Customer Shopping Behavior - Data Analytics Project

Overview

This project demonstrates an end-to-end Data Analytics workflow using Python, MySQL, and Power BI. The project includes data loading, data cleaning, exploratory data analysis (EDA), SQL-based analysis, dashboard creation, and business reporting.

The goal of this project is to extract meaningful insights from raw data and present them through interactive dashboards and reports for better decision-making.

Dataset

• Dataset Source: (customer_shopping_behavior.csv)

• File Format: CSV / Excel

• Total Records: (3900)

• Features Included:

  • Customer Information
  • Product Details
  • Sales Data
  • Transaction History
  • Ratings & Reviews

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Tools & Technologies Used

Programming & Analysis

• Python
• Pandas

Database

• MySQL

Visualization & Reporting

• Power BI
• Gamma (for presentation creation)

Development Environment

• Jupyter Notebook
• MySQL Workbench

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Project Workflow

1. Data Loading

• Imported dataset using Python
• Loaded data into Pandas DataFrame
• Checked data structure and column details

2. Data Cleaning

• Removed duplicate records
• Handled missing/null values
• Standardized column names
• Fixed inconsistent values

3. Exploratory Data Analysis (EDA)

Performed detailed analysis to identify:

• Sales trends
• Customer behavior
• Product performance
• Revenue insights
• Correlation between variables

Used visualizations such as:

• Bar Charts
• Column Charts
• Donut Charts

4. SQL Analysis (MySQL)

Executed SQL queries for:

• Aggregations
• Subqueries
• Window Functions
• Customer segmentation
• Top-performing products analysis

5. Power BI Dashboard

Built interactive dashboards including:

• KPI Cards
• Sales Overview
• Customer Analysis
• Product Insights
• Trend Analysis
• Filters & Slicers

6. Reporting & Presentation

• Created business reports
• Generated presentation slides using Gamma
• Summarized key insights and recommendations

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Dashboard Highlights

The Power BI dashboard provides:

• Interactive visualizations
• Real-time filtering
• Business performance tracking
• Data-driven insights for decision-making

Key Metrics:

• Total Sales
• Total Profit
• Customer Count
• Top Products

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Results & Insights

Some key insights identified from the analysis:

• Top-performing product categories
• High-value customer segments
• Seasonal sales trends
• Revenue growth opportunities
• Customer purchasing behavior patterns

The project helps businesses make informed decisions using data-driven insights.

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Project Structure

Data-Analytics-Project/
│
├── Dataset/
├── Python Scripts/
├── SQL Queries/
├── Power BI Dashboard/
├── Reports/
├── Presentation/
└── README.md

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How to Run the Project

Step 1: Clone the Repository

git clone <repository-link>

Step 2: Install Required Libraries

pip install pandas numpy matplotlib seaborn mysql-connector-python

Step 3: Run Python Files

• Open Jupyter Notebook
• Execute EDA and data cleaning scripts

Step 4: Import Data into MySQL

• Create database in MySQL
• Import cleaned dataset
• Run SQL query files

Step 5: Open Power BI Dashboard

• Open .pbix file in Power BI Desktop
• Refresh data connections if required

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Future Improvements

• Add machine learning models
• Automate ETL pipeline
• Deploy dashboard online
• Integrate real-time data

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Author

Kapil Sanjay Mali Data Analyst | Python | SQL | Excel | Power BI LinkedIn: https://www.linkedin.com/in/kapil-mali/ GitHub: https://github.com/Kapilmali07