Python script for automated reporting using AI

automated_reporting.py

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

# Load data
# Replace 'your_data.csv' with your actual data file
# Make sure the CSV file is in the same directory as this script
# Or provide the full path to the file

# data = pd.read_csv('your_data.csv')

# Clean and preprocess data
# This is a placeholder. Replace with your actual data cleaning/preprocessing steps
# data = data.dropna()
# data = data.drop(['unnecessary_column'], axis=1)

# Scale data
# scaler = StandardScaler()
# data_scaled = scaler.fit_transform(data)

# Train model
# model = LinearRegression()
# model.fit(data_scaled[:, :-1], data_scaled[:, -1])

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

# Plot results
# plt.figure(figsize=(10, 6))
# sns.lineplot(data=data_scaled[:, -1], label='Actual')
# sns.lineplot(data=predictions, label='Predicted')
# plt.title('Actual vs Predicted')
# plt.show()

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