BTL_AI_Train_Test.ipynb

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        "<a href=\"https://colab.research.google.com/github/minhduc01168/ET_Tri_tue_nhan_tao/blob/master/BTL_AI_Train_Test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Modeling"
      ],
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        "id": "dsKEVaHlTlwM"
      }
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P2Ssoa6RPOyn"
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      "source": [
        "# Import libraries\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import scipy\n",
        "import sklearn\n",
        "import seaborn as sns\n",
        "import missingno as msno\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.inspection import permutation_importance\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score,roc_curve, accuracy_score\n",
        "from imblearn import under_sampling, over_sampling\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import svm\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from imblearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWCfu54pUFwr",
        "outputId": "e02a8f4c-a75b-4127-c9ca-d0073b44ecd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/AI_ML_DS/AI/BTL_AI/credit_risk_dataset_normalization.csv')"
      ],
      "metadata": {
        "id": "7yfe5lN-UJ4i"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()\n"
      ],
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      },
      "execution_count": null,
      "outputs": [
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              "   person_age  person_income  person_home_ownership  person_emp_length  \\\n",
              "0    0.038462       0.026807               0.666667             0.3125   \n",
              "1    0.038462       0.028263               0.666667             0.1250   \n",
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              "3    0.038462       0.028749               0.666667             0.1250   \n",
              "4    0.153846       0.032634               0.000000             0.5000   \n",
              "\n",
              "   loan_intent  loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
              "0          0.2        0.25   0.018450       0.400841          0.0   \n",
              "1          1.0        0.00   0.073801       0.120533          1.0   \n",
              "2          1.0        0.75   0.040590       0.653118          1.0   \n",
              "3          0.4        0.00   0.147601       0.224947          1.0   \n",
              "4          0.2        0.25   0.046125       0.390329          1.0   \n",
              "\n",
              "   loan_percent_income  cb_person_default_on_file  cb_person_cred_hist_length  \n",
              "0             0.191489                        0.0                    0.000000  \n",
              "1             0.510638                        0.0                    0.000000  \n",
              "2             0.319149                        0.0                    0.071429  \n",
              "3             0.936170                        0.0                    0.000000  \n",
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      "cell_type": "code",
      "source": [
        "X = pd.DataFrame(df)\n",
        "X = X.drop(['loan_status'], axis=1)\n",
        "y = df[['loan_status']]\n",
        "X.info()\n"
      ],
      "metadata": {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 29998 entries, 0 to 29997\n",
            "Data columns (total 11 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   person_age                  29998 non-null  float64\n",
            " 1   person_income               29998 non-null  float64\n",
            " 2   person_home_ownership       29998 non-null  float64\n",
            " 3   person_emp_length           29998 non-null  float64\n",
            " 4   loan_intent                 29998 non-null  float64\n",
            " 5   loan_grade                  29998 non-null  float64\n",
            " 6   loan_amnt                   29998 non-null  float64\n",
            " 7   loan_int_rate               29998 non-null  float64\n",
            " 8   loan_percent_income         29998 non-null  float64\n",
            " 9   cb_person_default_on_file   29998 non-null  float64\n",
            " 10  cb_person_cred_hist_length  29998 non-null  float64\n",
            "dtypes: float64(11)\n",
            "memory usage: 2.5 MB\n"
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            "text/plain": [
              "   person_age  person_income  person_home_ownership  person_emp_length  \\\n",
              "0    0.038462       0.026807               0.666667             0.3125   \n",
              "1    0.038462       0.028263               0.666667             0.1250   \n",
              "2    0.038462       0.028749               0.666667             0.3750   \n",
              "3    0.038462       0.028749               0.666667             0.1250   \n",
              "4    0.153846       0.032634               0.000000             0.5000   \n",
              "\n",
              "   loan_intent  loan_grade  loan_amnt  loan_int_rate  loan_percent_income  \\\n",
              "0          0.2        0.25   0.018450       0.400841             0.191489   \n",
              "1          1.0        0.00   0.073801       0.120533             0.510638   \n",
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              "4          0.2        0.25   0.046125       0.390329             0.319149   \n",
              "\n",
              "   cb_person_default_on_file  cb_person_cred_hist_length  \n",
              "0                        0.0                    0.000000  \n",
              "1                        0.0                    0.000000  \n",
              "2                        0.0                    0.071429  \n",
              "3                        0.0                    0.000000  \n",
              "4                        0.0                    0.000000  "
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              "          const element = document.querySelector('#df-8299728b-6ccb-4731-8f38-1bb4c4296774');\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",
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            ]
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          "metadata": {},
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      ]
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    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PV6XnYD0VJkJ",
        "outputId": "fc6208b8-b151-4cd2-8419-b0630a6e7747"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   loan_status\n",
              "0          0.0\n",
              "1          1.0\n",
              "2          1.0\n",
              "3          1.0\n",
              "4          1.0"
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              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>loan_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
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              "  </tbody>\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          const element = document.querySelector('#df-454ec784-26e0-4478-9fc0-fdd2bafb4631');\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",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = y.values.ravel()"
      ],
      "metadata": {
        "id": "fKF7U5mOo2mm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# K-best Method\n",
        "best_features = SelectKBest(score_func=chi2, k=11) #Chọn các tính năng theo k điểm cao nhất.\n",
        "fit = best_features.fit(X,y)\n",
        "df_scores = pd.DataFrame(fit.scores_)# Điểm số của các tính năng\n",
        "df_columns = pd.DataFrame(X.columns)\n",
        "feature_scores = pd.concat([df_columns, df_scores], axis=1)# Nối các đối tượng pandas dọc theo cột.\n",
        "feature_scores.columns = [\"Feature\",\"Score\"]\n",
        "print(feature_scores.nlargest(11,\"Score\"))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0eu6UFA_VZH6",
        "outputId": "95001ca3-9f4d-4e6b-93c6-14411f2057b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                       Feature       Score\n",
            "5                   loan_grade  979.486278\n",
            "9    cb_person_default_on_file  756.959610\n",
            "8          loan_percent_income  510.348486\n",
            "2        person_home_ownership  509.438695\n",
            "7                loan_int_rate  316.182079\n",
            "1                person_income  160.386595\n",
            "3            person_emp_length   40.968762\n",
            "6                    loan_amnt   26.140949\n",
            "4                  loan_intent   24.132526\n",
            "0                   person_age    3.029378\n",
            "10  cb_person_cred_hist_length    3.014954\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lst_accuracy = []"
      ],
      "metadata": {
        "id": "CmMQ7h-ishVJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **KNN**"
      ],
      "metadata": {
        "id": "7-59MfOHIqz4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Mô hình ban đầu**\n",
        "Tạo và các tham số được sử dụng mặc định."
      ],
      "metadata": {
        "id": "4f47vFow6qJZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### KNN Classifier\n",
        "# 1) Initial\n",
        "\n",
        "# Train, Test, Split\n",
        "X_train_1, X_test_1, Y_train_1, Y_test_1 = train_test_split(X, y, test_size = 0.2, random_state=152, stratify=y)\n",
        "\n",
        "# Define the model\n",
        "clf_1 = KNeighborsClassifier()\n",
        "\n",
        " # Fit the model\n",
        "clf_1.fit(X_train_1,Y_train_1)\n",
        "\n",
        "# Make prediction\n",
        "pred_1 = clf_1.predict(X_test_1)\n",
        "\n",
        "# Accuracy score\n",
        "print('Accuracy score of KNN model 1 is:', accuracy_score(Y_test_1, pred_1))\n",
        "# lst_accuracy.append(accuracy_score(Y_test_1, pred_1))\n",
        "\n",
        "# Confusion Matrix\n",
        "conmat_1 = confusion_matrix(Y_test_1, pred_1)\n",
        "print(conmat_1)\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.title('Confusion Matrix of KNN 1st Model')\n",
        "sns.heatmap(conmat_1, annot=True, cmap=\"Purples\", fmt=\"d\",cbar=True)\n",
        "\n",
        " # Classification Report\n",
        "print('Classification Report of KNN 1st Model')\n",
        "print(classification_report(Y_test_1, pred_1))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "zyw8uFkPnZ4o",
        "outputId": "cf2c3f36-9272-4ce5-8507-d889e73fcaef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of KNN model 1 is: 0.8846666666666667\n",
            "[[4593  166]\n",
            " [ 526  715]]\n",
            "Classification Report of KNN 1st Model\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.90      0.97      0.93      4759\n",
            "         1.0       0.81      0.58      0.67      1241\n",
            "\n",
            "    accuracy                           0.88      6000\n",
            "   macro avg       0.85      0.77      0.80      6000\n",
            "weighted avg       0.88      0.88      0.88      6000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# KNN classifier\n",
        "knn = KNeighborsClassifier()\n",
        "\n",
        "# Use k-fold cross validation with k=10\n",
        "scores = cross_val_score(knn, X, y, cv=10)\n",
        "\n",
        "# Print the mean and standard deviation of the scores\n",
        "print(\"Array acucuracy KNN \")\n",
        "print(scores)\n",
        "print(\"Accuracy: %0.2f\" % scores.mean())\n",
        "lst_accuracy.append(scores.mean())"
      ],
      "metadata": {
        "id": "Dk1yN9KDZG9i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcafde91-092c-42fb-954f-e9628d585b25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Array acucuracy KNN \n",
            "[0.88466667 0.85966667 0.87333333 0.88833333 0.88966667 0.86466667\n",
            " 0.84166667 0.86166667 0.88096032 0.87662554]\n",
            "Accuracy: 0.87\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Decision Tree Classifier**"
      ],
      "metadata": {
        "id": "kssf8F6zJGBm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1) Initial\n",
        "\n",
        "# Train, Test, Split\n",
        "X_train_2, X_test_2, Y_train_2, Y_test_2 = train_test_split(X, y, test_size = 0.2, random_state=152, stratify=y)\n",
        "\n",
        "# Define the model\n",
        "clf_2 = DecisionTreeClassifier()\n",
        "\n",
        "# Fit the model\n",
        "clf_2.fit(X_train_2,Y_train_2)\n",
        "\n",
        "# Make prediction\n",
        "pred_2 = clf_2.predict(X_test_2)\n",
        "\n",
        "# Accuracy score\n",
        "print('Accuracy score of Decision Tree model 1 is:', accuracy_score(Y_test_2, pred_2))\n",
        "# lst_accuracy.append(accuracy_score(Y_test_2, pred_2))\n",
        "\n",
        "# Confusion Matrix\n",
        "conmat_2 = confusion_matrix(Y_test_2, pred_2)\n",
        "print(conmat_2)\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.title('Confusion Matrix of Decision Tree 1st Model')\n",
        "sns.heatmap(conmat_2, annot=True, cmap=\"Purples\", fmt=\"d\",cbar=True)\n",
        "\n",
        "# Classification Report\n",
        "print('Classification Report of Decision Tree 1st Model')\n",
        "print(classification_report(Y_test_2, pred_2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "h3epKhCdJQRz",
        "outputId": "06fb7a91-90d5-4857-8e69-728fb2aaf635"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of Decision Tree model 1 is: 0.884\n",
            "[[4377  382]\n",
            " [ 314  927]]\n",
            "Classification Report of Decision Tree 1st Model\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.93      0.92      0.93      4759\n",
            "         1.0       0.71      0.75      0.73      1241\n",
            "\n",
            "    accuracy                           0.88      6000\n",
            "   macro avg       0.82      0.83      0.83      6000\n",
            "weighted avg       0.89      0.88      0.89      6000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Decision Tree classifier\n",
        "dt = DecisionTreeClassifier()\n",
        "\n",
        "# Use k-fold cross validation with k=10\n",
        "scores = cross_val_score(dt, X, y, cv=10)\n",
        "\n",
        "# Print the mean and standard deviation of the scores\n",
        "print(\"Array acucuracy Decision Tree Classifier \")\n",
        "print(scores)\n",
        "print(\"Accuracy: %0.2f\" % scores.mean())\n",
        "lst_accuracy.append(scores.mean())"
      ],
      "metadata": {
        "id": "E2dyss01ZcBi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2bd7bf6-3c9e-4d32-dde7-826a39afd07c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Array acucuracy Decision Tree Classifier \n",
            "[0.822      0.77066667 0.796      0.825      0.87733333 0.89833333\n",
            " 0.83966667 0.834      0.90196732 0.86862287]\n",
            "Accuracy: 0.84\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Random Forest Classifier**"
      ],
      "metadata": {
        "id": "1GEShT6oeINq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#1) Initial\n",
        "\n",
        "# Train, Test, Split\n",
        "X_train_3, X_test_3, Y_train_3, Y_test_3 = train_test_split(X, y, test_size = 0.2, random_state=152, stratify=y)\n",
        "\n",
        "# Define the model\n",
        "clf_3 = RandomForestClassifier()\n",
        "\n",
        "# Fit the model\n",
        "clf_3.fit(X_train_3,Y_train_3)\n",
        "\n",
        "# Make prediction\n",
        "pred_3 = clf_3.predict(X_test_3)\n",
        "\n",
        "# Accuracy score\n",
        "print('Accuracy score of Random Forest model is:', accuracy_score(Y_test_3, pred_3))\n",
        "# lst_accuracy.append(accuracy_score(Y_test_3, pred_3))\n",
        "\n",
        "# Confusion Matrix\n",
        "conmat_3 = confusion_matrix(Y_test_3, pred_3)\n",
        "print(conmat_3)\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.title('Confusion Matrix of Random Forest Model')\n",
        "sns.heatmap(conmat_3, annot=True, cmap=\"Purples\", fmt=\"d\",cbar=True)\n",
        "\n",
        "# Classification Report\n",
        "print('Classification Report of Random Forest Model')\n",
        "print(classification_report(Y_test_3, pred_3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "SVIH72XUeIdK",
        "outputId": "368fc795-0925-48d6-ee25-f9cc623fb450"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of Random Forest model is: 0.9281666666666667\n",
            "[[4734   25]\n",
            " [ 406  835]]\n",
            "Classification Report of Random Forest Model\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.92      0.99      0.96      4759\n",
            "         1.0       0.97      0.67      0.79      1241\n",
            "\n",
            "    accuracy                           0.93      6000\n",
            "   macro avg       0.95      0.83      0.88      6000\n",
            "weighted avg       0.93      0.93      0.92      6000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Random Forest Classifier\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "# Use k-fold cross validation with k=10\n",
        "scores = cross_val_score(rf, X, y, cv=10)\n",
        "\n",
        "# Print the mean and standard deviation of the scores\n",
        "print(\"Array acucuracy Random Forest Classifier \")\n",
        "print(scores)\n",
        "print(\"Accuracy: %0.2f\" % scores.mean())\n",
        "lst_accuracy.append(scores.mean())"
      ],
      "metadata": {
        "id": "SGV1z-uCZjYZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "003923de-a4bc-4340-cefd-ae61f2aef8d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Array acucuracy Random Forest Classifier \n",
            "[0.917      0.91366667 0.88733333 0.91833333 0.924      0.93266667\n",
            " 0.92766667 0.90533333 0.92530844 0.93897966]\n",
            "Accuracy: 0.92\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **SVM**"
      ],
      "metadata": {
        "id": "CFXJS-DWBbtD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "# 1) Initial\n",
        "\n",
        "# Train, Test, Split\n",
        "X_train_4, X_test_4, Y_train_4, Y_test_4 = train_test_split(X, y, test_size = 0.2, random_state=152, stratify=y)\n",
        "\n",
        "# Define the model\n",
        "clf_4 = SVC()\n",
        "\n",
        " # Fit the model\n",
        "clf_4.fit(X_train_4,Y_train_4)\n",
        "\n",
        "# Make prediction\n",
        "pred_4 = clf_4.predict(X_test_4)\n",
        "\n",
        "# Accuracy score\n",
        "print('Accuracy score of SVM model is:', accuracy_score(Y_test_4, pred_4))\n",
        "# lst_accuracy.append(accuracy_score(Y_test_4, pred_4))\n",
        "\n",
        "# Confusion Matrix\n",
        "conmat_4 = confusion_matrix(Y_test_4, pred_4)\n",
        "print(conmat_4)\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.title('Confusion Matrix of SVM Model')\n",
        "sns.heatmap(conmat_4, annot=True, cmap=\"Purples\", fmt=\"d\",cbar=True)\n",
        "\n",
        " # Classification Report\n",
        "print('Classification Report of SVM Model')\n",
        "print(classification_report(Y_test_4, pred_4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "87BCxUsaBeiU",
        "outputId": "f3306677-ad42-4881-91f7-32ab199bdfa5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of SVM model is: 0.8885\n",
            "[[4649  110]\n",
            " [ 559  682]]\n",
            "Classification Report of SVM Model\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.89      0.98      0.93      4759\n",
            "         1.0       0.86      0.55      0.67      1241\n",
            "\n",
            "    accuracy                           0.89      6000\n",
            "   macro avg       0.88      0.76      0.80      6000\n",
            "weighted avg       0.89      0.89      0.88      6000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# SVM classifier\n",
        "svm = SVC()\n",
        "\n",
        "# Use k-fold cross validation with k=10\n",
        "scores = cross_val_score(svm, X, y, cv=10)\n",
        "\n",
        "# Print the mean and standard deviation of the scores\n",
        "print(\"Array acucuracy SVM\")\n",
        "print(scores)\n",
        "print(\"Accuracy: %0.2f\" % scores.mean())\n",
        "lst_accuracy.append(scores.mean())"
      ],
      "metadata": {
        "id": "ggy6YM-QZfkq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70e0ad7d-7cd4-4347-8b28-5aa6b6f46ce0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Array acucuracy SVM\n",
            "[0.91566667 0.87466667 0.87066667 0.89033333 0.89466667 0.87166667\n",
            " 0.88033333 0.88133333 0.87729243 0.88729577]\n",
            "Accuracy: 0.88\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Naive Bayes**"
      ],
      "metadata": {
        "id": "L8IbikFbBfdb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import BernoulliNB\n",
        "# 1) Initial\n",
        "\n",
        "# Train, Test, Split\n",
        "X_train_5, X_test_5, Y_train_5, Y_test_5 = train_test_split(X, y, test_size = 0.2, random_state=152, stratify=y)\n",
        "\n",
        "# Define the model\n",
        "clf_5 = BernoulliNB()\n",
        "\n",
        " # Fit the model\n",
        "clf_5.fit(X_train_5, Y_train_5)\n",
        "\n",
        "# Make prediction\n",
        "pred_5 = clf_5.predict(X_test_5)\n",
        "\n",
        "# Accuracy score\n",
        "print('Accuracy score of Naive Bayes model is:', accuracy_score(Y_test_5, pred_5))\n",
        "# lst_accuracy.append(accuracy_score(Y_test_5, pred_5))\n",
        "\n",
        "# Confusion Matrix\n",
        "conmat_5 = confusion_matrix(Y_test_5, pred_5)\n",
        "print(conmat_5)\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.title('Confusion Matrix of Naive Bayes Model')\n",
        "sns.heatmap(conmat_5, annot=True, cmap=\"Purples\", fmt=\"d\",cbar=True)\n",
        "\n",
        " # Classification Report\n",
        "print('Classification Report of Naive Bayes Model')\n",
        "print(classification_report(Y_test_5, pred_5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "mWzyUaAVBi-1",
        "outputId": "6535b8af-133b-4259-ad57-3d58f896d563"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of Naive Bayes model is: 0.7931666666666667\n",
            "[[4601  158]\n",
            " [1083  158]]\n",
            "Classification Report of Naive Bayes Model\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.81      0.97      0.88      4759\n",
            "         1.0       0.50      0.13      0.20      1241\n",
            "\n",
            "    accuracy                           0.79      6000\n",
            "   macro avg       0.65      0.55      0.54      6000\n",
            "weighted avg       0.75      0.79      0.74      6000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Naive Bayes classifier\n",
        "nb = GaussianNB()\n",
        "\n",
        "# Use k-fold cross validation with k=10\n",
        "scores = cross_val_score(nb, X, y, cv=10)\n",
        "\n",
        "# Print the mean and standard deviation of the scores\n",
        "print(\"Array acucuracy Naive Bayes \")\n",
        "print(scores)\n",
        "print(\"Accuracy: %0.2f\" % scores.mean())\n",
        "lst_accuracy.append(scores.mean())"
      ],
      "metadata": {
        "id": "DxPwmaQvZll4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e36f7b48-af09-4443-9173-d035bc235312"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Array acucuracy Naive Bayes \n",
            "[0.79333333 0.79166667 0.79533333 0.79633333 0.836      0.799\n",
            " 0.78366667 0.80566667 0.84061354 0.8306102 ]\n",
            "Accuracy: 0.81\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Tổng kết**"
      ],
      "metadata": {
        "id": "qRmANvdGtfh5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import matplotlib.pyplot as plt\n",
        " \n",
        "# Define Axes\n",
        "X1 = ['KNN', 'Decision Tree', 'Random Forest', 'SVM', 'Naive Bayes']\n",
        "Y1 = lst_accuracy\n",
        " \n",
        "# Plot a graph\n",
        "plt.plot(X1, Y1, 'o-r', color='red')\n",
        " \n",
        "# Display graph\n",
        "# plotting\n",
        "plt.title(\"Accuracy: KNN, Decision Tree Classifier, Random Forest Classifier, SVM, Naive Bayes\")\n",
        "plt.xlabel(\"Model\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "UFrY1z4ItjXM",
        "outputId": "8fd79660-3594-46fb-b9e4-64ab568511be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}