Task7_SQLite_Using_Python

This project analyzes transactional sales data from a CSV using SQLite and Python. It loads data into a database, performs SQL queries inside Python, and visualizes top products by revenue using Matplotlib and Seaborn.

🛍️ Sales Data Analysis Project

This project analyzes transactional sales data using Python and SQLite. It demonstrates how to load data from a CSV, store it in a database, run SQL queries, and visualize key insights using Seaborn and Matplotlib.

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📌 Features

• Import CSV data into SQLite using Pandas
• Run SQL queries inside Python
• Generate:
  • Top 10 Products by Revenue (Bar Chart)
  • Revenue by Region
  • Revenue by Payment Method
  • Monthly Revenue Trend (Optional)
• Save visualizations as .png files

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Tech Stack:

• Python
• SQLite3
• Pandas
• Matplotlib
• Seaborn
• Jupyter Notebook

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python : Step 1: Import libraries import sqlite3 import pandas as pd import matplotlib.pyplot as plt import seaborn as sns

Step 2: Load data and connect df = pd.read_csv("trend (1).csv") conn = sqlite3.connect("sales_data.db") df.to_sql("sales", conn, if_exists="replace", index=False)

Step 3: SQL query + visualization query = """ SELECT [Product Name] AS product, SUM([Units Sold]) AS total_units, SUM([Total Revenue]) AS total_revenue FROM sales GROUP BY [Product Name] ORDER BY total_revenue DESC LIMIT 10 """ top_products = pd.read_sql_query(query, conn)

Step 4: Plot sns.barplot(data=top_products, x="total_revenue", y="product", palette="Blues") plt.title("Top 10 Products by Revenue") plt.xlabel("Revenue") plt.ylabel("Product") plt.tight_layout() plt.savefig("top_10_products_revenue.png") plt.show() also used