main.py

import pandas as pd
from src.analysis import run_analysis
from src.insights import generate_insights
from src.prediction import predict_future

# Step 1: Create synthetic dataset
def create_data():
    import numpy as np

    np.random.seed(42)

    dates = pd.date_range(start="2025-01-01", end="2025-03-31")

    categories = ['Food', 'Rent', 'Travel', 'Shopping', 'Bills', 'Entertainment']
    payment_methods = ['Cash', 'UPI', 'Card']

    data = {
        "Date": np.random.choice(dates, 200),
        "Category": np.random.choice(categories, 200),
        "Amount": np.random.randint(50, 5000, 200),
        "Payment_Method": np.random.choice(payment_methods, 200)
    }

    df = pd.DataFrame(data)
    df.to_csv("data/expenses.csv", index=False)

    return df

if __name__ == "__main__":
    df = create_data()

    df = run_analysis(df)
    generate_insights(df)
    predict_future(df)