HW4.1-DogukanAkgol.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\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 oxides 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 centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "\n",
    "raw_data = load_boston()\n",
    "print(raw_data.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
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       "<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",
       "      <th>PRICE</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",
       "      <td>24.0</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",
       "      <td>21.6</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",
       "      <td>34.7</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",
       "      <td>33.4</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",
       "      <td>36.2</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",
       "      <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",
       "      <td>22.4</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",
       "      <td>20.6</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",
       "      <td>23.9</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",
       "      <td>22.0</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",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 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  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",
       "..       ...     ...    ...    ...  \n",
       "501     21.0  391.99   9.67   22.4  \n",
       "502     21.0  396.90   9.08   20.6  \n",
       "503     21.0  396.90   5.64   23.9  \n",
       "504     21.0  393.45   6.48   22.0  \n",
       "505     21.0  396.90   7.88   11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(raw_data.data, columns=raw_data.feature_names)\n",
    "df[\"PRICE\"] = raw_data.target\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binning & Smooting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data içinde bulunan \"DIS\" değişkeninde Evin Bostondaki iş merkezlerine uzaklığı ağırlıklı olarak hesaplanmış. Bu değişkeni farklı yöntemler kullanarak Ayrıklaştırmak istiyoruz.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
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     "output_type": "execute_result"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bu scatterplotı Ayrıklaştırma işlemi yapıldıktan sonraki hali ile karşılaştırabilirsiniz\n",
    "DIS = df.DIS\n",
    "x = np.arange(len(DIS))\n",
    "plt.scatter(x, DIS)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2a47b3ff040>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# y değişkeninin için pd.cut fonksiyonunu kullanarak df.DIS 'ın ayrıklaştırılmış hali ile doldurun \n",
    "# NOT: bins parametresi için \"3\", labels için \"[0,1,2]\" ve retbins için \"True\" değerlerini kullanın\n",
    "new_DIS, bins = pd.cut(df.DIS, bins=3, retbins=True, labels=[0,1,2])\n",
    "x = np.arange(len(new_DIS))\n",
    "plt.scatter(x, new_DIS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>pd.cut</b> fonksiyonu, değişkeni ayrıklaştırırken aşağıdaki stratejilerden hangisini kullandı?<br>\n",
    "-- Equal-frequency(Eşit frekans)<br>\n",
    "-- Equal-width (eşit aralıklar)\n",
    "\n",
    "IPUCU: değişkenin min-max değerlerini ve value_count dağılımlarını kontrol edebilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min: 1.1296 Max: 12.1265\n",
      "Sınır değerleri: [ 1.1186031   4.79523333  8.46086667 12.1265    ]\n",
      "\n",
      " Ayrık değerlerin görülme sayısı\n",
      "0    365\n",
      "1    125\n",
      "2     16\n",
      "Name: DIS, dtype: int64 \n",
      "\n",
      "\n",
      "Cevabınızı bu print içine yazabilirsiniz.\n"
     ]
    }
   ],
   "source": [
    "print(\"Min:\", df.DIS.min(),\"Max:\", df.DIS.max())\n",
    "print(\"Sınır değerleri:\", bins)\n",
    "print(\"\\n Ayrık değerlerin görülme sayısı\")\n",
    "print(new_DIS.value_counts(), \"\\n\\n\")\n",
    "\n",
    "print(\"Cevabınızı bu print içine yazabilirsiniz.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soru "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2a47b46bdc0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# aynı işlemi pd.qcut kullanarak yapın\n",
    "# NOT: q parametresi için \"3\", labels için \"[0,1,2]\" ve retbins için \"True\" değerlerini kullanın\n",
    "new_DIS, bins = pd.qcut(df.DIS, q=3, retbins=True, labels=[0,1,2])\n",
    "x = np.arange(len(new_DIS))\n",
    "plt.scatter(x, new_DIS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>pd.qcut</b> fonksiyonu, değişkeni ayrıklaştırırken aşağıdaki stratejilerden hangisini kullandı?<br>\n",
    "-- Equal-frequency(Eşit frekans)<br>\n",
    "-- Equal-width (eşit aralıklar)\n",
    "\n",
    "IPUCU: değişkenin min-max değerlerini ve value_count dağılımlarını kontrol edebilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min: 1.1296 Max: 12.1265\n",
      "Sınır değerleri: [ 1.1296      2.38403333  4.3257     12.1265    ]\n",
      "\n",
      " Ayrık değerlerin görülme sayısı\n",
      "2    169\n",
      "0    169\n",
      "1    168\n",
      "Name: DIS, dtype: int64 \n",
      "\n",
      "\n",
      "Cevabınızı bu print içine yazabilirsiniz.\n"
     ]
    }
   ],
   "source": [
    "print(\"Min:\", df.DIS.min(),\"Max:\", df.DIS.max())\n",
    "print(\"Sınır değerleri:\", bins)\n",
    "print(\"\\n Ayrık değerlerin görülme sayısı\")\n",
    "print(new_DIS.value_counts(), \"\\n\\n\")\n",
    "\n",
    "print(\"Cevabınızı bu print içine yazabilirsiniz.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu bölümde var olan değişkenlerden yeni değişkenler üretelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "      <th>PRICE</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",
       "      <td>24.0</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",
       "      <td>21.6</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",
       "      <td>34.7</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",
       "      <td>33.4</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",
       "      <td>36.2</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  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  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seçtiğiniz iki değişkenin toplamından yeni bir değişken üretin\n",
    "df2[\"Sum\"] = df2[\"INDUS\"] + df2[\"DIS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seçtiğiniz iki değişkenin farkından yeni bir değişken üretin\n",
    "df2[\"Minus\"] = df2[\"INDUS\"] - df2[\"DIS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seçtiğiniz iki değişkenin bölümünden yeni bir değişken üretin\n",
    "df2[\"Sum\"] = df2[\"INDUS\"] / df2[\"DIS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seçtiğiniz iki değişkenin çarpımından yeni bir değişken üretin\n",
    "df2[\"Sum\"] = df2[\"INDUS\"] * df2[\"DIS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "      <th>PRICE</th>\n",
       "      <th>Sum</th>\n",
       "      <th>Minus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <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",
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       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.447900</td>\n",
       "      <td>-1.7800</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",
       "      <td>21.6</td>\n",
       "      <td>35.117397</td>\n",
       "      <td>2.1029</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",
       "      <td>34.7</td>\n",
       "      <td>35.117397</td>\n",
       "      <td>2.1029</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",
       "      <td>33.4</td>\n",
       "      <td>13.215596</td>\n",
       "      <td>-3.8822</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",
       "      <td>36.2</td>\n",
       "      <td>13.215596</td>\n",
       "      <td>-3.8822</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  PRICE        Sum   Minus  \n",
       "0     15.3  396.90   4.98   24.0   9.447900 -1.7800  \n",
       "1     17.8  396.90   9.14   21.6  35.117397  2.1029  \n",
       "2     17.8  392.83   4.03   34.7  35.117397  2.1029  \n",
       "3     18.7  394.63   2.94   33.4  13.215596 -3.8822  \n",
       "4     18.7  396.90   5.33   36.2  13.215596 -3.8822  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sklearn kütüphanesindeki PolynomialFeatures'ı kullanarak yeni featurelar üretin (NOT:Hedef değişkeni(PRICE) yeni featureları üretirken kullanmayın.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PolynomialFeatures kullandığınız python kodunu buraya yazabilirsiniz.\n",
    "df3 = df.copy()\n",
    "df3 = df3.drop(\"PRICE\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</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>...</th>\n",
       "      <th>TAX^2</th>\n",
       "      <th>TAX PTRATIO</th>\n",
       "      <th>TAX B</th>\n",
       "      <th>TAX LSTAT</th>\n",
       "      <th>PTRATIO^2</th>\n",
       "      <th>PTRATIO B</th>\n",
       "      <th>PTRATIO LSTAT</th>\n",
       "      <th>B^2</th>\n",
       "      <th>B LSTAT</th>\n",
       "      <th>LSTAT^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>117482.40</td>\n",
       "      <td>1474.08</td>\n",
       "      <td>234.09</td>\n",
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       "      <td>1976.5620</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\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",
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       "      <td>58564.0</td>\n",
       "      <td>4307.6</td>\n",
       "      <td>96049.80</td>\n",
       "      <td>2211.88</td>\n",
       "      <td>316.84</td>\n",
       "      <td>7064.820</td>\n",
       "      <td>162.692</td>\n",
       "      <td>157529.6100</td>\n",
       "      <td>3627.6660</td>\n",
       "      <td>83.5396</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.0</td>\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>...</td>\n",
       "      <td>58564.0</td>\n",
       "      <td>4307.6</td>\n",
       "      <td>95064.86</td>\n",
       "      <td>975.26</td>\n",
       "      <td>316.84</td>\n",
       "      <td>6992.374</td>\n",
       "      <td>71.734</td>\n",
       "      <td>154315.4089</td>\n",
       "      <td>1583.1049</td>\n",
       "      <td>16.2409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\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>...</td>\n",
       "      <td>49284.0</td>\n",
       "      <td>4151.4</td>\n",
       "      <td>87607.86</td>\n",
       "      <td>652.68</td>\n",
       "      <td>349.69</td>\n",
       "      <td>7379.581</td>\n",
       "      <td>54.978</td>\n",
       "      <td>155732.8369</td>\n",
       "      <td>1160.2122</td>\n",
       "      <td>8.6436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\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>...</td>\n",
       "      <td>49284.0</td>\n",
       "      <td>4151.4</td>\n",
       "      <td>88111.80</td>\n",
       "      <td>1183.26</td>\n",
       "      <td>349.69</td>\n",
       "      <td>7422.030</td>\n",
       "      <td>99.671</td>\n",
       "      <td>157529.6100</td>\n",
       "      <td>2115.4770</td>\n",
       "      <td>28.4089</td>\n",
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       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>501</th>\n",
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       "      <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",
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       "      <td>5733.0</td>\n",
       "      <td>107013.27</td>\n",
       "      <td>2639.91</td>\n",
       "      <td>441.00</td>\n",
       "      <td>8231.790</td>\n",
       "      <td>203.070</td>\n",
       "      <td>153656.1601</td>\n",
       "      <td>3790.5433</td>\n",
       "      <td>93.5089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>1.0</td>\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>...</td>\n",
       "      <td>74529.0</td>\n",
       "      <td>5733.0</td>\n",
       "      <td>108353.70</td>\n",
       "      <td>2478.84</td>\n",
       "      <td>441.00</td>\n",
       "      <td>8334.900</td>\n",
       "      <td>190.680</td>\n",
       "      <td>157529.6100</td>\n",
       "      <td>3603.8520</td>\n",
       "      <td>82.4464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>1.0</td>\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>...</td>\n",
       "      <td>74529.0</td>\n",
       "      <td>5733.0</td>\n",
       "      <td>108353.70</td>\n",
       "      <td>1539.72</td>\n",
       "      <td>441.00</td>\n",
       "      <td>8334.900</td>\n",
       "      <td>118.440</td>\n",
       "      <td>157529.6100</td>\n",
       "      <td>2238.5160</td>\n",
       "      <td>31.8096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>1.0</td>\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>...</td>\n",
       "      <td>74529.0</td>\n",
       "      <td>5733.0</td>\n",
       "      <td>107411.85</td>\n",
       "      <td>1769.04</td>\n",
       "      <td>441.00</td>\n",
       "      <td>8262.450</td>\n",
       "      <td>136.080</td>\n",
       "      <td>154802.9025</td>\n",
       "      <td>2549.5560</td>\n",
       "      <td>41.9904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>1.0</td>\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>...</td>\n",
       "      <td>74529.0</td>\n",
       "      <td>5733.0</td>\n",
       "      <td>108353.70</td>\n",
       "      <td>2151.24</td>\n",
       "      <td>441.00</td>\n",
       "      <td>8334.900</td>\n",
       "      <td>165.480</td>\n",
       "      <td>157529.6100</td>\n",
       "      <td>3127.5720</td>\n",
       "      <td>62.0944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1     CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  ...  \\\n",
       "0    1.0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  ...   \n",
       "1    1.0  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  ...   \n",
       "2    1.0  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  ...   \n",
       "3    1.0  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  ...   \n",
       "4    1.0  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  ...   \n",
       "..   ...      ...   ...    ...   ...    ...    ...   ...     ...  ...  ...   \n",
       "501  1.0  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  ...   \n",
       "502  1.0  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  ...   \n",
       "503  1.0  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  ...   \n",
       "504  1.0  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  ...   \n",
       "505  1.0  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  ...   \n",
       "\n",
       "       TAX^2  TAX PTRATIO      TAX B  TAX LSTAT  PTRATIO^2  PTRATIO B  \\\n",
       "0    87616.0       4528.8  117482.40    1474.08     234.09   6072.570   \n",
       "1    58564.0       4307.6   96049.80    2211.88     316.84   7064.820   \n",
       "2    58564.0       4307.6   95064.86     975.26     316.84   6992.374   \n",
       "3    49284.0       4151.4   87607.86     652.68     349.69   7379.581   \n",
       "4    49284.0       4151.4   88111.80    1183.26     349.69   7422.030   \n",
       "..       ...          ...        ...        ...        ...        ...   \n",
       "501  74529.0       5733.0  107013.27    2639.91     441.00   8231.790   \n",
       "502  74529.0       5733.0  108353.70    2478.84     441.00   8334.900   \n",
       "503  74529.0       5733.0  108353.70    1539.72     441.00   8334.900   \n",
       "504  74529.0       5733.0  107411.85    1769.04     441.00   8262.450   \n",
       "505  74529.0       5733.0  108353.70    2151.24     441.00   8334.900   \n",
       "\n",
       "     PTRATIO LSTAT          B^2    B LSTAT  LSTAT^2  \n",
       "0           76.194  157529.6100  1976.5620  24.8004  \n",
       "1          162.692  157529.6100  3627.6660  83.5396  \n",
       "2           71.734  154315.4089  1583.1049  16.2409  \n",
       "3           54.978  155732.8369  1160.2122   8.6436  \n",
       "4           99.671  157529.6100  2115.4770  28.4089  \n",
       "..             ...          ...        ...      ...  \n",
       "501        203.070  153656.1601  3790.5433  93.5089  \n",
       "502        190.680  157529.6100  3603.8520  82.4464  \n",
       "503        118.440  157529.6100  2238.5160  31.8096  \n",
       "504        136.080  154802.9025  2549.5560  41.9904  \n",
       "505        165.480  157529.6100  3127.5720  62.0944  \n",
       "\n",
       "[506 rows x 105 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf = PolynomialFeatures()\n",
    "pd.DataFrame(pf.fit_transform(df3), \n",
    "             columns=pf.get_feature_names(df3.columns))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalizasyon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sklearn kütüphanesindeki Min-max scalerı kullanarak datayı normalize edip yeni bir dataframe içine atayın "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.314815</td>\n",
       "      <td>0.577505</td>\n",
       "      <td>0.641607</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.208015</td>\n",
       "      <td>0.287234</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089680</td>\n",
       "      <td>0.422222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.547998</td>\n",
       "      <td>0.782698</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.553191</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.204470</td>\n",
       "      <td>0.368889</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.599382</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.553191</td>\n",
       "      <td>0.989737</td>\n",
       "      <td>0.063466</td>\n",
       "      <td>0.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.658555</td>\n",
       "      <td>0.441813</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.648936</td>\n",
       "      <td>0.994276</td>\n",
       "      <td>0.033389</td>\n",
       "      <td>0.631111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.687105</td>\n",
       "      <td>0.528321</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.648936</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.099338</td>\n",
       "      <td>0.693333</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.000633</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.420455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.386831</td>\n",
       "      <td>0.580954</td>\n",
       "      <td>0.681771</td>\n",
       "      <td>0.122671</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.164122</td>\n",
       "      <td>0.893617</td>\n",
       "      <td>0.987619</td>\n",
       "      <td>0.219095</td>\n",
       "      <td>0.386667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.000438</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.420455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.386831</td>\n",
       "      <td>0.490324</td>\n",
       "      <td>0.760041</td>\n",
       "      <td>0.105293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.164122</td>\n",
       "      <td>0.893617</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.202815</td>\n",
       "      <td>0.346667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.000612</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.420455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.386831</td>\n",
       "      <td>0.654340</td>\n",
       "      <td>0.907312</td>\n",
       "      <td>0.094381</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.164122</td>\n",
       "      <td>0.893617</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.107892</td>\n",
       "      <td>0.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.001161</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.420455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.386831</td>\n",
       "      <td>0.619467</td>\n",
       "      <td>0.889804</td>\n",
       "      <td>0.114514</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.164122</td>\n",
       "      <td>0.893617</td>\n",
       "      <td>0.991301</td>\n",
       "      <td>0.131071</td>\n",
       "      <td>0.377778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.000462</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.420455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.386831</td>\n",
       "      <td>0.473079</td>\n",
       "      <td>0.802266</td>\n",
       "      <td>0.125072</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.164122</td>\n",
       "      <td>0.893617</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.169702</td>\n",
       "      <td>0.153333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0    0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
       "1    0.000236  0.00  0.242302   0.0  0.172840  0.547998  0.782698  0.348962   \n",
       "2    0.000236  0.00  0.242302   0.0  0.172840  0.694386  0.599382  0.348962   \n",
       "3    0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4    0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "..        ...   ...       ...   ...       ...       ...       ...       ...   \n",
       "501  0.000633  0.00  0.420455   0.0  0.386831  0.580954  0.681771  0.122671   \n",
       "502  0.000438  0.00  0.420455   0.0  0.386831  0.490324  0.760041  0.105293   \n",
       "503  0.000612  0.00  0.420455   0.0  0.386831  0.654340  0.907312  0.094381   \n",
       "504  0.001161  0.00  0.420455   0.0  0.386831  0.619467  0.889804  0.114514   \n",
       "505  0.000462  0.00  0.420455   0.0  0.386831  0.473079  0.802266  0.125072   \n",
       "\n",
       "          RAD       TAX   PTRATIO         B     LSTAT     PRICE  \n",
       "0    0.000000  0.208015  0.287234  1.000000  0.089680  0.422222  \n",
       "1    0.043478  0.104962  0.553191  1.000000  0.204470  0.368889  \n",
       "2    0.043478  0.104962  0.553191  0.989737  0.063466  0.660000  \n",
       "3    0.086957  0.066794  0.648936  0.994276  0.033389  0.631111  \n",
       "4    0.086957  0.066794  0.648936  1.000000  0.099338  0.693333  \n",
       "..        ...       ...       ...       ...       ...       ...  \n",
       "501  0.000000  0.164122  0.893617  0.987619  0.219095  0.386667  \n",
       "502  0.000000  0.164122  0.893617  1.000000  0.202815  0.346667  \n",
       "503  0.000000  0.164122  0.893617  1.000000  0.107892  0.420000  \n",
       "504  0.000000  0.164122  0.893617  0.991301  0.131071  0.377778  \n",
       "505  0.000000  0.164122  0.893617  1.000000  0.169702  0.153333  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# python kodu:\n",
    "df_mms = df.copy()\n",
    "mms = MinMaxScaler()\n",
    "df_mms[df_mms.columns] = mms.fit_transform(df_mms[df_mms.columns])\n",
    "display(df_mms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sklearn kütüphanesindeki StandardScalerı kullanarak datayı standardize edip yeni bir dataframe içine atayın "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.419782</td>\n",
       "      <td>0.284830</td>\n",
       "      <td>-1.287909</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>-0.144217</td>\n",
       "      <td>0.413672</td>\n",
       "      <td>-0.120013</td>\n",
       "      <td>0.140214</td>\n",
       "      <td>-0.982843</td>\n",
       "      <td>-0.666608</td>\n",
       "      <td>-1.459000</td>\n",
       "      <td>0.441052</td>\n",
       "      <td>-1.075562</td>\n",
       "      <td>0.159686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.417339</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>-0.593381</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>-0.740262</td>\n",
       "      <td>0.194274</td>\n",
       "      <td>0.367166</td>\n",
       "      <td>0.557160</td>\n",
       "      <td>-0.867883</td>\n",
       "      <td>-0.987329</td>\n",
       "      <td>-0.303094</td>\n",
       "      <td>0.441052</td>\n",
       "      <td>-0.492439</td>\n",
       "      <td>-0.101524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.417342</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>-0.593381</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>-0.740262</td>\n",
       "      <td>1.282714</td>\n",
       "      <td>-0.265812</td>\n",
       "      <td>0.557160</td>\n",
       "      <td>-0.867883</td>\n",
       "      <td>-0.987329</td>\n",
       "      <td>-0.303094</td>\n",
       "      <td>0.396427</td>\n",
       "      <td>-1.208727</td>\n",
       "      <td>1.324247</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.416750</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>-1.306878</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>-0.835284</td>\n",
       "      <td>1.016303</td>\n",
       "      <td>-0.809889</td>\n",
       "      <td>1.077737</td>\n",
       "      <td>-0.752922</td>\n",
       "      <td>-1.106115</td>\n",
       "      <td>0.113032</td>\n",
       "      <td>0.416163</td>\n",
       "      <td>-1.361517</td>\n",
       "      <td>1.182758</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.412482</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>-1.306878</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>-0.835284</td>\n",
       "      <td>1.228577</td>\n",
       "      <td>-0.511180</td>\n",
       "      <td>1.077737</td>\n",
       "      <td>-0.752922</td>\n",
       "      <td>-1.106115</td>\n",
       "      <td>0.113032</td>\n",
       "      <td>0.441052</td>\n",
       "      <td>-1.026501</td>\n",
       "      <td>1.487503</td>\n",
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       "      <th>501</th>\n",
       "      <td>-0.413229</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>0.115738</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>0.158124</td>\n",
       "      <td>0.439316</td>\n",
       "      <td>0.018673</td>\n",
       "      <td>-0.625796</td>\n",
       "      <td>-0.982843</td>\n",
       "      <td>-0.803212</td>\n",
       "      <td>1.176466</td>\n",
       "      <td>0.387217</td>\n",
       "      <td>-0.418147</td>\n",
       "      <td>-0.014454</td>\n",
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       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>-0.415249</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>0.115738</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>0.158124</td>\n",
       "      <td>-0.234548</td>\n",
       "      <td>0.288933</td>\n",
       "      <td>-0.716639</td>\n",
       "      <td>-0.982843</td>\n",
       "      <td>-0.803212</td>\n",
       "      <td>1.176466</td>\n",
       "      <td>0.441052</td>\n",
       "      <td>-0.500850</td>\n",
       "      <td>-0.210362</td>\n",
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       "      <th>503</th>\n",
       "      <td>-0.413447</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>0.115738</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>0.158124</td>\n",
       "      <td>0.984960</td>\n",
       "      <td>0.797449</td>\n",
       "      <td>-0.773684</td>\n",
       "      <td>-0.982843</td>\n",
       "      <td>-0.803212</td>\n",
       "      <td>1.176466</td>\n",
       "      <td>0.441052</td>\n",
       "      <td>-0.983048</td>\n",
       "      <td>0.148802</td>\n",
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       "      <th>504</th>\n",
       "      <td>-0.407764</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>0.115738</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>0.158124</td>\n",
       "      <td>0.725672</td>\n",
       "      <td>0.736996</td>\n",
       "      <td>-0.668437</td>\n",
       "      <td>-0.982843</td>\n",
       "      <td>-0.803212</td>\n",
       "      <td>1.176466</td>\n",
       "      <td>0.403225</td>\n",
       "      <td>-0.865302</td>\n",
       "      <td>-0.057989</td>\n",
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       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>-0.415000</td>\n",
       "      <td>-0.487722</td>\n",
       "      <td>0.115738</td>\n",
       "      <td>-0.272599</td>\n",
       "      <td>0.158124</td>\n",
       "      <td>-0.362767</td>\n",
       "      <td>0.434732</td>\n",
       "      <td>-0.613246</td>\n",
       "      <td>-0.982843</td>\n",
       "      <td>-0.803212</td>\n",
       "      <td>1.176466</td>\n",
       "      <td>0.441052</td>\n",
       "      <td>-0.669058</td>\n",
       "      <td>-1.157248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "0   -0.419782  0.284830 -1.287909 -0.272599 -0.144217  0.413672 -0.120013   \n",
       "1   -0.417339 -0.487722 -0.593381 -0.272599 -0.740262  0.194274  0.367166   \n",
       "2   -0.417342 -0.487722 -0.593381 -0.272599 -0.740262  1.282714 -0.265812   \n",
       "3   -0.416750 -0.487722 -1.306878 -0.272599 -0.835284  1.016303 -0.809889   \n",
       "4   -0.412482 -0.487722 -1.306878 -0.272599 -0.835284  1.228577 -0.511180   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "501 -0.413229 -0.487722  0.115738 -0.272599  0.158124  0.439316  0.018673   \n",
       "502 -0.415249 -0.487722  0.115738 -0.272599  0.158124 -0.234548  0.288933   \n",
       "503 -0.413447 -0.487722  0.115738 -0.272599  0.158124  0.984960  0.797449   \n",
       "504 -0.407764 -0.487722  0.115738 -0.272599  0.158124  0.725672  0.736996   \n",
       "505 -0.415000 -0.487722  0.115738 -0.272599  0.158124 -0.362767  0.434732   \n",
       "\n",
       "          DIS       RAD       TAX   PTRATIO         B     LSTAT     PRICE  \n",
       "0    0.140214 -0.982843 -0.666608 -1.459000  0.441052 -1.075562  0.159686  \n",
       "1    0.557160 -0.867883 -0.987329 -0.303094  0.441052 -0.492439 -0.101524  \n",
       "2    0.557160 -0.867883 -0.987329 -0.303094  0.396427 -1.208727  1.324247  \n",
       "3    1.077737 -0.752922 -1.106115  0.113032  0.416163 -1.361517  1.182758  \n",
       "4    1.077737 -0.752922 -1.106115  0.113032  0.441052 -1.026501  1.487503  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "501 -0.625796 -0.982843 -0.803212  1.176466  0.387217 -0.418147 -0.014454  \n",
       "502 -0.716639 -0.982843 -0.803212  1.176466  0.441052 -0.500850 -0.210362  \n",
       "503 -0.773684 -0.982843 -0.803212  1.176466  0.441052 -0.983048  0.148802  \n",
       "504 -0.668437 -0.982843 -0.803212  1.176466  0.403225 -0.865302 -0.057989  \n",
       "505 -0.613246 -0.982843 -0.803212  1.176466  0.441052 -0.669058 -1.157248  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# python kodu:\n",
    "df_ss = df.copy()\n",
    "ss = StandardScaler()\n",
    "df_ss[df_ss.columns] = ss.fit_transform(df_ss[df_ss.columns])\n",
    "display(df_ss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sklearn kütüphanesindeki RobustScalerı kullanarak datayı standardize edip yeni bir dataframe içine atayın "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CRIM</th>\n",
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       "      <td>0.153016</td>\n",
       "      <td>-0.841579</td>\n",
       "      <td>1.529781</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.052144</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.581720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.457143</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.026382</td>\n",
       "      <td>-0.168916</td>\n",
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       "      <th>503</th>\n",
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       "      <td>-0.20</td>\n",
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       "      <td>0.696429</td>\n",
       "      <td>0.096414</td>\n",
       "      <td>-0.487756</td>\n",
       "      <td>0.100313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>-0.058164</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.173509</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>-0.241870</td>\n",
       "      <td>0.067278</td>\n",
       "      <td>-0.227459</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.147287</td>\n",
       "      <td>0.696429</td>\n",
       "      <td>0.261902</td>\n",
       "      <td>-0.347826</td>\n",
       "      <td>-1.166144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0   -0.069593  1.44 -0.571650   0.0  0.000000  0.496612 -0.250765  0.285777   \n",
       "1   -0.063755  0.00 -0.202943   0.0 -0.394286  0.287940  0.028542  0.569789   \n",
       "2   -0.063760  0.00 -0.202943   0.0 -0.394286  1.323171 -0.334353  0.569789   \n",
       "3   -0.062347  0.00 -0.581720   0.0 -0.457143  1.069783 -0.646279  0.924391   \n",
       "4   -0.052144  0.00 -0.581720   0.0 -0.457143  1.271680 -0.475025  0.924391   \n",
       "..        ...   ...       ...   ...       ...       ...       ...       ...   \n",
       "501 -0.053930  0.00  0.173509   0.0  0.200000  0.521003 -0.171254 -0.236007   \n",
       "502 -0.058759  0.00  0.173509   0.0  0.200000 -0.119919 -0.016310 -0.297887   \n",
       "503 -0.054450  0.00  0.173509   0.0  0.200000  1.039973  0.275229 -0.336744   \n",
       "504 -0.040867  0.00  0.173509   0.0  0.200000  0.793360  0.240571 -0.265053   \n",
       "505 -0.058164  0.00  0.173509   0.0  0.200000 -0.241870  0.067278 -0.227459   \n",
       "\n",
       "      RAD       TAX   PTRATIO         B     LSTAT     PRICE  \n",
       "0   -0.20 -0.087855 -1.339286  0.261902 -0.637681  0.351097  \n",
       "1   -0.15 -0.227390 -0.446429  0.261902 -0.221889  0.050157  \n",
       "2   -0.15 -0.227390 -0.446429  0.066675 -0.732634  1.692790  \n",
       "3   -0.10 -0.279070 -0.125000  0.153016 -0.841579  1.529781  \n",
       "4   -0.10 -0.279070 -0.125000  0.261902 -0.602699  1.880878  \n",
       "..    ...       ...       ...       ...       ...       ...  \n",
       "501 -0.20 -0.147287  0.696429  0.026382 -0.168916  0.150470  \n",
       "502 -0.20 -0.147287  0.696429  0.261902 -0.227886 -0.075235  \n",
       "503 -0.20 -0.147287  0.696429  0.261902 -0.571714  0.338558  \n",
       "504 -0.20 -0.147287  0.696429  0.096414 -0.487756  0.100313  \n",
       "505 -0.20 -0.147287  0.696429  0.261902 -0.347826 -1.166144  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# python kodu:\n",
    "df_rs = df.copy()\n",
    "rs = RobustScaler()\n",
    "df_rs[df_rs.columns] = rs.fit_transform(df_rs[df_rs.columns])\n",
    "display(df_rs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation & Covariance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu hücrede değişkenlerin birbiri ile olan korelasyonları heatmap kullanarak görselleştirildi.<br>\n",
    "Aşağıdaki sorular bu görseli incelemeye ve yorumlamaya yönelik olacaktır.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "sns.heatmap(df.corr(), vmin=-1, vmax=1, square=True, annot=True, fmt='.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aşağıda örnek iki değişken için scatter plot çizilmiştir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": [
    "df.plot.scatter(\"TAX\",\"CHAS\")\n",
    "plt.show()\n",
    "# scatter plot çizerken buradan kopya çekebilirsiniz :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yukarıdaki heatmapten birbiri ile pozitif yüksek korelasyona sahip iki değişken için scatter plot çiziniz. Bir iki cümle ile görseli yorumlamaya çalışınız. Not:Yorum yaparken değişkenlerin yukarıdaki açıklamalarından faydalanabilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oda sayısındaki artış fıyatlara da artış olarak yansımaktadır. Dolayısıyla bu iki değişken pozitif korelasyona sahiptir\n"
     ]
    },
    {
     "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": [
    "# python kodu buraya:\n",
    "sns.scatterplot(x=df.RM, y=df.PRICE)\n",
    "\n",
    "print(\"Oda sayısındaki artış fıyatlara da artış olarak yansımaktadır. Dolayısıyla bu iki değişken pozitif korelasyona sahiptir.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heatmapten faydalanarak negatif korelasyonlu iki değişken için scatter plot çiziniz. Sonucu yorumlayınız"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston merkezlerine olan uzaklık (DIS), nitrik oksit konsantrasyonu ile (NOX) negatif korelasyona sahiptir.Merkeze olan uzaklığın artması doğal olarak hava kirliliğinin, nitrik oksit konsantrasyonunun azalmasına sebep olmaktadır.\n"
     ]
    },
    {
     "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": [
    "# python kodu buraya:\n",
    "sns.scatterplot(x=df.DIS, y=df.NOX)\n",
    "\n",
    "\n",
    "print(\"Boston merkezlerine olan uzaklık (DIS), nitrik oksit konsantrasyonu ile (NOX) negatif korelasyona sahiptir.\"+\n",
    "     \"Merkeze olan uzaklığın artması doğal olarak hava kirliliğinin, nitrik oksit konsantrasyonunun azalmasına sebep olmaktadır.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heatmapten faydalanarak düşük korelasyonlu iki değişken için scatter plot çiziniz. Sonucu yorumlayınız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bölgenin Chas Nehrine komsu olup olmaması ile oda sayısı arasında %9 gibi düşük bir pozitif korelasyon bulunmaktadırDolayısıyla oda sayısı ve CHAS değişkeni arasında kayda değer bir etkileşim yoktur.\n"
     ]
    },
    {
     "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": [
    "# python kodu buraya:\n",
    "\n",
    "sns.scatterplot(x=df.RM, y=df.CHAS)\n",
    "\n",
    "print(\"Bölgenin Chas Nehrine komsu olup olmaması ile oda sayısı arasında %9 gibi düşük bir pozitif korelasyon bulunmaktadır\"+\n",
    "     \"Dolayısıyla oda sayısı ve CHAS değişkeni arasında kayda değer bir etkileşim yoktur.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soru"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Adım 1 :</b>df.cov() methodunu kullanarak datasetteki covariance değerlerini hesaplayın.<br> \n",
    "sns.heatmap ile bu methodu görselleştirin (IPUCU: Yukarıda korelasyon gösteriminde kullanılan yöntemin aynısı)\n",
    "heatmap için vmin ve vmax parametrelerini kullanmayın."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap için python kodunu bu hücreye yazın:\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "sns.heatmap(df.cov(), square=True, annot=True, fmt='.2f')\n",
    "plt.show()\n",
    "\n",
    "# Bilgi: Kovaryans değerinden anlam çıkartmak korelasyona göre daha zor çıkan grafikten bunu farkedebilirsiniz."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Adım 2 :</b> Şimdi sklearn.preprocessing paketi içindeki StandardScaler objesini kullanarak datayı standardize edin ve bunu ayrı bir dataframede tutun. Standardize edilmiş dataframe objesinin Kovaryans değerini tekrar hesaplatıp görselleştirin.\n",
    "\n",
    "<br>IPUCU : standardScalerdan gelen dönüştürülmüş datayı tekrar pandas DataFrame objesine çevirin ve bunu yaparken kolon adlarını kaybetmemeye çalışın örn: pd.DataFrame(standardize_numpy_datası, columns=df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x1224 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap için python kodunu bu hücreye yazın:\n",
    "df_ss2 = df.copy()\n",
    "ss = StandardScaler()\n",
    "df_ss2[df_ss2.columns] = ss.fit_transform(df_ss2[df_ss2.columns])\n",
    "\n",
    "plt.figure(figsize=(17,17))\n",
    "sns.heatmap(df_ss2.cov(), square=True, cbar=True, annot=True, fmt='.1f');\n",
    "# Bilgi: Ortaya çıkan grafik korelasyon grafiğine benziyor mu? :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>1.001980</td>\n",
       "      <td>-0.200866</td>\n",
       "      <td>0.407389</td>\n",
       "      <td>-0.056002</td>\n",
       "      <td>0.421805</td>\n",
       "      <td>-0.219681</td>\n",
       "      <td>0.353433</td>\n",
       "      <td>-0.380422</td>\n",
       "      <td>0.626744</td>\n",
       "      <td>0.583918</td>\n",
       "      <td>0.290520</td>\n",
       "      <td>-0.385826</td>\n",
       "      <td>0.456524</td>\n",
       "      <td>-0.389074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>-0.200866</td>\n",
       "      <td>1.001980</td>\n",
       "      <td>-0.534885</td>\n",
       "      <td>-0.042781</td>\n",
       "      <td>-0.517627</td>\n",
       "      <td>0.312608</td>\n",
       "      <td>-0.570665</td>\n",
       "      <td>0.665724</td>\n",
       "      <td>-0.312566</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.392454</td>\n",
       "      <td>0.175868</td>\n",
       "      <td>-0.413812</td>\n",
       "      <td>0.361159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.407389</td>\n",
       "      <td>-0.534885</td>\n",
       "      <td>1.001980</td>\n",
       "      <td>0.063063</td>\n",
       "      <td>0.765164</td>\n",
       "      <td>-0.392451</td>\n",
       "      <td>0.646055</td>\n",
       "      <td>-0.709429</td>\n",
       "      <td>0.596308</td>\n",
       "      <td>0.722187</td>\n",
       "      <td>0.384006</td>\n",
       "      <td>-0.357683</td>\n",
       "      <td>0.604995</td>\n",
       "      <td>-0.484683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>-0.056002</td>\n",
       "      <td>-0.042781</td>\n",
       "      <td>0.063063</td>\n",
       "      <td>1.001980</td>\n",
       "      <td>0.091383</td>\n",
       "      <td>0.091432</td>\n",
       "      <td>0.086689</td>\n",
       "      <td>-0.099372</td>\n",
       "      <td>-0.007383</td>\n",
       "      <td>-0.035657</td>\n",
       "      <td>-0.121756</td>\n",
       "      <td>0.048885</td>\n",
       "      <td>-0.054036</td>\n",
       "      <td>0.175607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>0.421805</td>\n",
       "      <td>-0.517627</td>\n",
       "      <td>0.765164</td>\n",
       "      <td>0.091383</td>\n",
       "      <td>1.001980</td>\n",
       "      <td>-0.302787</td>\n",
       "      <td>0.732919</td>\n",
       "      <td>-0.770753</td>\n",
       "      <td>0.612651</td>\n",
       "      <td>0.669346</td>\n",
       "      <td>0.189307</td>\n",
       "      <td>-0.380803</td>\n",
       "      <td>0.592049</td>\n",
       "      <td>-0.428167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>-0.219681</td>\n",
       "      <td>0.312608</td>\n",
       "      <td>-0.392451</td>\n",
       "      <td>0.091432</td>\n",
       "      <td>-0.302787</td>\n",
       "      <td>1.001980</td>\n",
       "      <td>-0.240741</td>\n",
       "      <td>0.205653</td>\n",
       "      <td>-0.210262</td>\n",
       "      <td>-0.292626</td>\n",
       "      <td>-0.356205</td>\n",
       "      <td>0.128322</td>\n",
       "      <td>-0.615024</td>\n",
       "      <td>0.696737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.353433</td>\n",
       "      <td>-0.570665</td>\n",
       "      <td>0.646055</td>\n",
       "      <td>0.086689</td>\n",
       "      <td>0.732919</td>\n",
       "      <td>-0.240741</td>\n",
       "      <td>1.001980</td>\n",
       "      <td>-0.749361</td>\n",
       "      <td>0.456925</td>\n",
       "      <td>0.507458</td>\n",
       "      <td>0.262033</td>\n",
       "      <td>-0.274076</td>\n",
       "      <td>0.603531</td>\n",
       "      <td>-0.377701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-0.380422</td>\n",
       "      <td>0.665724</td>\n",
       "      <td>-0.709429</td>\n",
       "      <td>-0.099372</td>\n",
       "      <td>-0.770753</td>\n",
       "      <td>0.205653</td>\n",
       "      <td>-0.749361</td>\n",
       "      <td>1.001980</td>\n",
       "      <td>-0.495567</td>\n",
       "      <td>-0.535490</td>\n",
       "      <td>-0.232931</td>\n",
       "      <td>0.292089</td>\n",
       "      <td>-0.497980</td>\n",
       "      <td>0.250424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.626744</td>\n",
       "      <td>-0.312566</td>\n",
       "      <td>0.596308</td>\n",
       "      <td>-0.007383</td>\n",
       "      <td>0.612651</td>\n",
       "      <td>-0.210262</td>\n",
       "      <td>0.456925</td>\n",
       "      <td>-0.495567</td>\n",
       "      <td>1.001980</td>\n",
       "      <td>0.912031</td>\n",
       "      <td>0.465661</td>\n",
       "      <td>-0.445293</td>\n",
       "      <td>0.489644</td>\n",
       "      <td>-0.382382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.583918</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>0.722187</td>\n",
       "      <td>-0.035657</td>\n",
       "      <td>0.669346</td>\n",
       "      <td>-0.292626</td>\n",
       "      <td>0.507458</td>\n",
       "      <td>-0.535490</td>\n",
       "      <td>0.912031</td>\n",
       "      <td>1.001980</td>\n",
       "      <td>0.461766</td>\n",
       "      <td>-0.442683</td>\n",
       "      <td>0.545071</td>\n",
       "      <td>-0.469464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>0.290520</td>\n",
       "      <td>-0.392454</td>\n",
       "      <td>0.384006</td>\n",
       "      <td>-0.121756</td>\n",
       "      <td>0.189307</td>\n",
       "      <td>-0.356205</td>\n",
       "      <td>0.262033</td>\n",
       "      <td>-0.232931</td>\n",
       "      <td>0.465661</td>\n",
       "      <td>0.461766</td>\n",
       "      <td>1.001980</td>\n",
       "      <td>-0.177735</td>\n",
       "      <td>0.374785</td>\n",
       "      <td>-0.508792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.385826</td>\n",
       "      <td>0.175868</td>\n",
       "      <td>-0.357683</td>\n",
       "      <td>0.048885</td>\n",
       "      <td>-0.380803</td>\n",
       "      <td>0.128322</td>\n",
       "      <td>-0.274076</td>\n",
       "      <td>0.292089</td>\n",
       "      <td>-0.445293</td>\n",
       "      <td>-0.442683</td>\n",
       "      <td>-0.177735</td>\n",
       "      <td>1.001980</td>\n",
       "      <td>-0.366812</td>\n",
       "      <td>0.334121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>0.456524</td>\n",
       "      <td>-0.413812</td>\n",
       "      <td>0.604995</td>\n",
       "      <td>-0.054036</td>\n",
       "      <td>0.592049</td>\n",
       "      <td>-0.615024</td>\n",
       "      <td>0.603531</td>\n",
       "      <td>-0.497980</td>\n",
       "      <td>0.489644</td>\n",
       "      <td>0.545071</td>\n",
       "      <td>0.374785</td>\n",
       "      <td>-0.366812</td>\n",
       "      <td>1.001980</td>\n",
       "      <td>-0.739123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRICE</th>\n",
       "      <td>-0.389074</td>\n",
       "      <td>0.361159</td>\n",
       "      <td>-0.484683</td>\n",
       "      <td>0.175607</td>\n",
       "      <td>-0.428167</td>\n",
       "      <td>0.696737</td>\n",
       "      <td>-0.377701</td>\n",
       "      <td>0.250424</td>\n",
       "      <td>-0.382382</td>\n",
       "      <td>-0.469464</td>\n",
       "      <td>-0.508792</td>\n",
       "      <td>0.334121</td>\n",
       "      <td>-0.739123</td>\n",
       "      <td>1.001980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "CRIM     1.001980 -0.200866  0.407389 -0.056002  0.421805 -0.219681  0.353433   \n",
       "ZN      -0.200866  1.001980 -0.534885 -0.042781 -0.517627  0.312608 -0.570665   \n",
       "INDUS    0.407389 -0.534885  1.001980  0.063063  0.765164 -0.392451  0.646055   \n",
       "CHAS    -0.056002 -0.042781  0.063063  1.001980  0.091383  0.091432  0.086689   \n",
       "NOX      0.421805 -0.517627  0.765164  0.091383  1.001980 -0.302787  0.732919   \n",
       "RM      -0.219681  0.312608 -0.392451  0.091432 -0.302787  1.001980 -0.240741   \n",
       "AGE      0.353433 -0.570665  0.646055  0.086689  0.732919 -0.240741  1.001980   \n",
       "DIS     -0.380422  0.665724 -0.709429 -0.099372 -0.770753  0.205653 -0.749361   \n",
       "RAD      0.626744 -0.312566  0.596308 -0.007383  0.612651 -0.210262  0.456925   \n",
       "TAX      0.583918 -0.315186  0.722187 -0.035657  0.669346 -0.292626  0.507458   \n",
       "PTRATIO  0.290520 -0.392454  0.384006 -0.121756  0.189307 -0.356205  0.262033   \n",
       "B       -0.385826  0.175868 -0.357683  0.048885 -0.380803  0.128322 -0.274076   \n",
       "LSTAT    0.456524 -0.413812  0.604995 -0.054036  0.592049 -0.615024  0.603531   \n",
       "PRICE   -0.389074  0.361159 -0.484683  0.175607 -0.428167  0.696737 -0.377701   \n",
       "\n",
       "              DIS       RAD       TAX   PTRATIO         B     LSTAT     PRICE  \n",
       "CRIM    -0.380422  0.626744  0.583918  0.290520 -0.385826  0.456524 -0.389074  \n",
       "ZN       0.665724 -0.312566 -0.315186 -0.392454  0.175868 -0.413812  0.361159  \n",
       "INDUS   -0.709429  0.596308  0.722187  0.384006 -0.357683  0.604995 -0.484683  \n",
       "CHAS    -0.099372 -0.007383 -0.035657 -0.121756  0.048885 -0.054036  0.175607  \n",
       "NOX     -0.770753  0.612651  0.669346  0.189307 -0.380803  0.592049 -0.428167  \n",
       "RM       0.205653 -0.210262 -0.292626 -0.356205  0.128322 -0.615024  0.696737  \n",
       "AGE     -0.749361  0.456925  0.507458  0.262033 -0.274076  0.603531 -0.377701  \n",
       "DIS      1.001980 -0.495567 -0.535490 -0.232931  0.292089 -0.497980  0.250424  \n",
       "RAD     -0.495567  1.001980  0.912031  0.465661 -0.445293  0.489644 -0.382382  \n",
       "TAX     -0.535490  0.912031  1.001980  0.461766 -0.442683  0.545071 -0.469464  \n",
       "PTRATIO -0.232931  0.465661  0.461766  1.001980 -0.177735  0.374785 -0.508792  \n",
       "B        0.292089 -0.445293 -0.442683 -0.177735  1.001980 -0.366812  0.334121  \n",
       "LSTAT   -0.497980  0.489644  0.545071  0.374785 -0.366812  1.001980 -0.739123  \n",
       "PRICE    0.250424 -0.382382 -0.469464 -0.508792  0.334121 -0.739123  1.001980  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ss2.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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