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Customer Churn Prediction project using Machine Learning. I analyzed customer data, compared Logistic Regression, Decision Tree, and Random Forest models, and evaluated their performance using accuracy, precision, recall, and ROC-AUC. I also performed threshold analysis to identify high-risk customers for better retention strategies.
The network for creativity
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Creatives on Contra have earned over $150M and we are just getting started
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“Machine learning project that predicts customer churn using data analysis, preprocessing, and classification models to identify customers at risk of leaving.”
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I developed a Sentiment Analysis project using Natural Language Processing (NLP) to classify customer messages based on their sentiment. I worked with customer text data, performed text preprocessing, and implemented both a lexicon-based approach and a DistilBERT transformer model for sentiment classification.
The project helped identify positive, negative, and neutral customer feedback and demonstrated how NLP and machine learning can be used to understand customer opinions from text data.
