The objective of this project is to apply classification learning models on retired or terminated customer’s dataset with 60 features for 500,000 customers and therefore to obtain a predictive model for identifying future customers who roll over assets to a competitor. This helps to increase the Rollover retention Rate, revenue, crossover customers, and improve client loyalty.

The objective of this project is to categorize whether a webshop session will end in a purchase or not. Predicting customer behavior increases customer satisfaction and sales, by facilitating an increase in customer experience through personalization, recommendations, and special offers.

Feedback analysis to classify each feedback (as excellent, good, or bad) to understand the customer opinion about the hotel which is useful for hoteliers to improve their performance based on feedback. This is a multilingual problem extracted other language reviews from the data and translated other languages other than English to English and implemented text preprocessing steps like cleaning data, tokenization, and convert text into numbers using TF-IDF Vectorizer. Built multiple models and analyzed the results.