House_Price_Prediction

import pandas as pd import matplotlib.pyplot as plt

Sample dataset

data = { 'Month': ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'], 'Sales': [100, 120, 130, 150, 160, 180, 200, 220, 210, 190, 160, 150] }

Convert data to DataFrame

df = pd.DataFrame(data)

Plotting monthly sales

plt.figure(figsize=(10, 6)) plt.plot(df['Month'], df['Sales'], marker='o') plt.title('Monthly Sales of Product X') plt.xlabel('Month') plt.ylabel('Sales') plt.xticks(rotation=45) plt.grid(True) plt.tight_layout()

Save plot as an image

plt.savefig('monthly_sales.png')

Write README file

with open('README.md', 'w') as readme: readme.write('# Monthly Sales Analysis\n\n') readme.write('This repository contains data and analysis of the monthly sales of Product X.\n\n') readme.write('## Dataset\n\n') readme.write('The dataset contains two columns: Month and Sales.\n\n') readme.write('### Example Data\n\n') readme.write('csv\n') readme.write(df.to_csv(index=False)) readme.write('\n\n') readme.write('## Graph\n\n') readme.write('\n\n') readme.write('## Analysis\n\n') readme.write('Add your analysis here.')

print("README.md and monthly_sales.png generated successfully!")