Autism_Input_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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      "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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              "...       ...  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...            ...   \n",
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              "\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",
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              "3             family member  Yes  \n",
              "4             family member  Yes  \n",
              "...                     ...  ...  \n",
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              "1051          family member  Yes  \n",
              "1052          family member   No  \n",
              "1053          family member  Yes  \n",
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              "    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-f23686b0-c6b7-4681-8223-d1afbabd89b8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3670cd54-bb5d-4d19-ae6f-13d1710f8409\">\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_3670cd54-bb5d-4d19-ae6f-13d1710f8409 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": null,
      "id": "56680621-b09c-4bd0-8037-00f1366bdd75",
      "metadata": {
        "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": null,
      "id": "19e408f1-a241-4ede-a863-a6f4b48598b7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19e408f1-a241-4ede-a863-a6f4b48598b7",
        "outputId": "344c79a4-63d1-407f-fe24-a2baddf2734f"
      },
      "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": null,
      "id": "b02c2fea-86df-4f27-8496-4a941729bb12",
      "metadata": {
        "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": null,
      "id": "e94e4dc3-7f4f-42e7-86e2-d8c7f6c6b22f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e94e4dc3-7f4f-42e7-86e2-d8c7f6c6b22f",
        "outputId": "4b6133fc-c366-486f-e7bb-7333c0c66270"
      },
      "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": null,
      "id": "0773180c-663e-4390-bec9-91caea98021f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0773180c-663e-4390-bec9-91caea98021f",
        "outputId": "2e472649-971c-4d81-8706-9f36d3879877"
      },
      "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": null,
      "id": "920d90a8-0201-4b61-9a00-4181c1cb566e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "920d90a8-0201-4b61-9a00-4181c1cb566e",
        "outputId": "d69140f5-7dbb-4450-d9b0-3231c71c9aa3"
      },
      "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": null,
      "id": "16f1d680-3b17-4e47-ad19-33efefb9f4b0",
      "metadata": {
        "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": null,
      "id": "ae7e21cc-5f7d-45b8-a63c-697921a5e764",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ae7e21cc-5f7d-45b8-a63c-697921a5e764",
        "outputId": "4efcd04e-1c27-47f4-eb8f-87f2128090c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0,\n",
              "       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",
              "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0,\n",
              "       1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1,\n",
              "       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",
              "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n",
              "       1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0,\n",
              "       1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n",
              "       0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0,\n",
              "       1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
              "       1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
              "       0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
              "       1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
              "       1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0,\n",
              "       0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
              "       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": null,
      "id": "a593b8d9-a5b7-477d-989d-b4cf1aea3da0",
      "metadata": {
        "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": null,
      "id": "275db9fa-51ae-4515-a531-ab642a400b8a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "275db9fa-51ae-4515-a531-ab642a400b8a",
        "outputId": "651054b3-4ca6-4688-e93d-fbfd0caa6200"
      },
      "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": null,
      "id": "dc100db6-d9c7-4aff-94b6-5f075384de2c",
      "metadata": {
        "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": null,
      "id": "08ad33ee-63b3-40c2-ad6f-e091123a7115",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08ad33ee-63b3-40c2-ad6f-e091123a7115",
        "outputId": "6ccdb534-4678-475c-a690-673267eb42a9"
      },
      "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": null,
      "id": "d9e54b16-5955-4b3b-b24c-7af6e5265aeb",
      "metadata": {
        "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": null,
      "id": "d8f0e9e4-dfa5-44d3-930c-a07b8b3a51e0",
      "metadata": {
        "id": "d8f0e9e4-dfa5-44d3-930c-a07b8b3a51e0"
      },
      "outputs": [],
      "source": [
        "feature_names = np.concatenate([obj_cols, float_cols])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fcab9b97-39af-4a89-9387-d1966d29678d",
      "metadata": {
        "id": "fcab9b97-39af-4a89-9387-d1966d29678d"
      },
      "outputs": [],
      "source": [
        "x_train_final = pd.DataFrame(x_train_processed, columns = feature_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ffa8308-abfd-4fc0-8d27-ea6a20e4c980",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ffa8308-abfd-4fc0-8d27-ea6a20e4c980",
        "outputId": "b935fa04-e8ba-4fef-db1e-550d36129209"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n",
            "[[ 66   0]\n",
            " [  0 145]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression()\n",
        "lr.fit(x_train_processed, y_train_processed)\n",
        "y_pred = lr.predict(x_test_processed)\n",
        "print(accuracy_score(y_test_processed, y_pred))\n",
        "print(confusion_matrix(y_test_processed, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b32898f1-d776-4463-8834-9b7013d22db3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b32898f1-d776-4463-8834-9b7013d22db3",
        "outputId": "0974de4c-f27e-4f86-a37d-5d3403ff402f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No', 'Yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "le.inverse_transform([0,1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "385d085f-da07-4f13-860a-c68b8877512f",
      "metadata": {
        "id": "385d085f-da07-4f13-860a-c68b8877512f"
      },
      "outputs": [],
      "source": [
        "df2 = pd.read_csv(r\"/content/Sample_Dataset.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Q71RUc1qmBuo",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q71RUc1qmBuo",
        "outputId": "4da6ba79-2d78-4396-bfb5-d3a9ff59a1a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11 entries, 0 to 10\n",
            "Data columns (total 15 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   Case_No                 10 non-null     float64\n",
            " 1   Reaction_Time           10 non-null     float64\n",
            " 2   Accuracy_Rate           10 non-null     float64\n",
            " 3   Q1                      10 non-null     float64\n",
            " 4   Q3                      10 non-null     float64\n",
            " 5   Q5                      10 non-null     float64\n",
            " 6   Q7                      10 non-null     float64\n",
            " 7   Q9                      10 non-null     float64\n",
            " 8   Q10                     10 non-null     float64\n",
            " 9   Age in Months           11 non-null     int64  \n",
            " 10  Gender                  11 non-null     object \n",
            " 11  Region                  11 non-null     object \n",
            " 12  Jaundice                11 non-null     object \n",
            " 13  Family_mem_with_ASD     11 non-null     object \n",
            " 14  Who completed the test  11 non-null     object \n",
            "dtypes: float64(9), int64(1), object(5)\n",
            "memory usage: 1.4+ KB\n"
          ]
        }
      ],
      "source": [
        "df2.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "HlKNsmyNnyKD",
      "metadata": {
        "id": "HlKNsmyNnyKD"
      },
      "outputs": [],
      "source": [
        "def predict_q2(reaction_time, accuracy_rate):\n",
        "    return 1 if reaction_time >= 2500.0 and accuracy_rate < 70.0 else 0\n",
        "\n",
        "def predict_q4(reaction_time, accuracy_rate):\n",
        "    return 1 if reaction_time >= 2200.0 and accuracy_rate < 78.0 else 0\n",
        "\n",
        "def predict_q6(reaction_time, accuracy_rate):\n",
        "    return 1 if reaction_time >= 2000.0 and accuracy_rate < 75.0 else 0\n",
        "\n",
        "def predict_q8(reaction_time, accuracy_rate):\n",
        "    return 1 if reaction_time >= 1800.0 and accuracy_rate < 80.0 else 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Z82cLrwRn1td",
      "metadata": {
        "id": "Z82cLrwRn1td"
      },
      "outputs": [],
      "source": [
        "df2['Q2'] = df2.apply(lambda row: predict_q2(row['Reaction_Time'], row['Accuracy_Rate']), axis=1)\n",
        "df2['Q4'] = df2.apply(lambda row: predict_q4(row['Reaction_Time'], row['Accuracy_Rate']), axis=1)\n",
        "df2['Q6'] = df2.apply(lambda row: predict_q6(row['Reaction_Time'], row['Accuracy_Rate']), axis=1)\n",
        "df2['Q8'] = df2.apply(lambda row: predict_q8(row['Reaction_Time'], row['Accuracy_Rate']), axis=1)\n",
        "df2['Score out of 10'] = df2[['Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10']].sum(axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "472f7cf4-2d7d-4e49-bf45-b34b9e7d3691",
      "metadata": {
        "id": "472f7cf4-2d7d-4e49-bf45-b34b9e7d3691"
      },
      "outputs": [],
      "source": [
        "cols = ['Q1',  'Q3',  'Q5',  'Q7',  'Q9', 'Q10', 'Score out of 10']\n",
        "df2[cols] = df2[cols].applymap(np.int64)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "er9qMTzScs0g",
        "outputId": "0e1c7be4-c7b2-4a73-ab6d-4964a276f52e"
      },
      "id": "er9qMTzScs0g",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10 entries, 0 to 9\n",
            "Data columns (total 20 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   Case_No                 10 non-null     float64\n",
            " 1   Reaction_Time           10 non-null     float64\n",
            " 2   Accuracy_Rate           10 non-null     float64\n",
            " 3   Q1                      10 non-null     int64  \n",
            " 4   Q3                      10 non-null     int64  \n",
            " 5   Q5                      10 non-null     int64  \n",
            " 6   Q7                      10 non-null     int64  \n",
            " 7   Q9                      10 non-null     int64  \n",
            " 8   Q10                     10 non-null     int64  \n",
            " 9   Age in Months           10 non-null     int64  \n",
            " 10  Gender                  10 non-null     object \n",
            " 11  Region                  10 non-null     object \n",
            " 12  Jaundice                10 non-null     object \n",
            " 13  Family_mem_with_ASD     10 non-null     object \n",
            " 14  Who completed the test  10 non-null     object \n",
            " 15  Q2                      10 non-null     int64  \n",
            " 16  Q4                      10 non-null     int64  \n",
            " 17  Q6                      10 non-null     int64  \n",
            " 18  Q8                      10 non-null     int64  \n",
            " 19  Score out of 10         10 non-null     int64  \n",
            "dtypes: float64(3), int64(12), object(5)\n",
            "memory usage: 1.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df2.drop('Case_No' , axis = 1)\n",
        "X = df2.drop('Reaction_Time' , axis = 1)\n",
        "X = df2.drop('Accuracy_Rate' , axis = 1)"
      ],
      "metadata": {
        "id": "WE_tFD_4dAwd"
      },
      "id": "WE_tFD_4dAwd",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "48f1e8f1-0e7f-4ac4-83a5-3315e478cb65",
      "metadata": {
        "id": "48f1e8f1-0e7f-4ac4-83a5-3315e478cb65",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8802c2c-0c5b-4a0f-93fd-03e8981b9584"
      },
      "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": 46
        }
      ],
      "source": [
        "obj_cols2 = X.select_dtypes(include = 'object').columns\n",
        "obj_cols2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "729aff4b-5709-4b88-83d8-c978e8f33630",
      "metadata": {
        "id": "729aff4b-5709-4b88-83d8-c978e8f33630",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc2a89e0-daa1-4bdd-cee2-4c0d68e5fad7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Q1', 'Q3', 'Q5', 'Q7', 'Q9', 'Q10', 'Age in Months', 'Q2', 'Q4', 'Q6',\n",
              "       'Q8', 'Score out of 10'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "float_cols2 = X.select_dtypes(include = 'int64').columns\n",
        "float_cols2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "164c529f-6b32-4627-ae9c-ac53d2b9aeb4",
      "metadata": {
        "id": "164c529f-6b32-4627-ae9c-ac53d2b9aeb4"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "oe = OrdinalEncoder(categories = [X[i].unique() for i in obj_cols2])\n",
        "oe.fit(X[obj_cols2])\n",
        "X_cat_encoded = oe.transform(X[obj_cols2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cb1051f8-5e8d-4140-b48f-bc1d8fdfaf74",
      "metadata": {
        "id": "cb1051f8-5e8d-4140-b48f-bc1d8fdfaf74",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc11ef22-3e55-40a1-a33b-4d94de191314"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0.],\n",
              "       [1., 1., 1., 0., 0.],\n",
              "       [0., 1., 1., 0., 0.],\n",
              "       [1., 0., 0., 1., 0.],\n",
              "       [1., 1., 1., 1., 0.],\n",
              "       [0., 1., 1., 1., 0.],\n",
              "       [1., 1., 1., 1., 0.],\n",
              "       [0., 0., 1., 1., 0.],\n",
              "       [1., 1., 0., 2., 0.],\n",
              "       [0., 0., 1., 1., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "X_cat_encoded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4ea836ae-9758-4816-acf2-a855e119f9d9",
      "metadata": {
        "id": "4ea836ae-9758-4816-acf2-a855e119f9d9"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(X[float_cols2])\n",
        "\n",
        "X_float_encoded = scaler.transform(X[float_cols2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d4489051-a1a0-49ec-a6c9-3de70d49e29c",
      "metadata": {
        "id": "d4489051-a1a0-49ec-a6c9-3de70d49e29c"
      },
      "outputs": [],
      "source": [
        "X_processed = np.hstack((X_cat_encoded, X_float_encoded))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f003222f-6935-4e98-839b-70c6f34677b5",
      "metadata": {
        "id": "f003222f-6935-4e98-839b-70c6f34677b5"
      },
      "outputs": [],
      "source": [
        "feature_names2 = np.concatenate([obj_cols2, float_cols2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7493c24c-a66d-47ca-b32f-437e82f5b5b8",
      "metadata": {
        "id": "7493c24c-a66d-47ca-b32f-437e82f5b5b8"
      },
      "outputs": [],
      "source": [
        "X_final = pd.DataFrame(X_processed, columns = feature_names2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ac3c0c4c-ab05-4a78-9b21-e8e894018e9e",
      "metadata": {
        "id": "ac3c0c4c-ab05-4a78-9b21-e8e894018e9e"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression()\n",
        "lr.fit(x_train_processed, y_train_processed)\n",
        "y_pred = lr.predict(X_processed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "79195c1c-d3bb-4e90-b737-3f95341080f7",
      "metadata": {
        "id": "79195c1c-d3bb-4e90-b737-3f95341080f7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b614f2f8-bf00-41d4-c4c0-cdea56ad79dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ASD Possible :  [0 1 0 0 0 1 0 1 0 1]\n"
          ]
        }
      ],
      "source": [
        "print(\"ASD Possible : \", y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv"
      ],
      "metadata": {
        "id": "PActUrg_erZX"
      },
      "id": "PActUrg_erZX",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cases = list(range(1, len(y_pred) + 1))\n",
        "predictions = [{'Case': i, 'ASD_Present': \"Chances\" if pred == 1 else \"No\"} for i, pred in zip(cases, y_pred)]"
      ],
      "metadata": {
        "id": "cPCOwZhqew46"
      },
      "id": "cPCOwZhqew46",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "csv_file = 'predictions.csv'"
      ],
      "metadata": {
        "id": "gr1zLO8NezXm"
      },
      "id": "gr1zLO8NezXm",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(csv_file, 'w', newline='') as file:\n",
        "    writer = csv.DictWriter(file, fieldnames=['Case', 'ASD_Present'])\n",
        "    writer.writeheader()\n",
        "    writer.writerows(predictions)"
      ],
      "metadata": {
        "id": "XiyxHZnAe1Y6"
      },
      "id": "XiyxHZnAe1Y6",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Predictions saved to '{csv_file}' successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OoUw_ijse3u-",
        "outputId": "36f6a83d-4bf7-41c3-db97-e5e61248d2f7"
      },
      "id": "OoUw_ijse3u-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions saved to 'predictions.csv' successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['ASD_Prediction'] = y_pred"
      ],
      "metadata": {
        "id": "kAmL2nVDjXrE"
      },
      "id": "kAmL2nVDjXrE",
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "asd_present = df2[df2['ASD_Prediction'] == 1]\n",
        "asd_not_present = df2[df2['ASD_Prediction'] == 0]"
      ],
      "metadata": {
        "id": "ccVLa6LPj5wh"
      },
      "id": "ccVLa6LPj5wh",
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(x='Reaction_Time', y='Accuracy_Rate', data=asd_not_present, color='blue', label='No ASD', s=100)\n",
        "sns.scatterplot(x='Reaction_Time', y='Accuracy_Rate', data=asd_present, color='red', label='ASD', s=100)\n",
        "\n",
        "plt.title('Reaction Time vs Accuracy Rate by Predicted ASD')\n",
        "plt.xlabel('Reaction Time')\n",
        "plt.ylabel('Accuracy Rate')\n",
        "plt.legend(title='ASD Prediction')\n",
        "\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "oJnUoKBEj80x",
        "outputId": "01732e73-d025-4cbf-edca-a1d1fdfccd30"
      },
      "id": "oJnUoKBEj80x",
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AlvnWCNzkRrk"
      },
      "id": "AlvnWCNzkRrk",
      "execution_count": 69,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "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": "3.10.11"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}