Handling_Imbalanced_Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cài đặt\n",
    "# !pip install xgboost\n",
    "# !pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "bx4A4MP-JizG"
   },
   "outputs": [],
   "source": [
    "# Import library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "id": "h4VGM2FFPYyP",
    "outputId": "423ac30d-7f4a-4738-b278-709e7ab660fa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Đọc dữ liệu từ file\n",
    "customer_data = pd.read_csv(\"Churn_Modelling.csv\")\n",
    "customer_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "91E-WpFQP1RV",
    "outputId": "28cd5612-e604-41df-cfbc-712de7a7f677"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    7963\n",
      "1    2037\n",
      "Name: Exited, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer_data['Exited'].value_counts().plot(kind=\"bar\");\n",
    "print(customer_data['Exited'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TH1: Khi Train model như bình thường"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "TxGs0om5QEfN"
   },
   "outputs": [],
   "source": [
    "dataset = customer_data.drop([\"RowNumber\",\"Surname\",\"CustomerId\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Ky470aqpQSoD"
   },
   "outputs": [],
   "source": [
    "# Tao ra onehot (biến giả)\n",
    "dataset_dummy = pd.get_dummies(dataset,drop_first=True)\n",
    "\n",
    "X = dataset_dummy.drop([\"Exited\"],axis=1)\n",
    "y = dataset_dummy['Exited']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y , test_size = 0.2, random_state=42)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']]= scaler.transform(X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "X_test[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']] = scaler.transform(X_test[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "\n",
    "#Performance evaluation\n",
    "def print_scores(y_true, y_pred):\n",
    "  print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train model voi imabalance data\n",
    "### LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pldp0aMxRVJK",
    "outputId": "ae51c7e6-4af4-46c5-cf55-b89f85f43a74"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.96      0.89      1607\n",
      "           1       0.57      0.19      0.29       393\n",
      "\n",
      "    accuracy                           0.81      2000\n",
      "   macro avg       0.70      0.58      0.59      2000\n",
      "weighted avg       0.78      0.81      0.77      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Khoi tao\n",
    "model = LogisticRegression() \n",
    "\n",
    "# Fit voi du lieu trian\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Du doan du lieu test\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# in ra ket qua\n",
    "print_scores( y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Nhận xét:**\n",
    "+ Chỉ có f1-score của lớp 0 là tốt (0.89)\n",
    "+ Cái cần quan tâm là f1-score của lớp 1, lại rất tệ (0.29)\n",
    "+ Suy ra, chỉ tốt trên phần dữ liệu lớp 0 (chiếm đa số)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Jcjh0fCNRtWb",
    "outputId": "006074dc-19be-48ab-dcd5-cbca395b1bdb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.72      0.80      1607\n",
      "           1       0.38      0.71      0.50       393\n",
      "\n",
      "    accuracy                           0.72      2000\n",
      "   macro avg       0.65      0.72      0.65      2000\n",
      "weighted avg       0.81      0.72      0.74      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Khoi tao\n",
    "model = LogisticRegression(class_weight=\"balanced\") \n",
    "\n",
    "# Fit voi du lieu train\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Du doan du lieu test\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# in ra ket qua\n",
    "print_scores( y_test, y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Nhận xét:** Các chỉ số có nâng lên khi tạo `class_weight=\"balanced\"`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jGyyxmDJR-3X",
    "outputId": "bb11b1fd-5f29-47fa-a145-b3b7da43ab8e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.97      0.92      1607\n",
      "           1       0.78      0.48      0.59       393\n",
      "\n",
      "    accuracy                           0.87      2000\n",
      "   macro avg       0.83      0.72      0.76      2000\n",
      "weighted avg       0.86      0.87      0.86      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Random Forest \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Khoi tao model random forest\n",
    "model_RFC = RandomForestClassifier(n_estimators=200, random_state=42) \n",
    "\n",
    "# Fit vao du lieu train\n",
    "model_RFC.fit(X_train, y_train)\n",
    "\n",
    "# Du doan \n",
    "y_pred = model_RFC.predict(X_test)\n",
    "\n",
    "# In ket qua\n",
    "print_scores( y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradientBoosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xgz0PrxcSVIg",
    "outputId": "4266762d-b91a-489c-9209-c2908cb9a622"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.98      0.92      1607\n",
      "           1       0.84      0.33      0.48       393\n",
      "\n",
      "    accuracy                           0.86      2000\n",
      "   macro avg       0.85      0.66      0.70      2000\n",
      "weighted avg       0.85      0.86      0.83      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "model = GradientBoostingClassifier(learning_rate=0.01, random_state=42,n_estimators=200)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print_scores( y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yK--Pk8lSbvl",
    "outputId": "3255e78e-c7ab-4b43-ce0a-a4d1a011f6b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.98      0.92      1607\n",
      "           1       0.84      0.33      0.48       393\n",
      "\n",
      "    accuracy                           0.86      2000\n",
      "   macro avg       0.85      0.66      0.70      2000\n",
      "weighted avg       0.85      0.86      0.83      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "model_xgb = xgb.XGBClassifier(random_state=42, n_estimators = 200)\n",
    "model_xgb.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "print_scores( y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6Snh-9FjSlvL",
    "outputId": "a678a9fc-9f5b-464d-f882-f5c8f3b4310f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7963\n",
       "1    2037\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Exited\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TH2: Khi fix lại dataset \n",
    "\n",
    "Có 2 hướng:\n",
    "+ 1 là giảm số lượng quan sát ở mẫu nhiều (undersampling)\n",
    "+ 2 là tăng số lượng quan sát ở mẫu ít (oversampling)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Dùng cho class có nhiều data hơn (undersampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YOls5qUpSxPR",
    "outputId": "95117941-6fc3-4bc6-e20a-aa314a547540"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7963, 11), (2037, 11))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_0 = dataset[dataset.Exited==0]\n",
    "dataset_1 = dataset[dataset.Exited==1]\n",
    "dataset_0.shape, dataset_1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cách 1: Tạo ra một dataset mới"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_Ah19ZtcTB-m",
    "outputId": "8746af9f-a0d6-4fff-c23c-7c80779d7c27"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2037, 11)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dùng hàm sample - lấy ngẫu nhiên từ data shape nào đó\n",
    "dataset_0_rus = dataset_0.sample(dataset_1.shape[0])\n",
    "dataset_0_rus.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZQMMVnVTTO1c",
    "outputId": "0cb5c224-e880-48ea-c7d3-bd648b073536"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4074, 11)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nối rus_0 với dataset_1\n",
    "dataset_random_under = pd.concat([dataset_0_rus,dataset_1])\n",
    "dataset_random_under.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training lại model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hPsDHDESTbPF",
    "outputId": "a26d7296-ccb3-442b-b60f-cc287caae575"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.74      0.75       443\n",
      "           1       0.70      0.71      0.71       372\n",
      "\n",
      "    accuracy                           0.73       815\n",
      "   macro avg       0.73      0.73      0.73       815\n",
      "weighted avg       0.73      0.73      0.73       815\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dataset_dummy = pd.get_dummies(dataset_random_under,drop_first=True)\n",
    "\n",
    "X = dataset_dummy.drop([\"Exited\"],axis=1)\n",
    "y = dataset_dummy['Exited']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y , test_size = 0.2, random_state=42)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']]= scaler.transform(X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "X_test[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']] = scaler.transform(X_test[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "\n",
    "model = LogisticRegression() #Instantiating the algorithm class and unpacking parameters if any\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print_scores( y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cách  2: Dùng NearMiss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J-N8xBmIToFW",
    "outputId": "55a77e61-6811-491c-d1b2-2f46ecfc583a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4074, 11), (4074,))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_dummy = pd.get_dummies(dataset,drop_first=True)\n",
    "\n",
    "X = dataset_dummy.drop([\"Exited\"],axis=1)\n",
    "y = dataset_dummy['Exited']\n",
    "\n",
    "from imblearn.under_sampling import NearMiss\n",
    "nm = NearMiss()\n",
    "\n",
    "X_res, y_res = nm.fit_resample(X,y)\n",
    "X_res.shape, y_res.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training lại model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VT5YkpCGT7dv",
    "outputId": "0333cf03-a12d-4ff2-aee7-e84c75154843"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.97      0.91       443\n",
      "           1       0.96      0.80      0.87       372\n",
      "\n",
      "    accuracy                           0.89       815\n",
      "   macro avg       0.90      0.88      0.89       815\n",
      "weighted avg       0.90      0.89      0.89       815\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, test_size = 0.2, random_state=42)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']]= scaler.transform(X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "X_test[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']] = scaler.transform(X_test[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print_scores( y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. Dùng cho class có ít data hơn (oversampling)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cách 1: Dùng Random Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AEYEOczpUDqv",
    "outputId": "e3884388-8f16-4d39-b821-034e99228ed0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((15926, 11), (15926,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Oversampling\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "ros = RandomOverSampler()\n",
    "\n",
    "dataset_dummy = pd.get_dummies(dataset, drop_first=True)\n",
    "\n",
    "X = dataset_dummy.drop([\"Exited\"],axis=1)\n",
    "y = dataset_dummy['Exited']\n",
    "\n",
    "X_ros, y_ros = ros.fit_resample(X,y)\n",
    "X_ros.shape, y_ros.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training lại model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QirwBULDUtmH",
    "outputId": "defa74a3-dfec-45b6-89f9-a10093b6570b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.70      0.71      1633\n",
      "           1       0.69      0.70      0.70      1553\n",
      "\n",
      "    accuracy                           0.70      3186\n",
      "   macro avg       0.70      0.70      0.70      3186\n",
      "weighted avg       0.70      0.70      0.70      3186\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_ros, y_ros, test_size = 0.2, random_state=42)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']]= scaler.transform(X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "X_test[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']] = scaler.transform(X_test[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "\n",
    "model = LogisticRegression() #Instantiating the algorithm class and unpacking parameters if any\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print_scores( y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cách 2: Dùng SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KZz6yJVtU1da",
    "outputId": "cc669fc1-32bc-4987-a6c9-b6996c500a25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((15926, 11), (15926,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SMOTE\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "sm = SMOTE()\n",
    "\n",
    "dataset_dummy = pd.get_dummies(dataset,drop_first=True)\n",
    "\n",
    "X = dataset_dummy.drop([\"Exited\"],axis=1)\n",
    "y = dataset_dummy['Exited']\n",
    "\n",
    "X_sm, y_sm = sm.fit_resample(X, y)\n",
    "X_sm.shape, y_sm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E8bkocCEVIPi",
    "outputId": "656687ba-9d49-4860-df13-479a25e847aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.77      0.78      1633\n",
      "           1       0.77      0.79      0.78      1553\n",
      "\n",
      "    accuracy                           0.78      3186\n",
      "   macro avg       0.78      0.78      0.78      3186\n",
      "weighted avg       0.78      0.78      0.78      3186\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_sm, y_sm, test_size = 0.2, random_state=42)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']]= scaler.transform(X_train[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "X_test[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']] = scaler.transform(X_test[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print_scores( y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kết luận:\n",
    "+ Như vậy, dựa theo các kếtg quả trên, ta thấy cách scaler dữ liệu cho ra kết quả tốt nhất là NearMiss, tiếp theo là SMOTE\n",
    "+ Tuy nhiên, không có 1 khẳng định nào chứng minh là cách nào tốt hơn cách nào. Do đó, khi sử dụng cần linh hoạt để tìm được kết quả tốt nhất."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hướng mở, chia dataset ra train và test. Sau đó, chỉ làm undersampling và oversampling trên train, để nguyên test để kiểm tra.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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