Progress/Week_6/Cassification_Social_Network_Ads.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Cassification_Social_Network_Ads.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "seW8khXx1pMJ"
      },
      "source": [
        "<h1 align='center'>Đỗ Trọng Khánh - 19521676</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mqhQFi5g03kM"
      },
      "source": [
        "<h1 align='center'>Cassification Social Network Ads</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qHwARiCr1Myc"
      },
      "source": [
        "- Bước 1: Đọc dữ liệu từ file .csv\n",
        "- Bước 2: Phân chia dữ liệu thành tập train và tập test theo tỉ lệ 8:2\n",
        "- Bước 3: Chuẩn hóa dữ liệu\n",
        "- Bước 4: Khởi tạo và huấn luyện các mô hình\n",
        "- Bước 5: Infer và đánh giá mô hình với độ đo accuracy\n",
        "- Bước 6: Trực quan hóa kết quả của mô hình vừa huấn luyện"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40hw1dJwO7Et"
      },
      "source": [
        "# Connect Driver"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MO84crKzOd8I",
        "outputId": "9bdc50e3-f12f-4043-a659-3463f2d3bdaa"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "szodd_2RPgZ7"
      },
      "source": [
        "# Import Libary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RqLHdbwDPjH6"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from pandas import DataFrame\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1ZpdpfoPSsv"
      },
      "source": [
        "# Prepare Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "le_CJv1kPeof"
      },
      "source": [
        "data_link = '/gdrive/MyDrive/Colab Notebooks/Lập trình Python cho Máy học/Week 6/Social_Network_Ads.csv'\n",
        "data = pd.read_csv(data_link)"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "U9K3DKXgQx0U",
        "outputId": "716ff02c-de4d-4477-95c6-2797e5b41be1"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35</td>\n",
              "      <td>20000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>57000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19</td>\n",
              "      <td>76000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  EstimatedSalary  Purchased\n",
              "0   19            19000          0\n",
              "1   35            20000          0\n",
              "2   26            43000          0\n",
              "3   27            57000          0\n",
              "4   19            76000          0"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcC4e6unR1pQ"
      },
      "source": [
        "df = pd.DataFrame(data)\n",
        "X_data = df.iloc[:, 0:-1];\n",
        "Y_data = df.iloc[:, -1:];"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "C7stx6I5Sqta",
        "outputId": "548a33f8-d821-44f7-b34b-442d30b690b5"
      },
      "source": [
        "X_data"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35</td>\n",
              "      <td>20000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>57000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19</td>\n",
              "      <td>76000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>46</td>\n",
              "      <td>41000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>51</td>\n",
              "      <td>23000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>50</td>\n",
              "      <td>20000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>36</td>\n",
              "      <td>33000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>49</td>\n",
              "      <td>36000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Age  EstimatedSalary\n",
              "0     19            19000\n",
              "1     35            20000\n",
              "2     26            43000\n",
              "3     27            57000\n",
              "4     19            76000\n",
              "..   ...              ...\n",
              "395   46            41000\n",
              "396   51            23000\n",
              "397   50            20000\n",
              "398   36            33000\n",
              "399   49            36000\n",
              "\n",
              "[400 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v0aTVkoAUEYm"
      },
      "source": [
        "# Chuẩn hoá dữ liệu với StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "xNe85xNKSO61",
        "outputId": "2a1cf647-d695-4de1-b80f-59ac9a166d74"
      },
      "source": [
        "X_data_np = X_data.to_numpy()\n",
        "Y_data_np = Y_data.to_numpy()\n",
        "std_X_np = StandardScaler().fit_transform(X_data_np)\n",
        "std_X_df = pd.DataFrame(std_X_np)\n",
        "std_X_df.head()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.781797</td>\n",
              "      <td>-1.490046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.253587</td>\n",
              "      <td>-1.460681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.113206</td>\n",
              "      <td>-0.785290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.017692</td>\n",
              "      <td>-0.374182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.781797</td>\n",
              "      <td>0.183751</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0         1\n",
              "0 -1.781797 -1.490046\n",
              "1 -0.253587 -1.460681\n",
              "2 -1.113206 -0.785290\n",
              "3 -1.017692 -0.374182\n",
              "4 -1.781797  0.183751"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ispoWfhwUN7G"
      },
      "source": [
        "# Split data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86ZX7GczStbo"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(std_X_np, Y_data_np, test_size=0.2)"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6dQhfyPhwcK"
      },
      "source": [
        "# Visualize results model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0r47ps4gKwd"
      },
      "source": [
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "def visualize_results(X_test, Y_test, model, title):\n",
        "  x_min, x_max = X_test[:, 0].min() - 0.5, X_test[:, 0].max() + 0.5\n",
        "  y_min, y_max = X_test[:, 1].min() - 0.5, X_test[:, 1].max() + 0.5\n",
        "  h = 0.02\n",
        "  xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
        "  Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "  Z = Z.reshape(xx.shape)\n",
        "\n",
        "  plt.figure(1, figsize=(6,6))\n",
        "  plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
        "  plt.scatter(X_test[:, 0], X_test[:, 1], c=Y_test, edgecolors='k', cmap=plt.cm.Paired)\n",
        "  plt.xlim(xx.min(), xx.max())\n",
        "  plt.ylim(yy.min(), yy.max())\n",
        "  plt.xticks(())\n",
        "  plt.yticks(())\n",
        "  plt.title(title)\n",
        "  plt.show()"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3DzaWr39i5pj"
      },
      "source": [
        "# Đánh model giá bằng K-Fold Cross Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ht74LpV0jBLj"
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "def evaluate_with_KFold(X_data_np, Y_data_np, model):\n",
        "    kfold = KFold(5, True, 1);\n",
        "    accuracy_list = [];\n",
        "    for train, test in kfold.split(X_data_np, Y_data_np):\n",
        "        X_train, X_test = X_data_np[train], X_data_np[test];\n",
        "        Y_train, Y_test = Y_data_np[train], Y_data_np[test]\n",
        "\n",
        "        model.fit(X_train, Y_train);\n",
        "        Y_pred = model.predict(X_test);\n",
        "        accuracy_list.append(accuracy_score(Y_test, Y_pred));\n",
        "\n",
        "    return np.mean(accuracy_list);"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9WfDoS39UTzR"
      },
      "source": [
        "# Tranning and evaluate model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X3TPWeO7UXcX"
      },
      "source": [
        "## Model Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_KB0gT8TQi4",
        "outputId": "d4827d07-7217-489d-8afb-ecd10fbb1e80"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "logistic_regression_model = LogisticRegression()\n",
        "logistic_regression_model.fit(X_train, Y_train)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r3q52Qa8Tvhh"
      },
      "source": [
        "Y_pred = logistic_regression_model.predict(X_test)"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6qXs-f4TWTH",
        "outputId": "f348337b-b08f-42b7-ab27-7dbfada68784"
      },
      "source": [
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "print(classification_report(Y_test, Y_pred))\n",
        "print('Accuracy: ', accuracy)\n",
        "print('--------------------------------------------')\n",
        "print('Accuracy with K-Fold: ',evaluate_with_KFold(std_X_np, Y_data_np, logistic_regression_model));"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.89      0.87        57\n",
            "           1       0.70      0.61      0.65        23\n",
            "\n",
            "    accuracy                           0.81        80\n",
            "   macro avg       0.77      0.75      0.76        80\n",
            "weighted avg       0.81      0.81      0.81        80\n",
            "\n",
            "Accuracy:  0.8125\n",
            "--------------------------------------------\n",
            "Accuracy with K-Fold:  0.8450000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "S3OI4D0UvTIn",
        "outputId": "9c5b0170-6781-49dd-b15f-54c635bc93e7"
      },
      "source": [
        "visualize_results(X_test, Y_test, logistic_regression_model, 'Logistic Regression')"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3ctggm5XU20"
      },
      "source": [
        "## Model KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-q9RPB1fXUNW"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "KNN_model = KNeighborsClassifier(n_neighbors=7)"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x4lV39PcXsMI",
        "outputId": "41f8a7b7-06bc-4d64-fbb4-44a416ecbccc"
      },
      "source": [
        "KNN_model.fit(X_train, Y_train)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3T8-W0lSXu9q",
        "outputId": "ff139252-ebd0-4f6f-8578-22880058085e"
      },
      "source": [
        "Y_pred = KNN_model.predict(X_test)\n",
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "print(classification_report(Y_test, Y_pred))\n",
        "print('Accuracy: ', accuracy)\n",
        "print('--------------------------------------------')\n",
        "print('Accuracy with K-Fold: ',evaluate_with_KFold(std_X_np, Y_data_np, KNN_model));"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.89      0.93        57\n",
            "           1       0.78      0.91      0.84        23\n",
            "\n",
            "    accuracy                           0.90        80\n",
            "   macro avg       0.87      0.90      0.88        80\n",
            "weighted avg       0.91      0.90      0.90        80\n",
            "\n",
            "Accuracy:  0.9\n",
            "--------------------------------------------\n",
            "Accuracy with K-Fold:  0.9075000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "VCazWtPFg6sx",
        "outputId": "27a53d32-6865-49f3-a82b-8f477f8060df"
      },
      "source": [
        "visualize_results(X_test, Y_test, KNN_model, 'KNN')"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5wVVAMsUnmW"
      },
      "source": [
        "## Model Support Vector Machine(SVM)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xd2sULktUqym"
      },
      "source": [
        "# SVM rbf\n",
        "from sklearn.svm import SVC\n",
        "svm_model = SVC(C=1, kernel='rbf', gamma=0.01)"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0eg3VXalVFK1",
        "outputId": "c930d9a4-0f01-40b4-a8f3-646b87594940"
      },
      "source": [
        "svm_model.fit(X_train, Y_train)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4orAcMJVVai9",
        "outputId": "c2d0c606-c278-440d-a455-7f075978988c"
      },
      "source": [
        "Y_pred = svm_model.predict(X_test)\n",
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "print(classification_report(Y_test, Y_pred))\n",
        "print('Accuracy: ', accuracy)\n",
        "print('--------------------------------------------')\n",
        "print('Accuracy with K-Fold: ',evaluate_with_KFold(std_X_np, Y_data_np, svm_model));"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.95      0.89        57\n",
            "           1       0.80      0.52      0.63        23\n",
            "\n",
            "    accuracy                           0.82        80\n",
            "   macro avg       0.82      0.73      0.76        80\n",
            "weighted avg       0.82      0.82      0.81        80\n",
            "\n",
            "Accuracy:  0.825\n",
            "--------------------------------------------\n",
            "Accuracy with K-Fold:  0.8175000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "n3iU2N4Fg91l",
        "outputId": "9aae8902-f88e-4565-c72f-bcac43a521a0"
      },
      "source": [
        "visualize_results(X_test, Y_test, svm_model, 'SVM kernel')"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kthnRqe5Wnnq"
      },
      "source": [
        "# SVM linear\n",
        "svm_model = SVC(C=1, kernel='linear', gamma=0.01)"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saSh7IK9Wsw8",
        "outputId": "0a6b1400-427f-47cc-fdbb-94157bfa6c45"
      },
      "source": [
        "svm_model.fit(X_train, Y_train)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma=0.01, kernel='linear',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "akJIYSZXWub-",
        "outputId": "6a55ada7-24a8-4383-f536-c31adf8df9a6"
      },
      "source": [
        "Y_pred = svm_model.predict(X_test)\n",
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "print(classification_report(Y_test, Y_pred))\n",
        "print('Accuracy: ', accuracy)\n",
        "print('--------------------------------------------')\n",
        "print('Accuracy with K-Fold: ',evaluate_with_KFold(std_X_np, Y_data_np, svm_model));"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.89      0.87        57\n",
            "           1       0.70      0.61      0.65        23\n",
            "\n",
            "    accuracy                           0.81        80\n",
            "   macro avg       0.77      0.75      0.76        80\n",
            "weighted avg       0.81      0.81      0.81        80\n",
            "\n",
            "Accuracy:  0.8125\n",
            "--------------------------------------------\n",
            "Accuracy with K-Fold:  0.8425\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "0MktKBAuhAOe",
        "outputId": "9fae6a71-04b9-4c14-e372-95b495a80800"
      },
      "source": [
        "visualize_results(X_test, Y_test, svm_model, 'SVM linear')"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-v15rdDYFR8"
      },
      "source": [
        "## Model Naive bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmATR37sX5Uy"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "naive_bayes_model = GaussianNB()"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItNiD-CzYKzP",
        "outputId": "4ad55eca-abed-4aa7-bbbe-d28e542beb95"
      },
      "source": [
        "naive_bayes_model.fit(X_train, Y_train)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None, var_smoothing=1e-09)"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6ul4egrYimm",
        "outputId": "1e93de62-253f-417a-a483-5687bb1a4fe3"
      },
      "source": [
        "Y_pred = naive_bayes_model.predict(X_test)\n",
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "print(classification_report(Y_test, Y_pred))\n",
        "print('Accuracy: ', accuracy)\n",
        "print('--------------------------------------------')\n",
        "print('Accuracy with K-Fold: ',evaluate_with_KFold(std_X_np, Y_data_np, naive_bayes_model));"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.93      0.93        57\n",
            "           1       0.83      0.83      0.83        23\n",
            "\n",
            "    accuracy                           0.90        80\n",
            "   macro avg       0.88      0.88      0.88        80\n",
            "weighted avg       0.90      0.90      0.90        80\n",
            "\n",
            "Accuracy:  0.9\n",
            "--------------------------------------------\n",
            "Accuracy with K-Fold:  0.8899999999999999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "opXs88GVhCqL",
        "outputId": "4a78fe10-d099-4861-be4d-ee212a783c3f"
      },
      "source": [
        "visualize_results(X_test, Y_test, naive_bayes_model, 'Naive bayes')"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l_iMfyaDYxB8"
      },
      "source": [
        "## Model Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ov1uRiNEYwDD"
      },
      "source": [
        "from sklearn import tree\n",
        "clf_model = tree.DecisionTreeClassifier()"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iQruSIb_ZKQN",
        "outputId": "3291fa0d-05e9-40de-c868-6b135cf91979"
      },
      "source": [
        "clf_model.fit(X_train, Y_train)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lWTPe0eWZP-6",
        "outputId": "b5f05da6-6434-4248-8852-d8c928fbdeb9"
      },
      "source": [
        "Y_pred = clf_model.predict(X_test)\n",
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "print(classification_report(Y_test, Y_pred))\n",
        "print('Accuracy: ', accuracy)\n",
        "print('--------------------------------------------')\n",
        "print('Accuracy with K-Fold: ',evaluate_with_KFold(std_X_np, Y_data_np, clf_model));"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.88      0.92        57\n",
            "           1       0.75      0.91      0.82        23\n",
            "\n",
            "    accuracy                           0.89        80\n",
            "   macro avg       0.86      0.90      0.87        80\n",
            "weighted avg       0.90      0.89      0.89        80\n",
            "\n",
            "Accuracy:  0.8875\n",
            "--------------------------------------------\n",
            "Accuracy with K-Fold:  0.8400000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "7p1-4VEshEwB",
        "outputId": "40cee4a7-96f4-4338-d355-a71005a27cb8"
      },
      "source": [
        "visualize_results(X_test, Y_test, clf_model, 'Decision Tree')"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_nDLenqrZhp3"
      },
      "source": [
        "## Model Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQ_6EEraZeTo"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc_model = RandomForestClassifier(max_depth=2, random_state=0)"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDmWMhyiaIaB",
        "outputId": "1ea066cc-04a9-46f9-c07d-ed6e448c3033"
      },
      "source": [
        "rfc_model.fit(X_train, Y_train)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=2, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9UfA84WtaM4C",
        "outputId": "5c6311ea-b733-42ae-d49c-464045a33327"
      },
      "source": [
        "Y_pred = rfc_model.predict(X_test)\n",
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "print(classification_report(Y_test, Y_pred))\n",
        "print('Accuracy: ', accuracy)\n",
        "print('--------------------------------------------')\n",
        "print('Accuracy with K-Fold: ',evaluate_with_KFold(std_X_np, Y_data_np, rfc_model));"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.89      0.94        57\n",
            "           1       0.79      1.00      0.88        23\n",
            "\n",
            "    accuracy                           0.93        80\n",
            "   macro avg       0.90      0.95      0.91        80\n",
            "weighted avg       0.94      0.93      0.93        80\n",
            "\n",
            "Accuracy:  0.925\n",
            "--------------------------------------------\n",
            "Accuracy with K-Fold:  0.9125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "gpTrZCwOhHCC",
        "outputId": "e0a536bb-9056-4c2f-d35b-d5cd88e7a9b6"
      },
      "source": [
        "visualize_results(X_test, Y_test, rfc_model, 'Model Random Forest')"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}