Nabid In Motion Nabid In Motion

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Imbalanced Data Project Tutorial

Step-by-step tutorial for handling imbalanced classification on fraud-style data.

Project: Fraud Detection

Step 1: Load Data and Analyze Imbalance

import pandas as pd
from collections import Counter
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, roc_auc_score

# Example: replace with your CSV (e.g. Kaggle credit card fraud)
# df = pd.read_csv("creditcard.csv")
# For demo, simulate imbalance:
import numpy as np
rng = np.random.default_rng(42)
X = rng.normal(size=(1000, 5))
y = np.array([0] * 980 + [1] * 20)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

print("Class counts (train):", Counter(y_train))

Step 2: Baseline Model (No Resampling)

baseline = RandomForestClassifier(random_state=42)
baseline.fit(X_train, y_train)
pred_b = baseline.predict(X_test)
proba_b = baseline.predict_proba(X_test)[:, 1]

print("Baseline report:\n", classification_report(y_test, pred_b))
print("Baseline ROC-AUC:", round(roc_auc_score(y_test, proba_b), 4))

High accuracy with poor recall on the minority class is common here.

Step 3: Apply SMOTE

from imblearn.over_sampling import SMOTE

smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X_train, y_train)
print("After SMOTE:", Counter(y_res))

Step 4: Train Balanced Model and Compare

model = RandomForestClassifier(class_weight="balanced", random_state=42)
model.fit(X_res, y_res)
pred = model.predict(X_test)
proba = model.predict_proba(X_test)[:, 1]

print("SMOTE + balanced RF report:\n", classification_report(y_test, pred))
print("ROC-AUC:", round(roc_auc_score(y_test, proba), 4))

Compare precision and recall on class 1 against the baseline.

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