🛍️ Sales Data Analysis & Visualization
This project analyzes and visualizes a Sales Dataset using Python, SQLAlchemy, and Matplotlib/Seaborn. It demonstrates database integration, data cleaning, SQL querying, and insight visualization — all in one workflow.
⚙️ Steps Overview
Load Data
Extracted from /content/salesforcourse-4fe2kehu.csv.zip
Uploaded to an SQLite database (sales_data.db)
Logged operations using logging module
SQL Operations
SELECT, GROUP BY, and DELETE queries via SQLAlchemy
Checked for NULLs and invalid data (Customer Age < 1)
Calculated total and category-wise revenue
Data Cleaning & EDA
Handled missing and invalid values
Computed age statistics (mean, median, mode)
Visualized major trends using charts
📊 Key Visualizations Chart Insight Bar Plot Revenue by Product Category Pie/Donut Chart Sub-Category Distribution Gender-wise Bar Plot Revenue by Customer Gender Line Chart Monthly Revenue Trend Bar Plot (Top 10 States) Highest Revenue States Scatter Plot Quantity vs Revenue Histogram Customer Age Distribution 🧠 Insights
Technology leads in total revenue.
Female customers slightly contribute more revenue.
Seasonal trend: revenue peaks in specific months.
Top states dominate overall sales.
Majority of customers are aged 30–40.
🧰 Tools & Libraries
Python, Pandas, NumPy, Matplotlib, Seaborn, SQLAlchemy, SQLite, Logging
📂 Output Files
🗄️ sales_data.db → SQLite Database
🧾 log_file.log → Process Logs
📈 Visuals → Displayed inline
✨ Future Improvements
Add interactive dashboards (Plotly / Power BI)
Include forecasting models for future revenue
Automate data refresh
👩💻 Author: Nikita 📊 Data Analyst | Python | SQL | Visualization