Step-by-step EDA and data preprocessing journey for customer churn prediction. Updated weekly with raw & processed datasets, notebooks, and ML-ready pipeline.
In this notebook, We will evaluate the five best known machine learning algorithms to determine which one is the best fit for this given telecom churn binary classification.
Predict customer churn using machine learning. This project employs a RandomForestClassifier to analyze customer data and determine the likelihood of churn. Explore the Jupyter Notebook for insights into the data and model, and contribute to the project's development.
This project explores customer churn trends for a company in California using an IBM dataset. Built in a Jupyter Notebook, it employs pandas, NumPy, matplotlib, seaborn, plotly, and scipy to clean, analyze, and visualize data. SKlearn predictive model was trained using three main algorithms Decision Tree, Naive Bayes, and Random Forest
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