This project analyzes Diwali sales data to uncover key insights about customer behavior, purchasing trends, and business performance using Python and data visualization libraries. The goal was to help understand which factors influence sales the most — such as gender, age, state, occupation, and product category — to support data-driven marketing and inventory strategies.
- Clean and preprocess raw sales data for accurate analysis.
- Explore demographic patterns influencing purchase decisions.
- Identify top-performing states, occupations, and products.
- Visualize insights through Matplotlib and Seaborn charts.
- Derive actionable business conclusions to improve future Diwali campaigns.
Through this project, I gained hands-on experience in:
- Data Cleaning: Handling null values, data type conversions, and removing inconsistencies.
- Exploratory Data Analysis (EDA): Using Pandas to explore, group, and summarize data.
- Visualization: Creating clear, meaningful plots with Matplotlib and Seaborn.
- Business Insights: Understanding how sales vary with demographics like gender, age group, occupation, and region.
- Data Storytelling: Translating raw data into actionable insights for better decision-making.
- Female buyers were more active and contributed higher total purchases than males.
- The age group 26–35 years made the highest number of purchases.
- Uttar Pradesh, Maharashtra, and Karnataka were top states in sales.
- Married women working in IT, Healthcare, and Aviation sectors were key customers.
- Clothing & Apparel, Food, and Electronics were the most popular product categories.
✅ Problem Solved: Helped identify potential customer segments and high-performing categories for targeted marketing and stock management during festive seasons.
- Language: Python 🐍
- Libraries: Pandas, NumPy, Matplotlib, Seaborn
- Environment: Jupyter Notebook
- Bar charts showing gender vs. purchase
- Age group and occupation-based sales analysis
- State-wise sales performance
- Product category comparisons
- Source: Kaggle (Diwali Sales Dataset)
- Type: CSV file containing customer demographics and purchase details
- Clone this repository
git clone https://github.com/garimaakashyap/Diwali-Sales-Analysis-using-Python.git
- Open the Jupyter Notebook
- Run all cells to view the full analysis and visualizations
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Garima Kashyap
“Turning raw data into meaningful stories through Python & Analytics.” 🌸