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Industrial-level analysis for predicting user retention

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Name		Name	Last commit message	Last commit date
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.gitattributes		.gitattributes
BankChurners.csv		BankChurners.csv
Bank_Churn_Prediction.html		Bank_Churn_Prediction.html
Bank_Churn_Prediction.ipynb		Bank_Churn_Prediction.ipynb
README.md		README.md

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User Retention Analysis

Procedures

Data Processing
EDA
Feature Engineering
Model Building
- Random Forest
- SVM
- Gradient Boosting
Fine Tune
- Random Search
- Grid Search

Prerequisites

numpy
sklearn
plotly

Usage

Preview and interact with the .ipynb file.

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Industrial-level analysis for predicting user retention

python data-science random-forest svm sklearn eda boosting fine-tuning

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