diff --git a/models/logistic_regression.pkl b/models/logistic_regression.pkl
new file mode 100644
index 0000000..de2f665
Binary files /dev/null and b/models/logistic_regression.pkl differ
diff --git a/src/train.py b/src/train.py
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--- a/src/train.py
+++ b/src/train.py
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+import os
+import joblib
+import pandas as pd
+
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import classification_report, roc_auc_score
+
+
+DATA_PATH = "data/processed/transactions_with_target.csv"
+MODEL_DIR = "models"
+TARGET_COL = "FraudResult"
+
+
+# Columns to drop (identifiers, not features)
+DROP_COLS = [
+    "TransactionId",
+    "BatchId",
+    "AccountId",
+    "SubscriptionId",
+    "CustomerId",
+    "TransactionStartTime"
+]
+
+
+def load_data(path: str) -> pd.DataFrame:
+    if not os.path.exists(path):
+        raise FileNotFoundError(f"Data file not found at {path}")
+    return pd.read_csv(path)
+
+
+def build_preprocessor(df: pd.DataFrame) -> ColumnTransformer:
+    numeric_features = [
+        "Amount",
+        "Value",
+        "PricingStrategy"
+    ]
+
+    categorical_features = [
+        "CurrencyCode",
+        "CountryCode",
+        "ProviderId",
+        "ProductId",
+        "ProductCategory",
+        "ChannelId"
+    ]
+
+    numeric_pipeline = Pipeline(
+        steps=[
+            ("scaler", StandardScaler())
+        ]
+    )
+
+    categorical_pipeline = Pipeline(
+        steps=[
+            ("onehot", OneHotEncoder(handle_unknown="ignore", sparse_output=False))
+        ]
+    )
+
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ("num", numeric_pipeline, numeric_features),
+            ("cat", categorical_pipeline, categorical_features)
+        ]
+    )
+
+    return preprocessor
+
+
+def build_model(preprocessor: ColumnTransformer) -> Pipeline:
+    model = Pipeline(
+        steps=[
+            ("preprocessor", preprocessor),
+            ("classifier", LogisticRegression(
+                max_iter=1000,
+                class_weight="balanced",
+                n_jobs=-1
+            ))
+        ]
+    )
+    return model
+
+
+def main():
+    print("Loading data...")
+    df = load_data(DATA_PATH)
+
+    df = df.drop(columns=DROP_COLS, errors="ignore")
+
+    X = df.drop(columns=[TARGET_COL])
+    y = df[TARGET_COL]
+
+    print("Splitting data...")
+    X_train, X_test, y_train, y_test = train_test_split(
+        X,
+        y,
+        test_size=0.2,
+        stratify=y,
+        random_state=42
+    )
+
+    print("Building model...")
+    preprocessor = build_preprocessor(df)
+    model = build_model(preprocessor)
+
+    print("Training model...")
+    model.fit(X_train, y_train)
+
+    print("Evaluating model...")
+    y_pred = model.predict(X_test)
+    y_proba = model.predict_proba(X_test)[:, 1]
+
+    print("\nClassification Report:")
+    print(classification_report(y_test, y_pred))
+
+    roc_auc = roc_auc_score(y_test, y_proba)
+    print(f"ROC-AUC: {roc_auc:.4f}")
+
+    os.makedirs(MODEL_DIR, exist_ok=True)
+    model_path = os.path.join(MODEL_DIR, "logistic_regression.pkl")
+    joblib.dump(model, model_path)
+
+    print(f"\nModel saved to {model_path}")
+
+
+if __name__ == "__main__":
+    main()