Assignment001.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "494391e8",
   "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": 4,
   "id": "3c415927",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"diamonds_new.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6a37a72",
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>table</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>price_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "      <td>163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "      <td>163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "      <td>163.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "      <td>167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "      <td>167.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  table     x     y     z  price_new\n",
       "0   0.23    Ideal     E     SI2   55.0  3.95  3.98  2.43      163.0\n",
       "1   0.21  Premium     E     SI1   61.0  3.89  3.84  2.31      163.0\n",
       "2   0.23     Good     E     VS1   65.0  4.05  4.07  2.31      163.5\n",
       "3   0.29  Premium     I     VS2   58.0  4.20  4.23  2.63      167.0\n",
       "4   0.31     Good     J     SI2   58.0  4.34  4.35  2.75      167.5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d830afc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53940, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9435a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['carat', 'cut', 'color', 'clarity', 'table', 'x', 'y', 'z',\n",
       "       'price_new'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "632526de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   carat      53841 non-null  float64\n",
      " 1   cut        53940 non-null  object \n",
      " 2   color      53884 non-null  object \n",
      " 3   clarity    53940 non-null  object \n",
      " 4   table      53877 non-null  float64\n",
      " 5   x          53940 non-null  float64\n",
      " 6   y          53940 non-null  float64\n",
      " 7   z          53940 non-null  float64\n",
      " 8   price_new  53940 non-null  float64\n",
      "dtypes: float64(6), object(3)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8cd1cef",
   "metadata": {},
   "source": [
    "# Handling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bc8d84d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat        99\n",
       "cut           0\n",
       "color        56\n",
       "clarity       0\n",
       "table        63\n",
       "x             0\n",
       "y             0\n",
       "z             0\n",
       "price_new     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "391d3da9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        E\n",
       "1        E\n",
       "2        E\n",
       "3        I\n",
       "4        J\n",
       "        ..\n",
       "53935    D\n",
       "53936    D\n",
       "53937    D\n",
       "53938    H\n",
       "53939    D\n",
       "Name: color, Length: 53940, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"color\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e805ec55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G    11236\n",
       "E     9797\n",
       "F     9542\n",
       "H     8304\n",
       "D     6775\n",
       "I     5422\n",
       "J     2808\n",
       "Name: color, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"color\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b0b261f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['color'] = data['color'].fillna('G')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "51ef83ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['carat'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "390a8897",
   "metadata": {},
   "outputs": [],
   "source": [
    "median_carat = data['carat'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "35c6ea31",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['carat'] = data['carat'].fillna(median_carat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "41c5e3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   carat      53940 non-null  float64\n",
      " 1   cut        53940 non-null  object \n",
      " 2   color      53940 non-null  object \n",
      " 3   clarity    53940 non-null  object \n",
      " 4   table      53877 non-null  float64\n",
      " 5   x          53940 non-null  float64\n",
      " 6   y          53940 non-null  float64\n",
      " 7   z          53940 non-null  float64\n",
      " 8   price_new  53940 non-null  float64\n",
      "dtypes: float64(6), object(3)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ab686e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat         0\n",
       "cut           0\n",
       "color         0\n",
       "clarity       0\n",
       "table        63\n",
       "x             0\n",
       "y             0\n",
       "z             0\n",
       "price_new     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7f939267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['table'])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ee527eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "median_table = data['table'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1cd46ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['table'] = data['table'].fillna(median_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "894e3f23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat        0\n",
       "cut          0\n",
       "color        0\n",
       "clarity      0\n",
       "table        0\n",
       "x            0\n",
       "y            0\n",
       "z            0\n",
       "price_new    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b3d60776",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   carat      53940 non-null  float64\n",
      " 1   cut        53940 non-null  object \n",
      " 2   color      53940 non-null  object \n",
      " 3   clarity    53940 non-null  object \n",
      " 4   table      53940 non-null  float64\n",
      " 5   x          53940 non-null  float64\n",
      " 6   y          53940 non-null  float64\n",
      " 7   z          53940 non-null  float64\n",
      " 8   price_new  53940 non-null  float64\n",
      "dtypes: float64(6), object(3)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f7908c",
   "metadata": {},
   "source": [
    "# Outliers detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1e9b635a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data['carat'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cf6cc0c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7e499d0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['zr'] = stats.zscore(data['carat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f447cdd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([13757, 14138, 15684, 16283, 16439, 16637, 17181, 17196, 17466,\n",
       "            17560,\n",
       "            ...\n",
       "            27679, 27681, 27684, 27685, 27726, 27727, 27731, 27739, 27744,\n",
       "            27749],\n",
       "           dtype='int64', length=439)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['zr'] < -3) | (data['zr']> 3)].index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8a39a726",
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>table</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>price_new</th>\n",
       "      <th>zr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "      <td>163.0</td>\n",
       "      <td>-1.198168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "      <td>163.0</td>\n",
       "      <td>-1.240361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "      <td>163.5</td>\n",
       "      <td>-1.198168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "      <td>167.0</td>\n",
       "      <td>-1.071587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "      <td>167.5</td>\n",
       "      <td>-1.029394</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>-0.164427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>-0.164427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>-0.206621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>0.130927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>-0.101137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53501 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  table     x     y     z  price_new  \\\n",
       "0       0.23      Ideal     E     SI2   55.0  3.95  3.98  2.43      163.0   \n",
       "1       0.21    Premium     E     SI1   61.0  3.89  3.84  2.31      163.0   \n",
       "2       0.23       Good     E     VS1   65.0  4.05  4.07  2.31      163.5   \n",
       "3       0.29    Premium     I     VS2   58.0  4.20  4.23  2.63      167.0   \n",
       "4       0.31       Good     J     SI2   58.0  4.34  4.35  2.75      167.5   \n",
       "...      ...        ...   ...     ...    ...   ...   ...   ...        ...   \n",
       "53935   0.72      Ideal     D     SI1   57.0  5.75  5.76  3.50     1378.5   \n",
       "53936   0.72       Good     D     SI1   55.0  5.69  5.75  3.61     1378.5   \n",
       "53937   0.70  Very Good     D     SI1   60.0  5.66  5.68  3.56     1378.5   \n",
       "53938   0.86    Premium     H     SI2   58.0  6.15  6.12  3.74     1378.5   \n",
       "53939   0.75      Ideal     D     SI2   55.0  5.83  5.87  3.64     1378.5   \n",
       "\n",
       "             zr  \n",
       "0     -1.198168  \n",
       "1     -1.240361  \n",
       "2     -1.198168  \n",
       "3     -1.071587  \n",
       "4     -1.029394  \n",
       "...         ...  \n",
       "53935 -0.164427  \n",
       "53936 -0.164427  \n",
       "53937 -0.206621  \n",
       "53938  0.130927  \n",
       "53939 -0.101137  \n",
       "\n",
       "[53501 rows x 10 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['zr'] > -3) & (data['zr'] < 3)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64efabda",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data = data.drop([13757, 14138, 15684, 16283, 16439, 16637, 17181, 17196, 17466,\n",
    "#            17560,\n",
    "#           ...\n",
    "#           27679, 27681, 27684, 27685, 27726, 27727, 27731, 27739, 27744,\n",
    "#           27749])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "80615bf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data['table'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5e20734b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data['x'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d4f0e852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data['y'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cb0b8770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data['z'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "12e5827f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data['price_new'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5a6a386",
   "metadata": {},
   "source": [
    "# Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "14a89071",
   "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>carat</th>\n",
       "      <th>table</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>price_new</th>\n",
       "      <th>zr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>5.394000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.797940</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.539635</td>\n",
       "      <td>1966.399861</td>\n",
       "      <td>2.444878e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.474011</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.703869</td>\n",
       "      <td>1994.719869</td>\n",
       "      <td>1.000009e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>-1.261458e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>2.910000</td>\n",
       "      <td>475.000000</td>\n",
       "      <td>-8.395232e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>3.530000</td>\n",
       "      <td>1200.500000</td>\n",
       "      <td>-2.066210e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.040000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>4.040000</td>\n",
       "      <td>2662.125000</td>\n",
       "      <td>5.106683e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.010000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>31.800000</td>\n",
       "      <td>9411.500000</td>\n",
       "      <td>8.886075e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              carat         table             x             y             z  \\\n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
       "mean       0.797940     57.457184      5.731157      5.734526      3.539635   \n",
       "std        0.474011      2.234491      1.121761      1.142135      0.703869   \n",
       "min        0.200000     43.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.400000     56.000000      4.710000      4.720000      2.910000   \n",
       "50%        0.700000     57.000000      5.700000      5.710000      3.530000   \n",
       "75%        1.040000     59.000000      6.540000      6.540000      4.040000   \n",
       "max        5.010000     95.000000     10.740000     58.900000     31.800000   \n",
       "\n",
       "          price_new            zr  \n",
       "count  53940.000000  5.394000e+04  \n",
       "mean    1966.399861  2.444878e-16  \n",
       "std     1994.719869  1.000009e+00  \n",
       "min      163.000000 -1.261458e+00  \n",
       "25%      475.000000 -8.395232e-01  \n",
       "50%     1200.500000 -2.066210e-01  \n",
       "75%     2662.125000  5.106683e-01  \n",
       "max     9411.500000  8.886075e+00  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fe8ec10b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "766a8ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "avr_rate = data['carat'].mean()\n",
    "std_rate = data['carat'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2d2e8b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['z_score_rate'] = (data['carat'] - avr_rate) / std_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3c26d22e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>table</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>price_new</th>\n",
       "      <th>zr</th>\n",
       "      <th>z_score_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "      <td>163.0</td>\n",
       "      <td>-1.198168</td>\n",
       "      <td>-1.198157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "      <td>163.0</td>\n",
       "      <td>-1.240361</td>\n",
       "      <td>-1.240350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "      <td>163.5</td>\n",
       "      <td>-1.198168</td>\n",
       "      <td>-1.198157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "      <td>167.0</td>\n",
       "      <td>-1.071587</td>\n",
       "      <td>-1.071577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "      <td>167.5</td>\n",
       "      <td>-1.029394</td>\n",
       "      <td>-1.029384</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>-0.164427</td>\n",
       "      <td>-0.164426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>-0.164427</td>\n",
       "      <td>-0.164426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>-0.206621</td>\n",
       "      <td>-0.206619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>0.130927</td>\n",
       "      <td>0.130926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>-0.101137</td>\n",
       "      <td>-0.101136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  table     x     y     z  price_new  \\\n",
       "0       0.23      Ideal     E     SI2   55.0  3.95  3.98  2.43      163.0   \n",
       "1       0.21    Premium     E     SI1   61.0  3.89  3.84  2.31      163.0   \n",
       "2       0.23       Good     E     VS1   65.0  4.05  4.07  2.31      163.5   \n",
       "3       0.29    Premium     I     VS2   58.0  4.20  4.23  2.63      167.0   \n",
       "4       0.31       Good     J     SI2   58.0  4.34  4.35  2.75      167.5   \n",
       "...      ...        ...   ...     ...    ...   ...   ...   ...        ...   \n",
       "53935   0.72      Ideal     D     SI1   57.0  5.75  5.76  3.50     1378.5   \n",
       "53936   0.72       Good     D     SI1   55.0  5.69  5.75  3.61     1378.5   \n",
       "53937   0.70  Very Good     D     SI1   60.0  5.66  5.68  3.56     1378.5   \n",
       "53938   0.86    Premium     H     SI2   58.0  6.15  6.12  3.74     1378.5   \n",
       "53939   0.75      Ideal     D     SI2   55.0  5.83  5.87  3.64     1378.5   \n",
       "\n",
       "             zr  z_score_rate  \n",
       "0     -1.198168     -1.198157  \n",
       "1     -1.240361     -1.240350  \n",
       "2     -1.198168     -1.198157  \n",
       "3     -1.071587     -1.071577  \n",
       "4     -1.029394     -1.029384  \n",
       "...         ...           ...  \n",
       "53935 -0.164427     -0.164426  \n",
       "53936 -0.164427     -0.164426  \n",
       "53937 -0.206621     -0.206619  \n",
       "53938  0.130927      0.130926  \n",
       "53939 -0.101137     -0.101136  \n",
       "\n",
       "[53940 rows x 11 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "339ff51c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5265fe03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53940, 11)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab6fb7cb",
   "metadata": {},
   "source": [
    "# Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e7df6b95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.cut.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d90885f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.color.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "aa180756",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c15d7381",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>table</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>price_new</th>\n",
       "      <th>zr</th>\n",
       "      <th>z_score_rate</th>\n",
       "      <th>cut_Fair</th>\n",
       "      <th>cut_Good</th>\n",
       "      <th>...</th>\n",
       "      <th>color_I</th>\n",
       "      <th>color_J</th>\n",
       "      <th>clarity_I1</th>\n",
       "      <th>clarity_IF</th>\n",
       "      <th>clarity_SI1</th>\n",
       "      <th>clarity_SI2</th>\n",
       "      <th>clarity_VS1</th>\n",
       "      <th>clarity_VS2</th>\n",
       "      <th>clarity_VVS1</th>\n",
       "      <th>clarity_VVS2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "      <td>163.0</td>\n",
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       "      <td>-1.198157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>61.0</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "      <td>163.0</td>\n",
       "      <td>-1.240361</td>\n",
       "      <td>-1.240350</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>65.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "      <td>163.5</td>\n",
       "      <td>-1.198168</td>\n",
       "      <td>-1.198157</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "      <td>167.0</td>\n",
       "      <td>-1.071587</td>\n",
       "      <td>-1.071577</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "      <td>167.5</td>\n",
       "      <td>-1.029394</td>\n",
       "      <td>-1.029384</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>-0.164427</td>\n",
       "      <td>-0.164426</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>-0.164427</td>\n",
       "      <td>-0.164426</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>-0.206621</td>\n",
       "      <td>-0.206619</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>0.130927</td>\n",
       "      <td>0.130926</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "      <td>1378.5</td>\n",
       "      <td>-0.101137</td>\n",
       "      <td>-0.101136</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat  table     x     y     z  price_new        zr  z_score_rate  \\\n",
       "0       0.23   55.0  3.95  3.98  2.43      163.0 -1.198168     -1.198157   \n",
       "1       0.21   61.0  3.89  3.84  2.31      163.0 -1.240361     -1.240350   \n",
       "2       0.23   65.0  4.05  4.07  2.31      163.5 -1.198168     -1.198157   \n",
       "3       0.29   58.0  4.20  4.23  2.63      167.0 -1.071587     -1.071577   \n",
       "4       0.31   58.0  4.34  4.35  2.75      167.5 -1.029394     -1.029384   \n",
       "...      ...    ...   ...   ...   ...        ...       ...           ...   \n",
       "53935   0.72   57.0  5.75  5.76  3.50     1378.5 -0.164427     -0.164426   \n",
       "53936   0.72   55.0  5.69  5.75  3.61     1378.5 -0.164427     -0.164426   \n",
       "53937   0.70   60.0  5.66  5.68  3.56     1378.5 -0.206621     -0.206619   \n",
       "53938   0.86   58.0  6.15  6.12  3.74     1378.5  0.130927      0.130926   \n",
       "53939   0.75   55.0  5.83  5.87  3.64     1378.5 -0.101137     -0.101136   \n",
       "\n",
       "       cut_Fair  cut_Good  ...  color_I  color_J  clarity_I1  clarity_IF  \\\n",
       "0             0         0  ...        0        0           0           0   \n",
       "1             0         0  ...        0        0           0           0   \n",
       "2             0         1  ...        0        0           0           0   \n",
       "3             0         0  ...        1        0           0           0   \n",
       "4             0         1  ...        0        1           0           0   \n",
       "...         ...       ...  ...      ...      ...         ...         ...   \n",
       "53935         0         0  ...        0        0           0           0   \n",
       "53936         0         1  ...        0        0           0           0   \n",
       "53937         0         0  ...        0        0           0           0   \n",
       "53938         0         0  ...        0        0           0           0   \n",
       "53939         0         0  ...        0        0           0           0   \n",
       "\n",
       "       clarity_SI1  clarity_SI2  clarity_VS1  clarity_VS2  clarity_VVS1  \\\n",
       "0                0            1            0            0             0   \n",
       "1                1            0            0            0             0   \n",
       "2                0            0            1            0             0   \n",
       "3                0            0            0            1             0   \n",
       "4                0            1            0            0             0   \n",
       "...            ...          ...          ...          ...           ...   \n",
       "53935            1            0            0            0             0   \n",
       "53936            1            0            0            0             0   \n",
       "53937            1            0            0            0             0   \n",
       "53938            0            1            0            0             0   \n",
       "53939            0            1            0            0             0   \n",
       "\n",
       "       clarity_VVS2  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "...             ...  \n",
       "53935             0  \n",
       "53936             0  \n",
       "53937             0  \n",
       "53938             0  \n",
       "53939             0  \n",
       "\n",
       "[53940 rows x 28 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8ceb50",
   "metadata": {},
   "source": [
    "# Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4327d458",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max = preprocessing.MinMaxScaler(feature_range=(0,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08381b94",
   "metadata": {},
   "source": [
    "We can also give training and validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9a7afe6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = min_max.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bf821194",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c747cdc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0ac0bacf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.071817</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.137214</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.572626</td>\n",
       "      <td>0.103905</td>\n",
       "      <td>0.117610</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.137214</td>\n",
       "      <td>0.137214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.114345</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.542831</td>\n",
       "      <td>0.099660</td>\n",
       "      <td>0.114465</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.114345</td>\n",
       "      <td>0.114345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         4         5         6   \\\n",
       "0      0.006237  0.230769  0.367784  0.067572  0.076415  0.000000  0.006237   \n",
       "1      0.002079  0.346154  0.362197  0.065195  0.072642  0.000000  0.002079   \n",
       "2      0.006237  0.423077  0.377095  0.069100  0.072642  0.000054  0.006237   \n",
       "3      0.018711  0.288462  0.391061  0.071817  0.082704  0.000433  0.018711   \n",
       "4      0.022869  0.288462  0.404097  0.073854  0.086478  0.000487  0.022869   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "53935  0.108108  0.269231  0.535382  0.097793  0.110063  0.131427  0.108108   \n",
       "53936  0.108108  0.230769  0.529795  0.097623  0.113522  0.131427  0.108108   \n",
       "53937  0.103950  0.326923  0.527002  0.096435  0.111950  0.131427  0.103950   \n",
       "53938  0.137214  0.288462  0.572626  0.103905  0.117610  0.131427  0.137214   \n",
       "53939  0.114345  0.230769  0.542831  0.099660  0.114465  0.131427  0.114345   \n",
       "\n",
       "             7    8    9   ...   18   19   20   21   22   23   24   25   26  \\\n",
       "0      0.006237  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
       "1      0.002079  0.0  0.0  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "2      0.006237  0.0  1.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "3      0.018711  0.0  0.0  ...  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0   \n",
       "4      0.022869  0.0  1.0  ...  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
       "...         ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "53935  0.108108  0.0  0.0  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "53936  0.108108  0.0  1.0  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "53937  0.103950  0.0  0.0  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "53938  0.137214  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
       "53939  0.114345  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
       "\n",
       "        27  \n",
       "0      0.0  \n",
       "1      0.0  \n",
       "2      0.0  \n",
       "3      0.0  \n",
       "4      0.0  \n",
       "...    ...  \n",
       "53935  0.0  \n",
       "53936  0.0  \n",
       "53937  0.0  \n",
       "53938  0.0  \n",
       "53939  0.0  \n",
       "\n",
       "[53940 rows x 28 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96a10e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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