ankitsingh1240 / CUSTOMER-CHURN-PREDICTIONLinks
INSAIDINSTRUCTIONS:You are required to come up with the solution of the given business case.Business Context:This case requires trainees to develop a model for predicting customer churn at a fictitious wireless telecom company and use insights from the model to develop an incentive plan for enticing would-be churners to remain with company.Data …
☆10Updated 3 years ago
Alternatives and similar repositories for CUSTOMER-CHURN-PREDICTION
Users that are interested in CUSTOMER-CHURN-PREDICTION are comparing it to the libraries listed below
Sorting:
- Customer churn Modelling☆10Updated 7 years ago
- ☆12Updated 6 years ago
- ☆136Updated 7 years ago
- Python implementation of the population stability index (PSI)☆143Updated 2 years ago
- A general-purpose framework for solving problems with machine learning applied to predicting customer churn☆423Updated last year
- These common credit score data sets are collected to empirical evaluations, and I will update dynamically.☆90Updated 6 years ago
- Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, …☆679Updated last year
- Python package that optimizes information value, weight-of-evidence monotonicity and representativeness of features for credit scorecard …☆117Updated 3 years ago
- Python package for stacking (machine learning technique)☆702Updated last month
- GBST is an optimized distributed gradient boosting survival trees library that is implemented based on the XGBoost☆37Updated 5 years ago
- Leave One Feature Out Importance☆854Updated 10 months ago
- Data, Benchmarks, and methods submitted to the M5 forecasting competition☆648Updated 2 years ago
- Improving XGBoost survival analysis with embeddings and debiased estimators☆342Updated last year
- A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.☆1,627Updated 3 years ago
- uplift modeling in scikit-learn style in python☆789Updated 2 years ago
- CatBoost tutorials repository☆1,084Updated last week
- Implementation of Bayesian Hyperparameter Optimization of Machine Learning Algorithms☆637Updated 2 years ago
- Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features…☆757Updated 5 months ago
- ☆367Updated last year
- Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018☆1,475Updated 5 years ago
- Uplift modeling package.☆376Updated 3 years ago
- Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ra…☆765Updated last year
- PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in…☆140Updated 9 months ago
- Use Machine Learning to Predict Bank Client's CD Purchase with XGBoost and Scikit Learn in Watson Studio☆41Updated 5 years ago
- Linear Prediction Model with Automated Feature Engineering and Selection Capabilities☆535Updated last week
- ☆85Updated 7 years ago
- A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.☆583Updated last year
- ☆296Updated 3 years ago
- A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning☆598Updated 7 years ago
- XGBoost for label-imbalanced data: XGBoost with weighted and focal loss functions☆333Updated last year