Data_Science_Projects_3

Holiday Events Analysis - Walmart Sales Prediction⚾👷🏻‍♀👷🏻‍♂🦃🎄

Problem: Predicting sales in the retail industry using Machine Learning Algorithms.

Walmart Sales Prediction Project

📌 Project Overview

This project aims to build an effective machine learning solution for Walmart, one of the leading retail stores in the US, to accurately predict sales and demand.

I applied 5(five) Regression Models–> Multiple Linear Regression (MLR), Ridge Regression, Lasso Regression, Elastic-Net Regression, and Polynomial Regression for predicting Sales and evaluated the model scores for comparison (R2, RMSE, RSS and MSE).

🔍 Business Problem

• Walmart often faces challenges due to unforeseen demand, leading to stockouts.
• Current machine learning algorithms fail to adequately account for external factors, resulting in inaccuracies.

📊 Data Insights

Scope: Historical sales data from 45 Walmart stores across various regions in the US.

Key Factors Influencing Sales:

• Holidays & Events: Prominent holidays like Super Bowl, Labor Day, Thanksgiving, and Christmas.

• Economic Indicators: CPI (Consumer Price Index), Unemployment Index.

• Markdown Events: Seasonal promotional markdowns preceding major holidays.

• Holiday Weighting: Weeks including major holidays are weighted five times higher than non-holiday weeks.

• The dataset is taken from Kaggle (https://www.kaggle.com/datasets/yasserh/walmart-dataset).

🚀 Project Goals

• Develop a robust ML algorithm to accurately predict demand and sales.
• Account for factors like economic conditions and promotional markdowns.
• Handle incomplete historical data for holiday weeks effectively.
• Model the effects of markdowns on sales during major holiday weeks.

🌟 Expected Outcomes

• Improved accuracy in demand forecasting.
• Enhanced inventory planning and stock availability.
• Better alignment of markdown events with predicted holiday demands.

🔍 Factor Analysis & SHAP

Sales Prediction Map

Holiday Events Animation