This project focuses on analyzing customer demographics, spending patterns, and revenue contributions using Exploratory Data Analysis (EDA). By leveraging Pandas, Seaborn, and Matplotlib, we uncover key insights from sales data to help businesses make data-driven decisions.
Python: Data manipulation and analysis Pandas: Data preprocessing and aggregation Seaborn & Matplotlib: Data visualization and trend analysis Jupyter Notebook: Development and execution environment Excel (Optional): Data export and further analysis
The dataset consists of 14 columns and 11,251 records, including: Customer Demographics: Gender, Age Group, Marital Status, and State Transaction Details: Purchase Amount, Product Category Geographical Data: State-wise distribution of customers and revenue
- Removed unnecessary columns with missing values
- Handled null values in critical fields
- Converted numerical values into appropriate data types
- Identified trends in customer spending behavior
- Analyzed gender-wise and age-group-wise purchase patterns
- Evaluated state-wise customer distribution and revenue generation
- Visualized top product categories contributing to sales
- Count Plots: Distribution of customers by gender, age group, and marital status
- Bar Charts: State-wise revenue and top purchasing demographics
- Pie Charts: Contribution of different categories to total sales
- Which gender and age group contributes the most revenue?
- Which state has the highest number of customers?
- What are the top spending categories among customers?
- How does marital status impact purchase decisions?
- Implement Machine Learning models to predict customer behavior
- Automate data processing and visualization dashboards
- Integrate real-time data streaming for dynamic insights
This project provides valuable insights into customer behavior using EDA techniques. Businesses can use these findings to optimize their marketing strategies, target the right audience, and enhance customer engagement.