I am excited about the opportunity to assist you in analyzing your e-commerce dataset using the Apache Spark ML API. Leveraging my expertise in data analytics, Apache Spark, and Jupyter Notebooks, I will provide actionable insights into customer behavior, sales performance, and product trends. Below is my detailed proposal for your project:
Project Scope
Dataset Processing
Load and preprocess the e-commerce dataset using Apache Spark to handle large-scale data efficiently.
Clean and prepare the data, including handling missing values, duplicates, and inconsistencies.
Data Analysis and Insights
Customer Behavior Analysis:
Identify buying patterns, repeat customers, and segmentation based on demographics or purchase history.
Sales Performance Metrics:
Analyze revenue trends, best-performing products, and underperforming categories.
Product Trends:
Generate insights on popular product categories, seasonal trends, and demand fluctuations.
Modeling with Spark ML
Apply machine learning models using Apache Spark ML API to uncover deeper insights:
Clustering for customer segmentation (e.g., K-Means).
Predictive modeling for sales forecasting.
Recommendation system for products.
Deliverables
A fully functional Jupyter Notebook containing the entire process, from data loading to analysis and visualization.
Spark application code ready for further reuse and adaptation.
Summary results and insights, provided as visualizations and raw processed data.