KNN_applied_to_Cars_Evaluation_Dataset.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "hvTMmMI4HOmX",
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        "yk8Io8_HXWzH",
        "c7mFmerHJPHx",
        "tWvk9GhlJRr7",
        "_DHLS6MXJUn2",
        "fydNo9rDacxw"
      ],
      "authorship_tag": "ABX9TyPp+wN1fV77vGi5mz1xLaaz",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/amosdisabatino/KNN-applied-to-Cars-Evaluation-Dataset/blob/main/KNN_applied_to_Cars_Evaluation_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Libraries"
      ],
      "metadata": {
        "id": "kSmBAeVL-dge"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z9YFfEuw-QDW"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "from sklearn.model_selection import GridSearchCV, train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Data"
      ],
      "metadata": {
        "id": "dXsIeSFs-xgb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "header = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class']\n",
        "cars_dataset = pd.read_csv('car.data', header=None, names=header)\n",
        "print(cars_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-iQNpaq-zEt",
        "outputId": "a49d4e5d-8167-4dd3-c564-bf1ed53bb85d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     buying  maint  doors persons lug_boot safety  class\n",
            "0     vhigh  vhigh      2       2    small    low  unacc\n",
            "1     vhigh  vhigh      2       2    small    med  unacc\n",
            "2     vhigh  vhigh      2       2    small   high  unacc\n",
            "3     vhigh  vhigh      2       2      med    low  unacc\n",
            "4     vhigh  vhigh      2       2      med    med  unacc\n",
            "...     ...    ...    ...     ...      ...    ...    ...\n",
            "1723    low    low  5more    more      med    med   good\n",
            "1724    low    low  5more    more      med   high  vgood\n",
            "1725    low    low  5more    more      big    low  unacc\n",
            "1726    low    low  5more    more      big    med   good\n",
            "1727    low    low  5more    more      big   high  vgood\n",
            "\n",
            "[1728 rows x 7 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(cars_dataset.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2pXXdfuAq_d",
        "outputId": "caf9d9bf-4765-4632-9217-97a3bd50ce43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "buying      object\n",
            "maint       object\n",
            "doors       object\n",
            "persons     object\n",
            "lug_boot    object\n",
            "safety      object\n",
            "class       object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(cars_dataset.isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aku414RVA8HB",
        "outputId": "bd19ca4a-ae9b-4168-dfda-3284fd571b7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "buying      0\n",
            "maint       0\n",
            "doors       0\n",
            "persons     0\n",
            "lug_boot    0\n",
            "safety      0\n",
            "class       0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cleaning and encoding the categorical features"
      ],
      "metadata": {
        "id": "4r0_n5DjAG9b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_mapping_n_to_str = {\n",
        "    0: 'unacc',\n",
        "    1: 'acc',\n",
        "    2: 'good',\n",
        "    3: 'vgood',\n",
        "}\n"
      ],
      "metadata": {
        "id": "8AGUMsGbAKBk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cars_dataset.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2NQcr6DEMqT",
        "outputId": "857d4577-ce2f-4004-8585-2bd47ed73252"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            buying        maint     lug_boot       safety        class\n",
            "count  1728.000000  1728.000000  1728.000000  1728.000000  1728.000000\n",
            "mean      1.500000     1.500000     1.000000     1.000000     0.414931\n",
            "std       1.118358     1.118358     0.816733     0.816733     0.740700\n",
            "min       0.000000     0.000000     0.000000     0.000000     0.000000\n",
            "25%       0.750000     0.750000     0.000000     0.000000     0.000000\n",
            "50%       1.500000     1.500000     1.000000     1.000000     0.000000\n",
            "75%       2.250000     2.250000     2.000000     2.000000     1.000000\n",
            "max       3.000000     3.000000     2.000000     2.000000     3.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Correlation Analysis"
      ],
      "metadata": {
        "id": "P9qKWqdukYDt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the 'Kendall' correlation between each feature and the target feature\n",
        "# 'class'.\n",
        "corr_with_target = cars_dataset.corr(method='kendall')['class'].drop('class')\n",
        "\n",
        "# Creation of the 'Barplot'.\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(\n",
        "    x=corr_with_target.index, y=corr_with_target.values, palette='coolwarm',\n",
        ")\n",
        "plt.title(\"Kendall's correlation between each feature and the target feature.\")\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Kendall Correlation')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "id": "oGTo3_m_kdYz",
        "outputId": "3df9d82f-77de-4638-effe-2037366773c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-35a5114a9f4f>:7: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(\n"
          ]
        },
        {
          "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": [
        "# Creating a KNN model using 'Grid Search'"
      ],
      "metadata": {
        "id": "hvTMmMI4HOmX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "jLrV0kSJZaxM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X = cars_dataset.iloc[:, 0:-1]\n",
        "y = cars_dataset[['class']]\n",
        "\n",
        "# Split the dataset in 'train' and 'test' set.\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.3, random_state=42, stratify=y)\n",
        "# KNN model training\n",
        "knn = GridSearchCV(\n",
        "    KNeighborsClassifier(),\n",
        "     {\n",
        "         'n_neighbors': np.arange(2, 21, 1),\n",
        "         'weights': ['uniform', 'distance'],\n",
        "    },\n",
        "    scoring='balanced_accuracy', cv=5,\n",
        "  )\n",
        "scaler.fit(X_train)\n",
        "knn.fit(scaler.transform(X_train), y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pRPnQ_-DHRft",
        "outputId": "7b1050f3-79c2-41ee-fa23-c0c9350cc745",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid={'n_neighbors': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
              "       19, 20]),\n",
              "                         'weights': ['uniform', 'distance']},\n",
              "             scoring='balanced_accuracy')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid={&#x27;n_neighbors&#x27;: array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
              "       19, 20]),\n",
              "                         &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
              "             scoring=&#x27;balanced_accuracy&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid={&#x27;n_neighbors&#x27;: array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
              "       19, 20]),\n",
              "                         &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
              "             scoring=&#x27;balanced_accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Printing Accuracy"
      ],
      "metadata": {
        "id": "KX6XjAN2XM8W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "vOfSlbATFHt3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test, knn.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7cfyJayFPok",
        "outputId": "896defb4-99aa-46be-92bb-ddabfdabf2d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9576107899807321"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_6nfKt3WxNJ",
        "outputId": "aff683cc-d8dd-4b33-eab3-601fba4646f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_neighbors': 6, 'weights': 'distance'}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Some samples to test the KNN prediction"
      ],
      "metadata": {
        "id": "MsV9BbBGaeYO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_mapping_n_to_str[knn.predict(X_test)[100]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "EfnWxDALXjT4",
        "outputId": "0246f80a-6948-46aa-d1f2-186db95b79a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'unacc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_mapping_n_to_str[int(y_test.iloc[100])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "vp6IIuZdZUm9",
        "outputId": "6319722e-0368-46c8-eef1-c991842b4130"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-7415ef77a999>:1: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
            "  class_mapping_n_to_str[int(y_test.iloc[100])]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'unacc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_mapping_n_to_str[knn.predict(X_test)[200]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "VrP5EMllaxpC",
        "outputId": "6970d2e4-1489-4681-e217-f4cddb196f3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'unacc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_mapping_n_to_str[int(y_test.iloc[200])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "kHD51lBjbVZ-",
        "outputId": "e44a4d28-2057-4242-bce9-4b927a762f69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-b189da5ec48c>:1: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
            "  class_mapping_n_to_str[int(y_test.iloc[200])]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'unacc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_mapping_n_to_str[knn.predict(X_test)[123]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "yVvkygwqbcQz",
        "outputId": "7651949d-b3ae-4cde-afaa-1c4f8ffbe266"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'unacc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_mapping_n_to_str[int(y_test.iloc[123])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "SaFkbjasbkhe",
        "outputId": "928d015a-7aec-41e8-b571-934e9059b8d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-d9f4a5c343e2>:1: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
            "  class_mapping_n_to_str[int(y_test.iloc[123])]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'unacc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New hand-created data"
      ],
      "metadata": {
        "id": "yk8Io8_HXWzH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = {\n",
        "    'buying': [0, 3, 2],\n",
        "    'maint': [0, 1, 0],\n",
        "    'doors': [0, 5, 4],\n",
        "    'persons': [0, 5, 4],\n",
        "    'lug_boot':[0, 2, 1],\n",
        "    'safety':[0, 3, 1],\n",
        "}\n",
        "\n",
        "new_df = pd.DataFrame(new_data, index=[0, 1, 2])\n",
        "\n",
        "\n",
        "transform_df = scaler.transform(new_df)\n",
        "\n",
        "transform_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wk4y3g1nbwiA",
        "outputId": "3037da69-b6ba-40b6-debc-3c25ca171a1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.37199669, -1.32957965, -3.1342116 , -2.93298514, -1.23546044,\n",
              "        -1.21218951],\n",
              "       [ 1.32368694, -0.44245945,  1.31119176,  1.05664023,  1.21117621,\n",
              "         2.46698316],\n",
              "       [ 0.42512573, -1.32957965,  0.42211109,  0.25871516, -0.01214212,\n",
              "         0.01420138]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## First Row"
      ],
      "metadata": {
        "id": "c7mFmerHJPHx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_mapping_n_to_str[knn.predict(transform_df)[0]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Y777Q-RoQLtR",
        "outputId": "fc3ce747-3f20-4359-c8a9-1193476220d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'unacc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.iloc[[0]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "4S2O-YpuPOeM",
        "outputId": "c6ddfcdf-7177-44d4-d674-67b5239a05f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   buying  maint  doors  persons  lug_boot  safety\n",
              "0       0      0      0        0         0       0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e8ccca09-3dae-4ae8-9f2a-71baa3249995\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>buying</th>\n",
              "      <th>maint</th>\n",
              "      <th>doors</th>\n",
              "      <th>persons</th>\n",
              "      <th>lug_boot</th>\n",
              "      <th>safety</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8ccca09-3dae-4ae8-9f2a-71baa3249995')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e8ccca09-3dae-4ae8-9f2a-71baa3249995 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e8ccca09-3dae-4ae8-9f2a-71baa3249995');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"buying\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"maint\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"doors\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"persons\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lug_boot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"safety\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Second Row"
      ],
      "metadata": {
        "id": "tWvk9GhlJRr7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_mapping_n_to_str[knn.predict(transform_df)[1]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ZLIS8DmUQOwB",
        "outputId": "3da95828-650e-484e-c488-33fbcd8dd5dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'acc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.iloc[[1]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "l0CBOCbvQfHu",
        "outputId": "fb5df832-cd12-42f2-d228-fc07c57a184b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   buying  maint  doors  persons  lug_boot  safety\n",
              "1       3      1      5        5         2       3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d1516482-edd5-4b02-bc6a-f90f4c92f22d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>buying</th>\n",
              "      <th>maint</th>\n",
              "      <th>doors</th>\n",
              "      <th>persons</th>\n",
              "      <th>lug_boot</th>\n",
              "      <th>safety</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1516482-edd5-4b02-bc6a-f90f4c92f22d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d1516482-edd5-4b02-bc6a-f90f4c92f22d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d1516482-edd5-4b02-bc6a-f90f4c92f22d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"buying\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"maint\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"doors\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"persons\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lug_boot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"safety\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Third Row"
      ],
      "metadata": {
        "id": "_DHLS6MXJUn2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_mapping_n_to_str[knn.predict(transform_df)[2]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "pXh3Kuw6Qs2D",
        "outputId": "53f91137-45f5-4591-f977-0b67edd3a7c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'acc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.iloc[[2]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "aoy_AYesQv9C",
        "outputId": "99d6ce0f-d942-46b6-a3c9-2f95b2df1ee8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   buying  maint  doors  persons  lug_boot  safety\n",
              "2       2      0      4        4         1       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b328aece-5192-4f87-8194-768a6a32ae03\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>buying</th>\n",
              "      <th>maint</th>\n",
              "      <th>doors</th>\n",
              "      <th>persons</th>\n",
              "      <th>lug_boot</th>\n",
              "      <th>safety</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b328aece-5192-4f87-8194-768a6a32ae03')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b328aece-5192-4f87-8194-768a6a32ae03 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b328aece-5192-4f87-8194-768a6a32ae03');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"buying\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"maint\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"doors\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 4,\n        \"max\": 4,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"persons\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 4,\n        \"max\": 4,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lug_boot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"safety\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating a Confusion Matrix"
      ],
      "metadata": {
        "id": "fydNo9rDacxw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction on the test set\n",
        "y_pred = knn.predict(X_test)\n",
        "\n",
        "# Confusion matrix computation\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Confusion matrix visualisation\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(\n",
        "    cm, annot=True, cmap='Blues', fmt='d',\n",
        "    xticklabels=class_mapping_n_to_str.values(),\n",
        "    yticklabels=class_mapping_n_to_str.values(),\n",
        ")\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "# Printing the classification report\n",
        "class_names = class_mapping_n_to_str.values()\n",
        "print(classification_report(y_test, y_pred,target_names = class_names))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "id": "GxTFCDAuae8Y",
        "outputId": "5e71b5dc-7738-4f82-fdb0-c45b50eb860e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       unacc       0.98      0.98      0.98       363\n",
            "         acc       0.89      0.94      0.92       115\n",
            "        good       1.00      0.62      0.76        21\n",
            "       vgood       0.90      0.95      0.93        20\n",
            "\n",
            "    accuracy                           0.96       519\n",
            "   macro avg       0.94      0.87      0.90       519\n",
            "weighted avg       0.96      0.96      0.96       519\n",
            "\n"
          ]
        }
      ]
    }
  ]
}