knn_in_python_demo.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRmhsV9txd4c"
      },
      "source": [
        "# K-Nearest Neighbors in Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i0R7Ps0Nxd4l"
      },
      "source": [
        "## 1 - Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "oFS4L3Fmxd4l"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pandas as pd \n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "import numpy as np \n",
        "from tqdm import tqdm \n",
        "\n",
        "iris = load_iris()\n",
        "X, y = iris.data[:, 2:], iris.target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPJBGy-Z1WkL",
        "outputId": "8ca05b89-234b-41d5-9927-995eeec0fa13"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IpGXVIaxxd4l"
      },
      "source": [
        "The next task is to divide the dataset into a training and a test set. Notice though that the first 10 entries are all zeros. This is because the dataset has not been shuffled yet. Hence, in Iris, the vector `y` will consists of 50 0's, 50 1's, and 50 2's (in that order).\n",
        "\n",
        "It would thus be a bit problematic if we assigned the first 100 entries to a training set and the 50 last entries to the test set.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DS0gyJBExd4l"
      },
      "source": [
        "## 2 - Shuffle Dataset and Create Training and Test Subsets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ivtBWnB6xd4l"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                                    test_size=0.3,\n",
        "                                                    random_state=123,\n",
        "                                                    shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bi-yMRiAxd4m",
        "outputId": "93cf89c0-37c3-46e4-8787-c0c7f228196f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X_train.shape: (105, 2)\n",
            "y_train.shape: (105,)\n",
            "X_test.shape: (45, 2)\n",
            "y_test.shape: (45,)\n"
          ]
        }
      ],
      "source": [
        "print(f'X_train.shape: {X_train.shape}')\n",
        "print(f'y_train.shape: {y_train.shape}')\n",
        "print(f'X_test.shape: {X_test.shape}')\n",
        "print(f'y_test.shape: {y_test.shape}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q2RSN4_Oxd4n"
      },
      "source": [
        "## 3 - Plot Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2hWE8ioxd4n"
      },
      "source": [
        "We are only working with 2 features, i.e., petal length and petal width (via `X, y = iris.data[:, 2:], iris.target` earlier). Create a scatter plot matrix of these 2 features for X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "LXwJ7C1Jxd4w",
        "outputId": "ceea6170-c119-4539-efc2-0b2704353a53"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from mlxtend.plotting import scatterplotmatrix\n",
        "\n",
        "\n",
        "names = ['petal length [cm]', 'petal width [cm]']\n",
        "\n",
        "fig, axes = scatterplotmatrix(X_train[y_train==0], figsize=(10, 8), alpha=0.5, label=\"Setosa\")\n",
        "fig, axes = scatterplotmatrix(X_train[y_train==1], fig_axes=(fig, axes), alpha=0.5, label=\"Versicolor\")\n",
        "fig, axes = scatterplotmatrix(X_train[y_train==2], fig_axes=(fig, axes), alpha=0.5, names=names, label=\"Virginica\")\n",
        "\n",
        "fig.legend()\n",
        "\n",
        "plt.suptitle(\"Features of Iris Dataset\", y=1.02, fontsize=16)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KTIC4hjWxd4x"
      },
      "source": [
        "## 4 - Fit k-Nearest Neighbor Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEU7Q2puxd4x"
      },
      "source": [
        "Next, we are going to use the `KNeighborsClassifier` class from scikit-learn and instantiate a new KNN object, which we call `knn_model`. Then, we \"fit\" this model the training dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "4mRRspfgxd4x",
        "outputId": "469df9bd-eed0-4eae-afb2-527983a725f2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#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>KNeighborsClassifier(n_neighbors=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=1)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=1)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "\n",
        "knn_model = KNeighborsClassifier(n_neighbors=1)\n",
        "knn_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0Z7uAVGxd4x"
      },
      "source": [
        "Q: What distance measure is used by default?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'algorithm': 'auto',\n",
              " 'leaf_size': 30,\n",
              " 'metric': 'minkowski',\n",
              " 'metric_params': None,\n",
              " 'n_jobs': None,\n",
              " 'n_neighbors': 1,\n",
              " 'p': 2,\n",
              " 'weights': 'uniform'}"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn_model.get_params()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- The distance measure by default is `minkowski`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yud8BTR4xd4x"
      },
      "source": [
        "## 5 - Use kNN Model to Make Predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YgLukhZfxd4x"
      },
      "source": [
        "After \"fitting\" the KNN model, we can now make predictions on new data points that the model has not seen before. The test set represents such data points. Of course, we have labels for the test set as well, but we only use these labels to evaluate the model -- i.e., we compare the predictions to the true labels in the test set in order to find out how good the model is:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "JHPcRLG0xd4y"
      },
      "outputs": [],
      "source": [
        "y_pred = knn_model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dtibrL4-xd4y",
        "outputId": "539cb1c0-37dd-4930-92cf-40b9d7d70c1b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train set accuracy:  99.05%\n",
            "Test set accuracy: 93.33%\n"
          ]
        }
      ],
      "source": [
        "num_correct_predictions = (y_pred == y_test).sum()\n",
        "accuracy = (num_correct_predictions / y_test.shape[0]) * 100\n",
        "\n",
        "print(f'Train set accuracy: {((knn_model.predict(X_train) == y_train).sum() / y_train.shape[0]) * 100: .2f}%')\n",
        "\n",
        "print(f'Test set accuracy: {accuracy:.2f}%')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iS3pt70lxd4y",
        "outputId": "759da6f6-854a-4309-d5f6-8423d08697ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test set accuracy: 93.33%\n",
            "Test set accuracy: 93.33%\n"
          ]
        }
      ],
      "source": [
        "# print('Test set accuracy: %.2f%%' % (knn_model.score(X_test, y_test)*100))\n",
        "\n",
        "print(f'Test set accuracy: {knn_model.score(X_test, y_test)*100:.2f}%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWMNt2d83xSj"
      },
      "source": [
        "Q1: Change K to maximize the performance on the test set\n",
        "\n",
        "Q2: Change the distance measure and analyse the model performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  0%|          | 0/24 [00:00<?, ?it/s]"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 67%|██████▋   | 16/24 [00:08<00:03,  2.30it/s]c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\pairwise.py:2181: DataConversionWarning: Data was converted to boolean for metric yule\n",
            "  warnings.warn(msg, DataConversionWarning)\n",
            "100%|██████████| 24/24 [00:11<00:00,  2.05it/s]\n"
          ]
        }
      ],
      "source": [
        "res = []\n",
        "\n",
        "metrics = ['p', 'hamming', 'euclidean', 'l2', 'chebyshev', 'sokalsneath',\n",
        "           'nan_euclidean', 'sqeuclidean', 'cosine', 'l1', 'russellrao', 'braycurtis', 'infinity', \n",
        "           'canberra', 'rogerstanimoto', 'minkowski', 'yule', 'cityblock', 'jaccard', \n",
        "           'correlation', 'dice', 'haversine', 'sokalmichener', 'manhattan'\n",
        "            ]\n",
        "\n",
        "ks = np.arange(1, 20, 1)\n",
        "\n",
        "for metric in tqdm(metrics):\n",
        "    # print(f'Processing metric: {metric}')\n",
        "    for n in ks:\n",
        "        knn_model = KNeighborsClassifier(n_neighbors=n, metric=metric)\n",
        "        knn_model.fit(X_train, y_train)\n",
        "        res.append({'metric': metric, 'k': n, 'accuracy': knn_model.score(X_test, y_test)})\n",
        "        "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>metric</th>\n",
              "      <th>k</th>\n",
              "      <th>accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>haversine</td>\n",
              "      <td>7</td>\n",
              "      <td>0.977778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>infinity</td>\n",
              "      <td>10</td>\n",
              "      <td>0.955556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>minkowski</td>\n",
              "      <td>13</td>\n",
              "      <td>0.955556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>minkowski</td>\n",
              "      <td>12</td>\n",
              "      <td>0.955556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>minkowski</td>\n",
              "      <td>11</td>\n",
              "      <td>0.955556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>minkowski</td>\n",
              "      <td>10</td>\n",
              "      <td>0.955556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>minkowski</td>\n",
              "      <td>9</td>\n",
              "      <td>0.955556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>minkowski</td>\n",
              "      <td>8</td>\n",
              "      <td>0.955556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>minkowski</td>\n",
              "      <td>7</td>\n",
              "      <td>0.955556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>minkowski</td>\n",
              "      <td>5</td>\n",
              "      <td>0.955556</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        metric   k  accuracy\n",
              "405  haversine   7  0.977778\n",
              "237   infinity  10  0.955556\n",
              "297  minkowski  13  0.955556\n",
              "296  minkowski  12  0.955556\n",
              "295  minkowski  11  0.955556\n",
              "294  minkowski  10  0.955556\n",
              "293  minkowski   9  0.955556\n",
              "292  minkowski   8  0.955556\n",
              "291  minkowski   7  0.955556\n",
              "289  minkowski   5  0.955556"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.DataFrame(res).sort_values(by='accuracy', ascending=False).head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(metric=&#x27;haversine&#x27;, n_neighbors=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(metric=&#x27;haversine&#x27;, n_neighbors=7)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsClassifier(metric='haversine', n_neighbors=7)"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "\n",
        "knn_model = KNeighborsClassifier(n_neighbors=7, metric='haversine')\n",
        "knn_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LRrdWmpVxd4y"
      },
      "source": [
        "## 6 - Visualize Decision Boundary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h3JAcPhYxd4y"
      },
      "source": [
        "Usually, in machine learning, we work with datasets that have more then 2 feature variables. For educational purposes, however, we chose a very simple dataset consisting of only two features here (the petal length and the petal width of Iris flowers). If we have only two features, we can easily visualize the decision boundary of the model -- here, we will be using a convenience function implemented in the MLxtend library:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "qeCDAx0gxd4y",
        "outputId": "5ad6678a-c070-43e0-f267-d3a90deb31b5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from mlxtend.plotting import plot_decision_regions\n",
        "\n",
        "\n",
        "plot_decision_regions(X_train, y_train, knn_model)\n",
        "plt.xlabel('petal length [cm]')\n",
        "plt.ylabel('petal width [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "UeQTZMgExd4z",
        "outputId": "ad77d116-9234-4cf9-a80e-3288d5f9487a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_decision_regions(X_test, y_test, knn_model)\n",
        "plt.xlabel('petal length [cm]')\n",
        "plt.ylabel('petal width [cm]')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}