diabetes_pred.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import the dataset\n",
        "diabetes_pred='/content/drive/MyDrive/Ml_datsets/diabetes2.csv'\n",
        "df=pd.read_csv(diabetes_pred)"
      ],
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        "df.head()"
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      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df.info()"
      ],
      "metadata": {
        "id": "NdVnJBufYK9V"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset has no missing values"
      ],
      "metadata": {
        "id": "mxoYcfZvYSu2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert te target variable from integer to an object\n",
        "df['Outcome'] = df['Outcome'].astype(str)"
      ],
      "metadata": {
        "id": "MKgmJZvjYPaW"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Analyze the Dependent Variable"
      ],
      "metadata": {
        "id": "YuuqmvNgYjOq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# count no of patients in each class of the target variable\n",
        "df.groupby('Outcome')['Glucose'].size()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zt1WPYN6Yn7G",
        "outputId": "7fdb5b9c-5617-4790-cc92-0564554c7d05"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Outcome\n",
              "0    500\n",
              "1    268\n",
              "Name: Glucose, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Grouping by 'Outcome' and getting the size of 'Glucose' in each group\n",
        "outcome_glucose_count = df.groupby('Outcome')['Glucose'].size()\n",
        "\n",
        "# Calculate percentage of total\n",
        "percentages = outcome_glucose_count / outcome_glucose_count.sum() * 100\n",
        "\n",
        "# Plotting the bar graph\n",
        "ax = outcome_glucose_count.plot(kind='bar', color=['blue', 'green'])  # You can customize colors as needed\n",
        "\n",
        "# Adding data labels showing percentages\n",
        "for i, v in enumerate(outcome_glucose_count):\n",
        "    ax.text(i, v + 0.5, f\"{percentages[i]:.1f}%\", ha='center', va='bottom')\n",
        "\n",
        "plt.title('Count of Patients by Outcome')\n",
        "plt.xlabel('Outcome')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()"
      ],
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        },
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        "outputId": "f296beb3-946c-4610-9bf6-3b8e41351fc9"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset is imbalanced.The positive class(1) constitutes 34.9% of the data. 65.1% of the dataset makes the negative class(0). An imbalanced dataset - the number of observations is not same for all classes in a classification model."
      ],
      "metadata": {
        "id": "NQA2UBzccXGG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analyzing the Independent Variables"
      ],
      "metadata": {
        "id": "kTx8LaCEcuhO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# descriptive statistics of the variables\n",
        "features_df=df.drop(columns=['Outcome'])\n",
        "features_df.describe()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "5Fxq7UONbyV9",
        "outputId": "7db4319b-dfa1-4533-fcec-ef3edf8e225d"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age  \n",
              "count  768.000000                768.000000  768.000000  \n",
              "mean    31.992578                  0.471876   33.240885  \n",
              "std      7.884160                  0.331329   11.760232  \n",
              "min      0.000000                  0.078000   21.000000  \n",
              "25%     27.300000                  0.243750   24.000000  \n",
              "50%     32.000000                  0.372500   29.000000  \n",
              "75%     36.600000                  0.626250   41.000000  \n",
              "max     67.100000                  2.420000   81.000000  "
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>31.972618</td>\n",
              "      <td>19.355807</td>\n",
              "      <td>15.952218</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
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              "    async function quickchart(key) {\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"features_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 269.85223453356366,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8450520833333335,\n          3.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 243.73802348295857,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          120.89453125,\n          117.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 252.8525053581062,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          69.10546875,\n          72.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 263.7684730531098,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          20.536458333333332,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 350.26059167945886,\n        \"min\": 0.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          79.79947916666667,\n          127.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 262.05117817552093,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.992578124999998,\n          32.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3005221658502,\n        \"min\": 0.078,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.47187630208333325,\n          0.3725,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260.1941178528413,\n        \"min\": 11.760231540678685,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.240885416666664,\n          29.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  # Creating a boxplot for Pregnancies\n",
        "plt.figure(figsize=(8, 6))\n",
        "df.boxplot(column='Pregnancies')\n",
        "plt.title('Pregnancies Boxplot')\n",
        "plt.xlabel('Data')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "f-HszeKheEgu",
        "outputId": "7c14b970-0aa4-4ac9-c2ee-5413ce0de441"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a boxplot for Glucose\n",
        "plt.figure(figsize=(8, 6))\n",
        "df.boxplot(column='Glucose')\n",
        "plt.title('Glucose Boxplot')\n",
        "plt.xlabel('Data')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "-TXSqH4DAxsk",
        "outputId": "d4e01db9-19d1-4e7e-faf1-9421bdae79c1"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Counting the number of rows where glucose level is 0\n",
        "glucose_zero_count = (features_df['Glucose'] == 0).sum()\n",
        "\n",
        "print(\"Number of rows where glucose level is 0:\", glucose_zero_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXQKzNUzCsST",
        "outputId": "7de66fcd-5fa5-4f0c-ae5c-377b198796a2"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows where glucose level is 0: 5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "glucose of zero is not realistic,therefore glucose evel of zero is an outlier. Replace Zeros with the mean of Glucose level"
      ],
      "metadata": {
        "id": "FwTjugdnB-B6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the mean of non-zero glucose values\n",
        "glucose_mean = df[df['Glucose'] != 0]['Glucose'].mean()\n",
        "\n",
        "# Replace zero values with the mean of non-zero glucose values\n",
        "df['Glucose'] = df['Glucose'].replace(0, glucose_mean)\n",
        "\n",
        "# print(features_df)"
      ],
      "metadata": {
        "id": "oQbaxZXrB5w3"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "  # Creating a boxplot for Glucose after removal of outliers\n",
        "plt.figure(figsize=(8, 6))\n",
        "df.boxplot(column='Glucose')\n",
        "plt.title('Glucose Boxplot without Outliers')\n",
        "plt.xlabel('Data')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "WgxLFgixj_u6",
        "outputId": "c4d55ba4-3bfd-49e0-d6cf-09a3b32815d4"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a boxplot for BMI\n",
        "plt.figure(figsize=(8, 6))\n",
        "df.boxplot(column='BMI')\n",
        "plt.title('BMI Boxplot')\n",
        "plt.xlabel('Data')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "t1UATVXGTL-x",
        "outputId": "9f773f37-9ec3-4d28-924e-b250d45b3f48"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the mean of non-zero BMI values\n",
        "BMI_mean = df[df['BMI'] != 0]['BMI'].mean()\n",
        "\n",
        "# Replace zero values with the mean of non-zero BMI values\n",
        "df['BMI'] = df['BMI'].replace(0, BMI_mean)\n",
        "\n",
        "# print(features_df)"
      ],
      "metadata": {
        "id": "qKyz0liVg9VB"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace the outlier value with lower limit values using IQR\n",
        "Q1 = df['BMI'].quantile(0.25)\n",
        "Q3 = df['BMI'].quantile(0.75)\n",
        "\n",
        "# Calculate the interquartile range (IQR)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "# Define the lower and upper bounds to identify outliers\n",
        "upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "# Replace outliers with the lower bound values\n",
        "df.loc[df['BMI'] > upper_bound, 'BMI'] = upper_bound\n",
        "print(upper_bound)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N41YlnwTRehc",
        "outputId": "a3ee6cd0-63f0-4aeb-b90c-d25c220ff3fd"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "50.25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  # Creating a boxplot for BloodPressure\n",
        "plt.figure(figsize=(8, 6))\n",
        "df.boxplot(column='BloodPressure')\n",
        "plt.title('BloodPressure Boxplot')\n",
        "plt.xlabel('Data')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "rn5DmjvxjsP-",
        "outputId": "e6337723-471d-4b4d-8a92-17c1089d204e"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Counting the number of rows where BloodPressure level is 0\n",
        "BloodPressure_zero_count = (df['BloodPressure'] == 0).sum()\n",
        "\n",
        "print(\"Number of rows where BloodPressure level is 0:\", BloodPressure_zero_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdsDJA6iTZcB",
        "outputId": "bddc7c1a-fd53-4124-95a8-d02dfb853027"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows where BloodPressure level is 0: 35\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace zore values with the mean of BloodPressure\n",
        "BloodPressure_mean = df[df['BloodPressure'] != 0]['BloodPressure'].mean()\n",
        "\n",
        "df['BloodPressure'] = df['BloodPressure'].replace(0, BloodPressure_mean)"
      ],
      "metadata": {
        "id": "0dXokMUJjLVp"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Replace the outlier value with lower limit values using IQR\n",
        "# # Calculate the first quartile (Q1)\n",
        "# Q1 = features_df['BloodPressure'].quantile(0.25)\n",
        "# Q3 = features_df['BloodPressure'].quantile(0.75)\n",
        "\n",
        "# # Calculate the interquartile range (IQR)\n",
        "# IQR = Q3 - Q1\n",
        "\n",
        "# # Define the lower and upper bounds to identify outliers\n",
        "# lower_bound = Q1 - 1.5 * IQR\n",
        "\n",
        "# # Replace outliers with the lower bound values\n",
        "# df.loc[df['BloodPressure'] < lower_bound, 'BloodPressure'] = lower_bound\n",
        "# print(lower_bound)"
      ],
      "metadata": {
        "id": "rEs0vRJKRwjF"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a boxplot for SkinThickness\n",
        "plt.figure(figsize=(8, 6))\n",
        "df.boxplot(column='SkinThickness')\n",
        "plt.title('SkinThickness Boxplot')\n",
        "plt.xlabel('Data')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "DYcUWihzkv0r",
        "outputId": "f493259f-b394-4873-b9f2-425cbcad6103"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Counting the number of rows where BloodPressure level is 0\n",
        "SkinThickness_zero_count = (df['SkinThickness'] == 0).sum()\n",
        "\n",
        "print(\"Number of rows where SkinThickness is 0:\", SkinThickness_zero_count)"
      ],
      "metadata": {
        "id": "FJ1XoPtke-XE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "761780fb-426d-4048-b055-904d33803b76"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows where SkinThickness is 0: 227\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace zore values with the mean of SkinThickness\n",
        "SkinThickness_mean = df[df['SkinThickness'] != 0]['SkinThickness'].mean()\n",
        "\n",
        "df['SkinThickness'] = df['SkinThickness'].replace(0, SkinThickness_mean)"
      ],
      "metadata": {
        "id": "nplE8HximJSO"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a boxplot for Insulin\n",
        "plt.figure(figsize=(8, 6))\n",
        "df.boxplot(column='Insulin')\n",
        "plt.title('Insulin Boxplot')\n",
        "plt.xlabel('Data')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "TR_SovNFm1FV",
        "outputId": "6b667ff3-3c87-48c5-cfe7-d5fd104f080f"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Counting the number of rows where BloodPressure level is 0\n",
        "Insulin_zero_count = (df['Insulin'] == 0).sum()\n",
        "\n",
        "print(\"Number of rows where Insulin is 0:\", Insulin_zero_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FBHUHWmvm1IY",
        "outputId": "73a60d38-1bc5-4d61-aec4-54def8f2f788"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows where Insulin is 0: 374\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace zore values with the mean of Insulin\n",
        "Insulin_mean = df[df['Insulin'] != 0]['Insulin'].mean()\n",
        "\n",
        "df['Insulin'] = df['Insulin'].replace(0, Insulin_mean)"
      ],
      "metadata": {
        "id": "ZdJ92-jdnICr"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(Insulin_mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtnd23KKnaTq",
        "outputId": "c83ff5f8-a546-4248-eebb-d814baf7be91"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "155.5482233502538\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Examine the relationship between the Independent Variables"
      ],
      "metadata": {
        "id": "ez4k52ZwfgBc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plot a correlation heat map\n",
        "plt.figure(figsize=(10, 6))\n",
        "dataplot = sns.heatmap(df.corr(), cmap=\"YlGnBu\", annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 751
        },
        "id": "NlgaG-Qbe-aM",
        "outputId": "e2c50dcb-89cc-4b48-dd7f-14a2e9025fa7"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-86-3524dd1c2e0f>:3: 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",
            "  dataplot = sns.heatmap(df.corr(), cmap=\"YlGnBu\", annot=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pregnancies has a weak correlation with all the other independet variables except Age, which exhibits as moderate positive corr of .54\n",
        "Skin thickness and insulin have a moderate corr of .44\n",
        "Insulin and glucose also have a weak positive corr of .33\n",
        "Most varibles have weak positive correlation btw each other. Note that if 2 independent variables have a strong positive or a strong negative correlation, it is advisable to drop one of the variables before going into the training step."
      ],
      "metadata": {
        "id": "P74X_9DuhPuP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Relationship btw the Independent Variables and the Target Variable"
      ],
      "metadata": {
        "id": "XCiefyySjcU0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EZJELx4Rku5I",
        "outputId": "36ffbe9d-6eec-414b-8c59-4259e2e4f243"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness     Insulin   BMI  \\\n",
              "0            6    148.0           72.0       35.00000  155.548223  33.6   \n",
              "1            1     85.0           66.0       29.00000  155.548223  26.6   \n",
              "2            8    183.0           64.0       29.15342  155.548223  23.3   \n",
              "3            1     89.0           66.0       23.00000   94.000000  28.1   \n",
              "4            0    137.0           40.0       35.00000  168.000000  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age Outcome  \n",
              "0                     0.627   50       1  \n",
              "1                     0.351   31       0  \n",
              "2                     0.672   32       1  \n",
              "3                     0.167   21       0  \n",
              "4                     2.288   33       1  "
            ],
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              "        document.querySelector('#df-ffa63e69-84e1-4dcf-b367-92ec2e75535a 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-ffa63e69-84e1-4dcf-b367-92ec2e75535a');\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-a0cc5dec-bd5d-414f-9aa6-3ed2d65590f0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a0cc5dec-bd5d-414f-9aa6-3ed2d65590f0')\"\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-a0cc5dec-bd5d-414f-9aa6-3ed2d65590f0 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\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.435948867207657,\n        \"min\": 44.0,\n        \"max\": 199.0,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151.0,\n          101.0,\n          112.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.096346184037937,\n        \"min\": 24.0,\n        \"max\": 122.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86.0,\n          46.0,\n          85.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.790941925624532,\n        \"min\": 7.0,\n        \"max\": 99.0,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7.0,\n          12.0,\n          48.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.02110776922126,\n        \"min\": 14.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52.0,\n          41.0,\n          183.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.667465771125937,\n        \"min\": 18.2,\n        \"max\": 50.25,\n        \"num_unique_values\": 242,\n        \"samples\": [\n          36.6,\n          31.0,\n          44.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l0aIJp2Xk56r",
        "outputId": "45622018-9bc8-4cb5-8dc3-030de5f18308"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    float64\n",
            " 2   BloodPressure             768 non-null    float64\n",
            " 3   SkinThickness             768 non-null    float64\n",
            " 4   Insulin                   768 non-null    float64\n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    object \n",
            "dtypes: float64(6), int64(2), object(1)\n",
            "memory usage: 54.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Age\n",
        "# DiabetesPedigreeFunction\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot histogram for DiabetesPedigreeFunction with color coding for Outcome\n",
        "sns.histplot(data=df, bins=20, x='Age', hue='Outcome', palette=['blue', 'red'], alpha=0.5)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Age Distribution by Outcome ')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "qTeWb43he-du",
        "outputId": "d80825d2-1d5d-42ad-bbf5-9e4ba77b1f0f"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# BloodPressure\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot histogram for DiabetesPedigreeFunction with color coding for Outcome\n",
        "sns.histplot(data=df, bins=20, x='BloodPressure', hue='Outcome', palette=['blue', 'red'], alpha=0.5)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('BloodPressure')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('BloodPressure Distribution by Outcome')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "OwmTK1Cce-gk",
        "outputId": "8eec5a73-6b27-4b34-fab4-cbfca65004d8"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# BMI\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot histogram for DiabetesPedigreeFunction with color coding for Outcome\n",
        "sns.histplot(data=df, bins=20, x='BMI', hue='Outcome', palette=['blue', 'red'], alpha=0.5)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('BMI')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('BMI Distribution by Outcome ')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "OZkcX-3amF9-",
        "outputId": "39715768-a556-4222-82de-a254bf0abea8"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DiabetesPedigreeFunction\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot histogram for DiabetesPedigreeFunction with color coding for Outcome\n",
        "sns.histplot(data=df, bins=20, x='DiabetesPedigreeFunction', hue='Outcome', palette=['blue', 'red'], alpha=0.5)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('DiabetesPedigreeFunction')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('DiabetesPedigreeFunction Distribution by Outcome ')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "-qksRQG4e-kT",
        "outputId": "57fde166-b726-4e9d-c488-47cdd71d29e3"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SkinThickness\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot histogram for DiabetesPedigreeFunction with color coding for Outcome\n",
        "sns.histplot(data=df, bins=20, x='SkinThickness', hue='Outcome', palette=['blue', 'red'], alpha=0.5)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('SkinThickness')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('SkinThickness Distribution by Outcome ')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "7pnGNNtpe-rU",
        "outputId": "6e170d8f-09a6-4d14-b4bc-42540af153da"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Insulin\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot histogram for DiabetesPedigreeFunction with color coding for Outcome\n",
        "sns.histplot(data=df, bins=20, x='Insulin', hue='Outcome', palette=['blue', 'red'], alpha=0.5)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Insulin')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Insulin Distribution by Outcome ')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "zRz8rgI-yplI",
        "outputId": "d0578f05-1e87-4afb-f1a8-dc00e233db70"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pregnancies\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot histogram for DiabetesPedigreeFunction with color coding for Outcome\n",
        "sns.histplot(data=df, bins=20, x='Pregnancies', hue='Outcome', palette=['blue', 'red'], alpha=0.5)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Pregnancies')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Pregnancies Distribution by Outcome ')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "CVOEuo3_zG32",
        "outputId": "afafa539-2f03-42d4-e9d2-2dfb7d96bfa2"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Glucose\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot histogram for DiabetesPedigreeFunction with color coding for Outcome\n",
        "sns.histplot(data=df, bins=20, x='Glucose', hue='Outcome', palette=['blue', 'red'], alpha=0.5)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Glucose')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Glucose Distribution by Outcome ')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Kd9QCRYOzG7v",
        "outputId": "86ae184b-914d-4931-cc7c-51129074a17b"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conclusion\n",
        "\n",
        "\n",
        "*  Majority of people who are 30 yrs of age and above tend to have diabetes. The proportion of this age group that are affected is higher than those below the age of 30.\n",
        "* Majority of those with diabetes have blood pressure of 25 and above\n",
        "* The higher the BMI index,skinthickness,glucose level and insulin,the higher chances of a person being infected\n",
        "*\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "48dZ4GZPOhsX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split the Data into Train and Test"
      ],
      "metadata": {
        "id": "ggLg7baYbnDH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#independent variable-features\n",
        "#dependent variale-target variable\n",
        "X=df.drop('Outcome',axis=1)\n",
        "y=df['Outcome']"
      ],
      "metadata": {
        "id": "BL816qsRcjSF"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split your data into training (70%) and the rest (30%)\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Split the remaining data into validation (15%) and testing (15%)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42,shuffle=True)\n"
      ],
      "metadata": {
        "id": "XSNUtLLcQ2Em"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(X_val.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)\n",
        "print(y_val.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSBW-doPZnRq",
        "outputId": "ffe7a0a6-d423-4840-b176-5c25e65ddbe4"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(537, 8)\n",
            "(116, 8)\n",
            "(115, 8)\n",
            "(537,)\n",
            "(116,)\n",
            "(115,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YWRNdN2ak98Z",
        "outputId": "ce20f8aa-dfc8-47dc-e52d-b2a1781f3690"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "334            1     95.0           60.0           18.0     58.0  23.9   \n",
              "139            5    105.0           72.0           29.0    325.0  36.9   \n",
              "485            0    135.0           68.0           42.0    250.0  42.3   \n",
              "547            4    131.0           68.0           21.0    166.0  33.1   \n",
              "18             1    103.0           30.0           38.0     83.0  43.3   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  \n",
              "334                     0.260   22  \n",
              "139                     0.159   28  \n",
              "485                     0.365   24  \n",
              "547                     0.160   28  \n",
              "18                      0.183   33  "
            ],
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              "      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-4ab12d70-de27-4cfa-822b-4537946e61f5 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-4ab12d70-de27-4cfa-822b-4537946e61f5');\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-6a3084a4-4cc3-4c2a-9a03-156c4e4fac42\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6a3084a4-4cc3-4c2a-9a03-156c4e4fac42')\"\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-6a3084a4-4cc3-4c2a-9a03-156c4e4fac42 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": "X_train",
              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 537,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          1,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.138496239021283,\n        \"min\": 44.0,\n        \"max\": 199.0,\n        \"num_unique_values\": 128,\n        \"samples\": [\n          158.0,\n          180.0,\n          143.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.204822884325463,\n        \"min\": 24.0,\n        \"max\": 122.0,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          50.0,\n          46.0,\n          90.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.475989577351253,\n        \"min\": 8.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          49.0,\n          15.0,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.0390990830924,\n        \"min\": 14.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          193.0,\n          230.0,\n          540.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.746934134792933,\n        \"min\": 18.2,\n        \"max\": 50.25,\n        \"num_unique_values\": 215,\n        \"samples\": [\n          46.3,\n          33.5,\n          44.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3420873632546252,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 402,\n        \"samples\": [\n          0.404,\n          0.426,\n          0.282\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          67,\n          46,\n          69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Featurer Scaling"
      ],
      "metadata": {
        "id": "z8xdpQ5rmbMr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "# Create a MinMaxScaler object\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Fit the scaler to the training data and transform it\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "\n",
        "# Apply the same transformation to the test data\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# print(\"Scaled Training Data:\\5\", X_train_scaled)\n",
        "# print(\"Scaled Test Data:\\5\", X_test_scaled)"
      ],
      "metadata": {
        "id": "usVKNGE2Zqco"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train_scaled"
      ],
      "metadata": {
        "id": "JFfbI3Wst9Z2"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Training"
      ],
      "metadata": {
        "id": "TLpslyUw5C2Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score,confusion_matrix"
      ],
      "metadata": {
        "id": "umscgVqRzzHW"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Logistice Regression"
      ],
      "metadata": {
        "id": "bRblJ9ZbAEcw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model = LogisticRegression()     # create an instance for logistic regression\n",
        "model.fit(X_train,y_train)      # train logisticregression model on the training data\n",
        "y_pred=model.predict(X_val)     # trained logistic regression model (model) makes predictions on the validation data (X_val) which is stored as y_pred\n",
        "print('The accuracy_score',accuracy_score(y_val,y_pred))\n",
        "print('The precision_score',precision_score(y_val,y_pred,pos_label='1'))\n",
        "print('The recall',recall_score(y_val,y_pred,pos_label='1'))\n",
        "print('The f1_score',f1_score(y_val,y_pred,pos_label='1'))\n",
        "\n",
        "print(\"Confusion Matrix:\\n\",confusion_matrix(y_val,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xg5Da-zZAEz3",
        "outputId": "45ef2903-32de-4f7c-f6cc-0e4463ee3d41"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy_score 0.7391304347826086\n",
            "The precision_score 0.6\n",
            "The recall 0.5675675675675675\n",
            "The f1_score 0.5833333333333333\n",
            "Confusion Matrix:\n",
            " [[64 14]\n",
            " [16 21]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " The fit() method adjusts the parameters of the logistic regression model to minimize the error between the predicted and actual labels"
      ],
      "metadata": {
        "id": "HuSTq1rVA6B_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "KNN"
      ],
      "metadata": {
        "id": "7KKjpnj9fBfH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# K-Nearest Neighbors\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "model_knn = KNeighborsClassifier(n_neighbors=5) # You can adjust n_neighbors based on your dataset\n",
        "model_knn.fit(X_train, y_train)\n",
        "y_pred_knn = model_knn.predict(X_val)\n",
        "\n",
        "print('\\nK-Nearest Neighbors Metrics:')\n",
        "print('Accuracy:', accuracy_score(y_val, y_pred_knn))\n",
        "print('Precision:', precision_score(y_val, y_pred_knn, pos_label='1'))\n",
        "print('Recall:', recall_score(y_val, y_pred_knn, pos_label='1'))\n",
        "print('F1 Score:', f1_score(y_val, y_pred_knn, pos_label='1'))\n",
        "print(\"Confusion Matrix:\\n\", confusion_matrix(y_val, y_pred_knn))"
      ],
      "metadata": {
        "id": "ELAli2z8fOmv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ebc7a04-f99e-4f92-f8cc-fb4fc06b2f0f"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "K-Nearest Neighbors Metrics:\n",
            "Accuracy: 0.7043478260869566\n",
            "Precision: 0.5348837209302325\n",
            "Recall: 0.6216216216216216\n",
            "F1 Score: 0.575\n",
            "Confusion Matrix:\n",
            " [[58 20]\n",
            " [14 23]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decision Tree"
      ],
      "metadata": {
        "id": "938t9Qwr5PEm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# Decision Tree\n",
        "model_dt = DecisionTreeClassifier()\n",
        "model_dt.fit(X_train, y_train)\n",
        "y_pred_dt = model_dt.predict(X_val)\n",
        "\n",
        "print('\\nDecision Tree Metrics:')\n",
        "print('Accuracy:', accuracy_score(y_val, y_pred_dt))\n",
        "print('Precision:', precision_score(y_val, y_pred_dt, pos_label='1'))\n",
        "print('Recall:', recall_score(y_val, y_pred_dt, pos_label='1'))\n",
        "print('F1 Score:', f1_score(y_val, y_pred_dt, pos_label='1'))\n",
        "print(\"Confusion Matrix:\\n\", confusion_matrix(y_val, y_pred_dt))"
      ],
      "metadata": {
        "id": "kjhtv4zlIsil",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd5a6568-6852-4ea2-a7b2-2d7bee6f7e1f"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Decision Tree Metrics:\n",
            "Accuracy: 0.7217391304347827\n",
            "Precision: 0.5490196078431373\n",
            "Recall: 0.7567567567567568\n",
            "F1 Score: 0.6363636363636364\n",
            "Confusion Matrix:\n",
            " [[55 23]\n",
            " [ 9 28]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rabdom Forest"
      ],
      "metadata": {
        "id": "dJIod4UY9mCF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Create the Random Forest model with a specified number of trees\n",
        "model_rf = RandomForestClassifier(n_estimators=100) # n_estimators represents the number of trees\n",
        "\n",
        "# Fit the model on the training data\n",
        "model_rf.fit(X_train, y_train)\n",
        "\n",
        "# Predict on the validation set\n",
        "y_pred_rf = model_rf.predict(X_val)\n",
        "\n",
        "# Evaluate the Random Forest model\n",
        "print('\\nRandom Forest Metrics:')\n",
        "print('Accuracy:', accuracy_score(y_val, y_pred_rf))\n",
        "print('Precision:', precision_score(y_val, y_pred_rf, pos_label='1'))\n",
        "print('Recall:', recall_score(y_val, y_pred_rf, pos_label='1'))\n",
        "print('F1 Score:', f1_score(y_val, y_pred_rf, pos_label='1'))\n",
        "print(\"Confusion Matrix:\\n\", confusion_matrix(y_val, y_pred_rf))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TPfDA8Ko9mUx",
        "outputId": "dd48c189-9e3c-4186-f954-45a27fb09834"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest Metrics:\n",
            "Accuracy: 0.7217391304347827\n",
            "Precision: 0.5641025641025641\n",
            "Recall: 0.5945945945945946\n",
            "F1 Score: 0.5789473684210528\n",
            "Confusion Matrix:\n",
            " [[61 17]\n",
            " [15 22]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this cases,the cost of a FN> that that of a FP, we therefore put much emphasis on Recall and an algorithm with the highest recall score.This is because Recall measures the ability of a model to correctly identify all positive instances, and in situations where missing a positive instance (FN) has serious consequences, maximizing Recall becomes critical.Choose Random Forest since it has a high recall of 0.62 and a relatively high accuracy of 0.739"
      ],
      "metadata": {
        "id": "-9K4_viN862-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The ROC"
      ],
      "metadata": {
        "id": "jatv06GSed4S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "# Predict probabilities for the positive class\n",
        "y_pred_proba_rf = model_rf.predict_proba(X_val)[:, 1]\n",
        "\n",
        "# Compute ROC curve and ROC area for the model\n",
        "fpr, tpr, _ = roc_curve(y_val, y_pred_proba_rf, pos_label='1')\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "IQX3CX1eeh5z",
        "outputId": "690c90e1-1de2-4bb3-d013-87526fc85d94"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "AUC represents the probability that a random positive (has diabetes) example is positioned to the right of a random negative (no diabetes).AUC of 0.79- there is a 79% probability that the model will accurately indetify a positive class."
      ],
      "metadata": {
        "id": "JS3s8JDxCSPm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Learning Curve"
      ],
      "metadata": {
        "id": "sk7_hfb7eHZF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "# Generate learning curves\n",
        "train_sizes, train_scores, test_scores = learning_curve(\n",
        "    RandomForestClassifier(n_estimators=50), X_train, y_train, cv=5,\n",
        "    scoring='accuracy', # Change this to any other performance metric if needed\n",
        "    n_jobs=-1, train_sizes=np.linspace(0.01, 1.0, 50))\n",
        "\n",
        "# Calculate mean and standard deviation for training set scores\n",
        "train_mean = np.mean(train_scores, axis=1)\n",
        "train_std = np.std(train_scores, axis=1)\n",
        "test_mean = np.mean(test_scores, axis=1)\n",
        "test_std = np.std(test_scores, axis=1)\n",
        "\n",
        "# Calculate mean and standard deviation for test set (validation) scores\n",
        "test_mean = np.mean(test_scores, axis=1)\n",
        "test_std = np.std(test_scores, axis=1)\n",
        "\n",
        "# Plot the learning curves\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(train_sizes, train_mean, label=\"Training score\", color=\"darkorange\", lw=2)\n",
        "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color=\"darkorange\", alpha=0.2)\n",
        "\n",
        "plt.plot(train_sizes, test_mean, label=\"Cross-validation score\", color=\"navy\", lw=2)\n",
        "plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color=\"navy\", alpha=0.2)\n",
        "\n",
        "plt.title(\"Learning Curve\")\n",
        "plt.xlabel(\"Training Set Size\")\n",
        "plt.ylabel(\"Accuracy Score\")\n",
        "plt.legend(loc=\"best\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "RmDZqNESBfq4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "0965a5c5-2edd-44a9-c995-23673fbb1279"
      },
      "execution_count": 109,
      "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": [
        "# # Generate learning curves\n",
        "# train_sizes, train_scores, test_scores = learning_curve(\n",
        "#     model, X_train, y_train, cv=5, n_jobs=-1,\n",
        "#     train_sizes=np.linspace(0.01, 1.0, 50),\n",
        "#     scoring='accuracy'  # You can use recall here instead of accuracy since you're interested in Recall\n",
        "# )\n",
        "\n",
        "# # Calculate the average and standard deviation of the train and test scores\n",
        "# train_mean = np.mean(train_scores, axis=1)\n",
        "# train_std = np.std(train_scores, axis=1)\n",
        "# test_mean = np.mean(test_scores, axis=1)\n",
        "# test_std = np.std(test_scores, axis=1)\n",
        "\n",
        "# # Plotting the learning curve\n",
        "# plt.figure(figsize=(10, 6))\n",
        "# plt.plot(train_sizes, train_mean, 'o-', color=\"r\", label=\"Training score\")\n",
        "# plt.plot(train_sizes, test_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n",
        "\n",
        "# plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, alpha=0.1, color=\"r\")\n",
        "# plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, alpha=0.1, color=\"g\")\n",
        "\n",
        "# plt.title(\"Learning Curve\")\n",
        "# plt.xlabel(\"Training Set Size\")\n",
        "# plt.ylabel(\"Accuracy Score\")\n",
        "# plt.legend(loc=\"best\")\n",
        "# plt.grid()\n",
        "# plt.show()"
      ],
      "metadata": {
        "id": "CjVExKXZIAuu"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X8MMfhgBDhjq"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hyper-Parameter Tuning"
      ],
      "metadata": {
        "id": "0L6nOJVS5fn3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Define the hyperparameter grid\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4],\n",
        "    'bootstrap': [True, False]\n",
        "}\n",
        "\n",
        "# Create a base model\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "# Instantiate the grid search model\n",
        "grid_search = GridSearchCV(estimator = rf, param_grid = param_grid,\n",
        "                           cv = 3, n_jobs = -1, verbose = 2, scoring = 'accuracy')\n",
        "\n",
        "# Fit the grid search to the data\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Get the best parameters\n",
        "best_params = grid_search.best_params_\n",
        "\n",
        "# Create a new model with the best parameters\n",
        "best_rf = RandomForestClassifier(**best_params)\n",
        "\n",
        "# Fit the best model to the data\n",
        "best_rf.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions with the best model\n",
        "y_pred_best = best_rf.predict(X_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w2b9Toug5j0O",
        "outputId": "298900ec-9ec9-4595-961e-6c6cef3c1d77"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 216 candidates, totalling 648 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the best hyperparameters and the corresponding best score from the grid search\n",
        "best_params = grid_search.best_params_\n",
        "best_score = grid_search.best_score_\n",
        "\n",
        "print(f\"The best hyperparameters from GridSearchCV are: {best_params}\")\n",
        "print(f\"The best cross-validation accuracy from GridSearchCV is: {best_score}\")\n",
        "\n",
        "# The best_score_ attribute gives you the best score observed during the optimization process,"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aBvmoRaY7f0L",
        "outputId": "8de325fa-833f-4897-a207-17e022126e69"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best hyperparameters from GridSearchCV are: {'bootstrap': False, 'max_depth': 10, 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 100}\n",
            "The best cross-validation accuracy from GridSearchCV is: 0.7783985102420856\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the test set using the be parameters\n",
        "y_pred_test = best_rf.predict(X_test)\n",
        "\n",
        "# Calculate probabilities for the ROC curve\n",
        "y_pred_proba_test = best_rf.predict_proba(X_test)[:, 1]\n",
        "\n",
        "# Calculate the various performance metrics\n",
        "accuracy = accuracy_score(y_test, y_pred_test)\n",
        "precision = precision_score(y_test, y_pred_test, pos_label='1')\n",
        "recall = recall_score(y_test, y_pred_test, pos_label='1')\n",
        "f1 = f1_score(y_test, y_pred_test, pos_label='1')\n",
        "\n",
        "# Print the performance metrics\n",
        "print(f'Accuracy: {accuracy:.4f}')\n",
        "print(f'Precision: {precision:.4f}')\n",
        "print(f'Recall: {recall:.4f}')\n",
        "print(f'F1 Score: {f1:.4f}')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwXpbly_7iQ-",
        "outputId": "d73cef86-7d91-4a23-8d2e-3ee3a445bd52"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7672\n",
            "Precision: 0.6739\n",
            "Recall: 0.7209\n",
            "F1 Score: 0.6966\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the ROC Curve and AUC\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba_test, pos_label='1')\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plot the ROC Curve\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC)')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "vzhQNtiEAl2t",
        "outputId": "a0efdc76-c21a-4dbc-f6e4-301f2cdec248"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHurnKKnA4WU",
        "outputId": "af18e9df-0b91-4b9f-9417-c0bd1ff2f590"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    float64\n",
            " 2   BloodPressure             768 non-null    float64\n",
            " 3   SkinThickness             768 non-null    float64\n",
            " 4   Insulin                   768 non-null    float64\n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    object \n",
            "dtypes: float64(6), int64(2), object(1)\n",
            "memory usage: 54.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Input Patient's Data"
      ],
      "metadata": {
        "id": "lH08k9CEGHgH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# This function will convert user input into a numpy array that can be fed into the model\n",
        "def input_features():\n",
        "     Pregnancies = int(input(\"Enter  Pregnancies value: \"))\n",
        "     Glucose = float(input(\"Enter Glucose value: \"))\n",
        "     BloodPressure = float(input(\"Enter BloodPressure value: \"))\n",
        "     SkinThickness=float(input(\"Enter SkinThickness value: \"))\n",
        "     Insulin=float(input(\"Enter Insulin value: \"))\n",
        "     BMI=float(input(\"Enter BMI value: \"))\n",
        "     DiabetesPedigreeFunction=float(input(\"Enter DiabetesPedigreeFunction value: \"))\n",
        "     Age=int(input(\"Enter Age value: \"))\n",
        "\n",
        "    # Create a numpy array from the input values\n",
        "     features_array = np.array([[Pregnancies, Glucose, BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age]])\n",
        "\n",
        "     return features_array\n",
        "\n",
        "# 'best_rf' is the trained RandomForestClassifier model\n",
        "# Let's get the input from the user\n",
        "user_input = input_features()\n",
        "\n",
        "# Predict the target variable based on the user input\n",
        "predicted_target = best_rf.predict(user_input)\n",
        "\n",
        "# Output the predicted target value\n",
        "print(f\"The predicted target variable is: {predicted_target[0]}\")\n",
        "print(f\"The predicted target variable is: {'POSITIVE' if predicted_target[0] == 1 else 'NEGATIVE'}\")\n"
      ],
      "metadata": {
        "id": "ZQ3gRtTyC-u-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PmnL8434E2vO"
      },
      "execution_count": 58,
      "outputs": []
    }
  ]
}