This project analyzes customer shopping behavior using 3,900 purchase records to identify spending patterns, customer segments, product preferences, and subscription trends. Using Python, SQL, PostgreSQL, and Power BI, the project delivers business insights through data analysis and interactive dashboards.
This project demonstrates a complete end-to-end Data Analytics workflow using Python, PostgreSQL, and Power BI. The goal of the project is to analyze a dataset, uncover insights, and present findings through interactive dashboards, reports, and presentations.
The project includes:
- Data loading and preprocessing using Python
- Exploratory Data Analysis (EDA)
- Data cleaning and transformation
- SQL analysis using PostgreSQL
- Interactive dashboard creation in Power BI
- Final business report and PowerPoint presentation
The dataset used in this project contains structured data related to business/industry operations and was analyzed to identify trends, patterns, and key performance indicators (KPIs).
- Multiple variables and attributes
- Numerical and categorical data
- Missing values and inconsistencies handled during cleaning
- Suitable for analytics and visualization tasks
| Tool | Purpose |
|---|---|
| Python | Data loading, cleaning, and EDA |
| Pandas & NumPy | Data manipulation |
| Matplotlib & Seaborn | Data visualization |
| PostgreSQL | SQL queries and database analysis |
| Power BI | Dashboard and data visualization |
| PowerPoint | Presentation creation |
| Jupyter Notebook | Development and analysis environment |
- Imported dataset using Python
- Verified structure, columns, and data types
- Performed initial inspection of records
- Analyzed distributions and relationships
- Identified trends, patterns, and anomalies
- Generated charts and summary statistics
- Handled missing values
- Removed duplicates
- Corrected inconsistent formats
- Prepared clean dataset for analysis
-
Created database and tables
-
Imported cleaned dataset into PostgreSQL
-
Executed SQL queries for:
- Aggregations
- Filtering
- Joins
- KPI calculations
- Trend analysis
Built an interactive dashboard to visualize:
- Key performance indicators
- Trends and comparisons
- Category-wise analysis
- Business insights
-
Prepared a detailed analytical report
-
Created a professional PowerPoint presentation summarizing:
- Objectives
- Methodology
- Findings
- Recommendations
The Power BI dashboard includes:
- Interactive filters and slicers
- KPI summary cards
- Trend analysis charts
- Comparative visualizations
- User-friendly interface for stakeholders
Some of the major outcomes from the analysis include:
- Identification of important business trends
- Insights into performance metrics
- Improved understanding of customer/operational behavior
- Data-driven recommendations for decision-making
project-folder/
│
├── data/
│ ├── raw_dataset.csv
│ └── cleaned_dataset.csv
│
├── notebooks/
│ └── eda_analysis.ipynb
│
├── sql/
│ └── analysis_queries.sql
│
├── powerbi/
│ └── dashboard.pbix
│
├── reports/
│ ├── final_report.pdf
│ └── presentation.pptx
│
└── README.mdInstall the following:
- Python 3.x
- PostgreSQL
- Power BI Desktop
- Jupyter Notebook
- Clone the repository:
git clone <repository-link>- Install required Python libraries:
pip install pandas numpy matplotlib seaborn psycopg2- Run the Jupyter Notebook:
jupyter notebook- Execute SQL scripts in PostgreSQL:
Run analysis_queries.sql- Open the Power BI dashboard:
dashboard.pbix- Automate ETL pipeline
- Deploy dashboards online
- Add predictive analytics models
- Integrate real-time data sources
This project demonstrates practical skills in data analytics, SQL, visualization, and business reporting. It highlights the complete analytics lifecycle from raw data processing to actionable insights and presentation-ready outputs.
Aftab Monye Data Analytics Project GitHub: https://github.com/aftabmonye LinkedIn: https://www.linkedin.com/in/aftab-monye-3774a13a3