KNN implementation(without_sklearn).ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import accuracy_score\n",
        "import math"
      ]
    },
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      "source": [
        "data = pd.read_csv('heart.csv')"
      ],
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              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
              "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
              "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
              "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
              "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   2     3       0  \n",
              "1   0     3       0  \n",
              "2   0     3       0  \n",
              "3   1     3       0  \n",
              "4   3     2       0  "
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              "      <td>0</td>\n",
              "      <td>138</td>\n",
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              "      <td>106</td>\n",
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              "      <td>1.9</td>\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1025 entries, 0 to 1024\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1025 non-null   int64  \n",
            " 1   sex       1025 non-null   int64  \n",
            " 2   cp        1025 non-null   int64  \n",
            " 3   trestbps  1025 non-null   int64  \n",
            " 4   chol      1025 non-null   int64  \n",
            " 5   fbs       1025 non-null   int64  \n",
            " 6   restecg   1025 non-null   int64  \n",
            " 7   thalach   1025 non-null   int64  \n",
            " 8   exang     1025 non-null   int64  \n",
            " 9   oldpeak   1025 non-null   float64\n",
            " 10  slope     1025 non-null   int64  \n",
            " 11  ca        1025 non-null   int64  \n",
            " 12  thal      1025 non-null   int64  \n",
            " 13  target    1025 non-null   int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 112.2 KB\n"
          ]
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              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
              "      dtype='object')"
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            "text/plain": [
              "(1025, 14)"
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        "corr = data.corr()\n",
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            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop('target', axis=1)"
      ],
      "metadata": {
        "id": "4Zu8p5TR2wX5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9EebaJ3C3D1u",
        "outputId": "c6ebe339-631f-4b55-9756-4f42b6bc9dc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
              "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
              "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
              "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
              "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
              "\n",
              "   ca  thal  \n",
              "0   2     3  \n",
              "1   0     3  \n",
              "2   0     3  \n",
              "3   1     3  \n",
              "4   3     2  "
            ],
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          "metadata": {},
          "execution_count": 9
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        "y = data['target']"
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      "outputs": []
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              "0    0\n",
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              "2    0\n",
              "3    0\n",
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      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "BE2a7__s3S6o"
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      "execution_count": null,
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    },
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        "Euclidean Distance"
      ],
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        "id": "alSoXraT3e1r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def euclidean_distance(x1, x2):\n",
        "    return math.sqrt(np.sum((x1 - x2)**2))"
      ],
      "metadata": {
        "id": "lN4qZsDY3eBx"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "def knn_euclidean_distance(X_train, y_train, X_test, k):\n",
        "    # List to store the predicted labels for the test set\n",
        "    y_pred = []\n",
        "\n",
        "    # Iterate over each point in the test set\n",
        "    for i in range(len(X_test)):\n",
        "        distances = []\n",
        "        # Iterate over each point in the training set\n",
        "        for j in range(len(X_train)):\n",
        "            # Calculate the distance between the two points using the Euclidean distance metric\n",
        "            dist = euclidean_distance(X_test[i], X_train[j])\n",
        "            distances.append((dist, y_train[j]))\n",
        "\n",
        "        # Sort the distances list by distance (ascending order)\n",
        "        distances.sort()\n",
        "\n",
        "        # Get the k nearest neighbors\n",
        "        neighbors = distances[:k]\n",
        "\n",
        "        # Count the votes for each class\n",
        "        counts = {}\n",
        "        for neighbor in neighbors:\n",
        "            label = neighbor[1]\n",
        "            if label in counts:\n",
        "                counts[label] += 1\n",
        "            else:\n",
        "                counts[label] = 1\n",
        "\n",
        "        # Get the class with the most votes\n",
        "        max_count = max(counts, key=counts.get)\n",
        "        y_pred.append(max_count)\n",
        "\n",
        "    return y_pred"
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      "outputs": []
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      "source": [
        "X_train_np = np.array(X_train)\n",
        "X_test_np = np.array(X_test)"
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        "id": "hdxB1EqyQGNb"
      },
      "execution_count": null,
      "outputs": []
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        "y_train_np = np.array(y_train)\n",
        "y_test_np = np.array(y_test)"
      ],
      "metadata": {
        "id": "a2M_Nx3_QKzz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k_values = list(range(1, 15))\n",
        "accuracies = []"
      ],
      "metadata": {
        "id": "_St7fhMFQNWh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for k in k_values:\n",
        "    y_pred = knn_euclidean_distance(X_train_np, y_train_np, X_test_np, k)\n",
        "    accuracy = accuracy_score(y_test_np, y_pred)\n",
        "    accuracies.append(accuracy)"
      ],
      "metadata": {
        "id": "Fz47lv-RR3oc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_df = pd.DataFrame({'k': k_values, 'Accuracy': accuracies})"
      ],
      "metadata": {
        "id": "1yvLxsk0R4HI"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "results_df.head(15)"
      ],
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        "colab": {
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          "height": 488
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        "id": "AiioD6WiR--Y",
        "outputId": "c0e676ee-ef7d-4f44-befe-13bec3d87f6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     k  Accuracy\n",
              "0    1  0.985366\n",
              "1    2  0.985366\n",
              "2    3  0.902439\n",
              "3    4  0.902439\n",
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              "11  12  0.712195\n",
              "12  13  0.678049\n",
              "13  14  0.756098"
            ],
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              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>0.717073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>0.756098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>0.687805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>0.712195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0.678049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0.756098</td>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.line(results_df, x='k', y='Accuracy', title='KNN Accuracy for Different Values of k', labels={'k': 'k', 'Accuracy': 'Accuracy'})\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "T-qwDvVqSEu0",
        "outputId": "38baf059-4ce5-4656-ac16-e05a8bb74bc0"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_k = k_values[accuracies.index(max(accuracies))]\n",
        "best_accuracy = max(accuracies)\n",
        "print(\"Best k value is:\", best_k , \"where accuracy is:\" ,best_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ptRtoBIzSaKm",
        "outputId": "4651698f-78f4-490a-80f2-b9e35dd622f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best k value is: 1 where accuracy is: 0.9853658536585366\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Minkowski Distance"
      ],
      "metadata": {
        "id": "3Ga98Fx4SoyI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def minkowski_distance(x1, x2, p):\n",
        "    return np.power(np.sum(np.power(np.abs(x1 - x2), p)), 1/p)"
      ],
      "metadata": {
        "id": "3jZk54RCSfwo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def knn_minkowski_distance(X_train, y_train, X_test, k, p):\n",
        "    # List to store the predicted labels for the test set\n",
        "    y_pred = []\n",
        "\n",
        "    # Iterate over each point in the test set\n",
        "    for i in range(len(X_test)):\n",
        "        distances = []\n",
        "        # Iterate over each point in the training set\n",
        "        for j in range(len(X_train)):\n",
        "            # Calculate the distance between the two points using the Minkowski distance metric\n",
        "            dist = minkowski_distance(X_test[i], X_train[j], p)\n",
        "            distances.append((dist, y_train[j]))\n",
        "\n",
        "        # Sort the distances list by distance (ascending order)\n",
        "        distances.sort()\n",
        "\n",
        "        # Get the k nearest neighbors\n",
        "        neighbors = distances[:k]\n",
        "\n",
        "        # Count the votes for each class\n",
        "        counts = {}\n",
        "        for neighbor in neighbors:\n",
        "            label = neighbor[1]\n",
        "            if label in counts:\n",
        "                counts[label] += 1\n",
        "            else:\n",
        "                counts[label] = 1\n",
        "\n",
        "        # Get the class with the most votes\n",
        "        max_count = max(counts, key=counts.get)\n",
        "        y_pred.append(max_count)\n",
        "\n",
        "    return y_pred"
      ],
      "metadata": {
        "id": "pqcc_pajSuPY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k_values = list(range(1, 15))\n",
        "p_values = list(range(1, 6))\n",
        "results = []"
      ],
      "metadata": {
        "id": "mjfsf7goS5Qo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for k in k_values:\n",
        "    for p in p_values:\n",
        "        y_pred = knn_minkowski_distance(X_train_np, y_train_np, X_test_np, k, p)\n",
        "        accuracy = accuracy_score(y_test_np, y_pred)\n",
        "        results.append((k, p, accuracy))"
      ],
      "metadata": {
        "id": "d7llCSP9TDvL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_df1 = pd.DataFrame(results, columns=['k', 'p', 'Accuracy'])"
      ],
      "metadata": {
        "id": "dBREY_CHTGHN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_df1.head(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "yvry4VLuTlsa",
        "outputId": "c14567cd-3bff-4df0-e84f-85dc3c782937"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    k  p  Accuracy\n",
              "0   1  1  0.985366\n",
              "1   1  2  0.985366\n",
              "2   1  3  1.000000\n",
              "3   1  4  1.000000\n",
              "4   1  5  1.000000\n",
              "5   2  1  0.985366\n",
              "6   2  2  0.985366\n",
              "7   2  3  1.000000\n",
              "8   2  4  1.000000\n",
              "9   2  5  1.000000\n",
              "10  3  1  0.892683\n",
              "11  3  2  0.902439\n",
              "12  3  3  0.917073\n",
              "13  3  4  0.917073\n",
              "14  3  5  0.917073"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>k</th>\n",
              "      <th>p</th>\n",
              "      <th>Accuracy</th>\n",
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              "    <tr>\n",
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              "      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-cf780be0-6e24-40b5-8793-d255d9eb9adc 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-cf780be0-6e24-40b5-8793-d255d9eb9adc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3c49d8fa-5f27-4fa2-b6e1-d43b11dfb74a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3c49d8fa-5f27-4fa2-b6e1-d43b11dfb74a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3c49d8fa-5f27-4fa2-b6e1-d43b11dfb74a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure(data=[go.Scatter3d(\n",
        "    x=results_df1['k'],\n",
        "    y=results_df1['p'],\n",
        "    z=results_df1['Accuracy'],\n",
        "    mode='markers',\n",
        "    marker=dict(\n",
        "        size=4,\n",
        "        color=results_df1['Accuracy'],\n",
        "        colorscale='Viridis',\n",
        "        showscale=True,\n",
        "        opacity=0.8\n",
        "    ),\n",
        "    text=[f\"k={k}, p={p}, Acc={acc:.2f}\" for k, p, acc in results]\n",
        ")])\n",
        "\n",
        "fig.update_layout(scene=dict(\n",
        "    xaxis_title='k',\n",
        "    yaxis_title='p',\n",
        "    zaxis_title='Accuracy'\n",
        "))\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "HK0fXLvXTo-B",
        "outputId": "dc035000-3e2e-4a66-94a9-57483709dbb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "Manhattan Distance"
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        "id": "e_ErtJh2U9Lc"
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      "source": [
        "def manhattan_distance(x1, x2):\n",
        "    return np.sum(np.abs(x1 - x2))"
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        "id": "wSQa38YHU_IK"
      },
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      "outputs": []
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        "def knn_manhattan_distance(X_train, y_train, X_test, k):\n",
        "    # List to store the predicted labels for the test set\n",
        "    y_pred = []\n",
        "\n",
        "    # Iterate over each point in the test set\n",
        "    for i in range(len(X_test)):\n",
        "        distances = []\n",
        "        # Iterate over each point in the training set\n",
        "        for j in range(len(X_train)):\n",
        "            # Calculate the distance between the two points using the Manhattan distance metric\n",
        "            dist = manhattan_distance(X_test[i], X_train[j])\n",
        "            distances.append((dist, y_train[j]))\n",
        "\n",
        "        # Sort the distances list by distance (ascending order)\n",
        "        distances.sort()\n",
        "\n",
        "        # Get the k nearest neighbors\n",
        "        neighbors = distances[:k]\n",
        "\n",
        "        # Count the votes for each class\n",
        "        counts = {}\n",
        "        for neighbor in neighbors:\n",
        "            label = neighbor[1]\n",
        "            if label in counts:\n",
        "                counts[label] += 1\n",
        "            else:\n",
        "                counts[label] = 1\n",
        "\n",
        "        # Get the class with the most votes\n",
        "        max_count = max(counts, key=counts.get)\n",
        "        y_pred.append(max_count)\n",
        "\n",
        "    return y_pred"
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      "outputs": []
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      "cell_type": "code",
      "source": [
        "k_values = list(range(1, 15))\n",
        "accuracies = []"
      ],
      "metadata": {
        "id": "Bxm0-RxDWMW1"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "for k in k_values:\n",
        "    y_pred = knn_manhattan_distance(X_train_np, y_train_np, X_test_np, k)\n",
        "    accuracy = accuracy_score(y_test_np, y_pred)\n",
        "    accuracies.append(accuracy)"
      ],
      "metadata": {
        "id": "Xct9QNiCV7Nn"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "results_df3 = pd.DataFrame({'k': k_values, 'Accuracy': accuracies})"
      ],
      "metadata": {
        "id": "SW6zWSYKV_f1"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
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        "id": "TtSXLofIWFO6",
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              "     k  Accuracy\n",
              "0    1  0.985366\n",
              "1    2  0.985366\n",
              "2    3  0.892683\n",
              "3    4  0.907317\n",
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              "9   10  0.760976\n",
              "10  11  0.712195\n",
              "11  12  0.746341\n",
              "12  13  0.731707\n",
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          "metadata": {},
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    },
    {
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      "source": [
        "fig = px.line(results_df, x='k', y='Accuracy', title='KNN Accuracy for Different k Values', labels={'k': 'k', 'Accuracy': 'Accuracy'})\n",
        "fig.show()"
      ],
      "metadata": {
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          "height": 542
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        "id": "wNTvTuX4WWc7",
        "outputId": "d19fc696-db0b-4fc7-b55d-65859e13e96c"
      },
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "best_k = k_values[accuracies.index(max(accuracies))]\n",
        "best_accuracy = max(accuracies)\n",
        "print(\"Best k value is:\", best_k , \"where accuracy is:\" ,best_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-UB6B67Wa6a",
        "outputId": "00a73a21-4cf9-4670-f527-7009535b2fda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best k value is: 1 where accuracy is: 0.9853658536585366\n"
          ]
        }
      ]
    },
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      "source": [],
      "metadata": {
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      "metadata": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "24gH6OqChtp2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}