Retail

Here you’ll find everything about machine learning use cases in the retail industry.

There are many potential machine learning use cases in the retail industry, such as:

  • Product recommendation: using collaborative filtering or content-based algorithms to recommend products to customers based on their past purchases or browsing behavior
  • Fraud detection: using supervised machine learning algorithms to identify and flag potential cases of fraud, such as fraudulent transactions or fake user accounts
  • Customer segmentation: Retail companies can use unsupervised learning algorithms to group customers into clusters or segments based on their characteristics, such as their purchase history or demographics
  • Sentiment analysis: using natural language processing (NLP) algorithms to analyze customer reviews and feedback and to identify the overall sentiment towards products or the company
  • Personalized pricing: using reinforcement learning algorithms to optimize prices for individual customers based on factors such as their purchase history and market conditions.

Create a Personalized Movie Recommendation Engine using Content-based Filtering in Python

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Content-based recommender systems are a popular type of machine learning algorithm that recommends relevant articles based on what a user has previously consumed or liked. This approach aims to identify items with certain keywords, understand what the customer likes, and then identify other items that are similar to items the … Read more

Build a High-Performing Movie Recommender System using Collaborative Filtering in Python

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The digital age presents us with an unmanageable number of decisions and even more options. Which series to watch today? What song to listen to next? Nowadays, the internet and its vast content offer too many choices. But there is hope – recommender systems are here to solve this problem … Read more

Forecasting Beer Sales with ARIMA in Python

Forecasting Beer Sales with ARIMA

Time series analysis and forecasting is a tough nut to crack, but the ARIMA model has been cracking it for decades. ARIMA, short for “Auto-Regressive Integrated Moving Average,” is a powerful statistical modeling technique for time series analysis. It’s particularly effective when the time series you’re analyzing follows a clear … Read more

Customer Churn Prediction – Understanding Models with Feature Permutation Importance using Python

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One of the primary goals of many service companies is to build solid and long-lasting relationships with their customers. Customers who churn cost a lot of money. Modern service companies who understand which customers tend to end the business relationship can take appropriate countermeasures early to retain them. The prerequisite … Read more

Sentiment Analysis with Naive Bayes and Logistic Regression in Python

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Get ready to dive into the world of social media sentiment analysis with Python! In this article, we’ll be exploring how companies are using machine learning to analyze the sentiments expressed in Twitter comments and how you can do the same. We’ll be training and comparing different classification models using … Read more