BTL_AI_Train_Test_SMOTE.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_SMOTE.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": "bor9FnrqbRwd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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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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        "id": "9vQ6asCSbYbS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41866f7b-cf20-4fb2-cb51-76d52bb4642b"
      },
      "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_SMOTE.csv')"
      ],
      "metadata": {
        "id": "pdlkFVxSbgyy"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
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          "height": 270
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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_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",
              "2          1.0        0.75   0.040590       0.653118             0.319149   \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  loan_status  \n",
              "0                        0.0                    0.000000          0.0  \n",
              "1                        0.0                    0.000000          1.0  \n",
              "2                        0.0                    0.071429          1.0  \n",
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              "4                        0.0                    0.000000          1.0  "
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      "source": [
        "X_smote = pd.DataFrame(df)\n",
        "X_smote = X_smote.drop(['loan_status'], axis=1)\n",
        "y_smote = df[['loan_status']]\n",
        "X_smote.info()"
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      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 47582 entries, 0 to 47581\n",
            "Data columns (total 11 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   person_age                  47582 non-null  float64\n",
            " 1   person_income               47582 non-null  float64\n",
            " 2   person_home_ownership       47582 non-null  float64\n",
            " 3   person_emp_length           47582 non-null  float64\n",
            " 4   loan_intent                 47582 non-null  float64\n",
            " 5   loan_grade                  47582 non-null  float64\n",
            " 6   loan_amnt                   47582 non-null  float64\n",
            " 7   loan_int_rate               47582 non-null  float64\n",
            " 8   loan_percent_income         47582 non-null  float64\n",
            " 9   cb_person_default_on_file   47582 non-null  float64\n",
            " 10  cb_person_cred_hist_length  47582 non-null  float64\n",
            "dtypes: float64(11)\n",
            "memory usage: 4.0 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           0.312500   \n",
              "1        0.038462       0.028263               0.666667           0.125000   \n",
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              "4        0.153846       0.032634               0.000000           0.500000   \n",
              "...           ...            ...                    ...                ...   \n",
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              "47581    0.302400       0.130062               1.000000           0.187500   \n",
              "\n",
              "       loan_intent  loan_grade  loan_amnt  loan_int_rate  loan_percent_income  \\\n",
              "0              0.2    0.250000   0.018450       0.400841             0.191489   \n",
              "1              1.0    0.000000   0.073801       0.120533             0.510638   \n",
              "2              1.0    0.750000   0.040590       0.653118             0.319149   \n",
              "3              0.4    0.000000   0.147601       0.224947             0.936170   \n",
              "4              0.2    0.250000   0.046125       0.390329             0.319149   \n",
              "...            ...         ...        ...            ...                  ...   \n",
              "47577          0.0    0.250000   0.333969       0.322192             0.814964   \n",
              "47578          0.4    0.000000   0.197977       0.060834             0.327337   \n",
              "47579          0.6    0.829248   0.719557       0.772293             0.384022   \n",
              "47580          0.6    0.250000   0.420625       0.399510             0.741456   \n",
              "47581          0.8    0.000000   0.445343       0.267021             0.847745   \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  \n",
              "...                          ...                         ...  \n",
              "47577                        0.0                    0.807116  \n",
              "47578                        0.0                    0.121564  \n",
              "47579                        1.0                    0.169001  \n",
              "47580                        0.0                    0.067819  \n",
              "47581                        0.0                    0.540629  \n",
              "\n",
              "[47582 rows x 11 columns]"
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              "      <td>0.071429</td>\n",
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              "      <td>0.666667</td>\n",
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              "      <td>0.046125</td>\n",
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              "    <tr>\n",
              "      <th>47578</th>\n",
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              "      <td>0.384022</td>\n",
              "      <td>1.0</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
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        "y_smote"
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          "height": 423
        },
        "outputId": "2dd8c89d-938f-422a-db59-eea3d407a176"
      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       loan_status\n",
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              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "y_smote = y_smote.values.ravel()"
      ],
      "metadata": {
        "id": "XWfkrab25EOQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lst_accuracy =[]"
      ],
      "metadata": {
        "id": "C82bNCM7rMv4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **KNN**"
      ],
      "metadata": {
        "id": "umKG4JwvcK37"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4) Smote\"\"\"\n",
        "\n",
        "# Train, Test, Split\n",
        "X_train_1, X_test_1, Y_train_1, Y_test_1 = train_test_split(X_smote, y_smote, test_size = 0.2, random_state=152, stratify=y_smote)\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 2 is:', accuracy_score(Y_test_1, pred_1))\n",
        "# lst_accuracy.append(accuracy_score(Y_test_1, pred_1))\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 2th Model')\n",
        "sns.heatmap(conmat_1, annot=True, cmap=\"Purples\", fmt=\"d\",cbar=True)\n",
        "\n",
        "# Classification Report\n",
        "print('Classification Report of KNN 2th Model')\n",
        "print(classification_report(Y_test_1, pred_1))"
      ],
      "metadata": {
        "id": "j_1Fc-olcOjI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "outputId": "1d75c5a3-431e-4d9d-d5aa-b121eafa6985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of KNN model 2 is: 0.8818955553220553\n",
            "[[3852  907]\n",
            " [ 217 4541]]\n",
            "Classification Report of KNN 2th Model\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.95      0.81      0.87      4759\n",
            "         1.0       0.83      0.95      0.89      4758\n",
            "\n",
            "    accuracy                           0.88      9517\n",
            "   macro avg       0.89      0.88      0.88      9517\n",
            "weighted avg       0.89      0.88      0.88      9517\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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3G1m8SJJUunrVLk4bSZKkspi8SJJUuKjZil2TF0mSVBSTF0mSCueCXUmSVJSazRo5bSRJkspi8iJJUulqFr2YvEiSpKKYvEiSVDgX7EqSpKLUbNbIaSNJklQWkxdJkkpXs+jF5EWSJBXF5EWSpML52UaSJEm9mMmLJEmFi5pFERYvkiSVzmkjSZKk3svkRZKkwtUseDF5kSRJZbF4kSSpcNEnun3r9JoR/SPiwYh4NCKeiIizq/bLI+LZiJhSbTtU7RERP4yIloh4LCJ2bDrXqIh4utpGdXZtp40kSSpdz8wbzQM+mZmvR8RKwL0RcXO175TM/Hmr/iOAodW2M3AhsHNErAOcCXwESODhiJiQmXPau7DJiyRJWmbZ8Hr1dKVqyw4OOQi4sjrufmBARGwI7AdMyszZVcEyCRje0bVNXmpmpZX78i8XH0K/lfvSt28fHrzjj/ziogfZ9qMbc/jJu9Engrf+8jYXnXknM6fN5RMHbs3hJ+3GnFlvAHDb+Me4+z+n8jdbDeKYMXuw6uors3DhQm649GHun9TSw69OKsf4n13LjTddTyZ8auTBHHroF3jttbmccdYYXpoxgw023JBzzv42a625Fv9x7ZXcNukWAN55ZwHPP/8cN02YxFprrd3Dr0K9RU8t2I2IvsDDwJbABZn5QEQcD4yNiDOAO4DTM3MeMBh4oenwaVVbe+3tsnipmflvv8PY425g3l/m07dfH8649NM8et/zHDNmD877ykRefG4Oe39uOw7++w9z0Vl3AnD/bU9zxXd/vcR55r01nwvPuJ2ZL8xlwKDV+OY1h/LYb/6HN19/uydellSUZ55p4cabrufii66kX79+fPWUf2K33T7OhBt/yYd33Ikjv3g0V119OVdffTlfOv6f+MLhR/GFw48C4N777mH8+P+wcNEKFxGjgdFNTeMyc1xzn8x8B9ghIgYA10fEdsAY4CVgZWAccBpwTneOzWmjGpr3l/kA9O3Xh779+pBAZrLqGisDsNoaKzPnT292eI6X/mcuM1+YC8CrL7/Ja7P/wpoDV12h45beL557/jmGbbMd/fv3p1+/fnxohx35r3vu5Nf3/hcjho8EYMTwkfz63ruXOvb2O25l7733e49HrN5uRSzYzcxxmfmRpm1ce9fPzFeBu4DhmTmjmhqaB/wE2KnqNh0Y0nTYxlVbe+3t6jR5iYitacxTLYpwpgMTMnNqZ8eqd4o+wdirD2X9IWszafzv+OPjM7nk3Ls45QcjmT9vAX95423OPPqv66w+utcWbL3jRrz0/Ktcdd59zJ75+hLn23zbD9BvpT7Mmjb3vX4pUpE232wLxl38I+bOfZVVVunPb+6/j60/uA1z5sxm0KBBAKy77rrMmTN7iePeeustHnjgN3zl5FN7YtjqzXpg3igi1gPmZ+arEbEqsA/wnYjYMDNnROPTIg8GHq8OmQCcGBHX0ViwO7fqdyvwrYgYWPXbl0Z6064Oi5eIOA04HLgOeLBq3hi4NiKuy8xvL/OrVY/LhcnXv/BTVltjZb78byPYeIt1GHHE9vzrSTfxx8dncsCRH+KIr3yMS869i0fueZb/vuUpFsxfyCc/vS3Hnb0X3zruhsXnGjBoNY4/Z28uOvMOsqNlWpIW23TTzfjiF47iy189kVX7r8rQLbeiT5++S/Rp/H9/yV9I9913D3/7t9s7ZaTeYkPgimrdSx9gfGbeFBF3VoVNAFOA46r+E4H9gRbgTeAYgMycHRHnApOrfudk5pKVeyudJS/HAttm5vzmxog4D3gCaLN4aZ4n22mTw9hy0Mc6uYx6wpuvv82TD01n+93+hk22GsQfH58JwP2Tnua0/3cgAK/Pnbe4/13/+SSHn7Tr4uerrr4SX/vBSH72owdoqY6V1DUjRx7MyJEHA3DRuAtYb70PMHDgOrz88ssMGjSIl19+mYEDBy5xzO133sbeezllpKX1xILdzHwM+FAb7Z9sp38CJ7Sz7zLgsq5eu7M1LwuBjdpo37Da16bmeTILl95lzQH9Wa1a27LSKn3ZbuchvPjcbFZbY2U22KTx19x2Ow9h+rON2+sHDFpt8bEf3mNTXqza+/brw8nf2597b/o9D97xx/f4VUjlWzQl9NLMl/ive+5kn72H87Hd9+DmW24C4OZbbuLjH9tjcf/XX3+dKVMeWaJNqqvOkpeTgTsi4mn+ehvTJjRuiTpxRQ5MK8aAQatz3Nl70advEBE8cHsLv/3181zyzbs4+V9HsHBh8sZr8xh3TuNOo/0O+zt2/MRmvPPOQt547S1+fNYdAOyyz5ZsveOGrLl2fz5x4DYAXHTWHTz/1Ms99tqkkvzzv5zKa3Pn0rdfP77y5dNYc801+eIRozjjzDH86lc3sP4GG3Lu2f93cf97fn0XO310Z1Zd1YXxWlpX3hH3/SSyk4UKEdGHxkrh5gW7k6vbozp1xIcvcCWE1AO+P/Gonh6CVFvrrb/me1pNnPjZa7r9d+2///yIXlsRdXq3UWYuBO5/D8YiSZKWQ9TsY6V9kzpJkkpXr9rFN6mTJEllMXmRJKlwdVuwa/IiSZKKYvIiSVLhXLArSZLK4rSRJElS72XyIklS4Wo2a2TyIkmSymLyIklS4VywK0mSyuKCXUmSpN7L5EWSpMLVbNbI5EWSJJXF5EWSpML52UaSJEm9mMmLJEmlq9miF4sXSZIKV7f3eXHaSJIkFcXkRZKkwkXNooiavVxJklQ6kxdJkgpXtzUvFi+SJJWuZsWL00aSJKkoJi+SJBXOBbuSJEm9mMmLJEmFc8GuJEkqix/MKEmS1HuZvEiSVLi6TRuZvEiSpKKYvEiSVLiaBS8mL5IkqSwmL5Ikla5mdxtZvEiSVDgX7EqSJPViJi+SJBWuZsGLyYskSVp2EdE/Ih6MiEcj4omIOLtq3ywiHoiIloj4aUSsXLWvUj1vqfZv2nSuMVX7HyJiv86ubfEiSVLp+kT3b52bB3wyM7cHdgCGR8QuwHeA8zNzS2AOcGzV/1hgTtV+ftWPiBgGHAZsCwwHfhQRfTt8ucv8BZIkSb1KRHT71plseL16ulK1JfBJ4OdV+xXAwdXjg6rnVPv3isaFDgKuy8x5mfks0ALs1NG1LV4kSdJyiYi+ETEFmAVMAv4IvJqZC6ou04DB1ePBwAsA1f65wLrN7W0c0yYX7EqSVLhYAe/zEhGjgdFNTeMyc1xzn8x8B9ghIgYA1wNbd/tA2mDxIkmSllIVKuM67djo+2pE3AXsCgyIiH5VurIxML3qNh0YAkyLiH7A2sArTe2LNB/TJqeNJEkqXayArbNLRqxXJS5ExKrAPsBU4C7gs1W3UcAN1eMJ1XOq/XdmZlbth1V3I20GDAUe7OjaJi+SJBWuh95hd0PgiurOoD7A+My8KSKeBK6LiG8CvwUurfpfClwVES3AbBp3GJGZT0TEeOBJYAFwQjUd1S6LF0mStMwy8zHgQ220P0Mbdwtl5lvA59o511hgbFevbfEiSVLhVsSC3d7MNS+SJKkoJi+SJBWubp8qbfEiSVLp6lW7OG0kSZLKYvIiSVLh6jZtZPIiSZKKYvIiSVLhaha8mLxIkqSymLxIklS4uiUvFi+SJBXOBbuSJEm9mMmLJEmFq1nwYvIiSZLKYvIiSVLh6rbmxeJFkqTC1ax2cdpIkiSVxeRFkqTC1W3ayORFkiQVxeRFkqTC1Sx4sXiRJKl0Qb2qF6eNJElSUUxeJEkqXN2mjUxeJElSUUxeJEkqnMmLJElSL2byIklS4er2JnUWL5IkFa5mtYvTRpIkqSwmL5Ikla5m0YvJiyRJKorJiyRJhatZ8GLxIklS6ep2t5HTRpIkqSgmL5IkFa5mwYvJiyRJKovJiyRJhavbmheLF0mSClez2sVpI0mSVBaTF0mSClez4MXkRZIklcXiRZKkwkVEt29duOaQiLgrIp6MiCci4qSq/ayImB4RU6pt/6ZjxkRES0T8ISL2a2ofXrW1RMTpnV3baSNJkrQ8FgBfzcxHImJN4OGImFTtOz8zv9fcOSKGAYcB2wIbAbdHxFbV7guAfYBpwOSImJCZT7Z3YYsXSZIK1xN3G2XmDGBG9fjPETEVGNzBIQcB12XmPODZiGgBdqr2tWTmMwARcV3Vt93ixWkjSZIK1xPTRq2uvynwIeCBqunEiHgsIi6LiIFV22DghabDplVt7bW3y+JFkiQtJSJGR8RDTdvodvqtAfwCODkzXwMuBLYAdqCRzPxbd4/NaSNJkgq3IqaNMnMcMK7j68ZKNAqXazLzl9VxM5v2XwzcVD2dDgxpOnzjqo0O2ttk8iJJkpZZNOaWLgWmZuZ5Te0bNnU7BHi8ejwBOCwiVomIzYChwIPAZGBoRGwWESvTWNQ7oaNrm7xIklS4Hvpso92BI4HfRcSUqu3rwOERsQOQwHPAPwBk5hMRMZ7GQtwFwAmZ+Q5ARJwI3Ar0BS7LzCc6urDFiyRJheuhu43upe03953YwTFjgbFttE/s6LjWnDaSJElFMXmRJKlwfqq0JElSL2byIklS4XpowW6PsXiRJKlwNatdnDaSJEllMXmRJKlwTht1sysfOH5FX0JSG/Za6ayeHoJUW3fnOT09hPc1kxdJkkpXr+DFNS+SJKksJi+SJBXONS+SJKkodStenDaSJElFMXmRJKlwNQteTF4kSVJZTF4kSSpc3da8WLxIklS4mtUuThtJkqSymLxIklS4uk0bmbxIkqSimLxIklS4uiUvFi+SJBWuZrWL00aSJKksJi+SJBWubtNGJi+SJKkoJi+SJBUu+pi8SJIk9VomL5IkFa5mS14sXiRJKp0LdiVJknoxkxdJkgpXs+DF5EWSJJXF5EWSpMLVbc2LxYskSYWrW/HitJEkSSqKyYskSYWrWfBi8iJJkspi8iJJUulqFr1YvEiSVDgX7EqSJPViJi+SJBWuZsGLyYskSSqLxYskSYWLPtHtW6fXjBgSEXdFxJMR8UREnFS1rxMRkyLi6erfgVV7RMQPI6IlIh6LiB2bzjWq6v90RIzq7NoWL5IkaXksAL6amcOAXYATImIYcDpwR2YOBe6ongOMAIZW22jgQmgUO8CZwM7ATsCZiwqe9li8SJJUuIju3zqTmTMy85Hq8Z+BqcBg4CDgiqrbFcDB1eODgCuz4X5gQERsCOwHTMrM2Zk5B5gEDO/o2i7YlSSpcCviVumIGE0jIVlkXGaOa6fvpsCHgAeA9TNzRrXrJWD96vFg4IWmw6ZVbe21t8viRZIkLaUqVNosVppFxBrAL4CTM/O15kIqMzMisrvH5rSRJEmFi4hu37p43ZVoFC7XZOYvq+aZ1XQQ1b+zqvbpwJCmwzeu2tprb5fFiyRJWmbRqHAuBaZm5nlNuyYAi+4YGgXc0NR+VHXX0S7A3Gp66VZg34gYWC3U3bdqa5fTRpIkFa6H3qRud+BI4HcRMaVq+zrwbWB8RBwLPA8cWu2bCOwPtABvAscAZObsiDgXmFz1OyczZ3d0YYsXSZIK1xOfbZSZ9wLtXXivNvoncEI757oMuKyr13baSJIkFcXkRZKkwvmp0pIkSb2YyYskSYWrWfBi8SJJUumcNpIkSerFTF4kSSqcyYskSVIvZvIiSVLhaha8mLxIkqSymLxIklS46FOv6MXiRZKkwjltJEmS1IuZvEiSVLho98Od359MXiRJUlFMXiRJKl29gheLF0mSSuc77EqSJPViJi+SJBWuZsGLyYskSSqLyYskSYWr25oXixdJkgpXs9rFaSNJklQWkxdJkgpXt2kjkxdJklQUkxdJkgpXs+DF5EWSJJXF5EWSpMLVbc2LxYskSYWrWe3itJEkSSqLyYskSYUzeZEkSerFTF4kSSpcUK/oxeJFkqTCOW0kSZLUi5m8SJJUuLq9z4vJiyRJKorJiyRJhatZ8GLxIklS6Zw2kiRJ6sVMXiRJKlzNgheTF0mStOwi4rKImBURjze1nRUR0yNiSrXt37RvTES0RMQfImK/pvbhVVtLRJzelWubvEiSVLgeWvNyOfDvwJWt2s/PzO81N0TEMOAwYFtgI+D2iNiq2n0BsA8wDZgcERMy88mOLmzxIklS6XqgdsnMeyJi0y52Pwi4LjPnAc9GRAuwU7WvJSds4I0AAAuhSURBVDOfAYiI66q+HRYvThtJkqTudGJEPFZNKw2s2gYDLzT1mVa1tdfeIYsXSZIKFxErYhsdEQ81baO7MJQLgS2AHYAZwL+tiNfrtJEkSVpKZo4Dxi3jMTMXPY6Ii4GbqqfTgSFNXTeu2uigvV0mL5IkFS6i+7flG0ds2PT0EGDRnUgTgMMiYpWI2AwYCjwITAaGRsRmEbEyjUW9Ezq7jsmLJElaZhFxLbAnMCgipgFnAntGxA5AAs8B/wCQmU9ExHgaC3EXACdk5jvVeU4EbgX6Apdl5hOdXdviRZKkwvXErdKZeXgbzZd20H8sMLaN9onAxGW5tsWLJEmFq9kb7LrmRZIklcXkRZKkwvmp0pIkSb2YyYskSYWrWfBi8SJJUumcNpIkSerFTF4kSSpczYIXkxdJklQWkxdJkgpn8qLamDFjBkcfPYqRB47kwE+N5KqrrgTglltv4cBPjWTb7Ybx+OOPL+5/4003csinD1m8bbvdMKZOndpTw5eK1KdPcPEjx/N/bzwCgNN/cgjXPvNlLvnt8Vzy2+PZcvsNluj/wY9sxB3zz2SPzwxb3Pbdm4/kpjljFp9Diohu33ozk5ca69evL6eeeirDhm3LG2+8wWc/9xl23XU3hm45lB/+4P9x1tlnLtH/wJEHcuDIAwF46qmn+Md/OpFtttmmJ4YuFeszJ+3K81P/xOprrbK47cen3Mp//eLJpfr26RP8w3f2ZfJtf1yi/bp/vY9VVluJT/3DR1b4eKXeyOSlxtZb7wMMG7YtAKuvvjqbb74Fs2bNZIsttmCzzTbr8NhfTfwVI0bs/14MU3rfWG/wWuxywFb86pKHu9T/0/+4C/f84klenfXGEu2P3PkMf/nzvBUxRBUqovu33sziRQBMnz6dqVOn8nd/t32X+t9yy80csL/Fi7QsTvz+CC469VZyYS7RfuzYvbn00S9xwnnDWWnlvgAM2mhNPnbINtxw4eSeGKrUq1m8iDfeeIOTTv4nxpx+OmussUan/R997FH69+/P0KFbvQejk94fdj1gK+bMeoOnHpmxRPu4MZM4ausfctxHL2LNdVbl8NM+DjQKnXGn3UZmtnU6aQmueemiiDgmM3/Szr7RwGiAC390If/n/4xe3stoBZs/fz4nn3wSIw84kH322bdLx9w8cSL773/ACh6Z9P6y3e6bsPunPsgu+w9l5f79WG2tVfjnqz7D2CN/AcD8t9/hlp/8ls9/bXcAPviRwZxx3ecAWHvQauy8/1DeWbCQe2/4fY+9Bqm3eDcLds8G2ixeMnMcMA7gnQUL/bOhl8pM/uWMb7D55ptz9NFHd+mYhQsXcsutt3DVlVev2MFJ7zMXf/12Lv767QDssMemfP5ruzP2yF+wzgZrMPul1wH42MHb8OzjswA4fPPzFx97+k8O4Tc3/cHCRap0WLxExGPt7QLW7/7h6L30yCOPMGHCBLbaaisO+fQhAJx88snMf/ttxn5rLLNnz+b4Lx3H1h/cmosvvgSAhx56iA022IAhQ4b05NCl941vXPNZBqy3OhHQMuUlzjvuxk6P+eE9x7LJ1oNYdY2V+dkLX+W7x97A5Nta3oPRqrfq7dM83S06mk+NiJnAfsCc1ruA/87MjTq7gMmL1DP2Wumsnh6CVFt35znvaTXR0vJKt/+u3XLLdXttRdTZtNFNwBqZOaX1joi4e4WMSJIkLZOaBS8dFy+ZeWwH+77Q/cORJEnqmLdKS5KkovjxAJIkFa5u00YmL5IkqSgmL5IkFS6oV/Ri8iJJkopi8iJJUunqFbxYvEiSVDoX7EqSJPViJi+SJBXOBbuSJEm9mMmLJEmlq1fwYvIiSZLKYvIiSVLhaha8WLxIklS6qNm90k4bSZKkopi8SJJUunoFLyYvkiSpLCYvkiQVrmbBi8WLJEmlc8GuJElSL2bxIkmSlllEXBYRsyLi8aa2dSJiUkQ8Xf07sGqPiPhhRLRExGMRsWPTMaOq/k9HxKiuXNviRZIkLY/LgeGt2k4H7sjMocAd1XOAEcDQahsNXAiNYgc4E9gZ2Ak4c1HB0xGLF0mSChfR/VtnMvMeYHar5oOAK6rHVwAHN7VfmQ33AwMiYkNgP2BSZs7OzDnAJJYuiJbigl1JkgrXixbsrp+ZM6rHLwHrV48HAy809ZtWtbXX3iGTF0mStJSIGB0RDzVto5fl+MxMIFfE2ExeJEnSUjJzHDBuGQ+bGREbZuaMalpoVtU+HRjS1G/jqm06sGer9rs7u4jJiyRJ6i4TgEV3DI0CbmhqP6q662gXYG41vXQrsG9EDKwW6u5btXXI5EWSpML1xJKXiLiWRmoyKCKm0bhr6NvA+Ig4FngeOLTqPhHYH2gB3gSOAcjM2RFxLjC56ndOZrZeBLwUixdJkrTMMvPwdnbt1UbfBE5o5zyXAZcty7UtXiRJKlzU7NONLF4kSSpdvWoXF+xKkqSymLxIklS43vMede8NkxdJklQUkxdJkgpXs+DF4kWSpOLVbN7IaSNJklQUkxdJkgpXr9zF5EWSJBXG5EWSpMLVbMmLxYskScWrWfXitJEkSSqKyYskSYWrV+5i8iJJkgpj8iJJUuFqtuTF5EWSJJXF5EWSpOLVK3qxeJEkqXBOG0mSJPViFi+SJKkoFi+SJKkornmRJKlwdVvzYvEiSVLx6lW9OG0kSZKKYvIiSVLh6jZtZPIiSZKKYvEiSZKK4rSRJEmlc9pIkiSp9zJ5kSSpcFGz6MXkRZIkFcXiRZIkFcXiRZIkFcU1L5IkFc43qZMkSerFLF4kSVJRnDaSJKl0NZs3MnmRJElFMXmRJKlw9cpdLF4kSSpfzaoXp40kSVJRLF4kSSpcrICtS9eNeC4ifhcRUyLioaptnYiYFBFPV/8OrNojIn4YES0R8VhE7Li8r9fiRZIkvRv/KzN3yMyPVM9PB+7IzKHAHdVzgBHA0GobDVy4vBe0eJEkqXQR3b8tv4OAK6rHVwAHN7VfmQ33AwMiYsPluYDFiyRJWkpEjI6Ih5q20W10S+C2iHi4af/6mTmjevwSsH71eDDwQtOx06q2ZebdRpIkaSmZOQ4Y10m3j2Xm9Ij4ADApIn7f6hwZEdndYzN5kSSpcD21YDczp1f/zgKuB3YCZi6aDqr+nVV1nw4MaTp846ptmVm8SJKkZRYRq0fEmoseA/sCjwMTgFFVt1HADdXjCcBR1V1HuwBzm6aXlonTRpIkla5n3qRufeD6aCzu7Qf8R2beEhGTgfERcSzwPHBo1X8isD/QArwJHLO8F7Z4kSRJyywznwG2b6P9FWCvNtoTOKE7rm3xIklS4aJmnw9g8SJJUunqVbu4YFeSJJXF5EWSpMLVLHgxeZEkSWUxeZEkqXQ1i14sXiRJKl69qhenjSRJUlFMXiRJKly9cheTF0mSVBiTF0mSSlez6MXiRZKkwtWsdnHaSJIklcXkRZKk0kW9sheTF0mSVBSLF0mSVBSLF0mSVBTXvEiSVLiaLXkxeZEkSWWxeJEkSUVx2kiSpMJFzeaNTF4kSVJRLF4kSVJRIjN7egzqxSJidGaO6+lxSHXjf3tS+0xe1JnRPT0Aqab8b09qh8WLJEkqisWLJEkqisWLOuOcu9Qz/G9PaocLdiVJUlFMXiRJUlEsXtSmiBgeEX+IiJaIOL2nxyPVRURcFhGzIuLxnh6L1FtZvGgpEdEXuAAYAQwDDo+IYT07Kqk2LgeG9/QgpN7M4kVt2QloycxnMvNt4DrgoB4ek1QLmXkPMLunxyH1ZhYvastg4IWm59OqNkmSepzFiyRJKorFi9oyHRjS9Hzjqk2SpB5n8aK2TAaGRsRmEbEycBgwoYfHJEkSYPGiNmTmAuBE4FZgKjA+M5/o2VFJ9RAR1wK/AT4YEdMi4tieHpPU2/gOu5IkqSgmL5IkqSgWL5IkqSgWL5IkqSgWL5IkqSgWL5IkqSgWL5IkqSgWL5IkqSgWL5IkqSj/Hx6r13oApepgAAAAAElFTkSuQmCC\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_smote, y_smote, 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": "6NsYzi3d3FQZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04d3d7ea-b6fc-4603-cf87-f9cc30e9ea84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Array acucuracy KNN \n",
            "[0.80647195 0.81781887 0.83375368 0.85897436 0.87683901 0.87641866\n",
            " 0.81357713 0.84068937 0.86696091 0.68726356]\n",
            "Accuracy: 0.83\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Decision Tree Classifier**"
      ],
      "metadata": {
        "id": "oa78lLW9dDsa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#4) Smote\n",
        "# Train, Test, Split\n",
        "X_train_2, X_test_2, Y_train_2, Y_test_2 = train_test_split(X_smote, y_smote, test_size = 0.2, random_state=152, stratify=y_smote)\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 is:', accuracy_score(Y_test_2, pred_2))\n",
        "# lst_accuracy.append(accuracy_score(Y_test_2, pred_2))\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 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 Model')\n",
        "print(classification_report(Y_test_2, pred_2))\n"
      ],
      "metadata": {
        "id": "-PO9ncBBdERa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "outputId": "3f5a46de-e1d6-4e83-eb31-3405c1e1109f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of Decision Tree model is: 0.8993380266890827\n",
            "[[4247  512]\n",
            " [ 446 4312]]\n",
            "Classification Report of Decision Tree Model\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.90      0.89      0.90      4759\n",
            "         1.0       0.89      0.91      0.90      4758\n",
            "\n",
            "    accuracy                           0.90      9517\n",
            "   macro avg       0.90      0.90      0.90      9517\n",
            "weighted avg       0.90      0.90      0.90      9517\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_smote, y_smote, 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": "YtQKECIM3RRR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16edbeee-738a-428d-b006-180f40970773"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Array acucuracy Decision Tree Classifier \n",
            "[0.809834   0.80794285 0.82576713 0.86359815 0.90416141 0.91656158\n",
            " 0.85140815 0.83900799 0.895124   0.61769651]\n",
            "Accuracy: 0.83\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Random Forest Classifier**"
      ],
      "metadata": {
        "id": "NcjOZ2K7fRqi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#\"\"\"4) Smote\"\"\"\n",
        "\n",
        "# Train, Test, Split\n",
        "X_train_3, X_test_3, Y_train_3, Y_test_3 = train_test_split(X_smote, y_smote, test_size = 0.2, random_state=152, stratify=y_smote)\n",
        "\n",
        "# Define the model\n",
        "clf_3 = RandomForestClassifier()\n",
        "\n",
        "# Fit tme 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",
        "# 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": {
        "id": "U6oGPArcfSXJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "outputId": "4a1aa440-c0d6-4964-c837-641843a6da9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of Random Forest model is: 0.9485131869286539\n",
            "[[4681   78]\n",
            " [ 412 4346]]\n",
            "Classification Report of Random Forest Model\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.92      0.98      0.95      4759\n",
            "         1.0       0.98      0.91      0.95      4758\n",
            "\n",
            "    accuracy                           0.95      9517\n",
            "   macro avg       0.95      0.95      0.95      9517\n",
            "weighted avg       0.95      0.95      0.95      9517\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_smote, y_smote, 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": "OAWtPelL3Voo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e1c6433-a39c-4cc5-a387-5f2a3f23b803"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Array acucuracy Random Forest Classifier \n",
            "[0.84366464 0.84093297 0.86359815 0.94745691 0.97772173 0.9642707\n",
            " 0.9186633  0.93926019 0.9726776  0.610971  ]\n",
            "Accuracy: 0.89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **SVM**"
      ],
      "metadata": {
        "id": "4ItxeS6V9lV8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4) Smote\"\"\"\n",
        "from sklearn.svm import SVC\n",
        "# Train, Test, Split\n",
        "X_train_4, X_test_4, Y_train_4, Y_test_4 = train_test_split(X_smote, y_smote, test_size = 0.2, random_state=152, stratify=y_smote)\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",
        "# 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": {
        "id": "Sjx9hF0f9kIM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "outputId": "d1d842f3-820e-4956-d957-3099965a867d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of SVM model is: 0.8225281075969318\n",
            "[[4184  575]\n",
            " [1114 3644]]\n",
            "Classification Report of SVM Model\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.79      0.88      0.83      4759\n",
            "         1.0       0.86      0.77      0.81      4758\n",
            "\n",
            "    accuracy                           0.82      9517\n",
            "   macro avg       0.83      0.82      0.82      9517\n",
            "weighted avg       0.83      0.82      0.82      9517\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_smote, y_smote, 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": "ylnw7E_53aHy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c20d7ba-7c51-470e-eea9-0355c26557a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Array acucuracy SVM\n",
            "[0.75225888 0.79155285 0.79361076 0.808533   0.8480454  0.81462799\n",
            " 0.79655317 0.81315679 0.8297604  0.58196721]\n",
            "Accuracy: 0.78\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Naive Bayes**"
      ],
      "metadata": {
        "id": "QfX7oXBj9rv8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4) Smote\"\"\"\n",
        "from sklearn . naive_bayes import BernoulliNB\n",
        "# Train, Test, Split\n",
        "X_train_5, X_test_5, Y_train_5, Y_test_5 = train_test_split(X_smote, y_smote, test_size = 0.2, random_state=152, stratify=y_smote)\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 2 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": {
        "id": "IqjkZGLR9sTT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab8b7f95-e454-4a2c-e11e-46a5d8326d98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of Naive Bayes model 2 is: 0.6575601555111905\n",
            "[[2817 1942]\n",
            " [1317 3441]]\n",
            "Classification Report of Naive Bayes Model\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.68      0.59      0.63      4759\n",
            "         1.0       0.64      0.72      0.68      4758\n",
            "\n",
            "    accuracy                           0.66      9517\n",
            "   macro avg       0.66      0.66      0.66      9517\n",
            "weighted avg       0.66      0.66      0.66      9517\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_smote, y_smote, 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": "koDASweH3eCo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c77a6f03-0b87-4fc6-9944-3e9aa6787d05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Array acucuracy Naive Bayes \n",
            "[0.72956503 0.73040555 0.68116856 0.72530475 0.79003783 0.75052543\n",
            " 0.74653216 0.76166456 0.79697352 0.51576293]\n",
            "Accuracy: 0.72\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Tổng kết**"
      ],
      "metadata": {
        "id": "LHDIQeGpq-rB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import matplotlib.pyplot as plt\n",
        " \n",
        "# Define Axes\n",
        "X = ['KNN', 'Decision Tree', 'Random Forest', 'SVM', 'Naive Bayes']\n",
        "Y = lst_accuracy\n",
        " \n",
        "# Plot a graph\n",
        "plt.plot(X, Y, '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": {
        "id": "14AvtORPrCtn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d90f9726-1e3e-4b32-f7b2-e2c96e9b4370"
      },
      "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"
          }
        }
      ]
    }
  ]
}