Task_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import pandas as pd  \n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston_dataset = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      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  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\gulmu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
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PPBJ//OMfY9u2bfH666/HNddck8eq69eYXiIibrrppjqzWblyZZ4qTuvXr1/84he/iB07dsS//vWv+OIXvxhf/epX4z//+U9EtJ65RDTcS0TrmQsAAABQ2HJZlmVn+6Zx48bF2LFj49e//nVERNTU1ET//v3jlltuiR/96EfNXmRLWr58eWzcuDF27tyZ71LOSS6Xiw0bNsTMmTMj4sOrkcvLy+O2226LxYsXR0REVVVV9OrVK9auXRtz587NY7UNO72fiA+vSD58+PBHrlRuDd58883o2bNnbNu2LSZOnBhVVVXRo0ePqKysjGuvvTYiIl566aX41Kc+Fdu3b4/x48fnueIzO72XiA+vfP30pz8d99xzT36La6LS0tK466674tprr221cznpZC8LFixo9XMBAAAACsdZX5H83nvvxY4dO2Ly5Mn/W+SCC2Ly5Mmxffv2Zi3ufHn55ZejvLw8Lr744rjuuuti3759+S7pnO3duzcOHDhQZ05du3aNcePGtdo5RURs3bo1evbsGUOHDo3vfve7cejQoXyX1ChVVVUR8WHIFxGxY8eOeP/99+vMZ9iwYTFgwICCn8/pvZz04IMPRllZWVx22WWxZMmSePfdd/NR3lk5ceJEPPTQQ1FdXR0TJkxo1XM5vZeTWuNcAAAAgMLT/mzf8NZbb8WJEyeiV69edY736tUrXnrppWYr7HwZN25crF27NoYOHRr79++PO++8M77whS/ECy+8EJ07d853eU124MCBiIh653TyXGszbdq0uOaaa2LQoEGxZ8+euOOOO2L69Omxffv2aNeuXb7LO6Oampq49dZb43Of+1xcdtllEfHhfIqKiqJbt251Xlvo86mvl4iIefPmxcCBA6O8vDyef/75+OEPfxi7d++O9evX57HaM9u1a1dMmDAhjh07Fp06dYoNGzbE8OHDY+fOna1uLmfqJaL1zQUAAAAoXGcdJLc106dPr30+atSoGDduXAwcODD+8Ic/xIIFC/JYGac79XYcI0eOjFGjRsXgwYNj69atMWnSpDxWlrZw4cJ44YUXWs29t1PO1Mu3v/3t2ucjR46MPn36xKRJk2LPnj0xePDg811mg4YOHRo7d+6Mqqqq+NOf/hTz58+Pbdu25busJjlTL8OHD291cwEAAAAK11nf2qKsrCzatWsXBw8erHP84MGD0bt372YrLF+6desWl156abzyyiv5LuWcnJxFW51TRMTFF18cZWVlBT2rm2++Of7yl7/Eli1bol+/frXHe/fuHe+9914cPny4zusLeT5n6qU+48aNi4go2NkUFRXFkCFDYsyYMbFixYoYPXp03Hvvva1yLmfqpT6FPhcAAACgcJ11kFxUVBRjxoyJJ554ovZYTU1NPPHEE3Xuy9lavfPOO7Fnz57o06dPvks5J4MGDYrevXvXmdORI0fi6aefbhNzioh47bXX4tChQwU5qyzL4uabb44NGzbE3/72txg0aFCd82PGjIkLL7ywznx2794d+/btK7j5NNRLfU5+eWUhzqY+NTU1cfz48VY1lzM52Ut9WttcAAAAgMLRpFtbVFRUxPz58+Ozn/1sXH755XHPPfdEdXV1fPOb32zu+lrc4sWL4+qrr46BAwfG66+/HsuWLYt27drFN77xjXyX1qB33nmnzpWFe/fujZ07d0ZpaWkMGDAgbr311vjZz34Wl1xySQwaNCiWLl0a5eXlMXPmzPwVnZDqp7S0NO68886YPXt29O7dO/bs2RO33357DBkyJKZOnZrHquu3cOHCqKysjIcffjg6d+5ce3/drl27RocOHaJr166xYMGCqKioiNLS0ujSpUvccsstMWHChBg/fnyeq6+roV727NkTlZWVMWPGjOjevXs8//zzsWjRopg4cWKMGjUqz9V/1JIlS2L69OkxYMCAOHr0aFRWVsbWrVtj8+bNrWouEeleWttcAAAAgAKXNdGvfvWrbMCAAVlRUVF2+eWXZ0899VRTl8qrOXPmZH369MmKioqyvn37ZnPmzMleeeWVfJfVKFu2bMki4iOP+fPnZ1mWZTU1NdnSpUuzXr16ZcXFxdmkSZOy3bt357fohFQ/7777bjZlypSsR48e2YUXXpgNHDgwu+mmm7IDBw7ku+x61ddHRGRr1qypfc1///vf7Hvf+172yU9+MuvYsWM2a9asbP/+/fkr+gwa6mXfvn3ZxIkTs9LS0qy4uDgbMmRI9oMf/CCrqqrKb+Fn8K1vfSsbOHBgVlRUlPXo0SObNGlS9thjj9Weby1zybJ0L61tLgAAAEBhy2VZlp3P4BoAAAAAgNblrO+RDAAAAADAx4sgGQAAAACAJEEyAAAAAABJgmQAAAAAAJIEyQAAAAAAJAmSAQAAAABIEiQDAAAAAJAkSAYAAAAAIEmQDA248cYbY+bMmfWe+/e//x1f+cpXomfPnlFSUhIXXXRRzJkzJ954441Yvnx55HK55OOkdevWRbt27WLhwoW1x6688srke6+88soW7hwAAAAAPiRIhiZ68803Y9KkSVFaWhqbN2+OF198MdasWRPl5eVRXV0dixcvjv3799c++vXrFz/5yU/qHDtp9erVcfvtt8e6devi2LFjERGxfv362tc988wzERHx17/+tfbY+vXr89I3AAAAAB8/7fNdALRW//jHP6Kqqiruv//+aN/+wx+lQYMGxVVXXVX7mk6dOtU+b9euXXTu3Dl69+5dZ529e/fGP//5z/jzn/8cW7ZsifXr18e8efOitLS09jUnw+Xu3bt/5P0AAAAA0NJckQxN1Lt37/jggw9iw4YNkWVZk9dZs2ZNfPnLX46uXbvG9ddfH6tXr27GKgEAAADg3AmSoYnGjx8fd9xxR8ybNy/Kyspi+vTpcdddd8XBgwcbvUZNTU2sXbs2rr/++oiImDt3bjz55JOxd+/eliobAAAAAM6aIBnOwc9//vM4cOBArFq1KkaMGBGrVq2KYcOGxa5duxr1/scffzyqq6tjxowZERFRVlYWX/rSl+KBBx5oybIBAAAA4KwIkuEcde/ePb72ta/F3XffHS+++GKUl5fH3Xff3aj3rl69Ot5+++3o0KFDtG/fPtq3bx+PPvpo/Pa3v42ampoWrhwAAAAAGseX7UEzKioqisGDB0d1dXWDrz106FA8/PDD8dBDD8WIESNqj584cSI+//nPx2OPPRbTpk1ryXIBAAAAoFEEydAIVVVVsXPnzjrHdu3aFZs3b465c+fGpZdeGlmWxSOPPBKPPvporFmzpsE1f/e730X37t3j61//euRyuTrnZsyYEatXrxYkAwAAAFAQBMnQCFu3bo3PfOYzdY5dddVVMWTIkLjtttvi1VdfjeLi4rjkkkvi/vvvjxtuuKHBNR944IGYNWvWR0LkiIjZs2fHDTfcEG+99VaUlZU1Wx8AAAAA0BS5LMuyfBcBAAAAAEDh8mV7AAAAAAAkCZIBAAAAAEgSJAMAAAAAkCRIBgAAAAAgSZAMAAAAAECSIBkAAAAAgCRBMgAAAAAASYJkAAAAAACSBMkAAAAAACQJkgEAAAAASBIkAwAAAACQJEgGAAAAACDp/wGt1/A+zIoImwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1800x3000 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting numerical columns\n",
    "c = 1\n",
    "plt.figure(figsize=(18,30))\n",
    "for i in df:\n",
    "    plt.subplot(6,3,c)\n",
    "    sns.boxplot(df[i])\n",
    "    c = c+1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mod_outlier(df):\n",
    "    col_vals = df.columns\n",
    "    df1 = df.copy()\n",
    "    df = df._get_numeric_data()\n",
    "\n",
    "    q1 = df.quantile(0.25)\n",
    "    q3 = df.quantile(0.75)\n",
    "\n",
    "    iqr = q3 - q1\n",
    "\n",
    "    lower_bound = q1 - (1.5 * iqr)\n",
    "    upper_bound = q3 + (1.5 * iqr)\n",
    "    for col in col_vals:\n",
    "        for i in range(0, len(df[col])):\n",
    "            if df[col][i] < lower_bound[col]:\n",
    "                df[col][i] = lower_bound[col]\n",
    "\n",
    "            if df[col][i] > upper_bound[col]:\n",
    "                df[col][i] = upper_bound[col]\n",
    "\n",
    "    for col in col_vals:\n",
    "        df1[col] = df[col]\n",
    "\n",
    "        return(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        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",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  \n",
       "0       15.3  396.90   4.98  \n",
       "1       17.8  396.90   9.14  \n",
       "2       17.8  392.83   4.03  \n",
       "3       18.7  394.63   2.94  \n",
       "4       18.7  396.90   5.33  \n",
       "..       ...     ...    ...  \n",
       "501     21.0  391.99   9.67  \n",
       "502     21.0  396.90   9.08  \n",
       "503     21.0  396.90   5.64  \n",
       "504     21.0  393.45   6.48  \n",
       "505     21.0  396.90   7.88  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_outlier(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,:-1]\n",
    "y = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import  train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "dt = DecisionTreeRegressor()\n",
    "knn = KNeighborsRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression()\n",
      "R2 score is: 0.7523649554283056\n",
      "Mean absolute error is: 2.630396306606111\n",
      "Mean squared error is: 12.124800747337677\n",
      "Root Mean squared error is: 3.4820684581635777\n",
      "-----------------------------------------------\n",
      "DecisionTreeRegressor()\n",
      "R2 score is: 0.6402987229240669\n",
      "Mean absolute error is: 2.9352450980392155\n",
      "Mean squared error is: 17.611830024509807\n",
      "Root Mean squared error is: 4.196645091559424\n",
      "-----------------------------------------------\n",
      "KNeighborsRegressor()\n",
      "R2 score is: 0.5196479098353745\n",
      "Mean absolute error is: 3.4702745098039216\n",
      "Mean squared error is: 23.519180784313725\n",
      "Root Mean squared error is: 4.849657800743649\n",
      "-----------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# model loop for training and testing\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "for i in [lr,dt,knn]:  # read all models\n",
    "    i.fit(X_train, y_train) # fit the model\n",
    "    pred = i.predict(X_test) # predict the model\n",
    "    text_score = r2_score(y_test, pred) # r2 score\n",
    "    train_score = r2_score(y_train, i.predict(X_train)) # r2 score\n",
    "    if abs(train_score-text_score) <= 0.5:\n",
    "        print(i)\n",
    "        print('R2 score is:', r2_score(y_test, pred))\n",
    "        print('Mean absolute error is:', mean_absolute_error(y_test, pred))\n",
    "        print('Mean squared error is:', mean_squared_error(y_test, pred))\n",
    "        print('Root Mean squared error is:', math.sqrt(mean_squared_error(y_test, pred)))\n",
    "        print('-----------------------------------------------')        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Linear Regression is Good for Training the Data\n",
    "- Decission Tree is Good for Both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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