Diamond_Price_Prediction_Lasso_Ridge_Elasticnet.ipynb

{
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  {
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   "source": [
    "## Life cycle of Machine learning Project\n",
    "1. Understanding the problem statement \n",
    "2. Data Collection\n",
    "3. Exploratory Data Analysis\n",
    "4. Data Cleaning\n",
    "5. Data Pre-Processing\n",
    "6. Model Training"
   ]
  },
  {
   "cell_type": "raw",
   "id": "9a1eac3a",
   "metadata": {},
   "source": [
    "Introduction About the Data :\n",
    "The dataset The goal is to predict price of given diamond (Regression Analysis).\n",
    "There are 10 independent variables (including id ):\n",
    "id : unique identifier of each diamond\n",
    "carat : Carat (ct.) refers to the unique unit of weight measurement used exclusively to weigh gemstones and diamonds.\n",
    "cut : Quality of Diamond Cut\n",
    "color : Color of Diamond\n",
    "clarity : Diamond clarity is a measure of the purity and rarity of the stone, graded by the visibility of these\n",
    "characteristics under 10-power magnification.\n",
    "depth : The depth of diamond is its height (in millimeters) measured from the culet (bottom tip) to the table\n",
    "(flat, top surface)\n",
    "table : A diamond's table is the facet which can be seen when the stone is viewed face up.\n",
    "x : Diamond X dimension\n",
    "y : Diamond Y dimension\n",
    "x : Diamond Z dimension"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2db1cb8",
   "metadata": {},
   "source": [
    "* Target variable:\n",
    "* price : Price of the given Diamond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b8e2ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Libraries\n",
    "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": 2,
   "id": "a864e82f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the Dataset\n",
    "# Data Ingestions step\n",
    "df = pd.read_csv('Diamond_Price_Prediction.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7727c4e9",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>53935</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>53936</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
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       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>53937</td>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>53938</td>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
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       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>53939</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 11 columns</p>\n",
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      "text/plain": [
       "          id  carat        cut color clarity  depth  table  price     x     y  \\\n",
       "0          0   0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98   \n",
       "1          1   0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
       "2          2   0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07   \n",
       "3          3   0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23   \n",
       "4          4   0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35   \n",
       "...      ...    ...        ...   ...     ...    ...    ...    ...   ...   ...   \n",
       "53935  53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76   \n",
       "53936  53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75   \n",
       "53937  53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68   \n",
       "53938  53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12   \n",
       "53939  53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87   \n",
       "\n",
       "          z  \n",
       "0      2.43  \n",
       "1      2.31  \n",
       "2      2.31  \n",
       "3      2.63  \n",
       "4      2.75  \n",
       "...     ...  \n",
       "53935  3.50  \n",
       "53936  3.61  \n",
       "53937  3.56  \n",
       "53938  3.74  \n",
       "53939  3.64  \n",
       "\n",
       "[53940 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f3a93e4e",
   "metadata": {},
   "outputs": [
    {
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       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
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       "      <td>61.0</td>\n",
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       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
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       "      <td>4.05</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
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      "text/plain": [
       "   id  carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the top 5 records\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "89caa91c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id         0\n",
       "carat      0\n",
       "cut        0\n",
       "color      0\n",
       "clarity    0\n",
       "depth      0\n",
       "table      0\n",
       "price      0\n",
       "x          0\n",
       "y          0\n",
       "z          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Missing value\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad31c84",
   "metadata": {},
   "source": [
    "* No missing values present in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "277f4db3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 11 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   id       53940 non-null  int64  \n",
      " 1   carat    53940 non-null  float64\n",
      " 2   cut      53940 non-null  object \n",
      " 3   color    53940 non-null  object \n",
      " 4   clarity  53940 non-null  object \n",
      " 5   depth    53940 non-null  float64\n",
      " 6   table    53940 non-null  float64\n",
      " 7   price    53940 non-null  int64  \n",
      " 8   x        53940 non-null  float64\n",
      " 9   y        53940 non-null  float64\n",
      " 10  z        53940 non-null  float64\n",
      "dtypes: float64(6), int64(2), object(3)\n",
      "memory usage: 4.5+ MB\n"
     ]
    }
   ],
   "source": [
    "#Summary of Dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "54c301e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26969.500000</td>\n",
       "      <td>0.797940</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.538734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15571.281097</td>\n",
       "      <td>0.474011</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.705699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13484.750000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>2.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26969.500000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>3.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40454.250000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5324.250000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>4.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>53939.000000</td>\n",
       "      <td>5.010000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>18823.000000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>31.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id         carat         depth         table         price  \\\n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
       "mean   26969.500000      0.797940     61.749405     57.457184   3932.799722   \n",
       "std    15571.281097      0.474011      1.432621      2.234491   3989.439738   \n",
       "min        0.000000      0.200000     43.000000     43.000000    326.000000   \n",
       "25%    13484.750000      0.400000     61.000000     56.000000    950.000000   \n",
       "50%    26969.500000      0.700000     61.800000     57.000000   2401.000000   \n",
       "75%    40454.250000      1.040000     62.500000     59.000000   5324.250000   \n",
       "max    53939.000000      5.010000     79.000000     95.000000  18823.000000   \n",
       "\n",
       "                  x             y             z  \n",
       "count  53940.000000  53940.000000  53940.000000  \n",
       "mean       5.731157      5.734526      3.538734  \n",
       "std        1.121761      1.142135      0.705699  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%        4.710000      4.720000      2.910000  \n",
       "50%        5.700000      5.710000      3.530000  \n",
       "75%        6.540000      6.540000      4.040000  \n",
       "max       10.740000     58.900000     31.800000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#descriptive summary of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f19fe1f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53940, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b320d12e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'carat', 'cut', 'color', 'clarity', 'depth', 'table', 'price',\n",
       "       'x', 'y', 'z'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check column names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c46f7d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the id column\n",
    "## Lets drop the id column\n",
    "df=df.drop(labels=['id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aad31ab4",
   "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>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</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>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</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>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</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>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</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>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</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>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cdd71098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat      float64\n",
       "cut         object\n",
       "color       object\n",
       "clarity     object\n",
       "depth      float64\n",
       "table      float64\n",
       "price        int64\n",
       "x          float64\n",
       "y          float64\n",
       "z          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the data type of every columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7e9510e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicated records\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d85ab0cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       carat        cut color clarity  depth  table  price     x     y     z\n",
      "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
      "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
      "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
      "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
      "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
      "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
      "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
      "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
      "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
      "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
      "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
      "\n",
      "[53794 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "# drop duplicates and create a new DataFrame\n",
    "df_no_duplicates = df.drop_duplicates()\n",
    "\n",
    "# print the new DataFrame\n",
    "print(df_no_duplicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "698b3c14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical columns: Index(['carat', 'depth', 'table', 'price', 'x', 'y', 'z'], dtype='object')\n",
      "Categorical Columns: Index(['cut', 'color', 'clarity'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "## segregate numerical and categorical columns\n",
    "numerical_columns=df.columns[df.dtypes!='object']\n",
    "\n",
    "categorical_columns=df.columns[df.dtypes=='object']\n",
    "\n",
    "print(f'Numerical columns: {numerical_columns}')\n",
    "print(f'Categorical Columns: {categorical_columns}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "691bada4",
   "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>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53940</td>\n",
       "      <td>53940</td>\n",
       "      <td>53940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>21551</td>\n",
       "      <td>11292</td>\n",
       "      <td>13065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cut  color clarity\n",
       "count   53940  53940   53940\n",
       "unique      5      7       8\n",
       "top     Ideal      G     SI1\n",
       "freq    21551  11292   13065"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[categorical_columns].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a722c496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ideal        21551\n",
       "Premium      13791\n",
       "Very Good    12082\n",
       "Good          4906\n",
       "Fair          1610\n",
       "Name: cut, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cut'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2a64644c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G    11292\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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['color'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8b4b3715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SI1     13065\n",
       "VS2     12258\n",
       "SI2      9194\n",
       "VS1      8171\n",
       "VVS2     5066\n",
       "VVS1     3655\n",
       "IF       1790\n",
       "I1        741\n",
       "Name: clarity, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['clarity'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ddcc52",
   "metadata": {},
   "source": [
    "## Histplot on Numerical columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae4cc05d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "x=0\n",
    "for i in numerical_columns:\n",
    "    sns.histplot(data=df, x=i, kde=True)\n",
    "    print()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2f3a7a15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check correlation\n",
    "sns.heatmap(df.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f8f16344",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Currently we will not execute this\n",
    "#df.drop(labels=['x','y','z'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "60a89f74",
   "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>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</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>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</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>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</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>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</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>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</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>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0b03e9ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ideal', 'Premium', 'Good', 'Very Good', 'Fair'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check unique in cut column\n",
    "df['cut'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cb3a95a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cut_map = {\"Fair\":1,\"Good\":2,\"Very Good\":3,\"Premium\":4,\"Ideal\":5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f2debea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SI2', 'SI1', 'VS1', 'VS2', 'VVS2', 'VVS1', 'I1', 'IF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['clarity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4085f10b",
   "metadata": {},
   "outputs": [],
   "source": [
    "clarity_map = {\"I1\":1,\"SI2\":2 ,\"SI1\":3 ,\"VS2\":4 , \"VS1\":5 , \"VVS2\":6 , \"VVS1\":7 ,\"IF\":8}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f6dbcb99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['E', 'I', 'J', 'H', 'F', 'G', 'D'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['color'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fe7aed0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "color_map = {\"D\":1 ,\"E\":2 ,\"F\":3 , \"G\":4 ,\"H\":5 , \"I\":6, \"J\":7}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b8529ef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cut'] = df['cut'].map(cut_map)\n",
    "df['clarity'] = df['clarity'].map(clarity_map)\n",
    "df['color'] = df['color'].map(color_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "60989af5",
   "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>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat  cut  color  clarity  depth  table  price     x     y     z\n",
       "0   0.23    5      2        2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21    4      2        3   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23    2      2        5   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29    4      6        4   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31    2      7        2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f833548",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "51128367",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(labels=['price'],axis=1)\n",
    "\n",
    "Y = df[['price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b648b3f6",
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>2757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>2757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>2757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>2757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>2757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       price\n",
       "0        326\n",
       "1        326\n",
       "2        327\n",
       "3        334\n",
       "4        335\n",
       "...      ...\n",
       "53935   2757\n",
       "53936   2757\n",
       "53937   2757\n",
       "53938   2757\n",
       "53939   2757\n",
       "\n",
       "[53940 rows x 1 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f8933134",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define which columns should be ordinal-encoded and which should be scaled\n",
    "categorical_cols = X.select_dtypes(include='object').columns\n",
    "\n",
    "numerical_cols = X.select_dtypes(exclude='object').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "559a49f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the custom ranking for each ordinal variable\n",
    "cut_categories = ['Fair', 'Good', 'Very Good','Premium','Ideal']\n",
    "\n",
    "color_categories = ['D', 'E', 'F', 'G', 'H', 'I', 'J']\n",
    "\n",
    "clarity_categories = ['I1','SI2','SI1','VS2','VS1','VVS2','VVS1','IF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3e76a69b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer ## Handling Missing Values.\n",
    "from sklearn.preprocessing import StandardScaler # Handling Feature Scaling.\n",
    "from sklearn.preprocessing import OrdinalEncoder # Ordinal Encoding.\n",
    "\n",
    "#pipelines\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f167d491",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Numerical Pipeline\n",
    "num_pipeline = Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())])\n",
    "\n",
    "# Categorigal Pipeline\n",
    "cat_pipeline = Pipeline(steps=[('imputer',SimpleImputer(strategy='most_frequent')),\n",
    "               ('ordinalencoder',OrdinalEncoder(categories=[cut_categories,color_categories,clarity_categories])),\n",
    "               ('scaler',StandardScaler())])\n",
    "\n",
    "preprocessor = ColumnTransformer([('num_pipeline',num_pipeline,numerical_cols),\n",
    "                                  ('cat_pipeline',cat_pipeline,categorical_cols)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "087fe3c6",
   "metadata": {},
   "source": [
    "## Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f425cd1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.30,random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "31a65fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model Training\n",
    "from sklearn.linear_model import LinearRegression,Lasso,Ridge,ElasticNet\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f5573e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression = LinearRegression()\n",
    "regression.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1fa050e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10833.64452368,   123.89487619,  -324.43513593,   501.64425375,\n",
       "          -85.295421  ,   -28.27039246,  -905.36763097,    32.38294639,\n",
       "          -26.63138913]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d0b4c9bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5930.59066139])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2f83ce8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def evaluate_model(true, predicted):\n",
    "    mae = mean_absolute_error(true, predicted)\n",
    "    mse = mean_squared_error(true, predicted)\n",
    "    rmse = np.sqrt(mean_squared_error(true, predicted))\n",
    "    r2_square = r2_score(true, predicted)\n",
    "    return mae, rmse, r2_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "99eb0b30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression\n",
      "Model Training Performance\n",
      "RMSE: 1217.9378815056311\n",
      "MAE: 808.4967647773293\n",
      "R2 score 90.75082981539356\n",
      "==============================\n",
      "\n",
      "Lasso\n",
      "Model Training Performance\n",
      "RMSE: 1217.6911591752678\n",
      "MAE: 810.7249333557039\n",
      "R2 score 90.75457671533343\n",
      "==============================\n",
      "\n",
      "Ridge\n",
      "Model Training Performance\n",
      "RMSE: 1217.8637978797808\n",
      "MAE: 808.9438357233588\n",
      "R2 score 90.7519549815191\n",
      "==============================\n",
      "\n",
      "ElasticNet\n",
      "Model Training Performance\n",
      "RMSE: 1749.5926225605283\n",
      "MAE: 1157.6598649390562\n",
      "R2 score 80.91349673321345\n",
      "==============================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Train multiple models\n",
    "models = {'LinearRegression':LinearRegression(), 'Lasso':Lasso(), 'Ridge':Ridge(),'ElasticNet':ElasticNet()}\n",
    "trained_model_list = []\n",
    "model_list = []\n",
    "r2_list = []\n",
    "\n",
    "for i in range(len(list(models))):\n",
    "    model = list(models.values())[i]\n",
    "    model.fit(X_train,y_train)\n",
    "\n",
    "     #Make Predictions\n",
    "    y_pred = model.predict(X_test)\n",
    "    mae, rmse, r2_square=evaluate_model(y_test,y_pred)\n",
    "    print(list(models.keys())[i])\n",
    "    model_list.append(list(models.keys())[i])\n",
    "    print('Model Training Performance')\n",
    "    print(\"RMSE:\",rmse)\n",
    "    print(\"MAE:\",mae)\n",
    "    print(\"R2 score\",r2_square*100)\n",
    "    r2_list.append(r2_square)\n",
    "    print('='*30)\n",
    "    print()  #Newline "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dcc4c94",
   "metadata": {},
   "source": [
    "Based on the performance metrics provided for each model on the training data:\n",
    "\n",
    "- Linear Regression, Lasso, and Ridge models have similar RMSE, MAE, and R2 scores, which indicates that they have similar accuracy and ability to explain the variance in the data.\n",
    "- The ElasticNet model has a significantly higher RMSE and MAE and a lower R2 score, indicating that it may not be as accurate or effective at explaining the variance in the data compared to the other models.\n",
    "\n",
    "Therefore, the conclusion is that Linear Regression, Lasso, and Ridge models may be equally suitable for this particular dataset, while the ElasticNet model may not be the best choice. However, it is also important to evaluate the models on a separate test dataset to ensure that they are not overfitting to the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1332a4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_list"
   ]
  }
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