Black Friday Sales Prediction Analysis Using Advanced Machine Learning Techniques for Enhanced Retail Decision-Making and Customer Targeting Strategies

Project Abstract:

Background: Black Friday sales events are a significant source of revenue for retailers, and accurate sales prediction models can provide valuable insights for retail decision-making, inventory management, and customer targeting strategies. The Black Friday Sales dataset contains information on customer demographics, product details, and purchase amounts, offering an opportunity to apply machine learning (ML) techniques for sales prediction analysis. This project aims to develop a robust and reliable sales prediction model using advanced machine learning techniques, leveraging the Black Friday Sales dataset to enhance retail decision-making and inform customer targeting strategies.

Objectives:

1. To preprocess and analyze the Black Friday Sales dataset, which includes data on customer demographics, product details, and purchase amounts.
2. To identify the most relevant features for effective sales prediction using feature selection techniques.
3. To implement various machine learning algorithms, including regression, ensemble methods, and deep learning, to create a high-performance sales prediction model.
4. To evaluate the performance of the prediction model using appropriate metrics and validate its effectiveness in predicting Black Friday sales.
5. To provide insights and recommendations for retail decision-making, inventory management, and customer targeting strategies based on the Black Friday sales prediction analysis.

Methods:

1. Data preprocessing: Data preprocessing steps, such as data cleaning, normalization, encoding, and handling missing values, will be performed to ensure the Black Friday Sales dataset is suitable for ML model training.
2. Feature selection: Techniques such as Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and correlation analysis will be used to identify the most relevant features for sales prediction.
3. Model development: ML algorithms, including Linear Regression, Ridge Regression, Lasso Regression, Decision Trees, Random Forest, Gradient Boosting, and deep learning models like Neural Networks, will be applied to develop the sales prediction model. Hyperparameter tuning and model selection will be conducted through cross-validation and grid search techniques.
4. Model evaluation: The performance of the ML models will be assessed using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.
5. Insights and recommendations: The Black Friday sales prediction analysis will be used to derive insights and recommendations for retail decision-making, inventory management, and customer targeting strategies.

Expected Outcomes: The project will result in a comprehensive sales prediction model capable of accurately predicting Black Friday sales. The implementation of this model in retail decision-making and customer targeting strategies will enable more informed decisions, leading to optimized revenue, better inventory management, and more effective marketing campaigns.

Keywords: Black Friday Sales dataset, sales prediction, machine learning, feature selection, data preprocessing, regression, ensemble methods, deep learning, retail decision-making, inventory management, customer targeting strategies.