Data Analysis project using SQL, Python, and Power BI to analyze customer behavior, uncover purchasing trends, perform data cleaning, and build interactive dashboards for business insights.
This is an end-to-end Data Analytics project that analyzes customer behavior using Python, SQL, and Power BI. The project follows a complete analytics workflow, including data loading, cleaning, exploratory data analysis (EDA), SQL-based business analysis, dashboard creation, and reporting to generate actionable business insights.
The dataset contains customer-related information such as purchasing behavior, demographics, and transaction details. It is used to identify patterns, analyze trends, and support data-driven business decisions.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- SQL
- PostgreSQL / MySQL / SQL Server
- Power BI
- Jupyter Notebook
- Microsoft Excel
- Imported the dataset into Python using Pandas.
- Performed initial inspection of the data.
- Removed duplicate records.
- Handled missing values.
- Corrected inconsistent data.
- Standardized data formats.
- Analyzed customer demographics.
- Identified purchasing patterns.
- Explored sales trends.
- Generated visualizations for key metrics.
- Imported the cleaned dataset into SQL.
- Wrote SQL queries to answer business questions.
- Performed aggregations, joins, filtering, grouping, and ranking.
- Extracted meaningful business insights.
- Built an interactive dashboard.
- Created KPI cards and charts.
- Added filters and slicers for dynamic analysis.
- Visualized customer and sales insights.
- Documented key findings and business recommendations.
- Summarized insights in a professional report.
The interactive Power BI dashboard provides insights into customer behavior, purchasing trends, sales performance, and key business metrics.
- Customer Overview
- Sales Performance
- Customer Segmentation
- Purchasing Trends
- Key Performance Indicators (KPIs)
- Interactive Filters and Slicers
- Identified customer purchasing trends.
- Discovered high-value customer segments.
- Analyzed product and sales performance.
- Generated actionable insights to support business decision-making.
- Presented findings through an interactive dashboard and professional presentation.
Customer-Behavior-Analysis/
│── Customer_Behavior_Analysis.ipynb
│── Customer_Behavior_Analysis.sql
│── Customer Behavior Analysis.pbix
│── customer_shopping_behavior.csv
│── sales_dashboard.png
│── README.md
└── LICENSE
- Clone this repository.
git clone https://github.com/shrutimalap/Customer-Behavior-Analysis.git- Install the required Python libraries.
pip install pandas numpy matplotlib seaborn-
Open the Jupyter Notebook and run the analysis.
-
Execute the SQL script in PostgreSQL, MySQL, or SQL Server.
-
Open the Power BI (.pbix) file to explore the interactive dashboard.
- Data Cleaning
- Exploratory Data Analysis (EDA)
- SQL Query Writing
- Data Visualization
- Business Intelligence
- Dashboard Development
- Reporting
- Data Storytelling
Shruti Malap
Aspiring Data Analyst passionate about transforming raw data into meaningful insights using Python, SQL, Excel, and Power BI.