This project was assigned as a mini project by my instructor. It focuses on data cleaning, analysis, and visualization of Cafeteria Sales Data using Python and Pandas library. Raw sales data was cleaned, processed, visualized using Matplotlib, and analyzed to generate meaningful insights including correlation analysis and strategic recommendations.
- Python — Core programming language
- Pandas — Data cleaning & analysis
- Matplotlib — Data visualization & plotting
- CSV — Raw and cleaned data format
- MS Word — Project documentation
| File Name | Description |
|---|---|
| Python and Pandas Mini Project Question.docx | Problem statement given by instructor |
| Python Pandas Mini Project Script.py | Main Python script for data cleaning & analysis |
| Cafe_Sales_Raw.csv | Raw cafeteria sales dataset |
| Cafe_Sales_Cleaned.csv | Cleaned dataset after data processing |
| plot1.png | Matplotlib plot – Quantity vs Total Spent |
| plot2.png | Matplotlib plot – Price Per Unit vs Total Spent |
| Cafeteria_Sales_Data_Analysis_Report.docx | Final analysis report with correlation findings |
- ✅ Loads raw cafeteria sales CSV data
- ✅ Performs data cleaning — handles missing values, duplicates & formatting issues
- ✅ Exports cleaned data as a new CSV file
- ✅ Creates 2 Matplotlib visualizations for sales insights
- ✅ Performs correlation analysis between numerical columns
- ✅ Generates strategic recommendations to improve revenue
Examining the Project Question Statement (Python_Pandas_Mini_Project_Question.docx) ↓ Raw Data (Cafe_sales_Raw.csv) ↓ Data Cleaning (Python Pandas Mini Project Script.py) ↓ Cleaned Data (cafe_sales_cleaned.csv) ↓ Visualization — Plot 1 & Plot 2 (Matplotlib) ↓ Correlation Analysis ↓ Final Report (Cafeteria_Sales_Data_Analysis_Report.docx)
- Cleaned and structured real-world cafeteria sales data
- Applied Pandas for efficient data manipulation
- Created 2 visualizations using Matplotlib for deeper insights
- Generated correlation matrix to find relationships in data
- Documented entire process with a professional analysis report
Vathada Swaroop Kumar
- GitHub: swaroop456
- LinkedIn: Vathada Swaroop Kumar