Expense Forecasting Tool

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_absolute_error, r2_score

# -----------------------------

# STEP 1: Create Sample Expense Data

# -----------------------------

# Example: Monthly total expenses (can be replaced with CSV input)

data = {

    "Month": [

        "Jan", "Feb", "Mar", "Apr", "May", "Jun",

        "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"

    ],

    "Expense": [21000, 19500, 22000, 25000, 24500, 26000, 27500, 28500, 29500, 31000, 32500, 34000]

}

df = pd.DataFrame(data)

df["Month_Number"] = np.arange(1, len(df) + 1)

# -----------------------------

# STEP 2: Train the Model

# -----------------------------

X = df[["Month_Number"]]

y = df["Expense"]

model = LinearRegression()

model.fit(X, y)

# -----------------------------

# STEP 3: Forecast Next 3 Months

# -----------------------------

future_months = np.arange(len(df) + 1, len(df) + 4).reshape(-1, 1)

future_predictions = model.predict(future_months)

# Combine results

forecast_df = pd.DataFrame({

    "Month_Number": future_months.flatten(),

    "Predicted_Expense": future_predictions

})

print("\n🔮 Expense Forecast:")

print(forecast_df)

# -----------------------------

# STEP 4: Visualize Results

# -----------------------------

plt.figure(figsize=(10, 6))

plt.plot(df["Month_Number"], df["Expense"], marker='o', label="Actual Expenses")

plt.plot(forecast_df["Month_Number"], forecast_df["Predicted_Expense"], marker='o', linestyle='--', color='orange', label="Predicted")

plt.title("Expense Forecasting Tool")

plt.xlabel("Month")

plt.ylabel("Expense (₹)")

plt.legend()

plt.grid(True)

plt.show()

# -----------------------------

# STEP 5: Evaluate Model

# -----------------------------

predictions = model.predict(X)

print("\n📊 Model Evaluation:")

print("MAE:", round(mean_absolute_error(y, predictions), 2))

print("R² Score:", round(r2_score(y, predictions), 3))