End-to-End Data Analytics Project using Python, SQL, and Power BI
This project analyzes customer shopping behavior using retail data to uncover meaningful insights related to purchasing patterns, discount usage, and customer segmentation. It demonstrates a complete data analytics workflow from raw data processing to dashboard visualization and business insights.
- Analyze customer purchasing behavior
- Understand the impact of discounts on spending
- Segment customers based on purchase history
- Identify high-value and loyal customers
The dataset contains retail customer transaction data, including:
- Customer ID
- Age
- Purchase amount
- Discount applied
- Previous purchases
- Product and transaction-related details
The data is initially processed using Python and later stored in a SQL database for querying and analysis.
- Python (Pandas) for data cleaning and exploratory data analysis
- MySQL for business-driven queries
- Power BI for interactive dashboards and data visualization
- Jupyter Notebook for development and analysis
- Loaded the dataset into Python using Pandas
- Performed initial inspection using basic functions
- Handled missing values
- Removed unnecessary columns
- Converted data types
- Created derived columns such as age groups and customer segments
- Analyzed customer demographics
- Identified spending trends
- Studied discount usage patterns
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Loaded cleaned data into a SQL database
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Executed queries to answer key business questions, including:
- Customers who used discounts but spent more than the average purchase amount
- Purchase behavior across different customer segments
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Built an interactive dashboard displaying:
- Customer segmentation
- Average purchase value
- Discount impact analysis
- Key performance indicators
- Loyal customers show higher average purchase values
- Discounts do not always reduce spending; some discounted customers spend more than average
- Customer segmentation helps identify high-value customers