GitHub / ohmthanap / Churn-Predictions-Telecom-Customer

Developed a churn prediction classification model using various techniques including: EDA, Decision trees, Naive Bayes, AdaBoost, MLP, Bagging, RF, KNN, logistic regression, SVM, Hyperparameter tuning using Grid Search CV and Randomized Search CV.

JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ohmthanap%2FChurn-Predictions-Telecom-Customer

Topics: adaboost, bagging, churn-prediction, decision-tree-classifier, exploratory-data-analysis, gridsearchcv, jupyter-notebook, k-nearest-neighbours, logistic-regression, matplotlib-pyplot, multilayer-perceptron, naive-bayes-classifier, numpy-library, pandas-library, python, random-forest, randomizedsearchcv, scikit-learn, seaborn, support-vector-machine

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