Reading
A complete end-to-end data science project that uses machine learning to predict weekly department-level sales for 45 Walmart stores, including data exploration, cleaning, visualization, modeling, and an interactive Streamlit application.
Section 01
A complete end-to-end data science project that uses machine learning to predict weekly department-level sales for 45 Walmart stores, including data exploration, cleaning, visualization, modeling, and an interactive Streamlit application.
Section 02
Section 03
This is a complete end-to-end data science project aimed at predicting weekly sales for each department across 45 Walmart stores. The project not only includes a traditional Jupyter Notebook analysis workflow but also builds an interactive Streamlit web application, allowing business users to intuitively explore data and perform real-time predictions.
The project has been deployed to Streamlit Cloud and can be accessed directly for experience: https://retail-data-analysis.streamlit.app/
Section 04
The dataset used in the project is quite substantial:
This is a typical time-series regression problem involving complex sales pattern prediction across multiple stores and departments.
Section 05
The Streamlit application includes six core modules that fully cover the data science lifecycle:
Section 06
Provides an interactive overview of three original datasets, including missing value analysis. Users can quickly understand data structure and quality issues to prepare for subsequent processing.
Section 07
A complete data cleaning workflow, including:
This module demonstrates how to transform raw data into a clean dataset ready for modeling.
Section 08
Interactive charts built using Plotly, including:
These visualizations help business personnel intuitively understand sales patterns and key driving factors.
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