Autism_Model.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
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        "id": "65222898-0e3c-4197-a9b3-fe6786d02d53"
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      "source": [
        "df = pd.read_csv(r\"/content/Toddler_Autism_dataset_July_2018.csv\")"
      ]
    },
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1054 entries, 0 to 1053\n",
            "Data columns (total 19 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   Case_No                 1054 non-null   int64 \n",
            " 1   A1                      1054 non-null   int64 \n",
            " 2   A2                      1054 non-null   int64 \n",
            " 3   A3                      1054 non-null   int64 \n",
            " 4   A4                      1054 non-null   int64 \n",
            " 5   A5                      1054 non-null   int64 \n",
            " 6   A6                      1054 non-null   int64 \n",
            " 7   A7                      1054 non-null   int64 \n",
            " 8   A8                      1054 non-null   int64 \n",
            " 9   A9                      1054 non-null   int64 \n",
            " 10  A10                     1054 non-null   int64 \n",
            " 11  Age_Mons                1054 non-null   int64 \n",
            " 12  Qchat-10-Score          1054 non-null   int64 \n",
            " 13  Sex                     1054 non-null   object\n",
            " 14  Ethnicity               1054 non-null   object\n",
            " 15  Jaundice                1054 non-null   object\n",
            " 16  Family_mem_with_ASD     1054 non-null   object\n",
            " 17  Who completed the test  1054 non-null   object\n",
            " 18  Class/ASD Traits        1054 non-null   object\n",
            "dtypes: int64(13), object(6)\n",
            "memory usage: 156.6+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
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              "      Case_No  Q1  Q2  Q3  Q4  Q5  Q6  Q7  Q8  Q9  Q10  Age in Months  \\\n",
              "0           1   0   0   0   0   0   0   1   1   0    1             28   \n",
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              "4           5   1   1   0   1   1   1   1   1   1    1             20   \n",
              "...       ...  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...            ...   \n",
              "1049     1050   0   0   0   0   0   0   0   0   0    1             24   \n",
              "1050     1051   0   0   1   1   1   0   1   0   1    0             12   \n",
              "1051     1052   1   0   1   1   1   1   1   1   1    1             18   \n",
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              "1053     1054   1   1   0   0   1   1   0   1   1    0             24   \n",
              "\n",
              "      Score out of 10 Gender          Region Jaundice Family_mem_with_ASD  \\\n",
              "0                   3      f  middle eastern      yes                  no   \n",
              "1                   4      m  White European      yes                  no   \n",
              "2                   4      m  middle eastern      yes                  no   \n",
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              "     Who completed the test  ASD  \n",
              "0             family member   No  \n",
              "1             family member  Yes  \n",
              "2             family member  Yes  \n",
              "3             family member  Yes  \n",
              "4             family member  Yes  \n",
              "...                     ...  ...  \n",
              "1049          family member   No  \n",
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              "1051          family member  Yes  \n",
              "1052          family member   No  \n",
              "1053          family member  Yes  \n",
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              "[1054 rows x 19 columns]"
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              "  }\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-6aa8b6f0-e922-4ed7-a690-7fdaa0c490ea button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7ccc1bae-b3d8-423b-9994-c5ee9d2103d7\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7ccc1bae-b3d8-423b-9994-c5ee9d2103d7 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\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\": 1054,\n  \"fields\": [\n    {\n      \"column\": \"Case_No\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 304,\n        \"min\": 1,\n        \"max\": 1054,\n        \"num_unique_values\": 1054,\n        \"samples\": [\n          261,\n          1026,\n          846\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q7\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q9\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age in Months\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 12,\n        \"max\": 36,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          25,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score out of 10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"m\",\n          \"f\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"south asian\",\n          \"middle eastern\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaundice\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Family_mem_with_ASD\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Who completed the test\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Health Care Professional\",\n          \"Others\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ASD\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df = df.rename(columns = {'Class/ASD Traits ' : 'ASD'})\n",
        "df = df.rename(columns = {'A1' : 'Q1'})\n",
        "df = df.rename(columns = {'A2' : 'Q2'})\n",
        "df = df.rename(columns = {'A3' : 'Q3'})\n",
        "df = df.rename(columns = {'A4' : 'Q4'})\n",
        "df = df.rename(columns = {'A5' : 'Q5'})\n",
        "df = df.rename(columns = {'A6' : 'Q6'})\n",
        "df = df.rename(columns = {'A7' : 'Q7'})\n",
        "df = df.rename(columns = {'A8' : 'Q8'})\n",
        "df = df.rename(columns = {'A9' : 'Q9'})\n",
        "df = df.rename(columns = {'A10' : 'Q10'})\n",
        "df = df.rename(columns = {'Age_Mons' : 'Age in Months'})\n",
        "df = df.rename(columns = {'Sex' : 'Gender'})\n",
        "df = df.rename(columns = {'Qchat-10-Score' : 'Score out of 10'})\n",
        "df = df.rename(columns = {'Ethnicity' : 'Region'})\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "56680621-b09c-4bd0-8037-00f1366bdd75",
      "metadata": {
        "tags": [],
        "id": "56680621-b09c-4bd0-8037-00f1366bdd75"
      },
      "outputs": [],
      "source": [
        "x = df.drop('Case_No' , axis = 1)\n",
        "x = x.drop('ASD', axis = 1)\n",
        "y = df['ASD']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "19e408f1-a241-4ede-a863-a6f4b48598b7",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19e408f1-a241-4ede-a863-a6f4b48598b7",
        "outputId": "7b90e734-927f-447a-aec9-450351a6b2e7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1054, 17), (1054,))"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "x.shape, y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "b02c2fea-86df-4f27-8496-4a941729bb12",
      "metadata": {
        "tags": [],
        "id": "b02c2fea-86df-4f27-8496-4a941729bb12"
      },
      "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 = 36)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "e94e4dc3-7f4f-42e7-86e2-d8c7f6c6b22f",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e94e4dc3-7f4f-42e7-86e2-d8c7f6c6b22f",
        "outputId": "651403e9-635e-4e44-e392-0799300782ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Gender', 'Region', 'Jaundice', 'Family_mem_with_ASD',\n",
              "       'Who completed the test'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "obj_cols = x_train.select_dtypes(include = 'object').columns\n",
        "obj_cols"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "0773180c-663e-4390-bec9-91caea98021f",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0773180c-663e-4390-bec9-91caea98021f",
        "outputId": "94db68b0-02a6-4297-dba7-0d20d24d16dd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10',\n",
              "       'Age in Months', 'Score out of 10'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "float_cols = x_train.select_dtypes(include = 'int64').columns\n",
        "float_cols"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "920d90a8-0201-4b61-9a00-4181c1cb566e",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "920d90a8-0201-4b61-9a00-4181c1cb566e",
        "outputId": "34caa5ac-a7d3-4d06-ccc6-eb73d6a659d4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LabelEncoder()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</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\">LabelEncoder</label><div class=\"sk-toggleable__content\"><pre>LabelEncoder()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "\n",
        "le.fit(y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "16f1d680-3b17-4e47-ad19-33efefb9f4b0",
      "metadata": {
        "tags": [],
        "id": "16f1d680-3b17-4e47-ad19-33efefb9f4b0"
      },
      "outputs": [],
      "source": [
        "y_train_processed = le.transform(y_train)\n",
        "y_test_processed = le.transform(y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "ae7e21cc-5f7d-45b8-a63c-697921a5e764",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ae7e21cc-5f7d-45b8-a63c-697921a5e764",
        "outputId": "59874079-44e8-45ed-d69c-815a00d7d8d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
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              "       1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0,\n",
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              "       1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0,\n",
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              "       1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
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              "       1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
              "       0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0,\n",
              "       1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
              "       1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "y_train_processed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "a593b8d9-a5b7-477d-989d-b4cf1aea3da0",
      "metadata": {
        "tags": [],
        "id": "a593b8d9-a5b7-477d-989d-b4cf1aea3da0"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "oe = OrdinalEncoder(categories = [x_train[i].unique() for i in obj_cols])\n",
        "oe.fit(x_train[obj_cols])\n",
        "x_train_cat_encoded = oe.transform(x_train[obj_cols])\n",
        "x_test_cat_encoded = oe.transform(x_test[obj_cols])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "275db9fa-51ae-4515-a531-ab642a400b8a",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "275db9fa-51ae-4515-a531-ab642a400b8a",
        "outputId": "c0a4234e-74f4-424b-e5c3-d3760b8f9635"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0.],\n",
              "       [1., 1., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "x_train_cat_encoded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "dc100db6-d9c7-4aff-94b6-5f075384de2c",
      "metadata": {
        "tags": [],
        "id": "dc100db6-d9c7-4aff-94b6-5f075384de2c"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(x_train[float_cols])\n",
        "\n",
        "x_train_float_encoded = scaler.transform(x_train[float_cols])\n",
        "x_test_float_encoded = scaler.transform(x_test[float_cols])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "08ad33ee-63b3-40c2-ad6f-e091123a7115",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08ad33ee-63b3-40c2-ad6f-e091123a7115",
        "outputId": "b025c367-ad5b-41c2-aeda-01f776f53671"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 1.        , 0.        , ..., 1.        , 0.70833333,\n",
              "        0.5       ],\n",
              "       [1.        , 1.        , 1.        , ..., 0.        , 1.        ,\n",
              "        0.8       ],\n",
              "       [1.        , 1.        , 0.        , ..., 1.        , 0.125     ,\n",
              "        0.7       ],\n",
              "       ...,\n",
              "       [1.        , 0.        , 1.        , ..., 0.        , 0.        ,\n",
              "        0.4       ],\n",
              "       [0.        , 1.        , 0.        , ..., 0.        , 0.75      ,\n",
              "        0.6       ],\n",
              "       [0.        , 0.        , 1.        , ..., 0.        , 0.5       ,\n",
              "        0.5       ]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "x_train_float_encoded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "d9e54b16-5955-4b3b-b24c-7af6e5265aeb",
      "metadata": {
        "tags": [],
        "id": "d9e54b16-5955-4b3b-b24c-7af6e5265aeb"
      },
      "outputs": [],
      "source": [
        "x_train_processed = np.hstack((x_train_cat_encoded, x_train_float_encoded))\n",
        "x_test_processed = np.hstack((x_test_cat_encoded, x_test_float_encoded))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "d8f0e9e4-dfa5-44d3-930c-a07b8b3a51e0",
      "metadata": {
        "tags": [],
        "id": "d8f0e9e4-dfa5-44d3-930c-a07b8b3a51e0"
      },
      "outputs": [],
      "source": [
        "feature_names = np.concatenate([obj_cols, float_cols])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "6VpbZQ2mnGV4"
      },
      "id": "6VpbZQ2mnGV4"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "fcab9b97-39af-4a89-9387-d1966d29678d",
      "metadata": {
        "tags": [],
        "id": "fcab9b97-39af-4a89-9387-d1966d29678d"
      },
      "outputs": [],
      "source": [
        "x_train_final = pd.DataFrame(x_train_processed, columns = feature_names)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "unique_classes_train, counts_train = np.unique(y_train_processed, return_counts=True)\n",
        "unique_classes_test, counts_test = np.unique(y_test_processed, return_counts=True)\n",
        "print(\"Training class distribution:\", dict(zip(unique_classes_train, counts_train)))\n",
        "print(\"Testing class distribution:\", dict(zip(unique_classes_test, counts_test)))\n"
      ],
      "metadata": {
        "id": "Ssc7hNOx0qds",
        "outputId": "2e608306-0723-4d94-b80f-731b1e4969e9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "Ssc7hNOx0qds",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training class distribution: {0: 260, 1: 583}\n",
            "Testing class distribution: {0: 66, 1: 145}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "2ffa8308-abfd-4fc0-8d27-ea6a20e4c980",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ffa8308-abfd-4fc0-8d27-ea6a20e4c980",
        "outputId": "d1af0102-d17d-4117-928a-e3e5d47d895a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 100.00%\n",
            "F1 Score: 100.00%\n",
            "Confusion Matrix:\n",
            "[[ 66   0]\n",
            " [  0 145]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        66\n",
            "           1       1.00      1.00      1.00       145\n",
            "\n",
            "    accuracy                           1.00       211\n",
            "   macro avg       1.00      1.00      1.00       211\n",
            "weighted avg       1.00      1.00      1.00       211\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, f1_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Assuming x_train_processed, y_train_processed, x_test_processed, y_test_processed are your processed data\n",
        "# Example training and testing data split\n",
        "# x_train_processed, x_test_processed, y_train_processed, y_test_processed = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Example: Logistic Regression model\n",
        "lr = LogisticRegression()\n",
        "lr.fit(x_train_processed, y_train_processed)\n",
        "y_pred = lr.predict(x_test_processed)\n",
        "\n",
        "# Calculate metrics\n",
        "accuracy = accuracy_score(y_test_processed, y_pred)\n",
        "f1 = f1_score(y_test_processed, y_pred)\n",
        "conf_matrix = confusion_matrix(y_test_processed, y_pred)\n",
        "class_report = classification_report(y_test_processed, y_pred)\n",
        "\n",
        "# Print results\n",
        "print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
        "print(f\"F1 Score: {f1 * 100:.2f}%\")\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "print(\"Classification Report:\")\n",
        "print(class_report)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "b32898f1-d776-4463-8834-9b7013d22db3",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b32898f1-d776-4463-8834-9b7013d22db3",
        "outputId": "aa9a4021-f632-46d7-d91c-0644de69d058"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No', 'Yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "le.inverse_transform([0,1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "f22a552f-763f-4869-adb0-37872e72a7ce",
      "metadata": {
        "tags": [],
        "id": "f22a552f-763f-4869-adb0-37872e72a7ce"
      },
      "outputs": [],
      "source": [
        "def pretty_confusion_matrix(y_test, y_pred, labels = ['Not_Diagnosed_with_ASD', 'Diagnosed_with_ASD']):\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    pred_labels = ['Predicted ' + i for i in labels]\n",
        "    df = pd.DataFrame(cm, columns = pred_labels, index = labels)\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "446229df-2435-45a3-8cdc-4a710e02249b",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "446229df-2435-45a3-8cdc-4a710e02249b",
        "outputId": "ae01e12b-ce5a-4047-cc3f-dadc02a7dc2f"
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              "                        Predicted Not_Diagnosed_with_ASD  \\\n",
              "Not_Diagnosed_with_ASD                                66   \n",
              "Diagnosed_with_ASD                                     0   \n",
              "\n",
              "                        Predicted Diagnosed_with_ASD  \n",
              "Not_Diagnosed_with_ASD                             0  \n",
              "Diagnosed_with_ASD                               145  "
            ],
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              "\n",
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              "        google.colab.notebook.generateWithVariable('results_plot');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_plot",
              "summary": "{\n  \"name\": \"results_plot\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Predicted Not_Diagnosed_with_ASD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46,\n        \"min\": 0,\n        \"max\": 66,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          66\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted Diagnosed_with_ASD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102,\n        \"min\": 0,\n        \"max\": 145,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          145,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "results_plot = pretty_confusion_matrix(y_test_processed, y_pred, ['Not_Diagnosed_with_ASD', 'Diagnosed_with_ASD'])\n",
        "results_plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "450d5588-7188-46ab-aaf5-dcc8e9e73ece",
      "metadata": {
        "tags": [],
        "id": "450d5588-7188-46ab-aaf5-dcc8e9e73ece",
        "outputId": "2b39bd68-8e3d-4afe-a1ad-6fccc3787095",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.heatmap(results_plot, cmap = 'Spectral')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "18d4c8fc-15e0-4b9f-afa9-6f46e6628726",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18d4c8fc-15e0-4b9f-afa9-6f46e6628726",
        "outputId": "9ecf588c-a0b2-4914-af0c-f15a86b9ee49"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.14901906, -0.04064787,  0.50390564, -0.17946135, -0.09388453,\n",
              "         2.24075121,  2.55477819,  1.99292749,  2.21891284,  2.14127321,\n",
              "         2.2604061 ,  2.27357885,  2.45267117,  2.5931873 ,  2.07729313,\n",
              "         0.04767249,  2.28057795]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "lr.coef_\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "10878f77-d9a2-438b-84f0-4e2902c9645b",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "10878f77-d9a2-438b-84f0-4e2902c9645b",
        "outputId": "0260e5e5-aca9-4f7e-992e-0f538e2bdf11"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Q1': -0.14901905604158722,\n",
              " 'Q2': -0.04064786635781323,\n",
              " 'Q3': 0.5039056422324285,\n",
              " 'Q4': -0.17946134988142562,\n",
              " 'Q5': -0.09388452964661184,\n",
              " 'Q6': 2.24075121465737,\n",
              " 'Q7': 2.5547781928574795,\n",
              " 'Q8': 1.9929274920302151,\n",
              " 'Q9': 2.2189128379995204,\n",
              " 'Q10': 2.141273206471033,\n",
              " 'Age in Months': 2.260406099556073,\n",
              " 'Score out of 10': 2.2735788503627132,\n",
              " 'Gender': 2.452671167731161,\n",
              " 'Region': 2.5931872950221986,\n",
              " 'Jaundice': 2.077293134321619,\n",
              " 'Family_mem_with_ASD': 0.04767249391793088,\n",
              " 'Who completed the test': 2.2805779491009384}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Match coef's of features to columns\n",
        "feature_dict = dict(zip(x.columns, list(lr.coef_[0])))\n",
        "feature_dict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "40b71e99-495c-4de6-91fd-dbab6ef654a2",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "40b71e99-495c-4de6-91fd-dbab6ef654a2",
        "outputId": "a41100f5-c489-4251-aee6-07f96569ab9a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Feature Importance'}>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualize feature importance\n",
        "feature_df = pd.DataFrame(feature_dict, index=[0])\n",
        "# plt.style.use('dark_background')\n",
        "feature_df.T.plot.bar(title=\"Feature Importance\", legend=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "783040e6",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "783040e6",
        "outputId": "384e6f26-2040-4a94-e2f4-4d7255d23243"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Age Distribution')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "yes_autism = df[df['ASD'] == 'Yes']\n",
        "ax = plt.subplot()\n",
        "# plt.style.use('dark_background')\n",
        "sns.countplot(x = \"Age in Months\", data = yes_autism)\n",
        "ax.set_ylabel('Number of Children')\n",
        "ax.set_title('Age Distribution')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "59ece12f",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "59ece12f",
        "outputId": "b7962b6b-9cc3-44cd-fceb-72c5bef15d70"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = plt.gcf()\n",
        "plt.pie(x['Gender'].value_counts(), labels = ('Boy','Girl'), explode = [0.1, 0], autopct='%1.1f%%', shadow = True, startangle = 90,\n",
        "        colors = ['orangered', 'royalblue'])\n",
        "plt.axis('equal')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa99cd67",
      "metadata": {
        "id": "aa99cd67"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4d163e53-41a5-4806-8b86-470768653c8d",
      "metadata": {
        "id": "4d163e53-41a5-4806-8b86-470768653c8d"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.11.4"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}