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Data_ScienceUse_Cases/Classification/Imbalanced_Tes.ipynb
Sang Putu Sandhyana Yogi cd70cdf758 Add files via upload
2024-04-23 23:56:47 +07:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "35edb7d6",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7c963881",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Role Satisfaction</th>\n",
" <th>Skill Utilization</th>\n",
" <th>Career Growth Opportunity</th>\n",
" <th>Supervisor Support</th>\n",
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" Role Satisfaction Skill Utilization Career Growth Opportunity \\\n",
"0 3 4 5 \n",
"1 2 3 1 \n",
"2 3 3 2 \n",
"3 3 3 4 \n",
"4 3 2 4 \n",
"\n",
" Supervisor Support Work-Life Balance Recognition & Appreciation \\\n",
"0 2 2 3 \n",
"1 2 4 3 \n",
"2 2 2 5 \n",
"3 4 3 1 \n",
"4 3 3 2 \n",
"\n",
" Company Culture Training & Development Communication Effectiveness \\\n",
"0 3 3 2 \n",
"1 4 3 2 \n",
"2 4 4 3 \n",
"3 4 4 4 \n",
"4 3 4 2 \n",
"\n",
" Diversity & Inclusion Work Environment Compensation Staff_Id \\\n",
"0 3 4 4 SA63171 \n",
"1 2 2 4 SP10211 \n",
"2 2 4 5 SA79627 \n",
"3 5 3 5 SA02310 \n",
"4 3 2 3 SA98565 \n",
"\n",
" Month_Of_Service Years_Of_Service Residence Residence_Code Net_Salary \\\n",
"0 1 0 Depok 4 5582218 \n",
"1 43 3 Jakarta 1 9213443 \n",
"2 10 0 Bekasi 3 5836455 \n",
"3 17 1 Depok 4 6035466 \n",
"4 17 1 Jakarta 1 5568101 \n",
"\n",
" Resigned \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Grab Data\n",
"df = pd.read_excel(\"https://raw.githubusercontent.com/youronlydimwit/Data_ScienceUse_Cases/main/Classification/Data/HRD_Survey_50.xlsx\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fcbe70d6",
"metadata": {},
"outputs": [
{
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"text/plain": [
" Role Satisfaction Skill Utilization Career Growth Opportunity \\\n",
"0 3 4 5 \n",
"1 2 3 1 \n",
"2 3 3 2 \n",
"3 3 3 4 \n",
"4 3 2 4 \n",
"\n",
" Supervisor Support Work-Life Balance Recognition & Appreciation \\\n",
"0 2 2 3 \n",
"1 2 4 3 \n",
"2 2 2 5 \n",
"3 4 3 1 \n",
"4 3 3 2 \n",
"\n",
" Company Culture Training & Development Communication Effectiveness \\\n",
"0 3 3 2 \n",
"1 4 3 2 \n",
"2 4 4 3 \n",
"3 4 4 4 \n",
"4 3 4 2 \n",
"\n",
" Diversity & Inclusion Work Environment Compensation Month_Of_Service \\\n",
"0 3 4 4 1 \n",
"1 2 2 4 43 \n",
"2 2 4 5 10 \n",
"3 5 3 5 17 \n",
"4 3 2 3 17 \n",
"\n",
" Years_Of_Service Residence_Code Net_Salary Resigned \n",
"0 0 4 5582218 0 \n",
"1 3 1 9213443 0 \n",
"2 0 3 5836455 0 \n",
"3 1 4 6035466 0 \n",
"4 1 1 5568101 0 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Making a copy of df, but with only numerical information\n",
"# Removing unnecessary columns\n",
"pred_df = df.drop(columns=['Staff_Id','Residence'])\n",
"pred_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "10a4fe36",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Role Satisfaction int64\n",
"Skill Utilization int64\n",
"Career Growth Opportunity int64\n",
"Supervisor Support int64\n",
"Work-Life Balance int64\n",
"Recognition & Appreciation int64\n",
"Company Culture int64\n",
"Training & Development int64\n",
"Communication Effectiveness int64\n",
"Diversity & Inclusion int64\n",
"Work Environment int64\n",
"Compensation int64\n",
"Month_Of_Service int64\n",
"Residence_Code int64\n",
"Net_Salary int64\n",
"Resigned int64\n",
"dtype: object"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pred_df = pred_df.drop(columns=[\"Years_Of_Service\"])\n",
"pred_df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a4bf1dae",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2dd56994",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b7c40c6f",
"metadata": {},
"outputs": [],
"source": [
"# Split the data into features (X) and labels (y)\n",
"X = pred_df.drop(columns=['Resigned'])\n",
"y = pred_df['Resigned']\n",
"\n",
"# Split the data into training and testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Define a dictionary to store results\n",
"results = {'Model': [], 'F1_score': [], 'Accuracy': [], 'Precision': [], 'Recall': []}"
]
},
{
"cell_type": "markdown",
"id": "319665bc",
"metadata": {},
"source": [
"# Norm"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e162bda0",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Asus\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"C:\\Users\\Asus\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
}
],
"source": [
"# Train Random Forest model\n",
"rf_model = RandomForestClassifier(random_state=42)\n",
"rf_model.fit(X_train, y_train)\n",
"\n",
"# Train Decision Tree model\n",
"dt_model = DecisionTreeClassifier(random_state=42)\n",
"dt_model.fit(X_train, y_train)\n",
"\n",
"# Train Logistic Regression model\n",
"lr_model = LogisticRegression(random_state=42)\n",
"lr_model.fit(X_train, y_train)\n",
"\n",
"# Evaluate models\n",
"models = {\"Random Forest\": rf_model, \"Decision Tree\": dt_model, \"Logistic Regression\": lr_model}\n",
"metrics = {\"Accuracy\": accuracy_score, \"Precision\": precision_score, \"Recall\": recall_score, \"F1 Score\": f1_score}\n",
"results = {}\n",
"\n",
"for name, model in models.items():\n",
" y_pred = model.predict(X_test)\n",
" result = {}\n",
" for metric_name, metric_func in metrics.items():\n",
" result[metric_name] = metric_func(y_test, y_pred)\n",
" results[name] = result\n",
"\n",
"# Convert results to DataFrame for easier plotting\n",
"results_df = pd.DataFrame(results)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "94ebdccc",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Random Forest</th>\n",
" <th>Decision Tree</th>\n",
" <th>Logistic Regression</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Accuracy</th>\n",
" <td>0.87</td>\n",
" <td>0.820000</td>\n",
" <td>0.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Precision</th>\n",
" <td>0.00</td>\n",
" <td>0.272727</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall</th>\n",
" <td>0.00</td>\n",
" <td>0.230769</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F1 Score</th>\n",
" <td>0.00</td>\n",
" <td>0.250000</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" </tbody>\n",
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"text/plain": [
" Random Forest Decision Tree Logistic Regression\n",
"Accuracy 0.87 0.820000 0.87\n",
"Precision 0.00 0.272727 0.00\n",
"Recall 0.00 0.230769 0.00\n",
"F1 Score 0.00 0.250000 0.00"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results_df"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e95e9b4b",
"metadata": {},
"outputs": [],
"source": [
"from imblearn.over_sampling import SMOTE"
]
},
{
"cell_type": "markdown",
"id": "96ab491e",
"metadata": {},
"source": [
"# SMOTE"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "4bed2a76",
"metadata": {},
"outputs": [],
"source": [
"# Apply SMOTE to oversample the minority class in the training data\n",
"smote = SMOTE(random_state=42)\n",
"X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c24a2a88",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Random Forest</th>\n",
" <th>Decision Tree</th>\n",
" <th>Logistic Regression</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Accuracy</th>\n",
" <td>0.83</td>\n",
" <td>0.720000</td>\n",
" <td>0.130000</td>\n",
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" <tr>\n",
" <th>Precision</th>\n",
" <td>0.00</td>\n",
" <td>0.105263</td>\n",
" <td>0.130000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall</th>\n",
" <td>0.00</td>\n",
" <td>0.153846</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F1 Score</th>\n",
" <td>0.00</td>\n",
" <td>0.125000</td>\n",
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"text/plain": [
" Random Forest Decision Tree Logistic Regression\n",
"Accuracy 0.83 0.720000 0.130000\n",
"Precision 0.00 0.105263 0.130000\n",
"Recall 0.00 0.153846 1.000000\n",
"F1 Score 0.00 0.125000 0.230088"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Train Random Forest model\n",
"rf_model = RandomForestClassifier(random_state=42)\n",
"rf_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Train Decision Tree model\n",
"dt_model = DecisionTreeClassifier(random_state=42)\n",
"dt_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Train Logistic Regression model\n",
"lr_model = LogisticRegression(random_state=42)\n",
"lr_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Evaluate models\n",
"models = {\"Random Forest\": rf_model, \"Decision Tree\": dt_model, \"Logistic Regression\": lr_model}\n",
"metrics = {\"Accuracy\": accuracy_score, \"Precision\": precision_score, \"Recall\": recall_score, \"F1 Score\": f1_score}\n",
"results = {}\n",
"\n",
"for name, model in models.items():\n",
" y_pred = model.predict(X_test)\n",
" result = {}\n",
" for metric_name, metric_func in metrics.items():\n",
" result[metric_name] = metric_func(y_test, y_pred)\n",
" results[name] = result\n",
"\n",
"# Convert results to DataFrame for easier plotting\n",
"results_df_resampled = pd.DataFrame(results)\n",
"\n",
"results_df_resampled"
]
},
{
"cell_type": "markdown",
"id": "45d27935",
"metadata": {},
"source": [
"# OVERSAMPLER"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "33d278e9",
"metadata": {},
"outputs": [],
"source": [
"from imblearn.over_sampling import RandomOverSampler"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c4b2b38c",
"metadata": {},
"outputs": [],
"source": [
"# Apply Random Oversampling to balance the training data\n",
"oversampler = RandomOverSampler(random_state=42)\n",
"X_train_resampled, y_train_resampled = oversampler.fit_resample(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "6e730981",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Asus\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"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>Random Forest</th>\n",
" <th>Decision Tree</th>\n",
" <th>Logistic Regression</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Accuracy</th>\n",
" <td>0.87</td>\n",
" <td>0.78</td>\n",
" <td>0.130000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Precision</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.130000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F1 Score</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.230088</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Random Forest Decision Tree Logistic Regression\n",
"Accuracy 0.87 0.78 0.130000\n",
"Precision 0.00 0.00 0.130000\n",
"Recall 0.00 0.00 1.000000\n",
"F1 Score 0.00 0.00 0.230088"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Train Random Forest model\n",
"rf_model = RandomForestClassifier(random_state=42)\n",
"rf_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Train Decision Tree model\n",
"dt_model = DecisionTreeClassifier(random_state=42)\n",
"dt_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Train Logistic Regression model\n",
"lr_model = LogisticRegression(random_state=42)\n",
"lr_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Evaluate models\n",
"models = {\"Random Forest\": rf_model, \"Decision Tree\": dt_model, \"Logistic Regression\": lr_model}\n",
"metrics = {\"Accuracy\": accuracy_score, \"Precision\": precision_score, \"Recall\": recall_score, \"F1 Score\": f1_score}\n",
"results = {}\n",
"\n",
"for name, model in models.items():\n",
" y_pred = model.predict(X_test)\n",
" result = {}\n",
" for metric_name, metric_func in metrics.items():\n",
" result[metric_name] = metric_func(y_test, y_pred)\n",
" results[name] = result\n",
"\n",
"# Convert results to DataFrame for easier plotting\n",
"results_df_resampled = pd.DataFrame(results)\n",
"\n",
"results_df_resampled"
]
},
{
"cell_type": "markdown",
"id": "bbe4b256",
"metadata": {},
"source": [
"# CLASS WEIGHTS - NORM"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "a1a034ad",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Asus\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"C:\\Users\\Asus\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"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>Random Forest</th>\n",
" <th>Decision Tree</th>\n",
" <th>Logistic Regression</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Accuracy</th>\n",
" <td>0.87</td>\n",
" <td>0.800000</td>\n",
" <td>0.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Precision</th>\n",
" <td>0.00</td>\n",
" <td>0.111111</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall</th>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F1 Score</th>\n",
" <td>0.00</td>\n",
" <td>0.090909</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Random Forest Decision Tree Logistic Regression\n",
"Accuracy 0.87 0.800000 0.87\n",
"Precision 0.00 0.111111 0.00\n",
"Recall 0.00 0.076923 0.00\n",
"F1 Score 0.00 0.090909 0.00"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Define class weights\n",
"class_weights = {0: 1, 1: 9}\n",
"\n",
"# Train Random Forest model\n",
"rf_model = RandomForestClassifier(random_state=42, class_weight=class_weights)\n",
"rf_model.fit(X_train, y_train)\n",
"\n",
"# Train Decision Tree model\n",
"dt_model = DecisionTreeClassifier(random_state=42, class_weight=class_weights)\n",
"dt_model.fit(X_train, y_train)\n",
"\n",
"# Train Logistic Regression model\n",
"lr_model = LogisticRegression(random_state=42, class_weight=class_weights)\n",
"lr_model.fit(X_train, y_train)\n",
"\n",
"# Evaluate models\n",
"models = {\"Random Forest\": rf_model, \"Decision Tree\": dt_model, \"Logistic Regression\": lr_model}\n",
"metrics = {\"Accuracy\": accuracy_score, \"Precision\": precision_score, \"Recall\": recall_score, \"F1 Score\": f1_score}\n",
"results = {}\n",
"\n",
"for name, model in models.items():\n",
" y_pred = model.predict(X_test)\n",
" result = {}\n",
" for metric_name, metric_func in metrics.items():\n",
" result[metric_name] = metric_func(y_test, y_pred)\n",
" results[name] = result\n",
"\n",
"# Convert results to DataFrame for easier plotting\n",
"results_df_resampled = pd.DataFrame(results)\n",
"\n",
"results_df_resampled"
]
},
{
"cell_type": "markdown",
"id": "00cb6be9",
"metadata": {},
"source": [
"# UNDERSAMPLER"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "06a4d1c7",
"metadata": {},
"outputs": [],
"source": [
"from imblearn.under_sampling import RandomUnderSampler"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "dcb0db52",
"metadata": {},
"outputs": [],
"source": [
"# Apply Random Oversampling to balance the training data\n",
"undersampler = RandomUnderSampler(random_state=42)\n",
"X_train_resampled, y_train_resampled = undersampler.fit_resample(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "a939e8fe",
"metadata": {},
"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>Random Forest</th>\n",
" <th>Decision Tree</th>\n",
" <th>Logistic Regression</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Accuracy</th>\n",
" <td>0.560000</td>\n",
" <td>0.490000</td>\n",
" <td>0.130000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Precision</th>\n",
" <td>0.155556</td>\n",
" <td>0.183333</td>\n",
" <td>0.130000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall</th>\n",
" <td>0.538462</td>\n",
" <td>0.846154</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F1 Score</th>\n",
" <td>0.241379</td>\n",
" <td>0.301370</td>\n",
" <td>0.230088</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Random Forest Decision Tree Logistic Regression\n",
"Accuracy 0.560000 0.490000 0.130000\n",
"Precision 0.155556 0.183333 0.130000\n",
"Recall 0.538462 0.846154 1.000000\n",
"F1 Score 0.241379 0.301370 0.230088"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Train Random Forest model\n",
"rf_model = RandomForestClassifier(random_state=42)\n",
"rf_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Train Decision Tree model\n",
"dt_model = DecisionTreeClassifier(random_state=42)\n",
"dt_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Train Logistic Regression model\n",
"lr_model = LogisticRegression(random_state=42)\n",
"lr_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Evaluate models\n",
"models = {\"Random Forest\": rf_model, \"Decision Tree\": dt_model, \"Logistic Regression\": lr_model}\n",
"metrics = {\"Accuracy\": accuracy_score, \"Precision\": precision_score, \"Recall\": recall_score, \"F1 Score\": f1_score}\n",
"results = {}\n",
"\n",
"for name, model in models.items():\n",
" y_pred = model.predict(X_test)\n",
" result = {}\n",
" for metric_name, metric_func in metrics.items():\n",
" result[metric_name] = metric_func(y_test, y_pred)\n",
" results[name] = result\n",
"\n",
"# Convert results to DataFrame for easier plotting\n",
"results_df_resampled = pd.DataFrame(results)\n",
"\n",
"results_df_resampled"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "3269fce1",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Calculate class frequencies for y_train_resampled and y_train\n",
"unique_train_resampled, counts_train_resampled = np.unique(y_train_resampled, return_counts=True)\n",
"unique_train, counts_train = np.unique(y_train, return_counts=True)\n",
"\n",
"# Plot bar plots\n",
"plt.figure(figsize=(10, 6))\n",
"\n",
"# Plot for y_train_resampled\n",
"plt.subplot(1, 2, 1)\n",
"plt.bar(unique_train_resampled, counts_train_resampled, color='red')\n",
"plt.title('Distribution of y_train_resampled')\n",
"plt.xlabel('Class')\n",
"plt.ylabel('Frequency')\n",
"plt.xticks(unique_train_resampled)\n",
"plt.ylim(0, max(max(counts_train_resampled), max(counts_train)) + 10)\n",
"\n",
"# Plot for y_train\n",
"plt.subplot(1, 2, 2)\n",
"plt.bar(unique_train, counts_train, color='blue')\n",
"plt.title('Distribution of y_train')\n",
"plt.xlabel('Class')\n",
"plt.ylabel('Frequency')\n",
"plt.xticks(unique_train)\n",
"plt.ylim(0, max(max(counts_train_resampled), max(counts_train)) + 10)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f16cadbc",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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