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app.py
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app.py
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import pandas as pd
import joblib
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder
def load_data(filepath):
return pd.read_csv(filepath)
def preprocess_data(data):
# Convert Date to datetime and extract Year, Month, Day
data['Date'] = pd.to_datetime(data['Date'])
data['Year'] = data['Date'].dt.year
data['Month'] = data['Date'].dt.month
data['Day'] = data['Date'].dt.day
data.drop(columns='Date', inplace=True)
# Label encode Location and Store
le = LabelEncoder()
data['Location'] = le.fit_transform(data['Location'])
data['Store'] = le.fit_transform(data['Store'])
return data
def split_data(data, target_column, test_size=0.2, random_state=42):
X = data.drop(columns=target_column)
y = data[target_column]
return train_test_split(X, y, test_size=test_size, random_state=random_state)
def train_model(X_train, y_train):
model = LogisticRegression()
model.fit(X_train, y_train)
return model
def save_model(model, filepath):
joblib.dump(model, filepath)
def test_model(model, X_test, y_test):
return model.score(X_test, y_test)
def main():
# Load the data
data = load_data('data/credit_card_records.csv')
# Preprocess the data
data = preprocess_data(data)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = split_data(data, 'Fraudulent')
# Train the model
model = train_model(X_train, y_train)
# Save the model
save_model(model, 'models/model.pkl')
# Test the model
score = test_model(model, X_test, y_test)
# print score is:
print("Model accuracy is: ", score)
return score
if __name__ == "__main__":
main()