https://github.com/ahmedshahriar/customer-churn-prediction
Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.
Science Score: 13.0%
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Low similarity (10.1%) to scientific vocabulary
Keywords
Repository
Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.
Basic Info
- Host: GitHub
- Owner: ahmedshahriar
- License: apache-2.0
- Language: HTML
- Default Branch: main
- Homepage: https://share.streamlit.io/ahmedshahriar/Telco-Customer-Churn-Prediction-Streamlit-App/main/app.py
- Size: 3.48 MB
Statistics
- Stars: 5
- Watchers: 1
- Forks: 3
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
IBM Telco Customer Churn Prediction
The objective of this notebook is to present an extensive analysis of the IBM Customer Churn Dataset and to predict the customer churn rate.
Dataset Source
Streamlit App repository
You can also view this notebook on kaggle
This project also serves as assignments for the courses below -
Summary
- Dataset mostly has categorical variables
- Data is not normally distributed, performed Nonparametric Statistical tests
- Performed statistical hypothesis test to check correlation , multicollinearity
- Imbalanced dataset, did experiment with different sampling techniques(e.g stratifying, imblearn - SMOTE etc)
- Tuned Hyperparameters using Optuna
- Performed single level Stacking Ensemble with Triple Gradient boosting algorithms and classic algorithms
- Number of months the customer has stayed with the company (tenure) and the contract term of the customer (contract) are the most important features that have strong correlation with churn of the customer
- Results from statiscial hypotheses testing reflects similarity with model feature importance
- With 80/20 train/test split triple boosting stacking ensemble model achieved an AUC of ~0.85
Built With
optuna==2.10.0
xgboost==0.90
catboost==1.0.1
lightgbm==2.2.3
plotly==4.4.1
scipy==1.7.1
scikit-learn==0.22.2.post1
Owner
- Name: Ahmed Shahriar Sakib
- Login: ahmedshahriar
- Kind: user
- Location: Ontario, Canada
- Company: @criticalml-uw
- Website: https://ahmedshahriar.com
- Twitter: ahmed__shahriar
- Repositories: 5
- Profile: https://github.com/ahmedshahriar
Software Engineer, an expert in web scraping & automation, data analytics, and machine learning. Kaggle Master.
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