL11X10iT/luRGVXVOVT1inPWQrBoSUlXXrqrTxslrJHlHVb0vybuTnNrd/7CoOgEAYCqLvFrI8Wu0P2xO22eT3Ht8/YkkN1tUXQAAsCju0AgAABMRrgEAYCLCNQAATES4BgCAiQjXAAAwkYVdLQTYOnbsOnXZJQDApYKeawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMZGHhuqpOrqrzq+qDM21Pq6pzq+qs8XHvNda9Z1V9tKrOrqpdi6oRAACmtMie61OS3HNO+x93983Hx2mrZ1bVIUn+NMm9ktw4yfFVdeMF1gkAAJNYWLju7rcn+dIBrHqrJGd39ye6+9tJXpbkfpMWBwAAC7CMMde/UlXvH4eNXG3O/COTfGZm+pyxDQAANrWNDtfPS/JDSW6e5Lwkf3SwG6yqE6rq9Ko6/YILLjjYzQEAwAHb0HDd3Z/v7u92938l+csMQ0BWOzfJdWamjxrb1trmSd29s7t3bt++fdqCAQBgP2xouK6qa81M/mSSD85Z7D1JblhV16uqyyd5SJLXbUR9AABwMLYtasNV9dIkxyY5oqrOSfLUJMdW1c2TdJLdSX5pXPbaSZ7f3ffu7j1V9StJ3pDkkCQnd/e/L6pOAACYysLCdXcfP6f5BWss+9kk956ZPi3JJS7TBwAAm5k7NAIAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACaybdkFwGazY9epc9t3n3jcBleyd2vVCQAsj55rAACYiHANAAATEa4BAGAiwjUAAExEuAYAgIkI1wAAMBHhGgAAJiJcAwDARIRrAACYiHANAAATEa4BAGAiwjUAAExEuAYAgIkI1wAAMBHhGgAAJiJcAwDARIRrAACYiHANAAATEa4BAGAiwjUAAExk27ILgHl27Dp1bvvuE4/b4Er2bSvVulWs9Z6uxXsNwGah5xoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIgsL11V1clWdX1UfnGl7RlV9pKreX1WvrqrD11h3d1V9oKrOqqrTF1UjAABMaZE916ckueeqtjcluUl33zTJx5I8aS/r36m7b97dOxdUHwAATGph4bq7357kS6va3tjde8bJdyY5alH7BwCAjbbMMde/mOT1a8zrJG+sqjOq6oQNrAkAAA7YUu7QWFX/O8meJC9ZY5Hbdfe5VfWDSd5UVR8Ze8LnbeuEJCckydFHH72QegEAYD02vOe6qh6W5D5Jfra7e94y3X3u+Hx+klcnudVa2+vuk7p7Z3fv3L59+wIqBgCA9dnQcF1V90zym0nu293fWGOZw6rqKiuvk9w9yQfnLQsAAJvJIi/F99Ik/5bkRlV1TlU9Islzk1wlw1CPs6rqz8dlr11Vp42rXiPJO6rqfUneneTU7v6HRdUJAABTWdiY6+4+fk7zC9ZY9rNJ7j2+/kSSmy2qLgAAWBR3aAQAgIkI1wAAMBHhGgAAJiJcAwDARIRrAACYyFLu0AgbZceuU9ect/vE4zawkuXb23sBAExDzzUAAExEuAYAgIkI1wAAMBHhGgAAJiJcAwDARIRrAACYyLrCdVX92KILAQCArW69Pdd/VlXvrqpfrqqrLrQiAADYotYVrrv79kl+Nsl1kpxRVX9TVXdbaGUAALDFrHvMdXd/PMmTkzwxyR2TPKeqPlJVP7Wo4gAAYCtZ75jrm1bVHyf5cJI7J/mf3f0/xtd/vMD6AABgy9i2zuX+JMnzk/xWd39zpbG7P1tVT15IZQAAsMWsN1wfl+Sb3f3dJKmqyyX5vu7+Rne/eGHVAQDAFrLeMdf/mOSKM9NXGtsAAIDResP193X311YmxtdXWkxJAACwNa03XH+9qo5ZmaiqH0/yzb0sDwAAlznrHXP9+CR/V1WfTVJJrpnkwYsqCgAAtqJ1hevufk9V/UiSG41NH+3u7yyuLAAA2HrW23OdJLdMsmNc55iqSne/aCFVAQDAFrSucF1VL07yQ0nOSvLdsbmTCNcAADBab8/1ziQ37u5eZDFwoHbsOnXZJRy0tX6G3Scet8GVHLj9/RkuDccNAGat92ohH8xwEiMAALCG9fZcH5HkQ1X17iTfWmns7vsupCoAANiC1huun7bIIgAA4NJgvZfie1tVXTfJDbv7H6vqSkkOWWxpAACwtaxrzHVVPSrJK5L8xdh0ZJLXLKgmAADYktZ7QuNjk/xEkouSpLs/nuQHF1UUAABsResN19/q7m+vTFTVtgzXuQYAAEbrDddvq6rfSnLFqrpbkr9L8veLKwsAALae9YbrXUkuSPKBJL+U5LQkT15UUQAAsBWt92oh/5XkL8cHAAAwx7rCdVV9MnPGWHf39SevCAAAtqj13kRm58zr70vywCRXn74cAADYutY15rq7vzjzOLe7n5XkuMWWBgAAW8t6h4UcMzN5uQw92evt9QYAgMuE9QbkP5p5vSfJ7iQPmrwaAADYwtZ7tZA7LboQAADY6tY7LOTX9za/u5+5xnonJ7lPkvO7+yZj29WTvDzJjow94N395TnrPjTfu5b273b3C9dTKwAALMt6byKzM8ljkhw5Ph6d5JgkVxkfazklyT1Xte1K8ubuvmGSN4/TFzMG8KcmuXWSWyV5alVdbZ21AgDAUqx3zPVRSY7p7q8mSVU9Lcmp3f1ze1upu99eVTtWNd8vybHj6xcmeWuSJ65a5h5J3tTdXxr396YMIf2l66wXAAA23Hp7rq+R5Nsz098e2w7ENbr7vPH159bYzpFJPjMzfc7YBgAAm9Z6e65flOTdVfXqcfr+GXqdD0p3d1Vd4s6P+6OqTkhyQpIcffTRB1sSC7Jj16lz23ef6HLplwVrHf/NyGcVgIOx3pvI/H9JHp7ky+Pj4d39ewe4z89X1bWSZHw+f84y5ya5zsz0UWPbvNpO6u6d3b1z+/btB1gSAAAcvPUOC0mSKyW5qLufneScqrreAe7zdUkeOr5+aJLXzlnmDUnuXlVXG09kvPvYBgAAm9a6wnVVPTXDSYdPGpsOTfLX61jvpUn+LcmNquqcqnpEkhOT3K2qPp7kruN0qmpnVT0/ScYTGX8nyXvGx9NXTm4EAIDNar1jrn8yyS2SnJkk3f3ZqtrbJfgyLnf8GrPuMmfZ05M8cmb65CQnr7M+AABYuvUOC/l2d3eSTpKqOmxxJQEAwNa03nD9t1X1F0kOr6pHJfnHJH+5uLIAAGDr2eewkKqqDLcr/5EkFyW5UZKndPebFlwbAABsKfsM1+O1qE/r7h9LIlADAMAa1jss5MyquuVCKwEAgC1uvVcLuXWSn6uq3Um+nqQydGrfdFGFAQDAVrPXcF1VR3f3p5PcY4PqAQCALWtfPdevSXJMd3+qql7Z3T+9ATUBAMCWtK8x1zXz+vqLLAQAALa6fYXrXuM1AACwyr6Ghdysqi7K0IN9xfF18r0TGr9/odUBAMAWstdw3d2HbFQhAACw1a33OtcAAMA+CNcAADAR4RoAACay3js0wkLs2HXqQpeHZVnrs7r7xOM2uBIANpKeawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMZNuyC4CtYseuU5ddAku01vHffeJxG1wJAJuZnmsAAJiIcA0AABMRrgEAYCLCNQAATES4BgCAiQjXAAAwkQ0P11V1o6o6a+ZxUVU9ftUyx1bVV2aWecpG1wkAAPtrw69z3d0fTXLzJKmqQ5Kcm+TVcxb95+6+zwaWBgAAB2XZw0LukuQ/uvtTS64DAAAO2rLD9UOSvHSNebetqvdV1eur6kfX2kBVnVBVp1fV6RdccMFiqgQAgHVYWriuqssnuW+Sv5sz+8wk1+3umyX5kySvWWs73X1Sd+/s7p3bt29fSK0AALAey+y5vleSM7v786tndPdF3f218fVpSQ6tqiM2ukAAANgfywzXx2eNISFVdc2qqvH1rTLU+cUNrA0AAPbbhl8tJEmq6rAkd0vySzNtj06S7v7zJA9I8piq2pPkm0ke0t29jFoBAGC9lhKuu/vrSX5gVdufz7x+bpLnbnRdAABwMJZ9tRAAALjUEK4BAGAiwjUAAExEuAYAgIkI1wAAMJGlXC2ErW/HrlPntu8+8bgNrmTrWeu9AwC2Pj3XAAAwEeEaAAAmIlwDAMBEhGsAAJiIcA0AABMRrgEAYCLCNQAATES4BgCAiQjXAAAwEeEaAAAmIlwDAMBEhGsAAJiIcA0AABMRrgEAYCLCNQAATES4BgCAiQjXAAAwEeEaAAAmIlwDAMBEhGsAAJjItmUXwMbasevUue27TzxugytZvrXei81mq9S5TJeG98h3E+DSQc81AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIksL11W1u6o+UFVnVdXpc+ZXVT2nqs6uqvdX1THLqBMAANZr25L3f6fu/sIa8+6V5Ibj49ZJnjc+AwDAprSZh4XcL8mLevDOJIdX1bWWXRQAAKxlmT3XneSNVdVJ/qK7T1o1/8gkn5mZPmdsO292oao6IckJSXL00Ucvrtol2rHr1Lntu088boMrYX+sddy4dHGcAZi1zJ7r23X3MRmGfzy2qu5wIBvp7pO6e2d379y+ffu0FQIAwH5YWrju7nPH5/OTvDrJrVYtcm6S68xMHzW2AQDAprSUcF1Vh1XVVVZeJ7l7kg+uWux1SX5hvGrIbZJ8pbvPCwAAbFLLGnN9jSSvrqqVGv6mu/+hqh6dJN3950lOS3LvJGcn+UaShy+pVgAAWJelhOvu/kSSm81p//OZ153ksRtZFwAAHIzNfCk+AADYUoRrAACYiHANAAATEa4BAGAiwjUAAExEuAYAgIkI1wAAMBHhGgAAJiJcAwDARIRrAACYiHANAAATEa4BAGAiwjUAAExEuAYAgIkI1wAAMJFtyy6AA7dj16lrztt94nEL38cUy8NlzbK+I/u736n+DQG4rNFzDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJbFt2ASzGjl2nLnR5AAAuSc81AABMRLgGAICJCNcAADAR4RoAACYiXAMAwEQ2PFxX1XWq6i1V9aGq+veqetycZY6tqq9U1Vnj4ykbXScAAOyvZVyKb0+SJ3T3mVV1lSRnVNWbuvtDq5b75+6+zxLqAwCAA7LhPdfdfV53nzm+/mqSDyc5cqPrAACAqS11zHVV7UhyiyTvmjP7tlX1vqp6fVX96MZWBgAA+29pd2isqisneWWSx3f3Ratmn5nkut39taq6d5LXJLnhGts5IckJSXL00UcvrmAAANiHpfRcV9WhGYL1S7r7Vavnd/dF3f218fVpSQ6tqiPmbau7T+rund29c/v27QutGwAA9mYZVwupJC9I8uHufuYay1xzXC5VdasMdX5x46oEAID9t4xhIT+R5OeTfKCqzhrbfivJ0UnS3X+e5AFJHlNVe5J8M8lDuruXUCsAAKzbhofr7n5HktrHMs9N8tyNqQgAAKbhDo0AADAR4RoAACYiXAMAwESEawAAmIhwDQAAE1naHRq5pB27Tl12CcAmc1n8d2Gtn3n3icdtcCUH7tLwMwAHRs81AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACaybdkFXBrs2HXq3PbdJx63wZUATGOtf9eS/f+3bap/IzfbdoCNs5W+t3quAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABMRLgGAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAE1lKuK6qe1bVR6vq7KraNWf+Farq5eP8d1XVjiWUCQAA+2XDw3VVHZLkT5PcK8mNkxxfVTdetdgjkny5u2+Q5I+T/P7GVgkAAPtvGT3Xt0pydnd/oru/neRlSe63apn7JXnh+PoVSe5SVbWBNQIAwH5bRrg+MslnZqbPGdvmLtPde5J8JckPbEh1AABwgKq7N3aHVQ9Ics/ufuQ4/fNJbt3dvzKzzAfHZc4Zp/9jXOYLc7Z3QpITxskbJfnogn8E1ueIJJc4Xmx6jtvW5dhtXY7d1uXYbV0He+yu293b583YdhAbPVDnJrnOzPRRY9u8Zc6pqm1Jrprki/M21t0nJTlpAXVyEKrq9O7euew62D+O29bl2G1djt3W5dhtXYs8dssYFvKeJDesqutV1eWTPCTJ61Yt87okDx1fPyDJP/VGd7EDAMB+2vCe6+7eU1W/kuQNSQ5JcnJ3/3tVPT3J6d39uiQvSPLiqjo7yZcyBHAAANjUljEsJN19WpLTVrU9Zeb1fyZ54EbXxaQM1dmaHLety7Hbuhy7rcux27oWduw2/IRGAAC4tHL7cwAAmIhwzUGpqutU1Vuq6kNV9e9V9bix/epV9aaq+vj4fLVl18p8VXVIVb23qv7vOH29qnpXVZ1dVS8fTzxmk6mqw6vqFVX1kar6cFXd1vdu86uq/3f8t/KDVfXSqvo+37nNqapOrqrzx8sDr7TN/Y7V4DnjMXx/VR2zvMpZ49g9Y/z38v1V9eqqOnxm3pPGY/fRqrrHwe5fuOZg7UnyhO6+cZLbJHnseDv7XUne3N03TPLmcZrN6XFJPjwz/ftJ/ri7b5Dky0kesZSq2JdnJ/mH7v6RJDfLcAx97zaxqjoyya8l2dndN8lwUv9D4ju3WZ2S5J6r2tb6jt0ryQ3HxwlJnrdBNTLfKbnksXtTkpt0902TfCzJk5JkzCwPSfKj4zp/VlWHHMzOhWsOSnef191njq+/muE/+CNz8VvYvzDJ/ZdSIHtVVUclOS7J88fpSnLnJK8YF3HsNqGqumqSO2S4slK6+9vdfWF877aCbUmuON7D4UpJzovv3KbU3W/PcMWyWWt9x+6X5EU9eGeSw6vqWhtSKJcw79h19xvHu34nyTsz3GclGY7dy7r7W939ySRnJ7nVwexfuGYyVbUjyS2SvCvJNbr7vHHW55JcY1l1sVfPSvKbSf5rnP6BJBfO/AN0ToZflthcrpfkgiR/NQ7peX5VHRbfu02tu89N8odJPp0hVH8lyRnxndtK1vqOHZnkMzPLOY6b2y8mef34evJjJ1wziaq6cpJXJnl8d180O2+8AZDL0mwyVXWfJOd39xnLroX9ti3JMUme1923SPL1rBoC4nu3+Yzjc++X4Zejayc5LJf80zVbhO/Y1lRV/zvDkNaXLGofwjUHraoOzRCsX9LdrxqbP7/yJ7Hx+fxl1ceafiLJfatqd5KXZfjT9LMz/Dlz5Rr4RyU5dznlsRfnJDmnu981Tr8iQ9j2vdvc7prkk919QXd/J8mrMnwPfee2jrW+Y+cmuc7Mco7jJlRVD0tynyQ/O3Pn78mPnXDNQRnH6L4gyYe7+5kzs2ZvYf/QJK/d6NrYu+5+Uncf1d07MpzM8U/d/bNJ3pLkAeNijt0m1N2fS/KZqrrR2HSXJB+K791m9+kkt6mqK43/dq4cN9+5rWOt79jrkvzCeNWQ2yT5yszwETaBqrpnhmGQ9+3ub8zMel2Sh1TVFarqehlOSn33Qe3LTWQ4GFV1uyT/nOQD+d643d/KMO76b5McneRTSR7U3atPDGGTqKpjk/yv7r5PVV0/Q0/21ZO8N8nPdfe3llgec1TVzTOciHr5JJ9I8vAMHSa+d5tYVf12kgdn+LP0e5M8MsP4Tt+5TaaqXprk2CRHJPl8kqcmeU3mfMfGX5aem2GYzzeSPLy7T19C2WTNY/ekJFdI8sVxsXd296PH5f93hnHYezIMb3396m3u1/6FawAAmIZhIQAAMBHhGgAAJiJcAwDARIRrAACYiHANAAATEa4BNpGq+tqq6YdV1XOXUMcpVfXJqjqrqs6sqtuusdzTq+quG10fwGa1bd+LAHAZ9Rvd/YqqunuSv0hy09mZVXVIdz9lOaUBbE56rgG2iKraUVX/VFXvr6o3V9XRY/spVfWAmeW+Nj5fq6rePvY+f7Cqbj+2372q/m3skf67qrryPnb99iQ3GNfdXVW/X1VnJnng7L6r6pZV9a9V9b6qendVXaWqDqmqZ1TVe8a6f2kBbw3ApiFcA2wuVxzD8FlVdVaSp8/M+5MkL+zumyZ5SZLn7GNbP5PkDd198yQ3S3JWVR2R5MlJ7trdxyQ5Pcmv72M7/zPDXVhXfLG7j+nul600VNXlk7w8yeO6+2ZJ7prkm0kekeFW0LdMcsskjxpvMQxwqWRYCMDm8s0xDCcZxlwn2TlO3jbJT42vX5zkD/axrfckObmqDk3ymu4+q6rumOTGSf5luGNzLp/k39ZY/xlV9eQkF2QIyStePmfZGyU5r7vfkyTdfdFY/92T3HSmZ/2qSW6Y5JP7qB1gSxKuAba+PRn/EllVl8sQmNPdb6+qOyQ5LskpVfXMJF9O8qbuPn4d2/2N7n7FnPav70dtleRXu/sN+7EOwJZlWAjA1vGvSR4yvv7ZJP88vt6d5MfH1/dNcmiSVNV1k3y+u/8yyfOTHJPknUl+oqpWxlAfVlU/PEFtH01yraq65bjdq1TVtiRvSPKYsfc8VfXDVXXYBPsD2JT0XANsHb+a5K+q6jcyDNV4+Nj+l0leW1XvS/IP+V7P8rFJfqOqvpPka0l+obsvGIeavLSqrjAu9+QkHzuYwrr721X14CR/UlVXzDDe+q4ZQv2OJGfWMA7lgiT3P5h9AWxm1d3LrgEAAC4VDAsBAICJCNcAADAR4RoAACYiXAMAwESEawAAmIhwDQAAExGuAQBgIsI1AABM5P8HndglL9SACicAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.hist(df[\"Y house price of unit area\"], \n",
    "         bins= 100)\n",
    "\n",
    "print(f'Mean price is: {df[\"Y house price of unit area\"].mean():.2f}')\n",
    "\n",
    "plt.axvline(df[\"Y house price of unit area\"].mean(), \n",
    "            color='Red', \n",
    "            linestyle='solid', \n",
    "            linewidth=3)\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.xlabel(\"House Price\")\n",
    "plt.title(\"Target Column Distribution\", fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "695c3ed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of DataFrame\n",
    "df_copy = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e1e5b24b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping outliers\n",
    "df_copy = df_copy[df_copy[\"Y house price of unit area\"] < 115]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c1175f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1 transaction date</th>\n",
       "      <th>X2 house age</th>\n",
       "      <th>X3 distance to the nearest MRT station</th>\n",
       "      <th>X4 number of convenience stores</th>\n",
       "      <th>X5 latitude</th>\n",
       "      <th>X6 longitude</th>\n",
       "      <th>Y house price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012.917</td>\n",
       "      <td>32.0</td>\n",
       "      <td>84.87882</td>\n",
       "      <td>10</td>\n",
       "      <td>24.98298</td>\n",
       "      <td>121.54024</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012.917</td>\n",
       "      <td>19.5</td>\n",
       "      <td>306.59470</td>\n",
       "      <td>9</td>\n",
       "      <td>24.98034</td>\n",
       "      <td>121.53951</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013.583</td>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013.500</td>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012.833</td>\n",
       "      <td>5.0</td>\n",
       "      <td>390.56840</td>\n",
       "      <td>5</td>\n",
       "      <td>24.97937</td>\n",
       "      <td>121.54245</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X1 transaction date  X2 house age  X3 distance to the nearest MRT station   \n",
       "0             2012.917          32.0                                84.87882  \\\n",
       "1             2012.917          19.5                               306.59470   \n",
       "2             2013.583          13.3                               561.98450   \n",
       "3             2013.500          13.3                               561.98450   \n",
       "4             2012.833           5.0                               390.56840   \n",
       "\n",
       "   X4 number of convenience stores  X5 latitude  X6 longitude   \n",
       "0                               10     24.98298     121.54024  \\\n",
       "1                                9     24.98034     121.53951   \n",
       "2                                5     24.98746     121.54391   \n",
       "3                                5     24.98746     121.54391   \n",
       "4                                5     24.97937     121.54245   \n",
       "\n",
       "   Y house price of unit area  \n",
       "0                        37.9  \n",
       "1                        42.2  \n",
       "2                        47.3  \n",
       "3                        54.8  \n",
       "4                        43.1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop \"No\" columns\n",
    "df_copy = df_copy.drop(columns=[\"No\"])\n",
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "670b79f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X2 house age</th>\n",
       "      <th>X3 distance to the nearest MRT station</th>\n",
       "      <th>X4 number of convenience stores</th>\n",
       "      <th>X5 latitude</th>\n",
       "      <th>X6 longitude</th>\n",
       "      <th>Y house price of unit area</th>\n",
       "      <th>X1 Transaction Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>84.87882</td>\n",
       "      <td>10</td>\n",
       "      <td>24.98298</td>\n",
       "      <td>121.54024</td>\n",
       "      <td>37.9</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.5</td>\n",
       "      <td>306.59470</td>\n",
       "      <td>9</td>\n",
       "      <td>24.98034</td>\n",
       "      <td>121.53951</td>\n",
       "      <td>42.2</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>47.3</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>54.8</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>390.56840</td>\n",
       "      <td>5</td>\n",
       "      <td>24.97937</td>\n",
       "      <td>121.54245</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X2 house age  X3 distance to the nearest MRT station   \n",
       "0          32.0                                84.87882  \\\n",
       "1          19.5                               306.59470   \n",
       "2          13.3                               561.98450   \n",
       "3          13.3                               561.98450   \n",
       "4           5.0                               390.56840   \n",
       "\n",
       "   X4 number of convenience stores  X5 latitude  X6 longitude   \n",
       "0                               10     24.98298     121.54024  \\\n",
       "1                                9     24.98034     121.53951   \n",
       "2                                5     24.98746     121.54391   \n",
       "3                                5     24.98746     121.54391   \n",
       "4                                5     24.97937     121.54245   \n",
       "\n",
       "   Y house price of unit area  X1 Transaction Year  \n",
       "0                        37.9                 2012  \n",
       "1                        42.2                 2012  \n",
       "2                        47.3                 2013  \n",
       "3                        54.8                 2013  \n",
       "4                        43.1                 2012  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_column(column):\n",
    "    column =  column.astype(\"str\")\n",
    "    # Split the column into two columns\n",
    "    df_copy [[\"X1 Transaction Year\", \"Date\"]] = column.str.split(\".\", expand = True).astype(int)\n",
    "    df_copy.drop(columns=[\"X1 transaction date\",\"Date\" ], inplace=True)\n",
    "    return df_copy.head()\n",
    "\n",
    "convert_column(df_copy[\"X1 transaction date\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93cfb667",
   "metadata": {},
   "source": [
    "## 8. \n",
    "\n",
    "#### Shuffling a DataFrame is important in machine learning to avoid any bias that may be introduced when the data is ordered in a certain way. Our data seem to have a particular order. The prices and sizes are ordered from smallest to largest. This may create bias. Using scikit-learn, shuffle the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "93da1fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X2 house age</th>\n",
       "      <th>X3 distance to the nearest MRT station</th>\n",
       "      <th>X4 number of convenience stores</th>\n",
       "      <th>X5 latitude</th>\n",
       "      <th>X6 longitude</th>\n",
       "      <th>Y house price of unit area</th>\n",
       "      <th>X1 Transaction Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>21.2</td>\n",
       "      <td>512.5487</td>\n",
       "      <td>4</td>\n",
       "      <td>24.97400</td>\n",
       "      <td>121.53842</td>\n",
       "      <td>42.5</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>13.2</td>\n",
       "      <td>492.2313</td>\n",
       "      <td>5</td>\n",
       "      <td>24.96515</td>\n",
       "      <td>121.53737</td>\n",
       "      <td>42.3</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>7.6</td>\n",
       "      <td>2175.0300</td>\n",
       "      <td>3</td>\n",
       "      <td>24.96305</td>\n",
       "      <td>121.51254</td>\n",
       "      <td>27.7</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>12.2</td>\n",
       "      <td>1360.1390</td>\n",
       "      <td>1</td>\n",
       "      <td>24.95204</td>\n",
       "      <td>121.54842</td>\n",
       "      <td>30.1</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>11.6</td>\n",
       "      <td>201.8939</td>\n",
       "      <td>8</td>\n",
       "      <td>24.98489</td>\n",
       "      <td>121.54121</td>\n",
       "      <td>55.9</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X2 house age  X3 distance to the nearest MRT station   \n",
       "395          21.2                                512.5487  \\\n",
       "350          13.2                                492.2313   \n",
       "401           7.6                               2175.0300   \n",
       "354          12.2                               1360.1390   \n",
       "181          11.6                                201.8939   \n",
       "\n",
       "     X4 number of convenience stores  X5 latitude  X6 longitude   \n",
       "395                                4     24.97400     121.53842  \\\n",
       "350                                5     24.96515     121.53737   \n",
       "401                                3     24.96305     121.51254   \n",
       "354                                1     24.95204     121.54842   \n",
       "181                                8     24.98489     121.54121   \n",
       "\n",
       "     Y house price of unit area  X1 Transaction Year  \n",
       "395                        42.5                 2012  \n",
       "350                        42.3                 2013  \n",
       "401                        27.7                 2013  \n",
       "354                        30.1                 2013  \n",
       "181                        55.9                 2013  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shuffled = shuffle(df_copy, random_state = 42)\n",
    "df_shuffled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "839e0468",
   "metadata": {},
   "source": [
    "## 9.\n",
    "#### Write a code to separate the DataFrame into X and y variables. X for feature columns and y for the target column. Use scikit-learn's train_test_split function to split the X and y variables into training and test sets. Make the test set to be 10% of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db0ee5ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating X and y variables\n",
    "X = df_shuffled.drop(columns=[\"Y house price of unit area\"])\n",
    "y = df_shuffled[\"Y house price of unit area\"]\n",
    "\n",
    "\n",
    "# Splitting the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.1, \n",
    "                                                    random_state=23)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cb720f1",
   "metadata": {},
   "source": [
    "## 10\n",
    "#### Using scikit-learn LinearRegression, select the model for your data based on the size of your dataset. Ensure that your data is scaled before fitting. Use the r2_score,  and MSE(mean squared error) metrics to evaluate the model. What conclusion can you draw from the score results?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "55eaa420",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2_score is: 0.75\n",
      "The mean squared error is: 37.22\n"
     ]
    }
   ],
   "source": [
    "# Scale the data using StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.fit_transform(X_test)\n",
    "\n",
    "# Instantiating model\n",
    "reg = LinearRegression()\n",
    "reg.fit(X_train, y_train)\n",
    "\n",
    "# Scoring the model on test dataset\n",
    "y_pred = reg.predict(X_test)\n",
    "r2_score = r2_score(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test,y_pred)\n",
    "print(f'The r2_score is: {r2_score:.2f}')\n",
    "print(f'The mean squared error is: {mse:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e4fd72",
   "metadata": {},
   "source": [
    "## 11\n",
    "#### Use the cross validation function to assess the performance of a model on unseen data. Use the LinearRegression model.Make the number of folds 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5ca53419",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score is 0.60\n"
     ]
    }
   ],
   "source": [
    "# instantianting model\n",
    "reg_2 = LinearRegression()\n",
    "scores = cross_val_score(reg_2, X, y, cv=10, scoring ='r2')\n",
    "print(f'The accuracy score is {scores.mean():.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c8b331",
   "metadata": {},
   "source": [
    "## 12. \n",
    "\n",
    "#### Save the model you fitted in questin 10 using joblib. Load the model and make predictions on x_test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2a679bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['regression_model.joblib']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving the model to a file\n",
    "joblib.dump(reg, 'regression_model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9d7c8dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27.09757879, 51.44825839, 34.04118703, 30.39047647, 13.82357914,\n",
       "       16.69349041, 42.60603813, 27.15964181, 45.86711546, 48.67321448,\n",
       "       45.85921106, 14.605422  , 33.08864684, 37.19218743, 37.48843452,\n",
       "       33.42672213, 39.26182668, 41.94741088, 51.41722688, 47.36001613,\n",
       "       41.62995642, 26.03667193, 51.2523784 , 43.91308039, 51.35516386,\n",
       "       32.61166218, 30.86537266, 31.62878771, 45.78739233, 44.53276692,\n",
       "       39.49156072, 47.34957458, 34.68217714, 44.10032808, 44.13135959,\n",
       "       45.16105813, 14.38079497, 42.60603813, 38.41266862, 43.52172732,\n",
       "       44.54134562, 34.93780812])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the model from file\n",
    "reg_loaded = joblib.load(\"regression_model.joblib\")\n",
    "\n",
    "# Making predictions with model\n",
    "model_predictions = reg_loaded.predict(X_test)\n",
    "model_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "725717cc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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