Diabetes_Prediction.ipynb

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      "authorship_tag": "ABX9TyO0WknRL6wymibCSh0zrpsI",
      "include_colab_link": true
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/SubbulakshmiSN/Diabetes_Prediction_application_using_streamlit/blob/main/Diabetes_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "# *`IMPORTING LIBRARIES`*"
      ],
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        "id": "_qU6SRdbAtuo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "uwJHPj0G_fOj"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *`DATA READING`*"
      ],
      "metadata": {
        "id": "bGZXrf6nAzHE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv(\"/content/diabetes_prediction_dataset.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "a33gqKcJA2FQ",
        "outputId": "e2962c3d-0c26-4521-8271-9c8f77e246b5"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender   age  hypertension  heart_disease smoking_history    bmi  \\\n",
              "0  Female  80.0             0              1           never  25.19   \n",
              "1  Female  54.0             0              0         No Info  27.32   \n",
              "2    Male  28.0             0              0           never  27.32   \n",
              "3  Female  36.0             0              0         current  23.45   \n",
              "4    Male  76.0             1              1         current  20.14   \n",
              "\n",
              "   HbA1c_level  blood_glucose_level  diabetes  \n",
              "0          6.6                  140         0  \n",
              "1          6.6                   80         0  \n",
              "2          5.7                  158         0  \n",
              "3          5.0                  155         0  \n",
              "4          4.8                  155         0  "
            ],
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 100000,\n  \"fields\": [\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Female\",\n          \"Male\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.516839871617023,\n        \"min\": 0.08,\n        \"max\": 80.0,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          29.0,\n          39.0,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hypertension\",\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\": \"heart_disease\",\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\": \"smoking_history\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"never\",\n          \"No Info\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.636783416648369,\n        \"min\": 10.01,\n        \"max\": 95.69,\n        \"num_unique_values\": 4247,\n        \"samples\": [\n          53.27,\n          32.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HbA1c_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0706720918835437,\n        \"min\": 3.5,\n        \"max\": 9.0,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          6.6,\n          5.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blood_glucose_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40,\n        \"min\": 80,\n        \"max\": 300,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          140,\n          80\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diabetes\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *`DATASET OBSERVATION `*\n",
        "\n"
      ],
      "metadata": {
        "id": "sWAC8CCE__w9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
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        "id": "Lfvvq2Nr_-RP",
        "outputId": "6fba8359-6002-49c4-9301-ad1c98295ba1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100000 entries, 0 to 99999\n",
            "Data columns (total 9 columns):\n",
            " #   Column               Non-Null Count   Dtype  \n",
            "---  ------               --------------   -----  \n",
            " 0   gender               100000 non-null  object \n",
            " 1   age                  100000 non-null  float64\n",
            " 2   hypertension         100000 non-null  int64  \n",
            " 3   heart_disease        100000 non-null  int64  \n",
            " 4   smoking_history      100000 non-null  object \n",
            " 5   bmi                  100000 non-null  float64\n",
            " 6   HbA1c_level          100000 non-null  float64\n",
            " 7   blood_glucose_level  100000 non-null  int64  \n",
            " 8   diabetes             100000 non-null  int64  \n",
            "dtypes: float64(3), int64(4), object(2)\n",
            "memory usage: 6.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
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          "height": 320
        },
        "id": "jtDX_fkSBHRj",
        "outputId": "c1acfe2f-6834-41e3-af5e-08b2ca10b9dd"
      },
      "execution_count": 5,
      "outputs": [
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              "                 age  hypertension  heart_disease            bmi  \\\n",
              "count  100000.000000  100000.00000  100000.000000  100000.000000   \n",
              "mean       41.885856       0.07485       0.039420      27.320767   \n",
              "std        22.516840       0.26315       0.194593       6.636783   \n",
              "min         0.080000       0.00000       0.000000      10.010000   \n",
              "25%        24.000000       0.00000       0.000000      23.630000   \n",
              "50%        43.000000       0.00000       0.000000      27.320000   \n",
              "75%        60.000000       0.00000       0.000000      29.580000   \n",
              "max        80.000000       1.00000       1.000000      95.690000   \n",
              "\n",
              "         HbA1c_level  blood_glucose_level       diabetes  \n",
              "count  100000.000000        100000.000000  100000.000000  \n",
              "mean        5.527507           138.058060       0.085000  \n",
              "std         1.070672            40.708136       0.278883  \n",
              "min         3.500000            80.000000       0.000000  \n",
              "25%         4.800000           100.000000       0.000000  \n",
              "50%         5.800000           140.000000       0.000000  \n",
              "75%         6.200000           159.000000       0.000000  \n",
              "max         9.000000           300.000000       1.000000  "
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>bmi</th>\n",
              "      <th>HbA1c_level</th>\n",
              "      <th>blood_glucose_level</th>\n",
              "      <th>diabetes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.00000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>41.885856</td>\n",
              "      <td>0.07485</td>\n",
              "      <td>0.039420</td>\n",
              "      <td>27.320767</td>\n",
              "      <td>5.527507</td>\n",
              "      <td>138.058060</td>\n",
              "      <td>0.085000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>22.516840</td>\n",
              "      <td>0.26315</td>\n",
              "      <td>0.194593</td>\n",
              "      <td>6.636783</td>\n",
              "      <td>1.070672</td>\n",
              "      <td>40.708136</td>\n",
              "      <td>0.278883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.010000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23.630000</td>\n",
              "      <td>4.800000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>43.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.320000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>29.580000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>95.690000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>300.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      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",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35341.63551512144,\n        \"min\": 0.08,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          41.885856,\n          43.0,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hypertension\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35355.27148175323,\n        \"min\": 0.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.07485,\n          1.0,\n          0.2631504702289164\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heart_disease\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35355.27673391837,\n        \"min\": 0.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.03942,\n          1.0,\n          0.1945930169980995\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35344.22862141613,\n        \"min\": 6.636783416648369,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          27.3207671,\n          27.32,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HbA1c_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35353.52600015908,\n        \"min\": 1.0706720918835437,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.527507,\n          5.8,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blood_glucose_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35307.04741219372,\n        \"min\": 40.708136048704134,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          138.05806,\n          140.0,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diabetes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35355.270174483856,\n        \"min\": 0.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.085,\n          1.0,\n          0.27888308976662174\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VKczgcYiBcTy",
        "outputId": "017f4814-e8bb-42cc-a37e-34f830f64c4f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['gender', 'age', 'hypertension', 'heart_disease', 'smoking_history',\n",
              "       'bmi', 'HbA1c_level', 'blood_glucose_level', 'diabetes'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "supervised learning\n",
        "dependent columns= 'diabetes'(Target)\n",
        "independent columns= 'gender', 'age', 'hypertension', 'heart_disease', 'smoking_history','bmi', 'HbA1c_level', 'blood_glucose_level'\n",
        "\n",
        "continuous_columns= 'age', 'bmi', 'HbA1c_level', 'blood_glucose_level'\n",
        "categorical_columns= 'gender', 'hypertension', 'heart_disease', 'smoking_history'\n",
        "\n",
        "since target is dependent and categorical we use classifier algorithm\n",
        "\n",
        "'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "t-L5oGwPGjRu",
        "outputId": "7a1317b7-661f-4b96-9313-de8665d93253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nsupervised learning\\ndependent columns= 'diabetes'\\nindependent columns= 'gender', 'age', 'hypertension', 'heart_disease', 'smoking_history','bmi', 'HbA1c_level', 'blood_glucose_level'\\n\\ncontinuous_columns= 'age', 'bmi', 'HbA1c_level', 'blood_glucose_level'\\ncategorical_columns= 'gender', 'hypertension', 'heart_disease', 'smoking_history'\\n\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "_gZAcMmLIygX",
        "outputId": "767b2d3f-9b73-4462-e59d-462bf30d6e00"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-2f6f6606aa2c>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          age  hypertension  heart_disease       bmi  \\\n",
              "age                  1.000000      0.251171       0.233354  0.337396   \n",
              "hypertension         0.251171      1.000000       0.121262  0.147666   \n",
              "heart_disease        0.233354      0.121262       1.000000  0.061198   \n",
              "bmi                  0.337396      0.147666       0.061198  1.000000   \n",
              "HbA1c_level          0.101354      0.080939       0.067589  0.082997   \n",
              "blood_glucose_level  0.110672      0.084429       0.070066  0.091261   \n",
              "diabetes             0.258008      0.197823       0.171727  0.214357   \n",
              "\n",
              "                     HbA1c_level  blood_glucose_level  diabetes  \n",
              "age                     0.101354             0.110672  0.258008  \n",
              "hypertension            0.080939             0.084429  0.197823  \n",
              "heart_disease           0.067589             0.070066  0.171727  \n",
              "bmi                     0.082997             0.091261  0.214357  \n",
              "HbA1c_level             1.000000             0.166733  0.400660  \n",
              "blood_glucose_level     0.166733             1.000000  0.419558  \n",
              "diabetes                0.400660             0.419558  1.000000  "
            ],
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              "      <th></th>\n",
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              "      <td>0.251171</td>\n",
              "      <td>0.233354</td>\n",
              "      <td>0.337396</td>\n",
              "      <td>0.101354</td>\n",
              "      <td>0.110672</td>\n",
              "      <td>0.258008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hypertension</th>\n",
              "      <td>0.251171</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.121262</td>\n",
              "      <td>0.147666</td>\n",
              "      <td>0.080939</td>\n",
              "      <td>0.084429</td>\n",
              "      <td>0.197823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>heart_disease</th>\n",
              "      <td>0.233354</td>\n",
              "      <td>0.121262</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.061198</td>\n",
              "      <td>0.067589</td>\n",
              "      <td>0.070066</td>\n",
              "      <td>0.171727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>0.337396</td>\n",
              "      <td>0.147666</td>\n",
              "      <td>0.061198</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.082997</td>\n",
              "      <td>0.091261</td>\n",
              "      <td>0.214357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HbA1c_level</th>\n",
              "      <td>0.101354</td>\n",
              "      <td>0.080939</td>\n",
              "      <td>0.067589</td>\n",
              "      <td>0.082997</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.166733</td>\n",
              "      <td>0.400660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>blood_glucose_level</th>\n",
              "      <td>0.110672</td>\n",
              "      <td>0.084429</td>\n",
              "      <td>0.070066</td>\n",
              "      <td>0.091261</td>\n",
              "      <td>0.166733</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.419558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diabetes</th>\n",
              "      <td>0.258008</td>\n",
              "      <td>0.197823</td>\n",
              "      <td>0.171727</td>\n",
              "      <td>0.214357</td>\n",
              "      <td>0.400660</td>\n",
              "      <td>0.419558</td>\n",
              "      <td>1.000000</td>\n",
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              "  }\n",
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              "\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",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30822859770604244,\n        \"min\": 0.10135366114510604,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.0,\n          0.25117112907443984,\n          0.11067226757038243\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hypertension\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.32805668611339533,\n        \"min\": 0.08093878240605538,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.25117112907443984,\n          1.0,\n          0.08442890446963615\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heart_disease\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3383011185017867,\n        \"min\": 0.061197818557051996,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.23335429063748483,\n          0.12126165398954841,\n          0.07006570638302391\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3330996327909288,\n        \"min\": 0.061197818557051996,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.3373957761861766,\n          0.14766567202390926,\n          0.09126140154856027\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HbA1c_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34174727753535783,\n        \"min\": 0.06758882503325052,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.10135366114510604,\n          0.08093878240605538,\n          0.16673292982890464\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blood_glucose_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3409002305566943,\n        \"min\": 0.07006570638302391,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.11067226757038243,\n          0.08442890446963615,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diabetes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29019436500003504,\n        \"min\": 0.17172684954885264,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.25800803335609984,\n          0.197823246408052,\n          0.41955799602056193\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *`DATAFRAME PREPROCESSING`*"
      ],
      "metadata": {
        "id": "agQxi-97_2_r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''no null values found in the dataset'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "JHWDogDX9lJH",
        "outputId": "56a8099d-953d-4d56-9ab5-43a9c852b515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'no null values found in the dataset'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode categorical features\n",
        "encoder = OrdinalEncoder()\n",
        "df[['gender']] = encoder.fit_transform(df[['gender']])\n",
        "\n",
        "# Define custom encoding for smoking history\n",
        "smoking_history_encoding = {'never': 0, 'No Info': 1, 'current': 2, 'former': 3, 'ever': 4, 'not current': 5}\n",
        "df['smoking_history'] = df['smoking_history'].map(smoking_history_encoding)\n"
      ],
      "metadata": {
        "id": "lLCqnbfeCzDR"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Nz9MsGZJC4QC",
        "outputId": "41b9bf36-f0d8-4ca3-d4b3-1d93ba7f5183"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender   age  hypertension  heart_disease  smoking_history    bmi  \\\n",
              "0     0.0  80.0             0              1                0  25.19   \n",
              "1     0.0  54.0             0              0                1  27.32   \n",
              "2     1.0  28.0             0              0                0  27.32   \n",
              "3     0.0  36.0             0              0                2  23.45   \n",
              "4     1.0  76.0             1              1                2  20.14   \n",
              "\n",
              "   HbA1c_level  blood_glucose_level  diabetes  \n",
              "0          6.6                  140         0  \n",
              "1          6.6                   80         0  \n",
              "2          5.7                  158         0  \n",
              "3          5.0                  155         0  \n",
              "4          4.8                  155         0  "
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
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              "      <th>heart_disease</th>\n",
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              "      <th>2</th>\n",
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              "      <td>36.0</td>\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-eff3adf7-d83e-4c66-b003-74f517b0cfe8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-eff3adf7-d83e-4c66-b003-74f517b0cfe8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ade1fdb5-f110-4747-8a94-37a4aa0f7580\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ade1fdb5-f110-4747-8a94-37a4aa0f7580')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ade1fdb5-f110-4747-8a94-37a4aa0f7580 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 100000,\n  \"fields\": [\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4930309475024381,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.516839871617023,\n        \"min\": 0.08,\n        \"max\": 80.0,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          29.0,\n          39.0,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hypertension\",\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\": \"heart_disease\",\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\": \"smoking_history\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.636783416648369,\n        \"min\": 10.01,\n        \"max\": 95.69,\n        \"num_unique_values\": 4247,\n        \"samples\": [\n          53.27,\n          32.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HbA1c_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0706720918835437,\n        \"min\": 3.5,\n        \"max\": 9.0,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          6.6,\n          5.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blood_glucose_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40,\n        \"min\": 80,\n        \"max\": 300,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          140,\n          80\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diabetes\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *`MACHINE LEARNING - GRADIENTBOOSTING CLASSIFIER`*"
      ],
      "metadata": {
        "id": "DYlNkTWoKvhs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split data into features and target\n",
        "x = df.drop(\"diabetes\", axis=1)\n",
        "y = df[\"diabetes\"]"
      ],
      "metadata": {
        "id": "1xEVecGvC8sF"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train the model\n",
        "model = GradientBoostingClassifier()\n",
        "model.fit(x_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "338mtsyAKrIW",
        "outputId": "5bb5245c-72ea-48c0-fcbb-d6eb9101a63b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier()"
            ],
            "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>GradientBoostingClassifier()</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\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=model.predict(x_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FdCUSlN9MR5T",
        "outputId": "e8a550a2-1268-4475-fe61-836a2fe3b7af"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "USMnEukTMmgN",
        "outputId": "edd4597b-cdc4-4b3f-c352-da9440af758f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "75721    0\n",
              "80184    0\n",
              "19864    0\n",
              "76699    0\n",
              "92991    1\n",
              "        ..\n",
              "32595    0\n",
              "29313    0\n",
              "37862    0\n",
              "53421    0\n",
              "42410    0\n",
              "Name: diabetes, Length: 20000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#enter values to predict your diabetes\n",
        "name= input(\"Enter your name: \")\n",
        "user_input= input(\"Enter values for gender, age, hypertension, heart_disease, smoking_history, bmi, HbA1c_level, blood_glucose_level: \")\n",
        "\n",
        "# Convert user input to a list\n",
        "user_input = [float(x) for x in user_input.split(',')]\n",
        "\n",
        "# Reshape the user input\n",
        "user_input = np.array(user_input).reshape(1, -1)\n",
        "\n",
        "# Make a prediction\n",
        "prediction = model.predict(user_input)\n",
        "\n",
        "if prediction == 1:\n",
        "    print(f\"Sorry {name}! Based on the provided information, you are predicted to have diabetes\")\n",
        "else:\n",
        "   print(f\"Congrats {name}! Based on the provided information, you are predicted to not have diabetes\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TUcqaLc1Mq-n",
        "outputId": "22466e32-f4a0-4c05-f827-7caa181f8f50"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter your name: Meena\n",
            "Enter values for gender, age, hypertension, heart_disease, smoking_history, bmi, HbA1c_level, blood_glucose_level: 0,23,1,0,0,23.5,6.3,155\n",
            "Congrats Meena! Based on the provided information, you are predicted to not have diabetes\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *`TEST EVALUATION METRICS`*"
      ],
      "metadata": {
        "id": "vW6RTs-oP3gH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model (accuracy, confusion matrix,precision,recall)\n",
        "accuracy = accuracy_score(y_test,y_pred)\n",
        "cm= confusion_matrix(y_test,y_pred)\n",
        "classification_rep = classification_report(y_test, y_pred)\n",
        "df_cm = pd.DataFrame(cm, range(2), range(2))\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Confusion Matrix:\\n\", cm)\n",
        "print(\"Classification Report:\\n\", classification_rep)\n",
        "df_cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "clUWlEbuQBhO",
        "outputId": "ab51df39-9734-46de-cf92-d1761e523326"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9724\n",
            "Confusion Matrix:\n",
            " [[18277    15]\n",
            " [  537  1171]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.99     18292\n",
            "           1       0.99      0.69      0.81      1708\n",
            "\n",
            "    accuracy                           0.97     20000\n",
            "   macro avg       0.98      0.84      0.90     20000\n",
            "weighted avg       0.97      0.97      0.97     20000\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       0     1\n",
              "0  18277    15\n",
              "1    537  1171"
            ],
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              "        width: 32px;\n",
              "      }\n",
              "\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_cm')\"\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_cm');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_cm",
              "summary": "{\n  \"name\": \"df_cm\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12544,\n        \"min\": 537,\n        \"max\": 18277,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          537,\n          18277\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 817,\n        \"min\": 15,\n        \"max\": 1171,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1171,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    }
  ]
}