diamond (1).ipynb

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      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split , cross_val_score ,KFold\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn import metrics\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn import tree\n",
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Reading Data"
      ],
      "metadata": {
        "id": "AnXGGkTPs1Tu"
      }
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    {
      "cell_type": "code",
      "source": [
        "diamonds = pd.read_csv('diamonds_data.csv')"
      ],
      "metadata": {
        "id": "QtiBoW5Xqb42"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = diamonds.copy()"
      ],
      "metadata": {
        "id": "wYa-dQvGtEnF"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "df.head()"
      ],
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              "   carat      cut color clarity  depth  table  price     x     y     z\n",
              "0   0.23    Ideal     E     SI2   61.5   55.0  326.0  3.95  3.98  2.43\n",
              "1   0.21  Premium     E     SI1   59.8   61.0  326.0  3.89  3.84  2.31\n",
              "2   0.23     Good     E     VS1   56.9   65.0  327.0  4.05  4.07  2.31\n",
              "3   0.29  Premium     I     VS2   62.4   58.0  334.0  4.20  4.23  2.63\n",
              "4   0.31     Good     J     SI2   63.3   58.0  335.0  4.34  4.35  2.75"
            ],
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              "      <th></th>\n",
              "      <th>carat</th>\n",
              "      <th>cut</th>\n",
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              "      <th>clarity</th>\n",
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              "      <td>0.23</td>\n",
              "      <td>Ideal</td>\n",
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              "      <td>61.5</td>\n",
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              "      <td>Premium</td>\n",
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              "      <td>Good</td>\n",
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              "      <td>VS1</td>\n",
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              "      <td>4.05</td>\n",
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              "    <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>334.0</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>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>335.0</td>\n",
              "      <td>4.34</td>\n",
              "      <td>4.35</td>\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 53940 entries, 0 to 53939\n",
            "Data columns (total 10 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   carat    53936 non-null  float64\n",
            " 1   cut      53929 non-null  object \n",
            " 2   color    53938 non-null  object \n",
            " 3   clarity  53935 non-null  object \n",
            " 4   depth    53938 non-null  float64\n",
            " 5   table    53939 non-null  float64\n",
            " 6   price    53934 non-null  float64\n",
            " 7   x        53935 non-null  float64\n",
            " 8   y        53937 non-null  float64\n",
            " 9   z        53936 non-null  float64\n",
            "dtypes: float64(7), object(3)\n",
            "memory usage: 4.1+ MB\n"
          ]
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      "source": [
        "df['color']"
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        "outputId": "311e0fb4-eb14-481f-adb5-2bc249069976"
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          "data": {
            "text/plain": [
              "0        E\n",
              "1        E\n",
              "2        E\n",
              "3        I\n",
              "4        J\n",
              "        ..\n",
              "53935    D\n",
              "53936    D\n",
              "53937    D\n",
              "53938    H\n",
              "53939    D\n",
              "Name: color, Length: 53940, dtype: object"
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            "text/plain": [
              "carat       4\n",
              "cut        11\n",
              "color       2\n",
              "clarity     5\n",
              "depth       2\n",
              "table       1\n",
              "price       6\n",
              "x           5\n",
              "y           3\n",
              "z           4\n",
              "dtype: int64"
            ]
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          "execution_count": 10
        }
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      "source": [
        "df = df.apply(lambda x: x.fillna(x.mode().iloc[0]) if x.dtype == 'object' else x, axis=0)\n"
      ],
      "metadata": {
        "id": "gpRL8RomtbSt"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "df.fillna(df.mean(), inplace=True)\n"
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        "id": "Y3nBS_9ct1IF",
        "outputId": "2c2e8f58-42b5-4a01-adb7-15c2fbd46cd4"
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      "execution_count": null,
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-bf934754a5b1>:1: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df.fillna(df.mean(), inplace=True)\n"
          ]
        }
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      "source": [
        "df.isnull().sum()"
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        "outputId": "02f6ec25-bf54-4be4-e0fe-33827aabd975"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
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          "height": 300
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        "outputId": "65fa0300-b45f-4689-8bc2-0a9ecd576af4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              carat         depth         table         price             x  \\\n",
              "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
              "mean       0.797954     61.749590     57.457229   3932.869136      5.731179   \n",
              "std        0.474006      1.432286      2.234466   3989.269805      1.121742   \n",
              "min        0.200000     43.000000     43.000000    326.000000      0.000000   \n",
              "25%        0.400000     61.000000     56.000000    950.000000      4.710000   \n",
              "50%        0.700000     61.800000     57.000000   2401.000000      5.700000   \n",
              "75%        1.040000     62.500000     59.000000   5324.000000      6.540000   \n",
              "max        5.010000     79.000000     95.000000  18823.000000     10.740000   \n",
              "\n",
              "                  y             z  \n",
              "count  53940.000000  53940.000000  \n",
              "mean       5.734519      3.538731  \n",
              "std        1.142134      0.705699  \n",
              "min        0.000000      0.000000  \n",
              "25%        4.720000      2.910000  \n",
              "50%        5.710000      3.530000  \n",
              "75%        6.540000      4.040000  \n",
              "max       58.900000     31.800000  "
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              "      <th>carat</th>\n",
              "      <th>depth</th>\n",
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              "      <th>price</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>53940.000000</td>\n",
              "      <td>53940.000000</td>\n",
              "      <td>53940.000000</td>\n",
              "      <td>53940.000000</td>\n",
              "      <td>53940.000000</td>\n",
              "      <td>53940.000000</td>\n",
              "      <td>53940.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.797954</td>\n",
              "      <td>61.749590</td>\n",
              "      <td>57.457229</td>\n",
              "      <td>3932.869136</td>\n",
              "      <td>5.731179</td>\n",
              "      <td>5.734519</td>\n",
              "      <td>3.538731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.474006</td>\n",
              "      <td>1.432286</td>\n",
              "      <td>2.234466</td>\n",
              "      <td>3989.269805</td>\n",
              "      <td>1.121742</td>\n",
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              "      <td>0.705699</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\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>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>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>1.040000</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>5324.000000</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>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>\n",
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              "\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-5cfb2672-b281-43e3-a726-b0eab23846a4 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-5cfb2672-b281-43e3-a726-b0eab23846a4');\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",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-47600c4a-3ab5-42f9-a6df-869a704e1c57 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jr2g6tjNvpig",
        "outputId": "6dcc3825-e520-43aa-c5fc-13d6771ec7cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "146"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "DuUER--0vv4J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qOUbvJAiv42C",
        "outputId": "91771351-4211-4d45-a3ce-83d28830e68a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data analysis & Preprocessing"
      ],
      "metadata": {
        "id": "nVcdD0R4ugbR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['clarity'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vnpj0i02K6P",
        "outputId": "bf1e8626-8028-4de0-af44-fee2a95a7366"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SI1     13034\n",
              "VS2     12227\n",
              "SI2      9150\n",
              "VS1      8156\n",
              "VVS2     5056\n",
              "VVS1     3647\n",
              "IF       1784\n",
              "I1        740\n",
              "Name: clarity, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dic1 = {\n",
        "    \"I1\":0,\n",
        "    \"SI2\": 1,\n",
        "    \"SI1\": 2,\n",
        "    \"VS2\": 3,\n",
        "    \"VS1\": 4,\n",
        "    \"VVS2\": 5,\n",
        "    \"VVS1\": 6,\n",
        "    \"IF\": 7,\n",
        "}\n",
        "\n",
        "df['clarity']= df['clarity'].map(dic1)"
      ],
      "metadata": {
        "id": "wRQJQifU-BZE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['clarity'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0AAs2qZ-BL0",
        "outputId": "e04e20d1-da5b-496b-d11d-61ce3e86db7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    13034\n",
              "3    12227\n",
              "1     9150\n",
              "4     8156\n",
              "5     5056\n",
              "6     3647\n",
              "7     1784\n",
              "0      740\n",
              "Name: clarity, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['cut'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNcbTps82O7V",
        "outputId": "2bbe1f63-475c-47f2-aa51-140b996f7c3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ideal        21497\n",
              "Premium      13745\n",
              "Very Good    12067\n",
              "Good          4888\n",
              "Fair          1597\n",
              "Name: cut, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dic2 = {\n",
        "    \"Ideal\":0,\n",
        "    \"Fair\": 1,\n",
        "    \"Good\": 2,\n",
        "    \"Very Good\": 3,\n",
        "    \"Premium\": 4,\n",
        "}\n",
        "\n",
        "df['cut']= df['cut'].map(dic2)\n"
      ],
      "metadata": {
        "id": "bBzNe76uyGOy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['cut'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "no8bJtJr9DHl",
        "outputId": "8022d215-e38d-4689-b976-f137e696670a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    21497\n",
              "4    13745\n",
              "3    12067\n",
              "2     4888\n",
              "1     1597\n",
              "Name: cut, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['color'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sr-33BDJ-IOF",
        "outputId": "6db1a615-3c48-41b4-e31c-01d2c134609e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "G    11264\n",
              "E     9775\n",
              "F     9520\n",
              "H     8272\n",
              "D     6755\n",
              "I     5407\n",
              "J     2801\n",
              "Name: color, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dic3 = {\n",
        "    \"G\":0,\n",
        "    \"E\": 1,\n",
        "    \"F\": 2,\n",
        "    \"H\": 3,\n",
        "    \"D\": 4,\n",
        "    \"I\": 5,\n",
        "    \"J\": 6,\n",
        "}\n",
        "\n",
        "df['color']= df['color'].map(dic3)"
      ],
      "metadata": {
        "id": "J5fveO6n9ddg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['color'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sG8uAu---han",
        "outputId": "1880e6e3-ee92-491b-9ca5-90ddd17520fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    11264\n",
              "1     9775\n",
              "2     9520\n",
              "3     8272\n",
              "4     6755\n",
              "5     5407\n",
              "6     2801\n",
              "Name: color, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KGyWF1b78Ra4",
        "outputId": "b7192105-49a6-460d-ce7a-86719a7b0b34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   carat  cut  color  clarity  depth  table  price     x     y     z\n",
              "0   0.23    0      1        1   61.5   55.0  326.0  3.95  3.98  2.43\n",
              "1   0.21    4      1        2   59.8   61.0  326.0  3.89  3.84  2.31\n",
              "2   0.23    2      1        4   56.9   65.0  327.0  4.05  4.07  2.31\n",
              "3   0.29    4      5        3   62.4   58.0  334.0  4.20  4.23  2.63\n",
              "4   0.31    2      6        1   63.3   58.0  335.0  4.34  4.35  2.75"
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              "      <th>carat</th>\n",
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              "      <th>clarity</th>\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(df.corr(),annot=True,cmap='crest')"
      ],
      "metadata": {
        "id": "CwH83p-F8RYv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "outputId": "b16e584e-f34d-4909-91d1-55343c56185c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop('color', axis=1)\n",
        "df = df.drop('depth', axis=1)\n"
      ],
      "metadata": {
        "id": "kde0p6Rx8RV_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Training and Testing"
      ],
      "metadata": {
        "id": "w1sXhE_4BTOi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.drop(columns=['price'])\n",
        "y = df['price']"
      ],
      "metadata": {
        "id": "KPRAWIEr_nuG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "B4feH_a_CUyu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GntCnO5yCed4",
        "outputId": "36f1f401-5325-4ee5-a820-4c8cc7c467fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(43035, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "44mSD_N4CjAb",
        "outputId": "34fbded8-0e46-44e3-8417-3bc823916ee4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10759, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Helpers"
      ],
      "metadata": {
        "id": "EfKRAnVnCquS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def pred_model(model, X_train, y_train, X_test, y_test):\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    y_pred_train = model.predict(X_train)\n",
        "    y_pred_test = model.predict(X_test)\n",
        "\n",
        "    print(\"MAE For Train: \", metrics.mean_absolute_error(y_train, y_pred_train))\n",
        "    print(\"MAE For Test: \", metrics.mean_absolute_error(y_test, y_pred_test))\n",
        "\n",
        "    print(\"R2 Score For Train: \", r2_score(y_train, y_pred_train))\n",
        "    print(\"R2 Score For Test: \", r2_score(y_test, y_pred_test))"
      ],
      "metadata": {
        "id": "PRArNqEZCi-L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {\n",
        "    'max_depth': [5, 10, 15],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4]\n",
        "}\n"
      ],
      "metadata": {
        "id": "8Hh_dYLC23MW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model with train_test"
      ],
      "metadata": {
        "id": "MrH6vSpwC8sV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest Model\n"
      ],
      "metadata": {
        "id": "5jxJBTXUuXUD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestRegressor()\n",
        "pred_model(rf, X_train, y_train, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j8eYFxvWCi7T",
        "outputId": "c2312b0b-9a83-4cb4-a2bb-774885f9d9ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE For Train:  195.3991896213768\n",
            "MAE For Test:  511.6403168033698\n",
            "R2 Score For Train:  0.9914587337820153\n",
            "R2 Score For Test:  0.9402983027251767\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##LinearRegression"
      ],
      "metadata": {
        "id": "QH2L5mKGuZn0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()\n",
        "model = pred_model(lr, X_train, y_train, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FOzvzlJ0ub3s",
        "outputId": "a332e390-bc31-4439-e55f-dabf0df188c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE For Train:  868.1936368438787\n",
            "MAE For Test:  852.8240539519135\n",
            "R2 Score For Train:  0.8883391549400597\n",
            "R2 Score For Test:  0.8815428842228731\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Lasso"
      ],
      "metadata": {
        "id": "iAtvbne8u3RX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Ls= Lasso(alpha=1.0)\n",
        "pred_model(Ls, X_train, y_train, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WN7vAF0mu4-A",
        "outputId": "ce9d4a70-03e2-434f-d91e-033faa50fab6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE For Train:  870.3925842893645\n",
            "MAE For Test:  854.7391869849606\n",
            "R2 Score For Train:  0.8883221002450665\n",
            "R2 Score For Test:  0.8823035180891738\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Ridge"
      ],
      "metadata": {
        "id": "Gl1xjF5NvV-a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Rid= Ridge(alpha=1.0)\n",
        "pred_model(Rid, X_train, y_train, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2umxIZCUvZn8",
        "outputId": "699b382c-cef6-41a5-e5ba-f1d2e1ac5916"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE For Train:  868.5266058837238\n",
            "MAE For Test:  853.127714346171\n",
            "R2 Score For Train:  0.8883388242042034\n",
            "R2 Score For Test:  0.8816130534214441\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##tree"
      ],
      "metadata": {
        "id": "4qr6Qxr0v8R9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = tree.DecisionTreeRegressor(max_depth=3)\n",
        "pred_model(clf, X_train, y_train, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQrCJ6DIv-Km",
        "outputId": "9713cef9-0f9e-4137-9f7e-16fa08a6b3b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE For Train:  774.2602058335028\n",
            "MAE For Test:  766.3077556823578\n",
            "R2 Score For Train:  0.8885540601753098\n",
            "R2 Score For Test:  0.8875572762065174\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model with cross validation"
      ],
      "metadata": {
        "id": "XEFrjA2ZEgFB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest Model\n"
      ],
      "metadata": {
        "id": "dYNRLAi_1A8x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestRegressor()\n",
        "\n",
        "k_folds = 5\n",
        "kf = KFold(n_splits=k_folds, shuffle=True, random_state=42)\n",
        "\n",
        "mae_scores = -1*cross_val_score(rf, X, y, cv=kf, scoring='neg_mean_absolute_error')\n",
        "r2_scores = cross_val_score(rf, X, y, cv=kf, scoring='r2')\n",
        "\n",
        "# Print the average MAE and R2 score across all folds\n",
        "print(\"Average MAE: {:.2f}\".format(mae_scores.mean()))\n",
        "print(\"Average R2 Score: {:.2f}\".format(r2_scores.mean()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-Qno0WHJGkQ",
        "outputId": "ccfa6200-2e5a-41a1-ea60-a1fa59da8a8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average MAE: 519.89\n",
            "Average R2 Score: 0.94\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##LinearRegression"
      ],
      "metadata": {
        "id": "aTTdaVOM1G27"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()\n",
        "\n",
        "k_folds = 5\n",
        "kf = KFold(n_splits=k_folds, shuffle=True, random_state=42)\n",
        "\n",
        "mae_scores = -1*cross_val_score(lr, X, y, cv=kf, scoring='neg_mean_absolute_error')\n",
        "r2_scores = cross_val_score(lr, X, y, cv=kf, scoring='r2')\n",
        "\n",
        "# Print the average MAE and R2 score across all folds\n",
        "print(\"Average MAE: {:.2f}\".format(mae_scores.mean()))\n",
        "print(\"Average R2 Score: {:.2f}\".format(r2_scores.mean()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EW9tvH4q1AWR",
        "outputId": "346d8114-8c3a-45a7-8ae0-401911b65f11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average MAE: 868.63\n",
            "Average R2 Score: 0.89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Lasso"
      ],
      "metadata": {
        "id": "CFqv5ADA1Y3c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Ls= Lasso(alpha=1.0)\n",
        "\n",
        "k_folds = 5\n",
        "kf = KFold(n_splits=k_folds, shuffle=True, random_state=42)\n",
        "\n",
        "mae_scores = -1*cross_val_score(Ls, X, y, cv=kf, scoring='neg_mean_absolute_error')\n",
        "r2_scores = cross_val_score(Ls, X, y, cv=kf, scoring='r2')\n",
        "\n",
        "# Print the average MAE and R2 score across all folds\n",
        "print(\"Average MAE: {:.2f}\".format(mae_scores.mean()))\n",
        "print(\"Average R2 Score: {:.2f}\".format(r2_scores.mean()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ER4Mjvzd1dHt",
        "outputId": "1edb43d0-c501-4ca1-953d-9668e3cc1c09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average MAE: 870.29\n",
            "Average R2 Score: 0.89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Ridge"
      ],
      "metadata": {
        "id": "HcQNoINQ1p6K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Rid= Ridge(alpha=1.0)\n",
        "k_folds = 5\n",
        "kf = KFold(n_splits=k_folds, shuffle=True, random_state=42)\n",
        "\n",
        "mae_scores = -1*cross_val_score(Rid, X, y, cv=kf, scoring='neg_mean_absolute_error')\n",
        "r2_scores = cross_val_score(Rid, X, y, cv=kf, scoring='r2')\n",
        "\n",
        "# Print the average MAE and R2 score across all folds\n",
        "print(\"Average MAE: {:.2f}\".format(mae_scores.mean()))\n",
        "print(\"Average R2 Score: {:.2f}\".format(r2_scores.mean()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rG_0Qc5b1pXa",
        "outputId": "3cd8d891-11ef-4f32-b739-19f8e55a1ca3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average MAE: 868.57\n",
            "Average R2 Score: 0.89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##DecisionTreeRegressor"
      ],
      "metadata": {
        "id": "fg9DuGSW12KL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = tree.DecisionTreeRegressor(max_depth=3)\n",
        "k_folds = 5\n",
        "kf = KFold(n_splits=k_folds, shuffle=True, random_state=42)\n",
        "\n",
        "mae_scores = -1*cross_val_score(clf, X, y, cv=kf, scoring='neg_mean_absolute_error')\n",
        "r2_scores = cross_val_score(clf, X, y, cv=kf, scoring='r2')\n",
        "\n",
        "# Print the average MAE and R2 score across all folds\n",
        "print(\"Average MAE: {:.2f}\".format(mae_scores.mean()))\n",
        "print(\"Average R2 Score: {:.2f}\".format(r2_scores.mean()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FO84P1l13MX",
        "outputId": "58d4ba3e-d7f3-4b2b-cbb8-e836ddec7cf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average MAE: 772.72\n",
            "Average R2 Score: 0.89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Conclusion\n",
        "The price of diamonds is influenced by a combination of factors, commonly known as the \"Four Cs\": carat weight, cut, color, and clarity. Each of these factors plays a role in determining the overall quality and value of a diamond, but in this dataset, all of the Four Cs have no effect, it's possible that the dataset lacks diversity in terms of the ranges covered by each of the Four Cs or that other factors, not included in the dataset, are influencing diamond prices. It's also essential to consider potential outliers, as they could skew the analysis.\n",
        "\n",
        "Random forest is the best between the five models (DecisionTreeRegressor, Ridge, Lasso , Linear regression, Random forest)"
      ],
      "metadata": {
        "id": "8o_12H4HQStl"
      }
    }
  ]
}