diamond.ipynb

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      "cell_type": "code",
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      "metadata": {
        "id": "99450f9e"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd "
      ],
      "id": "99450f9e"
    },
    {
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      "execution_count": 2,
      "metadata": {
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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>carat</th>\n",
              "      <th>cut</th>\n",
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              "      <th>depth</th>\n",
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              "\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "train = pd.read_csv('diamonds_train.csv')\n",
        "train.head()"
      ],
      "id": "7dcb9e87"
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            "text/plain": [
              "      carat        cut color clarity  depth  table     x     y     z\n",
              "0      0.73      Ideal     G    VVS2   61.9   55.0  5.83  5.77  3.59\n",
              "1      0.61    Premium     F    VVS2   59.7   58.0  5.56  5.53  3.31\n",
              "2      1.55    Premium     I     VS1   58.2   60.0  7.69  7.59  4.45\n",
              "3      0.46       Good     F      IF   56.2   61.0  5.16  5.24  2.92\n",
              "4      1.10  Very Good     F     VS2   60.6   58.0  6.67  6.77  4.07\n",
              "...     ...        ...   ...     ...    ...    ...   ...   ...   ...\n",
              "3935   1.58    Premium     I     VS1   61.1   59.0  7.52  7.44  4.57\n",
              "3936   1.00  Very Good     F     SI1   63.1   59.0  6.29  6.22  3.95\n",
              "3937   1.13    Premium     H     VS1   58.9   58.0  6.81  6.78  4.00\n",
              "3938   1.51      Ideal     E     VS2   62.8   56.0  7.31  7.24  4.57\n",
              "3939   1.55      Ideal     G     SI2   60.7   55.0  7.54  7.51  4.57\n",
              "\n",
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              "      <td>0.46</td>\n",
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              "      <td>2.92</td>\n",
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              "      <th>4</th>\n",
              "      <td>1.10</td>\n",
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              "      <td>1.58</td>\n",
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              "      <th>3936</th>\n",
              "      <td>1.00</td>\n",
              "      <td>Very Good</td>\n",
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              "      <td>63.1</td>\n",
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              "      <th>3937</th>\n",
              "      <td>1.13</td>\n",
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              "      <td>1.51</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "test = pd.read_csv('diamonds_test.csv')\n",
        "test"
      ],
      "id": "b9ed3cff"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "366c8ae9",
        "outputId": "d8a2bd4a-e6b7-49ee-a244-a1d272b194ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Ideal', 'Premium', 'Good', 'Very Good', 'Fair'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "train['cut'].unique()"
      ],
      "id": "366c8ae9"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "FXGhuv12gWr3"
      },
      "outputs": [],
      "source": [
        "cut = {'Fair': 0,\n",
        "           'Very Good' : 1,\n",
        "           'Good' : 2,\n",
        "           'Premium' : 3 ,\n",
        "           'Ideal':4}\n",
        "\n",
        "train['cut'] = train['cut'].map(cut)\n",
        "test['cut'] = test['cut'].map(cut)\n"
      ],
      "id": "FXGhuv12gWr3"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hPdyU81eNdZ1",
        "outputId": "9df35800-ee34-4d1e-fc1d-eb48c10d0068"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['E', 'I', 'J', 'H', 'F', 'G', 'D'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "train['color'].unique()"
      ],
      "id": "hPdyU81eNdZ1"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1MR4hrZNdhU",
        "outputId": "f505786a-d278-4c51-a56f-bb89f3bc0cff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['SI2', 'SI1', 'VS1', 'VS2', 'VVS2', 'VVS1', 'I1', 'IF'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "train['clarity'].unique()"
      ],
      "id": "O1MR4hrZNdhU"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "vMFGzNRnmDgY"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "train['color'] = label_encoder.fit_transform(train['color'])\n",
        "test['color'] = label_encoder.fit_transform(test['color'])\n",
        "\n",
        "train['clarity'] = label_encoder.fit_transform(train['clarity'])\n",
        "test['clarity'] = label_encoder.fit_transform(test['clarity'])"
      ],
      "id": "vMFGzNRnmDgY"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MqLGADcsN2Xj",
        "outputId": "ce700bb3-8ff6-462b-ee0e-287da1aca977"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21472 entries, 0 to 21471\n",
            "Data columns (total 10 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   carat    21472 non-null  float64\n",
            " 1   cut      21472 non-null  int64  \n",
            " 2   color    21472 non-null  int64  \n",
            " 3   clarity  21472 non-null  int64  \n",
            " 4   depth    21472 non-null  float64\n",
            " 5   table    21472 non-null  float64\n",
            " 6   price    21471 non-null  float64\n",
            " 7   x        21471 non-null  float64\n",
            " 8   y        21471 non-null  float64\n",
            " 9   z        21471 non-null  float64\n",
            "dtypes: float64(7), int64(3)\n",
            "memory usage: 1.6 MB\n"
          ]
        }
      ],
      "source": [
        "train.info()"
      ],
      "id": "MqLGADcsN2Xj"
    },
    {
      "cell_type": "code",
      "source": [
        "train = train.dropna(axis=0, how='all', subset=['price'])"
      ],
      "metadata": {
        "id": "OuQcovWaJ0xT"
      },
      "id": "OuQcovWaJ0xT",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSVXoKGsJbGJ",
        "outputId": "0e5d481c-3290-48c7-b104-85512d1587b2"
      },
      "id": "tSVXoKGsJbGJ",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['carat', 'cut', 'color', 'clarity', 'depth', 'table', 'price', 'x', 'y',\n",
              "       'z'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ZFyZbnwohmel",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "459567cb-5962-44e4-b9d6-a5b0736d68a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[col] = igetitem(value, i)\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[col] = igetitem(value, i)\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[col] = igetitem(value, i)\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[col] = igetitem(value, i)\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[col] = igetitem(value, i)\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[col] = igetitem(value, i)\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[col] = igetitem(value, i)\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[col] = igetitem(value, i)\n",
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[col] = igetitem(value, i)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "for i in ['carat', 'cut', 'color', 'clarity', 'depth', 'table', 'x', 'y','z']:\n",
        "  train[[i]] = scaler.fit_transform(train[[i]])\n",
        "  test[[i]] = scaler.fit_transform(test[[i]])\n"
      ],
      "id": "ZFyZbnwohmel"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "fCrehMYyjHaN",
        "outputId": "108d8afc-635e-4463-cd9f-f69d0faf6c27"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          carat       cut     color   clarity     depth     table    price  \\\n",
              "0     -2.289609  1.083954 -1.046940 -0.367520 -0.205010 -1.245798    326.0   \n",
              "1     -2.348527  0.297419 -1.046940 -0.996087 -1.314032  1.471646    326.0   \n",
              "2     -2.289609 -0.489116 -1.046940  0.261047 -3.205892  3.283275    327.0   \n",
              "3     -2.112855  0.297419  1.273218  0.889613  0.382119  0.112924    334.0   \n",
              "4     -2.053937 -0.489116  1.853257 -0.367520  0.969248  0.112924    335.0   \n",
              "...         ...       ...       ...       ...       ...       ...      ...   \n",
              "21466  2.040852 -0.489116  0.113139 -0.996087 -2.357817  2.830367  11180.0   \n",
              "21467  0.567906 -0.489116 -0.466901  1.518180  1.164958 -0.339983  11182.0   \n",
              "21468  0.656283  1.083954  0.113139  1.518180 -0.400720 -0.792890  11182.0   \n",
              "21469  0.508989  1.083954 -1.046940  2.146747 -0.139774  0.112924  11184.0   \n",
              "21470  2.954079  0.297419  1.853257 -0.996087 -0.987849 -0.792890  11188.0   \n",
              "\n",
              "              x         y         z  \n",
              "0     -2.886380 -2.887948 -2.882565  \n",
              "1     -2.959410 -3.060670 -3.117024  \n",
              "2     -2.764665 -2.776912 -3.117024  \n",
              "3     -2.582092 -2.579515 -2.491800  \n",
              "4     -2.411690 -2.431468 -2.257342  \n",
              "...         ...       ...       ...  \n",
              "21466  1.909210  1.886584  1.318154  \n",
              "21467  0.509482  0.480133  0.732007  \n",
              "21468  0.752913  0.714541  0.634316  \n",
              "21469  0.558168  0.615843  0.556163  \n",
              "21470  2.067440  2.022294  1.787071  \n",
              "\n",
              "[21471 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c707bf6-2f4e-456e-8736-565b6a4344f5\">\n",
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              "      <th>carat</th>\n",
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              "<p>21471 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c707bf6-2f4e-456e-8736-565b6a4344f5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "    <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",
              "  <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-8c707bf6-2f4e-456e-8736-565b6a4344f5 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-8c707bf6-2f4e-456e-8736-565b6a4344f5');\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": 18
        }
      ],
      "source": [
        "train"
      ],
      "id": "fCrehMYyjHaN"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "TtGgq3rRVYH2"
      },
      "outputs": [],
      "source": [
        "y=  train[['price']]"
      ],
      "id": "TtGgq3rRVYH2"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "-6jHFC1uVb6_"
      },
      "outputs": [],
      "source": [
        "x = train.copy()\n",
        "x.drop(columns= ['price'], inplace= True)"
      ],
      "id": "-6jHFC1uVb6_"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, shuffle=True)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, shuffle=True)"
      ],
      "metadata": {
        "id": "N3U7iGIbLal5"
      },
      "id": "N3U7iGIbLal5",
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "03d20a30"
      },
      "outputs": [],
      "source": [
        "# modeling\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "model =LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_val)"
      ],
      "id": "03d20a30"
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3f005b7",
        "outputId": "1ebcf066-ef23-4964-e199-673613f6b5af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7916146448978976"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# evaluate your model on train data \n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "r2_score(y_val, y_pred)"
      ],
      "id": "f3f005b7"
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate model on tast data\n",
        "y_pred = model.predict(X_test)\n",
        "r2_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lnPVhIuDL_3-",
        "outputId": "d80ba1b6-badd-4d23-e714-d0e0a1dcb744"
      },
      "id": "lnPVhIuDL_3-",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8022807517734093"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.0 (tags/v3.8.0:fa919fd, Oct 14 2019, 19:37:50) [MSC v.1916 64 bit (AMD64)]"
    },
    "vscode": {
      "interpreter": {
        "hash": "44e7e1b8fa2096bd5707ed7fd18b1724a2db25f4c565a7673f8b6e7bfc49d25d"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}