Customer_Behaviour_Analysis

Data Analytics Project showcasing customer behaviour analysis using Python, SQL and PowerBI

Overview This project demonstrates a full data analytics workflow, including data loading, exploratory data analysis (EDA), data cleaning, SQL querying (MySQL), visualization with Power BI, report writing, and final presentation using Gamma.​

Dataset

• Description: A transactional customer shopping behavior dataset containing detailed records of purchases across multiple product categories. It includes customer demographics, purchase details, shopping behavior attributes, and subscription information for each transaction.
• Source: Public
• Format: CSV (Comma-Separated Values)

Tools

• Python (pandas, numpy, matplotlib, seaborn)
• SQL (MySQL)
• Power BI
• Gamma (for presentation)

Steps

1. Load Dataset in Python: Use pandas to import and preview data.​
2. Perform EDA: Visualize and summarize main insights, spot anomalies.​
3. Clean Data: Handle missing values, inconsistencies, and data types.​
4. Run SQL Queries: Analyze data using SQL joins, aggregations, and filters.​
5. Build Power BI Dashboard: Visualize results in interactive dashboards.​
6. Create Report: Document findings, actionable insights, and recommendations.​
7. Make PPT with Gamma: Turn key results into a clean, recruiter-ready presentation.​

Dashboard

• Main Metrics: Total Revenue Trends, Sales by Region/Location, Purchases by Product Category, Average Review Rating, Customer Subscription Rates, Frequency of Purchases (Weekly, Monthly, etc.), Discounts and Promo Code Usage, Preferred Payment Methods, Seasonality of Purchases, Customer Segmentation (by demographics/behaviors)
• Access: Power BI dashboard Screenshot

Results

• Summary of EDA and SQL findings​
• Visualization highlights (Power BI)​
• Conclusions and recommendations

How to Run

1. Clone repository.
2. Install dependencies: pip install -r requirements.txt.
3. Place dataset in /data folder.
4. Run python eda_and_cleaning.py for analysis.
5. Use SQL scripts for querying in DBMS of choice.
6. Open PowerBI_dashboard.pbix in Power BI.
7. Review Gamma_presentation.pptx for project summary.