5 Innovative Machine Learning Projects in Retail For 2023

The retail sector constantly evolves due to shifting consumer buying habits and the market's transition to complex ecosystems. Emerging technologies are drastically changing the industry. Buyers are swamped with enticing offers competing for their attention on every channel, including online (web to mobile apps) and in-store. Organizations may maximize the value of their consumer data by integrating machine learning with marketing initiatives. By analyzing user data and offering relevant insights, machine learning helps businesses provide their customers with a more customized experience. This blog explores five exciting project ideas around the practical applications of machine learning in retail.

Below are five popular machine learning projects in retail that will help you understand the role of data science in the retail sector.

1. Customer Churn Prediction

Let us begin with the most important and popular machine learning project idea in the retail industry, customer churn prediction. For this machine learning project, you will focus on developing a classification model for determining whether or not a customer for an e-commerce company will churn in the upcoming month. The decision tree classifier is an ideal choice for this project's solution. Use the E-commerce Customer Churn Analysis and Prediction dataset from Kaggle.

Source Code- Customer Churn Prediction

2. Market Basket Analysis

Market basket analysis is one of the most efficient strategies retail chains use to identify correlations between products. You can analyze the Instacart customer data of 3 million grocery orders in this project idea. You can use customer segmentation for targeted marketing and predicting customer behavior. You will also create a machine learning model using XGBoost and neural networks.

Source Code- Market Basket Analysis

3. Sentiment Analysis

Businesses can better understand the overall sentiment of their customers toward their products by classifying the vast amounts of customer reviews. For this project, you will perform sentiment analysis based on opinion words from the Amazon product reviews dataset. For this project idea, you can employ various machine learning algorithms, such as Logistic Regression, Decision Tree, Gaussian Naive Bayes, Random Forest, K-Nearest Neighbour, and SVM.

Source Code- Sentiment Analysis

4. Retail Sales Forecasting

Retailers employ sales forecasting to foresee future sales by analyzing previous sales, identifying patterns, and drawing predictions. This project will mainly use cutting-edge machine learning techniques to incorporate external data (from Google Trends) into retail sales forecasting. In predicting future sales, you will compare the accuracy of different machine learning (ML) models, such as the SARIMA model, FBProphet, XGBoost, and LSTM.

Source Code- Real-time Patient Behavior Detection System

5. E-commerce Fraud Detection

E-commerce companies can identify high-risk transactions and assess risk indicators using fraud detection, which helps them detect and minimize fraudulent online activities. E-commerce businesses are particularly vulnerable to fraudulent activities when transactions are made on insecure websites or mobile devices. In this project, you will leverage machine learning to identify online payment frauds in an e-commerce transaction dataset by Vesta Corporation and the IEEE Computational Intelligence Society (IEEE-CIS). You will implement the logistic regression, Random Forest, LGBM, and XGBoost models in this project.

Source Code- Ecommerce Fraud Detection

You can also check out platforms like GitHub and Kaggle for more project ideas around machine learning in retail.

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