E-commerce Customer Segmentation and Sales Prediction

This project leverages machine learning techniques to analyze e-commerce transaction data for segmenting customers and predicting future sales trends. The objective is to provide actionable insights for targeted marketing strategies and efficient inventory management.

Key Features:
- Data Preprocessing: Imported, cleaned, and transformed e-commerce transaction data, addressing missing values, and ensuring data types are appropriate for analysis.
- Exploratory Data Analysis (EDA): Conducted EDA to understand the data distribution, identify patterns, and detect anomalies. This involved visualizing data distributions, correlations, and key statistics to gain insights into customer behavior and sales trends.
- Customer Segmentation: Utilized K-means clustering to identify distinct customer segments based on purchasing behavior, enabling targeted marketing efforts.
- Sales Prediction: Developed regression models to forecast future sales, incorporating features such as historical sales data, customer demographics, and product categories.
- Model Evaluation: Used metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) to assess and validate model accuracy and reliability.