This repository hosts a data science project focused on analyzing and predicting Walmart's sales data. Utilizing Python with libraries like Pandas for data manipulation, Matplotlib and Seaborn for visualization, and scikit-learn for machine learning, this project provides a thorough exploration of Walmart's sales across 45 stores. Key features of the project include:
- Data Cleaning and Preprocessing: Rigorous data preparation processes to ensure accurate and meaningful analysis.
- Exploratory Data Analysis (EDA): Visual and statistical analysis to uncover underlying patterns and trends in sales data.
- Clustering Analysis: Application of clustering techniques to segment stores into meaningful groups based on sales performance and external factors, facilitating targeted marketing and strategic planning.
- Predictive Modeling: Development and validation of a Random Forest model to forecast weekly sales, emphasizing model accuracy and generalizability.
- Performance Evaluation: Detailed evaluation of predictive models to assess their efficacy and reliability in real-world scenarios.
The project aims to enhance strategic decision-making at Walmart by providing detailed insights and predictions on sales trends, contributing to more informed and effective business strategies.