Fresh_Start.ipynb

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      "metadata": {
        "id": "yjUrOobLxOkP"
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      "source": [
        "import pandas as pd\n",
        "import pickle\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GhMgLxsyAYg"
      },
      "source": [
        "#DATA UNDERSTANDING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "p0AUlPEyx4fF"
      },
      "outputs": [],
      "source": [
        "makeupdata = pd.read_csv(\"https://raw.githubusercontent.com/htetlwink/AIProjectTest/main/dataset/makeupdataset.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDRsaZzCym22",
        "outputId": "f9e6635c-a1b0-4ed5-f0d9-28993e76777a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(172, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "makeupdata.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7fdNghkytCz",
        "outputId": "220fe52d-5d39-4976-8ae4-18baec1251bc"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Hex_Code', 'R', 'G', 'B', 'Core_Skin_Type', 'Skin_Type', 'Core_Color',\n",
              "       'Foundation_Color', 'Blush_Color', 'Lipstick_Color', 'Eyeshadow_Color',\n",
              "       'Highlighter_Color', 'Contour_Color'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "makeupdata.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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          "data": {
            "text/plain": [
              "Core_Skin_Type\n",
              "Dry            78\n",
              "Oily           35\n",
              "Sensitive      33\n",
              "Combination    26\n",
              "Name: count, dtype: int64"
            ],
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Core_Skin_Type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Dry</th>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oily</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sensitive</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Combination</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "makeupdata[\"Core_Skin_Type\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
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        "id": "VAw_keVuy7YB",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Core_Color\n",
              "Mocha      74\n",
              "Natural    58\n",
              "Pale       24\n",
              "Golden     16\n",
              "Name: count, dtype: int64"
            ],
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Core_Color</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Mocha</th>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Natural</th>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pale</th>\n",
              "      <td>24</td>\n",
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              "    <tr>\n",
              "      <th>Golden</th>\n",
              "      <td>16</td>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
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      "source": [
        "makeupdata[\"Core_Color\"].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "#DATA PROCESSING"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6qpp4u1zEl3"
      },
      "source": [
        "WE DELETE UN NESSEARY COLUMNS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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            "text/plain": [
              "     R    G    B Core_Skin_Type Core_Color\n",
              "0  255  250  250           Oily       Pale\n",
              "1  255  240  225            Dry       Pale\n",
              "2  250  231  211    Combination       Pale\n",
              "3  240  213  190      Sensitive       Pale\n",
              "4  216  185  152    Combination    Natural"
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "makeupdata",
              "summary": "{\n  \"name\": \"makeupdata\",\n  \"rows\": 172,\n  \"fields\": [\n    {\n      \"column\": \"R\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72,\n        \"min\": 23,\n        \"max\": 255,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          43,\n          160,\n          110\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72,\n        \"min\": 0,\n        \"max\": 250,\n        \"num_unique_values\": 104,\n        \"samples\": [\n          67,\n          196,\n          224\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 67,\n        \"min\": 0,\n        \"max\": 250,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          14,\n          169,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Core_Skin_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Dry\",\n          \"Sensitive\",\n          \"Oily\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Core_Color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Natural\",\n          \"Mocha\",\n          \"Pale\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "makeupdata = makeupdata.drop(columns=[\"Hex_Code\",\"Skin_Type\",\"Foundation_Color\",\"Blush_Color\",\"Lipstick_Color\",\"Eyeshadow_Color\",\"Highlighter_Color\",\"Contour_Color\"])\n",
        "makeupdata.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q8V6hJZ6zWyn"
      },
      "source": [
        "Duplicate data set for encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "IP-IHzieyKaa"
      },
      "outputs": [],
      "source": [
        "df = makeupdata.copy()\n",
        "target = 'Core_Color'\n",
        "encode = ['Core_Skin_Type']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MxwM5JS1EEm"
      },
      "source": [
        "WE use ONE HOT ENCODING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "UlM_6clizZ9q"
      },
      "outputs": [],
      "source": [
        "for col in encode:\n",
        "    dummy = pd.get_dummies(df[col], prefix=col)\n",
        "    df = pd.concat([df,dummy], axis=1)\n",
        "    del df[col]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "vs6uy1DB0oqz",
        "outputId": "f4882de3-b18d-4bc8-e371-f0f8bc5fc8c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     R    G    B Core_Color  Core_Skin_Type_Combination  Core_Skin_Type_Dry  \\\n",
              "0  255  250  250       Pale                       False               False   \n",
              "1  255  240  225       Pale                       False                True   \n",
              "2  250  231  211       Pale                        True               False   \n",
              "3  240  213  190       Pale                       False               False   \n",
              "4  216  185  152    Natural                        True               False   \n",
              "\n",
              "   Core_Skin_Type_Oily  Core_Skin_Type_Sensitive  \n",
              "0                 True                     False  \n",
              "1                False                     False  \n",
              "2                False                     False  \n",
              "3                False                      True  \n",
              "4                False                     False  "
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 172,\n  \"fields\": [\n    {\n      \"column\": \"R\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72,\n        \"min\": 23,\n        \"max\": 255,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          43,\n          160,\n          110\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72,\n        \"min\": 0,\n        \"max\": 250,\n        \"num_unique_values\": 104,\n        \"samples\": [\n          67,\n          196,\n          224\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 67,\n        \"min\": 0,\n        \"max\": 250,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          14,\n          169,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Core_Color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Natural\",\n          \"Mocha\",\n          \"Pale\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Core_Skin_Type_Combination\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Core_Skin_Type_Dry\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Core_Skin_Type_Oily\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Core_Skin_Type_Sensitive\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kBDsLfNbzmJf"
      },
      "source": [
        "We Map our target to specific Number"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "WY5NlyKGzeyp"
      },
      "outputs": [],
      "source": [
        "target_mapper = {'Pale':0, 'Natural':1, 'Golden':2, 'Mocha':3}\n",
        "def target_encode(val):\n",
        "    return target_mapper[val]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "36ZEhRJWzpww"
      },
      "outputs": [],
      "source": [
        "df['Core_Color'] = df['Core_Color'].apply(target_encode)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "AA9HsEoEz0B9",
        "outputId": "5b740cb9-390a-4dd6-f071-3961e24ca70c"
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     R    G    B  Core_Color  Core_Skin_Type_Combination  Core_Skin_Type_Dry  \\\n",
              "0  255  250  250           0                       False               False   \n",
              "1  255  240  225           0                       False                True   \n",
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              "3  240  213  190           0                       False               False   \n",
              "4  216  185  152           1                        True               False   \n",
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              "   Core_Skin_Type_Oily  Core_Skin_Type_Sensitive  \n",
              "0                 True                     False  \n",
              "1                False                     False  \n",
              "2                False                     False  \n",
              "3                False                      True  \n",
              "4                False                     False  "
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef13bccc-51e7-4b92-849c-efd1013d4eaa')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-ef13bccc-51e7-4b92-849c-efd1013d4eaa 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-ef13bccc-51e7-4b92-849c-efd1013d4eaa');\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",
              "\n",
              "\n",
              "<div id=\"df-de280653-e716-42d3-9e27-92ddea877cba\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-de280653-e716-42d3-9e27-92ddea877cba')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\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",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    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-de280653-e716-42d3-9e27-92ddea877cba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 172,\n  \"fields\": [\n    {\n      \"column\": \"R\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72,\n        \"min\": 23,\n        \"max\": 255,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          43,\n          160,\n          110\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72,\n        \"min\": 0,\n        \"max\": 250,\n        \"num_unique_values\": 104,\n        \"samples\": [\n          67,\n          196,\n          224\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 67,\n        \"min\": 0,\n        \"max\": 250,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          14,\n          169,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Core_Color\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Core_Skin_Type_Combination\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Core_Skin_Type_Dry\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Core_Skin_Type_Oily\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Core_Skin_Type_Sensitive\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGcoJG6R1l2K"
      },
      "source": [
        "Seperate X and Y data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "rpYVaR4yz2Bh"
      },
      "outputs": [],
      "source": [
        "X = df.drop('Core_Color', axis=1)\n",
        "Y = df['Core_Color']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0tbExFV2vzt"
      },
      "source": [
        "Split the dataset into 80% train data and 20 % test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "lN2mX0vz2ojj"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(X,Y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mform8jT1vIV"
      },
      "source": [
        "#Fit in the Model using Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "2WQ6DlRL1qqo",
        "outputId": "4a166575-2994-42bd-8ae2-bd6c40ae033b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier()\n",
        "clf.fit(X, Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LBszz4ZV1-oa"
      },
      "source": [
        "#Export the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "F2OXjdgC155O"
      },
      "outputs": [],
      "source": [
        "with open('Trained_model.pkl', 'wb') as file:\n",
        "    pickle.dump(clf, file)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Load the Model"
      ],
      "metadata": {
        "id": "-fFlq4pdcjAH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('Trained_model.pkl', 'rb') as file:\n",
        "    clf = pickle.load(file)"
      ],
      "metadata": {
        "id": "qFceimQ7coIn"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZjZY4fy17cI"
      },
      "source": [
        "Model Prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "LZT9s9MR1_zI"
      },
      "outputs": [],
      "source": [
        "y_pred = clf.predict(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mTkF1nCU2Ajv"
      },
      "source": [
        "Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9CXBqRU2Ckj",
        "outputId": "acefa938-690f-43e4-d363-bf231f023336"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 3  0  0  0]\n",
            " [ 0 13  0  1]\n",
            " [ 0  0  3  0]\n",
            " [ 0  0  0 15]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "print(confusion_matrix(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Ke0veq62HWK"
      },
      "source": [
        "Result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ciib4x3U2KBj",
        "outputId": "98885390-6bbd-4c4a-dbe8-0a58f211978a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score is 97.14285714285714 %\n",
            "Precision score is 98.4375 %\n",
            "Recall score is 98.21428571428572 %\n",
            "F1 score is 98.26762246117084 %\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "print(\"Accuracy score is\", accuracy_score(y_test, y_pred)*100,\"%\")\n",
        "print(\"Precision score is\" ,precision_score(y_test, y_pred, average = \"macro\")*100,\"%\")\n",
        "print(\"Recall score is\", recall_score(y_test, y_pred, average = \"macro\")*100,\"%\")\n",
        "print(\"F1 score is\", f1_score(y_test, y_pred, average = \"macro\")*100,\"%\")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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