Iris_Flowers_Classification_(ML,_Difficulty_3).ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "ah8uQbgajU5c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install dash\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSNwWukcSnHN",
        "outputId": "95619583-8a06-4255-db2a-79991de9f4b6"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jeysMT7pjN09"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install dash-bootstrap-components\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7UJE97mW9II",
        "outputId": "70a0404a-0477-46f8-dac1-ee1ee7209c07"
      },
      "execution_count": 166,
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        {
          "output_type": "stream",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "po0uJDIHjYA0"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gWYsBxRLVqxx"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TuVeltJtVd0E"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tx-Lx1PtVd6u"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "----"
      ],
      "metadata": {
        "id": "51owZ7AljZ58"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries"
      ],
      "metadata": {
        "id": "Myes5znXVeIw"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "6y9-GoQwVeMS"
      },
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "vlQc9_FRVed7"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "NKsgPs7lYL5Y"
      },
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "8LJNWmefYOpo"
      },
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "5O-br_PmVeha"
      },
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "hzBht__cYWON"
      },
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n"
      ],
      "metadata": {
        "id": "sN9b1eJ3YaFH"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "2xSlJOimVelC"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "NKCSBBs_VeoL"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "6L_lMM5BVeq9"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib"
      ],
      "metadata": {
        "id": "xHKFE48oVeu0"
      },
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dash"
      ],
      "metadata": {
        "id": "db725C2cYi3s"
      },
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dash_core_components as dcc"
      ],
      "metadata": {
        "id": "OTjfDBp-Yi7E"
      },
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dash_html_components as html"
      ],
      "metadata": {
        "id": "NIuR4SZbYi-V"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from dash.dependencies import Input, Output"
      ],
      "metadata": {
        "id": "ZVgIEUmmYjBU"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "----"
      ],
      "metadata": {
        "id": "CZ1ZUnP1jdJn"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z1VFt4TCYsJK"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "prZ-jMJWYsMD"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lCfHbngRYsPc"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "----"
      ],
      "metadata": {
        "id": "PK6ki4Fsje9u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Iris dataset from the UCI repository"
      ],
      "metadata": {
        "id": "l0F6XpmIYsSp"
      },
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "column_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n"
      ],
      "metadata": {
        "id": "NrqgRbtybSfD"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',\n",
        "                 header=None, names=column_names)\n"
      ],
      "metadata": {
        "id": "hOFY2-JXbSiy"
      },
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4l6uosl5d619"
      },
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the dataset to a CSV file in the Colab environment"
      ],
      "metadata": {
        "id": "3aIuyVvyd65q"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('/content/iris_dataset.csv', index=False)"
      ],
      "metadata": {
        "id": "r9f-4Vk2d6-h"
      },
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7mJ0X-RzeAh5"
      },
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Verify by loading and displaying the saved file"
      ],
      "metadata": {
        "id": "JytGfFjjeAmt"
      },
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "saved_df = pd.read_csv('/content/iris_dataset.csv')"
      ],
      "metadata": {
        "id": "prJYGmrZd7DM"
      },
      "execution_count": 189,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(saved_df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5nXK7dwzd7HL",
        "outputId": "089f11bd-6b0a-404d-fa2e-25a6459ae901"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   sepal_length  sepal_width  petal_length  petal_width      species\n",
            "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
            "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
            "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
            "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
            "4           5.0          3.6           1.4          0.2  Iris-setosa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rO_tTrMZeG8I"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3KXsOJH1eHAf"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset from the provided path"
      ],
      "metadata": {
        "id": "Fu9PbnVNeHE4"
      },
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd = pd.read_csv('/content/iris_dataset.csv')"
      ],
      "metadata": {
        "id": "bEoVFtUQeHKO"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "oKO4Ouf8jjUN"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R0d0DKdfjkV-"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qTM0-sbujkfj"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kzOoq_oyeO_J"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "iophEFffjlp-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataset"
      ],
      "metadata": {
        "id": "HZxXWKVcePEP"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First few rows of the dataset:\")\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmyLYvn5eHO4",
        "outputId": "28642f45-f8c6-4922-a9c3-4c15566f16ea"
      },
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of the dataset:\n",
            "   sepal_length  sepal_width  petal_length  petal_width      species\n",
            "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
            "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
            "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
            "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
            "4           5.0          3.6           1.4          0.2  Iris-setosa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R2H6ZO-reR_A"
      },
      "execution_count": 194,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1lnQLvFeeSDh"
      },
      "execution_count": 194,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display summary statistics of the dataset"
      ],
      "metadata": {
        "id": "slx_WrQneSIL"
      },
      "execution_count": 195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSummary statistics:\")\n",
        "print(df.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NnPqEDbveHTv",
        "outputId": "61b98fa2-3e05-4369-e2ab-0ac0572587d4"
      },
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics:\n",
            "       sepal_length  sepal_width  petal_length  petal_width\n",
            "count    150.000000   150.000000    150.000000   150.000000\n",
            "mean       5.843333     3.054000      3.758667     1.198667\n",
            "std        0.828066     0.433594      1.764420     0.763161\n",
            "min        4.300000     2.000000      1.000000     0.100000\n",
            "25%        5.100000     2.800000      1.600000     0.300000\n",
            "50%        5.800000     3.000000      4.350000     1.300000\n",
            "75%        6.400000     3.300000      5.100000     1.800000\n",
            "max        7.900000     4.400000      6.900000     2.500000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SgZFl_xmeWem"
      },
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7hA4-y8heWjC"
      },
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display information about the dataset"
      ],
      "metadata": {
        "id": "PrMIKt80eWqy"
      },
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset Info:\")\n",
        "print(df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFuXgws5eaU_",
        "outputId": "381dd003-2f41-4673-802e-147b6777aab0"
      },
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   sepal_length  150 non-null    float64\n",
            " 1   sepal_width   150 non-null    float64\n",
            " 2   petal_length  150 non-null    float64\n",
            " 3   petal_width   150 non-null    float64\n",
            " 4   species       150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "twElYtjoeaZr"
      },
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VliuBz-xeae1"
      },
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values"
      ],
      "metadata": {
        "id": "BQGrRz9Weajs"
      },
      "execution_count": 199,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing values:\")\n",
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2dvgzBbVeaom",
        "outputId": "45995664-39d2-471c-bd48-b3df69b74614"
      },
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values:\n",
            "sepal_length    0\n",
            "sepal_width     0\n",
            "petal_length    0\n",
            "petal_width     0\n",
            "species         0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "r2cA93Xkeg3N"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bSTvHERZeg8C"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicate rows"
      ],
      "metadata": {
        "id": "PzqSuq0rehAs"
      },
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDuplicate rows:\")\n",
        "print(df.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1S9K0DTjehIK",
        "outputId": "ca6f1ac0-416b-4ae3-ce2d-487d20d1a8c0"
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Duplicate rows:\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cO-3IAGJehUH"
      },
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m8dwcew0ehZM"
      },
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicate rows"
      ],
      "metadata": {
        "id": "jHQFPUR_ehex"
      },
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleaned = df.drop_duplicates()"
      ],
      "metadata": {
        "id": "5rqVgP08eatU"
      },
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "npj1Txj8eoqV"
      },
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if duplicates are removed"
      ],
      "metadata": {
        "id": "ggD3dlAYeovP"
      },
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDuplicate rows after removal:\", df_cleaned.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnyzO4zkeo0n",
        "outputId": "7ae5ee9c-7a7c-4382-e6fb-ee8f2aa31836"
      },
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Duplicate rows after removal: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oU94zAOoeo5E"
      },
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the cleaned dataset"
      ],
      "metadata": {
        "id": "q0CQBPVDeo9_"
      },
      "execution_count": 207,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleaned.to_csv('/content/iris_dataset_cleaned.csv', index=False)"
      ],
      "metadata": {
        "id": "ptrkym8kepDG"
      },
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gfa0WvIveyKf"
      },
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the cleaned dataset's first few rows"
      ],
      "metadata": {
        "id": "haZZutBTeyP9"
      },
      "execution_count": 209,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nCleaned dataset:\")\n",
        "print(df_cleaned.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7H9y5ndseyU0",
        "outputId": "b6edbffa-3a91-415b-8b4f-ff6dd8add036"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Cleaned dataset:\n",
            "   sepal_length  sepal_width  petal_length  petal_width      species\n",
            "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
            "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
            "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
            "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
            "4           5.0          3.6           1.4          0.2  Iris-setosa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jazBJWlaeyaZ"
      },
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Eg0e7z5Weyfq"
      },
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set style for plots"
      ],
      "metadata": {
        "id": "fEq-5WApeykn"
      },
      "execution_count": 211,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(style=\"ticks\")"
      ],
      "metadata": {
        "id": "_hdBPtg8epK8"
      },
      "execution_count": 212,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xVcIK1DOe6rb"
      },
      "execution_count": 212,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pairplot to visualize relationships between features"
      ],
      "metadata": {
        "id": "aEOYWsrIe6wZ"
      },
      "execution_count": 213,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df_cleaned, hue='species')\n",
        "plt.suptitle(\"Pairplot of Iris Dataset\", y=1.02)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "nzJt_n7ne65k",
        "outputId": "357e0252-0976-4964-cde4-3fc4e4e1626c"
      },
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1158.25x1000 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4_-SQBSne6_I"
      },
      "execution_count": 214,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PnfhJBtMe7Ee"
      },
      "execution_count": 214,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation heatmap"
      ],
      "metadata": {
        "id": "2I_g-cN1epSh"
      },
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df_cleaned.drop('species', axis=1).corr()  # Drop 'species' column for correlation calculation\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=1, linecolor='black')\n",
        "plt.title(\"Correlation Heatmap\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "0YtOlSljd7Lk",
        "outputId": "a68e1a08-4256-4ffb-e193-fb5b13348e79"
      },
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "3nH95GE7jsJx"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RKTGOsaRfDlc"
      },
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hfMTHhodfDq6"
      },
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BXKy3wQtfDwR"
      },
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "U36HW5Lgju1P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target (y)"
      ],
      "metadata": {
        "id": "nt7VB4VHfD3C"
      },
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_cleaned.drop('species', axis=1)  # Features (sepal_length, sepal_width, petal_length, petal_width)\n"
      ],
      "metadata": {
        "id": "3zHqDb81d7Pw"
      },
      "execution_count": 218,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df_cleaned['species']  # Target variable (species)"
      ],
      "metadata": {
        "id": "X2WR150jd7Uu"
      },
      "execution_count": 219,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PQZTSgAsfNL4"
      },
      "execution_count": 219,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training (80%) and testing (20%) sets"
      ],
      "metadata": {
        "id": "2JDjLwDSfNSP"
      },
      "execution_count": 220,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "HgU_yLaDfNXm"
      },
      "execution_count": 221,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E1YJKeakfNc2"
      },
      "execution_count": 221,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale the features using StandardScaler"
      ],
      "metadata": {
        "id": "u_yRicPRfNiR"
      },
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "qu9b4BoqfNnV"
      },
      "execution_count": 223,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_scaled = scaler.fit_transform(X_train)  # Fit and transform on training data\n"
      ],
      "metadata": {
        "id": "z2R7wPgVfNv-"
      },
      "execution_count": 224,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_scaled = scaler.transform(X_test)  # Only transform on test data"
      ],
      "metadata": {
        "id": "ocmdh68HbSm4"
      },
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cOvNLcaWfa4H"
      },
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the shapes of the datasets"
      ],
      "metadata": {
        "id": "dVwkjlvEfa-5"
      },
      "execution_count": 226,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"X_train shape: {X_train_scaled.shape}\")\n",
        "print(f\"X_test shape: {X_test_scaled.shape}\")\n",
        "print(f\"y_train shape: {y_train.shape}\")\n",
        "print(f\"y_test shape: {y_test.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5rCUWstkfbIB",
        "outputId": "682559f1-74c1-45a3-9c2f-8baee4f92d4b"
      },
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (117, 4)\n",
            "X_test shape: (30, 4)\n",
            "y_train shape: (117,)\n",
            "y_test shape: (30,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "vrWQu6pijxOZ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "egWVndRtfgFR"
      },
      "execution_count": 227,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dFPavTF9fgLA"
      },
      "execution_count": 227,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ydOkgorrfgQW"
      },
      "execution_count": 227,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "x8y-r62mjy7H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate KNN for different k values (from 1 to 20)"
      ],
      "metadata": {
        "id": "wlScx1z0fgVm"
      },
      "execution_count": 228,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k_values = range(1, 21)"
      ],
      "metadata": {
        "id": "70qkezsKfkCX"
      },
      "execution_count": 229,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_accuracies = []"
      ],
      "metadata": {
        "id": "hu7VcWCQfkIn"
      },
      "execution_count": 230,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_accuracies = []"
      ],
      "metadata": {
        "id": "ttO0B_awfnPo"
      },
      "execution_count": 231,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for k in k_values:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X_train_scaled, y_train)\n",
        "\n",
        "    # Accuracy on training set\n",
        "    train_acc = knn.score(X_train_scaled, y_train)\n",
        "    train_accuracies.append(train_acc)\n",
        "\n",
        "    # Accuracy on test set\n",
        "    test_acc = knn.score(X_test_scaled, y_test)\n",
        "    test_accuracies.append(test_acc)"
      ],
      "metadata": {
        "id": "3hCrATx3fkOp"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "bWlfwAdrj1Vh"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fPvAvBYXfueq"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uL5lwzMxfuku"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "A2gZIXz0fuqg"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "uHB4yYKtj209"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the accuracy for different k values"
      ],
      "metadata": {
        "id": "QJk61CC2fuxB"
      },
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(k_values, train_accuracies, label='Train Accuracy', marker='o')\n",
        "plt.plot(k_values, test_accuracies, label='Test Accuracy', marker='x')\n",
        "plt.xlabel('k Value')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('KNN Accuracy for Different k Values')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "oUhP5PxAf1n0",
        "outputId": "347e4e81-f10a-4f11-c848-1ac8549f513e"
      },
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "Ya-52NR5j41r"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0Am_5bn3fgbB"
      },
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "geO8A5ZGfbNR"
      },
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Wxp_o8NkfbTK"
      },
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "5ZNtEdEdj7VM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Choose the best k (highest test accuracy)"
      ],
      "metadata": {
        "id": "PUQ8uhbnfbYq"
      },
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_k = k_values[test_accuracies.index(max(test_accuracies))]"
      ],
      "metadata": {
        "id": "6sUlTL9mbSqW"
      },
      "execution_count": 236,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Best k value: {best_k}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LHEejnksbStz",
        "outputId": "da0d7760-46be-431f-b4f9-92a5bb9f75a0"
      },
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best k value: 20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "QJIASZcqj-PZ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T2q2J1hZf9Z9"
      },
      "execution_count": 237,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kd9RdwHLf9gy"
      },
      "execution_count": 237,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iJcjrpD2f9mq"
      },
      "execution_count": 237,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "dfb_Dbqwj_2E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the final KNN model with the best k value"
      ],
      "metadata": {
        "id": "XlofJTASf9to"
      },
      "execution_count": 238,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_knn = KNeighborsClassifier(n_neighbors=best_k)"
      ],
      "metadata": {
        "id": "6gTf9Kc1f9zz"
      },
      "execution_count": 239,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_knn.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "uopYpYzzbSxh",
        "outputId": "adb15749-9476-4fef-9ffc-7e0029d41da3"
      },
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=20)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @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-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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: 0.5em;\n",
              "  text-align: center;\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-3 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-3 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-3 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-3 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=20)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=20)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gz5HIHYLgF7J"
      },
      "execution_count": 240,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iywLxKqCgGBf"
      },
      "execution_count": 240,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_RLgG87QgGHo"
      },
      "execution_count": 240,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "87mr0a-xkCl0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model's performance on the test set"
      ],
      "metadata": {
        "id": "xu30PqpdgGRN"
      },
      "execution_count": 241,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = final_knn.predict(X_test_scaled)"
      ],
      "metadata": {
        "id": "yaQrRvZfgGXf"
      },
      "execution_count": 242,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "id": "6zQNryTmbS1S"
      },
      "execution_count": 243,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_report = classification_report(y_test, y_pred)"
      ],
      "metadata": {
        "id": "BuVx_tgMgMaV"
      },
      "execution_count": 244,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix = confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "id": "5ZMxWY4kgMha"
      },
      "execution_count": 245,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-r3F0LwybS5f"
      },
      "execution_count": 245,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display accuracy, classification report, and confusion matrix"
      ],
      "metadata": {
        "id": "2UUt1BD5bS9F"
      },
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Accuracy on test set: {accuracy:.4f}\")\n",
        "print(\"Classification Report:\")\n",
        "print(class_report)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZjB1gjmbTAy",
        "outputId": "636f57b9-7787-465c-e282-0a5081b6412a"
      },
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 0.9667\n",
            "Classification Report:\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        11\n",
            "Iris-versicolor       0.91      1.00      0.95        10\n",
            " Iris-virginica       1.00      0.89      0.94         9\n",
            "\n",
            "       accuracy                           0.97        30\n",
            "      macro avg       0.97      0.96      0.96        30\n",
            "   weighted avg       0.97      0.97      0.97        30\n",
            "\n",
            "Confusion Matrix:\n",
            "[[11  0  0]\n",
            " [ 0 10  0]\n",
            " [ 0  1  8]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "4UOsaLLikEkI"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XPKyOTXLgWDv"
      },
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZMT-cSYjgWOL"
      },
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HzsaUOHRkHic"
      },
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "tFvlPLi9kHz7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the confusion matrix"
      ],
      "metadata": {
        "id": "W9e5rzbegWUN"
      },
      "execution_count": 248,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=y.unique(), yticklabels=y.unique())\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "dyIl20YXgWZp",
        "outputId": "d60ca271-8364-4e52-956a-e2d72c46b1f4"
      },
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "oUl-pvAhkKGR"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c_Ore4BKgdWu"
      },
      "execution_count": 249,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QJB_pJHagdeD"
      },
      "execution_count": 249,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Gpw9vZofgdkI"
      },
      "execution_count": 249,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "----"
      ],
      "metadata": {
        "id": "WfkChbdjkL02"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform 3-fold cross-validation on the training data"
      ],
      "metadata": {
        "id": "Z8yUvDuKgdqT"
      },
      "execution_count": 250,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_cv = KNeighborsClassifier(n_neighbors=best_k)"
      ],
      "metadata": {
        "id": "OML9b-0Qgdwo"
      },
      "execution_count": 251,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NUnpHKp7gd5-"
      },
      "execution_count": 251,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cross-validation scores (3 folds)"
      ],
      "metadata": {
        "id": "W-6raCbOgeFg"
      },
      "execution_count": 252,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_scores = cross_val_score(knn_cv, X_train_scaled, y_train, cv=3)"
      ],
      "metadata": {
        "id": "jD2A7V2ggeMT"
      },
      "execution_count": 253,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BJDJFh46gpcW"
      },
      "execution_count": 253,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the cross-validation scores"
      ],
      "metadata": {
        "id": "YXVARDHRgpi1"
      },
      "execution_count": 254,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Cross-validation scores: {cv_scores}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8O9Yx5G5gpqL",
        "outputId": "58b76d0d-c7a7-43df-e3da-0f4bd00f3855"
      },
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validation scores: [0.97435897 0.87179487 0.94871795]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8sCjWczYgpyF"
      },
      "execution_count": 255,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OsbyQzZ9gp5G"
      },
      "execution_count": 255,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the mean and standard deviation of the cross-validation scores"
      ],
      "metadata": {
        "id": "KUnXpRV0gqAP"
      },
      "execution_count": 256,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_mean = np.mean(cv_scores)"
      ],
      "metadata": {
        "id": "K9clHLMQgqG3"
      },
      "execution_count": 257,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_std = np.std(cv_scores)"
      ],
      "metadata": {
        "id": "GkPrTQj5geVv"
      },
      "execution_count": 258,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IUVeqq7vgWgD"
      },
      "execution_count": 258,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display mean and standard deviation"
      ],
      "metadata": {
        "id": "YP7sgxUfbTEc"
      },
      "execution_count": 259,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Mean of cross-validation scores: {cv_mean:.4f}\")\n",
        "print(f\"Standard deviation of cross-validation scores: {cv_std:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "naHzDBPwbTIi",
        "outputId": "927afe5c-37b2-4c63-c4ce-75f2c4d5fa5e"
      },
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean of cross-validation scores: 0.9316\n",
            "Standard deviation of cross-validation scores: 0.0436\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "brR5IB-4kRT5"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5Clno8KubTMh"
      },
      "execution_count": 260,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0-8slr5zbTQR"
      },
      "execution_count": 260,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yxn15Z55bTXF"
      },
      "execution_count": 260,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "jdL_Nqz3kTdx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the Logistic Regression model"
      ],
      "metadata": {
        "id": "00WlLXZvg9Ms"
      },
      "execution_count": 261,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logreg = LogisticRegression(max_iter=200)"
      ],
      "metadata": {
        "id": "D8Mfp-_1g-6d"
      },
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logreg.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "lhdkW7ekg_Bw",
        "outputId": "faae8378-74ae-41fb-e742-c01b71b7730b"
      },
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=200)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @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-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 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-4 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-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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: 0.5em;\n",
              "  text-align: center;\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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=200)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=200)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "oCVcf8MwkV0f"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tbPtgPwGg_ID"
      },
      "execution_count": 263,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "V4MLWjeNkXDs"
      },
      "execution_count": 263,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7afGPBkGkXOK"
      },
      "execution_count": 263,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "----"
      ],
      "metadata": {
        "id": "G4awS-L0kXYQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the feature coefficients"
      ],
      "metadata": {
        "id": "pJ0CPBQUg_P9"
      },
      "execution_count": 264,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "coefficients = logreg.coef_[0]"
      ],
      "metadata": {
        "id": "TfP5xLY4g_XD"
      },
      "execution_count": 265,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NmQxfPnYhJCy"
      },
      "execution_count": 265,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the feature importance using a bar plo"
      ],
      "metadata": {
        "id": "AcYEIEwrhJJ6"
      },
      "execution_count": 266,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = X.columns  # Feature names\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.barh(features, coefficients, color='skyblue')\n",
        "plt.xlabel('Coefficient Value')\n",
        "plt.title('Feature Importance (Logistic Regression Coefficients)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "YC_qVFUGhJP5",
        "outputId": "55396fbf-e487-4ab0-d798-0bb72b35ae49"
      },
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "----"
      ],
      "metadata": {
        "id": "2C0OsmdDkaGs"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "asJSjy5dhPam"
      },
      "execution_count": 267,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wc1UvagIhPhb"
      },
      "execution_count": 267,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DCqAqL7FhPo-"
      },
      "execution_count": 267,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "----"
      ],
      "metadata": {
        "id": "EvIziudRkeUC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the trained KNN model, scaler, and the performance results"
      ],
      "metadata": {
        "id": "x6HsiajfhP0U"
      },
      "execution_count": 268,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(final_knn, '/content/final_knn_model.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdsAlk78hJXJ",
        "outputId": "77eb5801-8097-4139-c3f8-ec3db28073e9"
      },
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/final_knn_model.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(scaler, '/content/scaler.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQb1NsRfhJd4",
        "outputId": "89ce90d7-1f32-466c-dc32-10c0bfc80934"
      },
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/scaler.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7w2pYkJGhU4M"
      },
      "execution_count": 270,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save accuracy, classification report, and confusion matrix to CSV(Optional)"
      ],
      "metadata": {
        "id": "qXV89soAhU-z"
      },
      "execution_count": 295,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#results = pd.DataFrame({\n",
        "#    'Accuracy': [accuracy],\n",
        "#    'Classification Report': [class_report],\n",
        "#    'Confusion Matrix': [conf_matrix]\n",
        "#})"
      ],
      "metadata": {
        "id": "9df8nFH5hVGD"
      },
      "execution_count": 296,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results.to_csv('/content/model_performance.csv', index=False)"
      ],
      "metadata": {
        "id": "kSnpix-Ihag8"
      },
      "execution_count": 275,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "r4Ue9CwjmD_X"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Dp7DbXT9hgIf"
      },
      "execution_count": 275,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G4wFII7ahgWu"
      },
      "execution_count": 275,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aCWD5uQBhghP"
      },
      "execution_count": 275,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "R4tzZ4KHmGIP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the saved KNN model and scaler"
      ],
      "metadata": {
        "id": "m6sdaA9rhgpN"
      },
      "execution_count": 276,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_loaded = joblib.load('/content/final_knn_model.joblib')"
      ],
      "metadata": {
        "id": "C3oRZzDdhgw6"
      },
      "execution_count": 277,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler_loaded = joblib.load('/content/scaler.joblib')"
      ],
      "metadata": {
        "id": "pu7n_t66hg4-"
      },
      "execution_count": 278,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZUslYeSfhnyC"
      },
      "execution_count": 278,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cEkD1iGohn8e"
      },
      "execution_count": 278,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function for making predictions"
      ],
      "metadata": {
        "id": "wBGpXK8DhoEn"
      },
      "execution_count": 279,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_species(sepal_length, sepal_width, petal_length, petal_width):\n",
        "    # Prepare the input data\n",
        "    input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]])\n",
        "\n",
        "    # Scale the input data using the loaded scaler\n",
        "    input_scaled = scaler_loaded.transform(input_data)\n",
        "\n",
        "    # Make the prediction using the loaded model\n",
        "    prediction = knn_loaded.predict(input_scaled)\n",
        "\n",
        "    return prediction[0]"
      ],
      "metadata": {
        "id": "Pcbi6ItghoLp"
      },
      "execution_count": 280,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zsaFeX1Nhu5R"
      },
      "execution_count": 281,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the prediction function with a sample flower"
      ],
      "metadata": {
        "id": "BN6o8Q-7hvAA"
      },
      "execution_count": 282,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_flower = {'sepal_length': 5.1, 'sepal_width': 3.5, 'petal_length': 1.4, 'petal_width': 0.2}\n"
      ],
      "metadata": {
        "id": "l287eS2hhyQo"
      },
      "execution_count": 281,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_species = predict_species(**sample_flower)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AGdjH0nrhyXU",
        "outputId": "4ac89790-8d13-4186-c5ef-905da1bd3b9f"
      },
      "execution_count": 283,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning:\n",
            "\n",
            "X does not have valid feature names, but StandardScaler was fitted with feature names\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VCti5dWRh3xZ"
      },
      "execution_count": 283,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Predicted species for the sample flower: {predicted_species}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HzA7PpBHh338",
        "outputId": "91f2f69d-f144-4aff-93ac-16498e057bc2"
      },
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted species for the sample flower: Iris-setosa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "qMMfis3Cmsfh"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yuyLh0OIh-Wg"
      },
      "execution_count": 284,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QefdNCRZh-dm"
      },
      "execution_count": 284,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s5XDNluph-ld"
      },
      "execution_count": 284,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "----"
      ],
      "metadata": {
        "id": "PaT383nEmu3W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the Dash app"
      ],
      "metadata": {
        "id": "mU8--UCMh-sq"
      },
      "execution_count": 285,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = dash.Dash(__name__)"
      ],
      "metadata": {
        "id": "RKiRF1aHh3_1"
      },
      "execution_count": 286,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mDbmT8MAhyfj"
      },
      "execution_count": 286,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the layout of the app"
      ],
      "metadata": {
        "id": "pEF1GW5ciLPg"
      },
      "execution_count": 288,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app.layout = html.Div([\n",
        "    html.H1(\"Iris Flower Prediction\"),\n",
        "\n",
        "    # Input fields for flower features\n",
        "    html.Label(\"Sepal Length:\"),\n",
        "    dcc.Input(id='sepal-length', type='number', value=5.1),\n",
        "\n",
        "    html.Label(\"Sepal Width:\"),\n",
        "    dcc.Input(id='sepal-width', type='number', value=3.5),\n",
        "\n",
        "    html.Label(\"Petal Length:\"),\n",
        "    dcc.Input(id='petal-length', type='number', value=1.4),\n",
        "\n",
        "    html.Label(\"Petal Width:\"),\n",
        "    dcc.Input(id='petal-width', type='number', value=0.2),\n",
        "\n",
        "    # Button to trigger prediction\n",
        "    html.Button('Predict', id='predict-button', n_clicks=0),\n",
        "\n",
        "    # Display prediction output\n",
        "    html.Div(id='prediction-output'),\n",
        "\n",
        "    # Graph for Correlation Heatmap\n",
        "    html.Div([\n",
        "        html.H3(\"Correlation Heatmap\"),\n",
        "        dcc.Graph(id='heatmap')\n",
        "    ]),\n",
        "\n",
        "    # Graph for Pairplot\n",
        "    html.Div([\n",
        "        html.H3(\"Pairplot\"),\n",
        "        dcc.Graph(id='pairplot')\n",
        "    ])\n",
        "])"
      ],
      "metadata": {
        "id": "3criKbfEiLYA"
      },
      "execution_count": 289,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fQj9uSN4ixGZ"
      },
      "execution_count": 289,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define callback for updating output and graphs"
      ],
      "metadata": {
        "id": "3-y-jfSbixOs"
      },
      "execution_count": 290,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@app.callback(\n",
        "    [Output('prediction-output', 'children'),\n",
        "     Output('heatmap', 'figure'),\n",
        "     Output('pairplot', 'figure')],\n",
        "    [Input('sepal-length', 'value'),\n",
        "     Input('sepal-width', 'value'),\n",
        "     Input('petal-length', 'value'),\n",
        "     Input('petal-width', 'value'),\n",
        "     Input('predict-button', 'n_clicks')]\n",
        ")\n",
        "def update_output(sepal_length, sepal_width, petal_length, petal_width, n_clicks):\n",
        "    try:\n",
        "        prediction_output = \"Enter flower features and click 'Predict'\"\n",
        "        heatmap_figure = None\n",
        "        pairplot_figure = None\n",
        "\n",
        "        if n_clicks > 0:\n",
        "            # Prepare input data for prediction\n",
        "            input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]])\n",
        "\n",
        "            # Scale input data\n",
        "            scaled_input = scaler.transform(input_data)\n",
        "\n",
        "            # Make prediction\n",
        "            prediction = knn_model.predict(scaled_input)\n",
        "\n",
        "            # Prepare prediction output\n",
        "            prediction_output = f\"The predicted species is: {prediction[0]}\"\n",
        "\n",
        "        # Prepare correlation heatmap\n",
        "        numeric_data = iris_data.select_dtypes(include=[np.number])  # Select numeric columns\n",
        "        corr_matrix = numeric_data.corr()  # Calculate correlation matrix\n",
        "        heatmap_figure = px.imshow(corr_matrix, title=\"Correlation Heatmap\", labels={\"x\": \"Features\", \"y\": \"Features\"})\n",
        "\n",
        "        # Prepare pairplot\n",
        "        pairplot_figure = px.scatter_matrix(iris_data, color=\"species\", title=\"Pairplot\")\n",
        "\n",
        "        # Save prediction results to CSV\n",
        "        result_df = pd.DataFrame({\n",
        "            'Sepal Length': [sepal_length],\n",
        "            'Sepal Width': [sepal_width],\n",
        "            'Petal Length': [petal_length],\n",
        "            'Petal Width': [petal_width],\n",
        "            'Predicted Species': [prediction[0]] if n_clicks > 0 else [\"N/A\"]\n",
        "        })\n",
        "        result_df.to_csv('/content/prediction_results.csv', mode='a', header=False, index=False)\n",
        "\n",
        "        return prediction_output, heatmap_figure, pairplot_figure\n",
        "\n",
        "    except Exception as e:\n",
        "        # Handle errors\n",
        "        print(f\"Error: {e}\")\n",
        "        return f\"An error occurred: {e}\", {}, {}\n"
      ],
      "metadata": {
        "id": "_g038ZcjixW2"
      },
      "execution_count": 291,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JaCbnwUHi4aP"
      },
      "execution_count": 291,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the app"
      ],
      "metadata": {
        "id": "9u-_h7V0i4hA"
      },
      "execution_count": 292,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == '__main__':\n",
        "    app.run_server(debug=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "w3JweHnmhVNW",
        "outputId": "769346bc-f75d-4bdc-d434-08007da2d8ed"
      },
      "execution_count": 293,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "    if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "      return;\n",
              "    }\n",
              "    element.appendChild(document.createTextNode(''));\n",
              "    const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "    const iframe = document.createElement('iframe');\n",
              "    iframe.src = new URL(path, url).toString();\n",
              "    iframe.height = height;\n",
              "    iframe.width = width;\n",
              "    iframe.style.border = 0;\n",
              "    iframe.allow = [\n",
              "        'accelerometer',\n",
              "        'autoplay',\n",
              "        'camera',\n",
              "        'clipboard-read',\n",
              "        'clipboard-write',\n",
              "        'gyroscope',\n",
              "        'magnetometer',\n",
              "        'microphone',\n",
              "        'serial',\n",
              "        'usb',\n",
              "        'xr-spatial-tracking',\n",
              "    ].join('; ');\n",
              "    element.appendChild(iframe);\n",
              "  })(8050, \"/\", \"100%\", 650, false, window.element)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "wsV5NEPymyNE"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rWSbgdW1lVWy"
      },
      "execution_count": 296,
      "outputs": []
    }
  ]
}