Python script for automated reporting using AI with financial data

automated_reporting_financial.py

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import seaborn as sns

# Example Balance Sheet and Income Statement Data
# Replace with your actual data

balance_sheet_data = {
    'Year': [2020, 2021, 2022],
    'Assets': [100000, 120000, 140000],
    'Liabilities': [50000, 60000, 70000],
    'Equity': [50000, 60000, 70000]
}

income_statement_data = {
    'Year': [2020, 2021, 2022],
    'Revenue': [200000, 220000, 240000],
    'Expenses': [150000, 160000, 170000],
    'Net Income': [50000, 60000, 70000]
}

# Convert to DataFrame
balance_sheet = pd.DataFrame(balance_sheet_data)
income_statement = pd.DataFrame(income_statement_data)

# Merge DataFrames on Year
financial_data = pd.merge(balance_sheet, income_statement, on='Year')

# Scale data
scaler = StandardScaler()
financial_data_scaled = scaler.fit_transform(financial_data)

# Train model to predict next year's Net Income based on previous financial data
model = LinearRegression()
model.fit(financial_data_scaled[:, :-1], financial_data_scaled[:, -1])

# Make predictions
predictions = model.predict(financial_data_scaled[:, :-1])

# Plot results
plt.figure(figsize=(10, 6))
plt.plot(financial_data['Year'], financial_data_scaled[:, -1], label='Actual')
plt.plot(financial_data['Year'], predictions, label='Predicted')
plt.title('Actual vs Predicted Net Income')
plt.legend()
plt.show()

# Save results to CSV
results = pd.DataFrame({'Year': financial_data['Year'], 'Actual': financial_data_scaled[:, -1], 'Predicted': predictions})
results.to_csv('results.csv', index=False)