Lab_2.ipynb

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        "<a href=\"https://colab.research.google.com/github/jovanadobreva/Labs-I2DS/blob/main/Lab_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFMjGridvepn"
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      "source": [
        "#<font  color='Orange'>Data Preparation & KNN Classification</font>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jaojhkPLyq8I"
      },
      "source": [
        "# <font color = 'Orange'> Read your Dataset (.csv)</font>\n",
        "run the code below for downloading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1CkUp_wtuauTNL9aOW-K52jDlTXqD4KWS"
      ],
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          "base_uri": "https://localhost:8080/"
        },
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      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1CkUp_wtuauTNL9aOW-K52jDlTXqD4KWS\n",
            "To: /content/diabetes.csv\n",
            "\r  0% 0.00/23.8k [00:00<?, ?B/s]\r100% 23.8k/23.8k [00:00<00:00, 13.1MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "VNuwigsVwtaP"
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      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/diabetes.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
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        "id": "xmpLTb2LzlQK",
        "outputId": "8d742531-4768-4d3c-a99a-218cfe94a15f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
              "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
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      "source": [
        "# <font color = 'Orange'> Data preprocessing </font>\n",
        "\n",
        "Context\n",
        "This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.\n",
        "\n",
        "Content\n",
        "The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Input columns: Pregnancies, Glucose, BloodPressure,SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age\n",
        "\n",
        "Output columns: Outcome 0-->doesn't have diabetes / 1--> has diabetes"
      ],
      "metadata": {
        "id": "Yv22PXmvzjbJ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WpZdCckk0Z8W"
      },
      "source": [
        "## <font color = 'Orange'>Detect the Missing values</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gw4SkFaB1QBr"
      },
      "source": [
        "Count the percentage of missing values in every column of the Dataset."
      ]
    },
    {
      "cell_type": "code",
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        "id": "KI_OUR0r1XOH",
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      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 14\n",
              "Glucose                      0\n",
              "BloodPressure                0\n",
              "SkinThickness                8\n",
              "Insulin                      0\n",
              "BMI                         10\n",
              "DiabetesPedigreeFunction     0\n",
              "Age                          0\n",
              "Outcome                      0\n",
              "dtype: int64"
            ],
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              "      <td>0</td>\n",
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              "      <th>BMI</th>\n",
              "      <td>10</td>\n",
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        "missing_percent = df.isnull().sum() / len(df) * 100\n",
        "missing_percent = missing_percent.reset_index().rename(columns={\"index\": \"column\", 0: \"percent_missing\"})\n",
        "missing_percent"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     column  percent_missing\n",
              "0               Pregnancies         1.822917\n",
              "1                   Glucose         0.000000\n",
              "2             BloodPressure         0.000000\n",
              "3             SkinThickness         1.041667\n",
              "4                   Insulin         0.000000\n",
              "5                       BMI         1.302083\n",
              "6  DiabetesPedigreeFunction         0.000000\n",
              "7                       Age         0.000000\n",
              "8                   Outcome         0.000000"
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              "type": "dataframe",
              "variable_name": "missing_percent",
              "summary": "{\n  \"name\": \"missing_percent\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"column\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Age\",\n          \"Glucose\",\n          \"BMI\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent_missing\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7223661882418939,\n        \"min\": 0.0,\n        \"max\": 1.8229166666666667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          1.3020833333333335,\n          1.8229166666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bsZjZCi92J8g"
      },
      "source": [
        "## <font color = 'Orange'> Find reasons for the missing values</font>\n",
        "\n",
        "\n",
        "With the help of visualization matrix, heatmap, dendrogram, show the dependence between the columns with missing values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SS5AuHms1bnh"
      },
      "source": [
        "Visualize the missing values using Missingno library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JE-uvOiL32-v"
      },
      "source": [
        "import missingno as msno\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "msno.bar(df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "roqB5gruhS_y",
        "outputId": "c0c0df80-9f8d-434d-9b89-d717bfce4485"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msno.heatmap(df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 899
        },
        "id": "Sog4mvf7YW0t",
        "outputId": "5c26658b-800f-44cf-8cfc-ede3c59b7042"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msno.dendrogram(df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        },
        "id": "ububYdHmhmQA",
        "outputId": "b79940d3-70eb-4d72-b306-aa05d7161173"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot.scatter(x='SkinThickness', y='Pregnancies')\n",
        "plt.xticks(rotation=90)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "EMZl6tLyD20O",
        "outputId": "2f7d1776-d314-491b-d194-634f35fda7f2"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-20.,   0.,  20.,  40.,  60.,  80., 100., 120.]),\n",
              " [Text(-20.0, 0, '−20'),\n",
              "  Text(0.0, 0, '0'),\n",
              "  Text(20.0, 0, '20'),\n",
              "  Text(40.0, 0, '40'),\n",
              "  Text(60.0, 0, '60'),\n",
              "  Text(80.0, 0, '80'),\n",
              "  Text(100.0, 0, '100'),\n",
              "  Text(120.0, 0, '120')])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8TiFswE5TsD"
      },
      "source": [
        "## <font color = 'Orange'>Handle the missing values</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gbzez457dgX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3830d9c-8c0c-4b26-d58c-c92e7b1ad94d"
      },
      "source": [
        "# For Pregnancies, use mode as it's a discrete count\n",
        "df['Pregnancies'].fillna(df['Pregnancies'].mode()[0], inplace=True)\n",
        "\n",
        "# For Insulin, BMI, and SkinThickness, use median as they are continuous variables\n",
        "df['Insulin'].fillna(df['Insulin'].median(), inplace=True)\n",
        "df['BMI'].fillna(df['BMI'].median(), inplace=True)\n",
        "df['SkinThickness'].fillna(df['SkinThickness'].median(), inplace=True)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-40-d394b01d3ece>:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  df['Pregnancies'].fillna(df['Pregnancies'].mode()[0], inplace=True)\n",
            "<ipython-input-40-d394b01d3ece>:5: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  df['Insulin'].fillna(df['Insulin'].median(), inplace=True)\n",
            "<ipython-input-40-d394b01d3ece>:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  df['BMI'].fillna(df['BMI'].median(), inplace=True)\n",
            "<ipython-input-40-d394b01d3ece>:7: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  df['SkinThickness'].fillna(df['SkinThickness'].median(), inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NKNTFYHG5y-",
        "outputId": "99c6284c-2cb7-48b9-f6de-21d085834133"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pregnancies                 0\n",
            "Glucose                     0\n",
            "BloodPressure               0\n",
            "SkinThickness               0\n",
            "Insulin                     0\n",
            "BMI                         0\n",
            "DiabetesPedigreeFunction    0\n",
            "Age                         0\n",
            "Outcome                     0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXjNJf9v8H2Q"
      },
      "source": [
        "## <font color = 'Orange'>Save the new Dataset(.csv) without the missing values</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cSczPJg8HGo"
      },
      "source": [
        "df.to_csv('new_diabetes.csv', index=False)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KSKgKX6D8Tip"
      },
      "source": [
        "Print the first rows of your final Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fh2KI6iF8R9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "49f20a5e-1942-4cb3-f502-cc3f782c0d4c"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0          6.0      148             72           35.0        0  33.6   \n",
              "1          1.0       85             66           29.0        0  26.6   \n",
              "2          8.0      183             64            0.0        0  23.3   \n",
              "3          1.0       89             66           23.0       94  28.1   \n",
              "4          0.0      137             40           35.0      168  43.1   \n",
              "5          5.0      116             74           23.0        0  25.6   \n",
              "6          3.0       78             50           32.0       88  31.0   \n",
              "7          1.0      115              0            0.0        0  35.3   \n",
              "8          2.0      197             70           45.0      543  30.5   \n",
              "9          8.0      125             96            0.0        0   0.0   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
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              "7                     0.134   29        0  \n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.364629268929979,\n        \"min\": 0.0,\n        \"max\": 17.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6.0,\n          1.0,\n          3.0\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\": 15.890063970470932,\n        \"min\": 0.0,\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\": 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\": 7.843368036335641,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          23.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33132859501277484,\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": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <font color='orange'>KNN Classification</font>"
      ],
      "metadata": {
        "id": "JQieWcUKzE8G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<font color = 'Orange'>Split the dataset for training and testing in ratio 80:20 </font>\n"
      ],
      "metadata": {
        "id": "BfleC1qMWP3h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X = df.drop('Outcome', axis=1)\n",
        "y = df['Outcome']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "-cfUrp-AWIGX"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = 'Orange'>Initialize the KNN Classification model, and use the fit function for training the model</font>"
      ],
      "metadata": {
        "id": "4K3TOKFMWfHO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier = KNeighborsClassifier(n_neighbors=5)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "e1yWFy4mWYdl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "6ab3dd60-028c-4249-dcbe-0cadc14d3f49"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
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              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = 'Orange'>Predict the outcomes for X test</font>"
      ],
      "metadata": {
        "id": "C-9eBoB3Wh2J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "Ss1TZyPmWj0h"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color = 'Orange'>See the model performance, by using sklearn metrics for classification</font>\n",
        "\n"
      ],
      "metadata": {
        "id": "HVgEX-dRWuPD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
      ],
      "metadata": {
        "id": "afpul_fIWy1Q"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GejQUmb-Q9Vy",
        "outputId": "7bf3f139-46a3-43ee-9f63-3dd3a55370e4"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6688311688311688\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oW3Pek3WQ9IS",
        "outputId": "6ba2de2c-66da-4314-b6a4-70cbe8fa6405"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.71      0.73        99\n",
            "           1       0.53      0.60      0.56        55\n",
            "\n",
            "    accuracy                           0.67       154\n",
            "   macro avg       0.65      0.65      0.65       154\n",
            "weighted avg       0.68      0.67      0.67       154\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxA0dNwCQ8-n",
        "outputId": "e7789910-5062-41a0-e442-87b6b29c53be"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[70 29]\n",
            " [22 33]]\n"
          ]
        }
      ]
    }
  ]
}