This project analyzes a Supermarket Sales Dataset using Data Science techniques.Leveraged advanced data mapping techniques and implemented beyond-the-course algorithms to enhance descriptive, predictive analytics. Integrated PySpark for efficient big data processing, enabling high-performance data manipulation and visualization. Conducted a comprehensive analysis and delivered a well-structured presentation, receiving outstanding feedback for depth, clarity, and insights.
✔ Data Cleaning & Transformation: Handled missing values, duplicates, and incorrect data types. Applied advanced data mapping techniques to standardize and enrich the dataset. Used PySpark for efficient big data processing and transformation, ensuring scalability for large datasets.
✔ Exploratory Data Analysis (EDA): Analyzed sales distribution across branches & cities. Performed customer segmentation by gender, membership type, and purchasing behavior. Identified top-selling product lines and seasonal demand fluctuations. Examined peak sales hours and their correlation with customer traffic patterns.
✔ Advanced Analytics: Implemented beyond-the-course algorithms for in-depth descriptive, predictive, and prescriptive analytics. Built predictive models using PySpark MLlib to forecast sales trends and customer demand. Conducted association rule mining to uncover relationships between product categories. Applied clustering techniques (e.g., K-Means) for customer segmentation based on spending habits.
✔ Visualization & Insights: Developed interactive dashboards with Matplotlib, Seaborn, and PySpark SQL visualization. Created correlation heatmaps, time-series plots, and decision trees to illustrate key insights. Visualized revenue patterns and purchase behavior using big data analytics techniques.
✔ Impact & Presentation: Delivered a structured, data-driven presentation with compelling insights and business recommendations. Earned excellent feedback for analytical depth, clarity, and the effectiveness of predictive insights.
- Google Colab
- Python (Pandas, NumPy, Matplotlib, Seaborn)
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Clone the Repository
git clone https://github.com/SriAshritha/supermarket-analysis.git cd supermarket-analysis -
Open Google Colab and upload
supermarket_analysis.ipynb. -
Install Dependencies by running the following command:
!pip install pandas numpy matplotlib seaborn
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Execute the cells sequentially.
- Enhanced Data Processing Efficiency: 🚀 Implemented PySpark for big data handling, reducing processing time and improving scalability.
- Advanced Analytical Depth: 🔍 Leveraged beyond-the-course algorithms for descriptive, predictive, and prescriptive analytics, uncovering key business insights.
- Optimized Sales Forecasting: 📊 Built predictive models to estimate future sales trends and demand fluctuations with high accuracy.
- Improved Customer Segmentation: 👥 Applied clustering techniques to identify distinct customer groups based on purchasing behavior.
- Actionable Business Insights: 💡 Provided data-driven recommendations for inventory management, peak sales strategies, and customer engagement.
- High-Impact Presentation: 🎯 Delivered a structured analysis with clear visualizations and compelling insights, earning excellent feedback.
For any inquiries, reach out via:
🔗 LinkedIn: Sri Ashritha P