Diamond_Price_Prediction.ipynb

{
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      "source": [
        "# Diamond Price Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZpDOcDfdvX0n"
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      "source": [
        "**The aim of this analysis is to predict the price of diamonds based on their characteristics. The dataset used for this analysis is the Diamonds dataset from Kaggle. The dataset contains 53940 observations and 10 variables. The variables are as follows:**\n",
        "\n",
        "\n",
        "|Column Name|Description|\n",
        "|-----------|-----------|\n",
        "|carat|Weight of the diamond|\n",
        "|cut|Quality of the cut (Fair, Good, Very Good, Premium, Ideal)|\n",
        "|color|Diamond colour, from J (worst) to D (best)|\n",
        "|clarity|How clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))|\n",
        "|x|Length in mm|\n",
        "|y|Width in mm|\n",
        "|z|Depth in mm|\n",
        "|depth|Total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)|\n",
        "|table|Width of top of diamond relative to widest point (43--95)|\n",
        "|price|Price in US dollars (326--18,823)|\n"
      ]
    },
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      "execution_count": 35,
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      "source": [
        "#importing the libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
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            "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"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>carat</th>\n",
              "      <th>cut</th>\n",
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              "      <th>clarity</th>\n",
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              "    <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",
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              "      <th>1</th>\n",
              "      <td>0.21</td>\n",
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              "      <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>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",
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          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "#loading the dataset\n",
        "df = pd.read_csv('/content/diamonds.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7OXIXRUvX0p"
      },
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "MykIcVH0vX0q",
        "outputId": "45f8172c-a14a-4b3a-fd5c-f833b9807e2e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "I1_0DaxKvX0q",
        "outputId": "4166f8b6-cf44-427c-9bde-f2466ccc4b17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50000 entries, 0 to 49999\n",
            "Data columns (total 10 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   carat    50000 non-null  float64\n",
            " 1   cut      50000 non-null  object \n",
            " 2   color    50000 non-null  object \n",
            " 3   clarity  50000 non-null  object \n",
            " 4   depth    50000 non-null  float64\n",
            " 5   table    50000 non-null  float64\n",
            " 6   price    50000 non-null  int64  \n",
            " 7   x        50000 non-null  float64\n",
            " 8   y        50000 non-null  float64\n",
            " 9   z        50000 non-null  float64\n",
            "dtypes: float64(6), int64(1), object(3)\n",
            "memory usage: 3.8+ MB\n"
          ]
        }
      ],
      "source": [
        "#checking for null values\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IIAAbAAfvX0q"
      },
      "outputs": [],
      "source": [
        "#checking descriptive statistics\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dZOoVADivX0r",
        "outputId": "c4237076-8d87-4739-d1c0-b8f3bcb0dc82"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ideal        19938\n",
            "Premium      12806\n",
            "Very Good    11204\n",
            "Good          4557\n",
            "Fair          1495\n",
            "Name: cut, dtype: int64 \n",
            " G    10452\n",
            "E     9085\n",
            "F     8864\n",
            "H     7711\n",
            "D     6224\n",
            "I     5058\n",
            "J     2606\n",
            "Name: color, dtype: int64 \n",
            " SI1     12115\n",
            "VS2     11404\n",
            "SI2      8519\n",
            "VS1      7579\n",
            "VVS2     4694\n",
            "VVS1     3369\n",
            "IF       1632\n",
            "I1        688\n",
            "Name: clarity, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "#values count of categorical variables\n",
        "print(df.cut.value_counts(),'\\n',df.color.value_counts(),'\\n',df.clarity.value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
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          "height": 363
        },
        "id": "Rbu5p2LDvX0r",
        "outputId": "528fbaaa-a892-4397-e685-cfea1ee475ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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            "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\n",
              "5   0.24  Very Good     J    VVS2   62.8   57.0    336  3.94  3.96  2.48\n",
              "6   0.24  Very Good     I    VVS1   62.3   57.0    336  3.95  3.98  2.47\n",
              "7   0.26  Very Good     H     SI1   61.9   55.0    337  4.07  4.11  2.53\n",
              "8   0.22       Fair     E     VS2   65.1   61.0    337  3.87  3.78  2.49\n",
              "9   0.23  Very Good     H     VS1   59.4   61.0    338  4.00  4.05  2.39"
            ],
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              "      <th></th>\n",
              "      <th>carat</th>\n",
              "      <th>cut</th>\n",
              "      <th>color</th>\n",
              "      <th>clarity</th>\n",
              "      <th>depth</th>\n",
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              "      <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",
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              "      <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",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.24</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>J</td>\n",
              "      <td>VVS2</td>\n",
              "      <td>62.8</td>\n",
              "      <td>57.0</td>\n",
              "      <td>336</td>\n",
              "      <td>3.94</td>\n",
              "      <td>3.96</td>\n",
              "      <td>2.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.24</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>I</td>\n",
              "      <td>VVS1</td>\n",
              "      <td>62.3</td>\n",
              "      <td>57.0</td>\n",
              "      <td>336</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.98</td>\n",
              "      <td>2.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.26</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>H</td>\n",
              "      <td>SI1</td>\n",
              "      <td>61.9</td>\n",
              "      <td>55.0</td>\n",
              "      <td>337</td>\n",
              "      <td>4.07</td>\n",
              "      <td>4.11</td>\n",
              "      <td>2.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.22</td>\n",
              "      <td>Fair</td>\n",
              "      <td>E</td>\n",
              "      <td>VS2</td>\n",
              "      <td>65.1</td>\n",
              "      <td>61.0</td>\n",
              "      <td>337</td>\n",
              "      <td>3.87</td>\n",
              "      <td>3.78</td>\n",
              "      <td>2.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.23</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>H</td>\n",
              "      <td>VS1</td>\n",
              "      <td>59.4</td>\n",
              "      <td>61.0</td>\n",
              "      <td>338</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.05</td>\n",
              "      <td>2.39</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5888f51f-2fc0-4aa1-a85a-31d5ee271ac9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-8a2dbf36-7f23-4eec-850d-409bdb693bcd\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8a2dbf36-7f23-4eec-850d-409bdb693bcd')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-8a2dbf36-7f23-4eec-850d-409bdb693bcd button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5888f51f-2fc0-4aa1-a85a-31d5ee271ac9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5888f51f-2fc0-4aa1-a85a-31d5ee271ac9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVKxHY9fvX0s"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ed0a8GbFvX0s",
        "outputId": "354b1c20-f4cb-4a41-d137-8c508c73ab3e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='price', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.histplot(df['price'],bins = 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "O-aFIMALvX0s",
        "outputId": "5d0bbd8f-e522-4ee4-82bd-dfdac88d990c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='carat', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.histplot(df['carat'],bins=20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fjsbMQaavX0s"
      },
      "source": [
        "***Most of the diamonds are less then 1 carat in weight.***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "fBhxsOk_vX0s",
        "outputId": "85ed4a26-0034-4227-8abc-9473cfac79c0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "plt.pie(df['cut'].value_counts(),labels=['Ideal','Premium','Very Good','Good','Fair'],autopct='%1.1f%%')\n",
        "plt.title('Cut')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "THZ-00N2vX0t",
        "outputId": "076836ef-5950-46aa-8390-78effba07d0a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "plt.bar(df['color'].value_counts().index,df['color'].value_counts())\n",
        "plt.ylabel(\"Number of Diamonds\")\n",
        "plt.xlabel(\"Color\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "1HZzfJFWvX0t",
        "outputId": "4b46e34a-8709-4ef8-bd0c-c5222170b7fa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "plt.bar(df['clarity'].value_counts().index,df['clarity'].value_counts())\n",
        "plt.title('Clarity')\n",
        "plt.ylabel(\"Number of Diamonds\")\n",
        "plt.xlabel(\"Clarity\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "nPcqqax6vX0t",
        "outputId": "3ecf0fbf-36cc-48d2-af46-1f01fe601217"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.histplot(df['table'],bins=10)\n",
        "plt.title('Table')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ouH6hcwvX0t"
      },
      "source": [
        "***Comparing Diamond's features with Price***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "OZCAmLwFvX0t",
        "outputId": "43e60b8e-28bf-47f9-824f-e4fea39672a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='cut', ylabel='price'>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.barplot(x='cut',y='price',data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "dnKFv_R_vX0t",
        "outputId": "9fc5ab5c-de71-41c7-ccd4-64d40a1532b8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.barplot(x='color',y='price',data=df)\n",
        "plt.title('Price vs Color')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "PWG3QG5avX0t",
        "outputId": "03dafe26-d68a-403a-b77a-516feccbf45c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='clarity', ylabel='price'>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.barplot(x = 'clarity', y = 'price', data = df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jgl9s9cOvX0u"
      },
      "source": [
        "*J color and I1 clarity are worst featiures for a diamond, however when the data is plotted on bar graph, it is seen that the price of diamonds with J color and I1 clarity is higher than the price of diamonds with D color and IF clarity, which is opposite to what I expected.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPFg29O0vX0u"
      },
      "source": [
        "## Data Preprocessing 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "X2_cq1T_vX0u"
      },
      "outputs": [],
      "source": [
        "#changing categorical variables to numerical variables\n",
        "df['cut'] = df['cut'].map({'Ideal':5,'Premium':4,'Very Good':3,'Good':2,'Fair':1})\n",
        "df['color'] = df['color'].map({'D':7,'E':6,'F':5,'G':4,'H':3,'I':2,'J':1})\n",
        "df['clarity'] = df['clarity'].map({'IF':8,'VVS1':7,'VVS2':6,'VS1':5,'VS2':4,'SI1':3,'SI2':2,'I1':1})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KI3RgSd3vX0v"
      },
      "source": [
        "## Coorelation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "-iyVX2dqvX0v",
        "outputId": "91cc5a26-bb6d-4849-d680-344c6df584af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            carat       cut     color   clarity     depth     table     price  \\\n",
              "carat    1.000000 -0.135135 -0.291530 -0.352435  0.027734  0.183639  0.921804   \n",
              "cut     -0.135135  1.000000  0.019548  0.189024 -0.223898 -0.432154 -0.053537   \n",
              "color   -0.291530  0.019548  1.000000 -0.026056 -0.047426 -0.027513 -0.172629   \n",
              "clarity -0.352435  0.189024 -0.026056  1.000000 -0.067329 -0.159967 -0.146941   \n",
              "depth    0.027734 -0.223898 -0.047426 -0.067329  1.000000 -0.293012 -0.012731   \n",
              "table    0.183639 -0.432154 -0.027513 -0.159967 -0.293012  1.000000  0.129848   \n",
              "price    0.921804 -0.053537 -0.172629 -0.146941 -0.012731  0.129848  1.000000   \n",
              "x        0.975037 -0.125738 -0.270529 -0.371355 -0.025563  0.197198  0.884919   \n",
              "y        0.950035 -0.121335 -0.263395 -0.357226 -0.029809  0.185248  0.864393   \n",
              "z        0.952700 -0.149830 -0.268388 -0.366218  0.094337  0.153161  0.860963   \n",
              "\n",
              "                x         y         z  \n",
              "carat    0.975037  0.950035  0.952700  \n",
              "cut     -0.125738 -0.121335 -0.149830  \n",
              "color   -0.270529 -0.263395 -0.268388  \n",
              "clarity -0.371355 -0.357226 -0.366218  \n",
              "depth   -0.025563 -0.029809  0.094337  \n",
              "table    0.197198  0.185248  0.153161  \n",
              "price    0.884919  0.864393  0.860963  \n",
              "x        1.000000  0.972977  0.970122  \n",
              "y        0.972977  1.000000  0.950030  \n",
              "z        0.970122  0.950030  1.000000  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-fc2fba36-8c19-49c0-890f-648a37a56f88\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        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>carat</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.135135</td>\n",
              "      <td>-0.291530</td>\n",
              "      <td>-0.352435</td>\n",
              "      <td>0.027734</td>\n",
              "      <td>0.183639</td>\n",
              "      <td>0.921804</td>\n",
              "      <td>0.975037</td>\n",
              "      <td>0.950035</td>\n",
              "      <td>0.952700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cut</th>\n",
              "      <td>-0.135135</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.019548</td>\n",
              "      <td>0.189024</td>\n",
              "      <td>-0.223898</td>\n",
              "      <td>-0.432154</td>\n",
              "      <td>-0.053537</td>\n",
              "      <td>-0.125738</td>\n",
              "      <td>-0.121335</td>\n",
              "      <td>-0.149830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>color</th>\n",
              "      <td>-0.291530</td>\n",
              "      <td>0.019548</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.026056</td>\n",
              "      <td>-0.047426</td>\n",
              "      <td>-0.027513</td>\n",
              "      <td>-0.172629</td>\n",
              "      <td>-0.270529</td>\n",
              "      <td>-0.263395</td>\n",
              "      <td>-0.268388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clarity</th>\n",
              "      <td>-0.352435</td>\n",
              "      <td>0.189024</td>\n",
              "      <td>-0.026056</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.067329</td>\n",
              "      <td>-0.159967</td>\n",
              "      <td>-0.146941</td>\n",
              "      <td>-0.371355</td>\n",
              "      <td>-0.357226</td>\n",
              "      <td>-0.366218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>depth</th>\n",
              "      <td>0.027734</td>\n",
              "      <td>-0.223898</td>\n",
              "      <td>-0.047426</td>\n",
              "      <td>-0.067329</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.293012</td>\n",
              "      <td>-0.012731</td>\n",
              "      <td>-0.025563</td>\n",
              "      <td>-0.029809</td>\n",
              "      <td>0.094337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>table</th>\n",
              "      <td>0.183639</td>\n",
              "      <td>-0.432154</td>\n",
              "      <td>-0.027513</td>\n",
              "      <td>-0.159967</td>\n",
              "      <td>-0.293012</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.129848</td>\n",
              "      <td>0.197198</td>\n",
              "      <td>0.185248</td>\n",
              "      <td>0.153161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>0.921804</td>\n",
              "      <td>-0.053537</td>\n",
              "      <td>-0.172629</td>\n",
              "      <td>-0.146941</td>\n",
              "      <td>-0.012731</td>\n",
              "      <td>0.129848</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.884919</td>\n",
              "      <td>0.864393</td>\n",
              "      <td>0.860963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x</th>\n",
              "      <td>0.975037</td>\n",
              "      <td>-0.125738</td>\n",
              "      <td>-0.270529</td>\n",
              "      <td>-0.371355</td>\n",
              "      <td>-0.025563</td>\n",
              "      <td>0.197198</td>\n",
              "      <td>0.884919</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.972977</td>\n",
              "      <td>0.970122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y</th>\n",
              "      <td>0.950035</td>\n",
              "      <td>-0.121335</td>\n",
              "      <td>-0.263395</td>\n",
              "      <td>-0.357226</td>\n",
              "      <td>-0.029809</td>\n",
              "      <td>0.185248</td>\n",
              "      <td>0.864393</td>\n",
              "      <td>0.972977</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.950030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>z</th>\n",
              "      <td>0.952700</td>\n",
              "      <td>-0.149830</td>\n",
              "      <td>-0.268388</td>\n",
              "      <td>-0.366218</td>\n",
              "      <td>0.094337</td>\n",
              "      <td>0.153161</td>\n",
              "      <td>0.860963</td>\n",
              "      <td>0.970122</td>\n",
              "      <td>0.950030</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc2fba36-8c19-49c0-890f-648a37a56f88')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-9a6f1885-2e03-49c8-9e3c-28bffd49fa68\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9a6f1885-2e03-49c8-9e3c-28bffd49fa68')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-9a6f1885-2e03-49c8-9e3c-28bffd49fa68 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fc2fba36-8c19-49c0-890f-648a37a56f88 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fc2fba36-8c19-49c0-890f-648a37a56f88');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "#coorelation matrix\n",
        "df.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "n9DCyqDpvX0v",
        "outputId": "8e6ccf5e-2d57-4aaa-d2da-42b4fe61bfee"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#plotting the correlation heatmap\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(df.corr(),annot=True,cmap='coolwarm')\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_oobRk95vX0v"
      },
      "source": [
        "#### Ploting the relationship between Price and Carat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "Dli-zGHhvX0v"
      },
      "outputs": [],
      "source": [
        "sns.lineplot(x='carat',y='price',data=df)\n",
        "plt.title('Carat vs Price')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1E_qqfxevX0v"
      },
      "source": [
        "From the lineplot it is quite clear that the price of the diamond increases with the increase in the carat of the diamond. However, diamonds with less carat also have high price. This is because of the other factors that affect the price of the diamond."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bq_4OgOGvX0w"
      },
      "outputs": [],
      "source": [
        "fig, ax = plt.subplots(2,3,figsize=(15,5))\n",
        "sns.scatterplot(x='x',y='carat',data=df, ax=ax[0,0])\n",
        "sns.scatterplot(x='y',y='carat',data=df, ax=ax[0,1])\n",
        "sns.scatterplot(x='z',y='carat',data=df, ax=ax[0,2])\n",
        "sns.scatterplot(x='x',y='price',data=df, ax=ax[1,0])\n",
        "sns.scatterplot(x='y',y='price',data=df, ax=ax[1,1])\n",
        "sns.scatterplot(x='z',y='price',data=df, ax=ax[1,2])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aLtMrHonvX0w"
      },
      "source": [
        "***Majority of the diamonds have x values between 4 and 8, y values between 4 and 10 and z values between 2 and 6. Diamonds with other dimensions are very rare.***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bBEuMkQKvX0w"
      },
      "source": [
        "## Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZTfCKw9dvX0w"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_test,x_train,y_test,y_train = train_test_split(df.drop('price',axis=1),df['price'],test_size=0.2,random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWHEXYiovX0w"
      },
      "source": [
        "## Model Building"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjCmjU0SvX0w"
      },
      "source": [
        "**Decision Tree Regressor**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7VIBHVR9vX0w"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dt = DecisionTreeRegressor()\n",
        "dt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EEm-ios2vX0w"
      },
      "outputs": [],
      "source": [
        "#training the model\n",
        "dt.fit(x_train,y_train)\n",
        "#train accuracy\n",
        "dt.score(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YqJzCJ1LvX0x"
      },
      "outputs": [],
      "source": [
        "#predicting the test set\n",
        "dt_pred = dt.predict(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83P0QopWvX0x"
      },
      "source": [
        "### Random Forest Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_UV2EgKYvX0x"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf = RandomForestRegressor()\n",
        "rf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KmG967ZGvX0x"
      },
      "outputs": [],
      "source": [
        "#training the model\n",
        "rf.fit(x_train,y_train)\n",
        "#train accuracy\n",
        "rf.score(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BE0Y3nMyvX0x"
      },
      "outputs": [],
      "source": [
        "#predicting the test set\n",
        "rf_pred = rf.predict(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_JHxnguAvX0x"
      },
      "source": [
        "**Model Evaluation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X3tqKOVevX0y"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error,mean_absolute_error"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zS-kUbXbvX0y"
      },
      "source": [
        "**Decision Tree Regressor**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5q0dMtGpvX0y"
      },
      "outputs": [],
      "source": [
        "#distribution plot for actual and predicted values\n",
        "ax = sns.distplot(y_test,hist=False,color='r',label='Actual Value')\n",
        "sns.distplot(dt_pred,hist=False,color='b',label='Fitted Values',ax=ax)\n",
        "plt.title('Actual vs Fitted Values for Price')\n",
        "plt.xlabel('Price')\n",
        "plt.ylabel('Proportion of Diamonds')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t_E8Vk6KvX0y"
      },
      "outputs": [],
      "source": [
        "print('Decision Tree Regressor RMSE:',np.sqrt(mean_squared_error(y_test,dt_pred)))\n",
        "print('Decision Tree Regressor Accuracy:',dt.score(x_test,y_test))\n",
        "print('Decision Tree Regressor MAE:',mean_absolute_error(y_test,dt_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hMu2FRF4vX0y"
      },
      "source": [
        "### Random Forest Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AF_R4Ea5vX0y"
      },
      "outputs": [],
      "source": [
        "#distribution plot for actual and predicted values\n",
        "ax = sns.distplot(y_test,hist=False,color='r',label='Actual Value')\n",
        "sns.distplot(rf_pred,hist=False,color='b',label='Fitted Values',ax=ax)\n",
        "plt.title('Actual vs Fitted Values for Price')\n",
        "plt.xlabel('Price')\n",
        "plt.ylabel('Proportion of Diamonds')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TJpHWhNGvX0y"
      },
      "outputs": [],
      "source": [
        "print('Random Forest Regressor RMSE:',np.sqrt(mean_squared_error(y_test,rf_pred)))\n",
        "print('Random Forest Regressor Accuracy:',rf.score(x_test,y_test))\n",
        "print('Random Forest Regressor MAE:',mean_absolute_error(y_test,rf_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N5vnGW0xvX0y"
      },
      "source": [
        "## Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OpNfKxwhvX0y"
      },
      "source": [
        "Both the models have almost same accuracy. However, the Random Forest Regressor model is slightly better than the Decision Tree Regressor model.\n",
        "\n",
        "There is something interesting about the data. The price of the diamonds with J color and I1 clarity is higher than the price of the diamonds with D color and IF clarity which couldn't be explained by the models. This could be because of the other factors that affect the price of the diamond.\n",
        "\n"
      ]
    }
  ],
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