This repo consists of data visualization project done for wealth management dataset from Kaggle. I have used various Machine Learning classifiers to calculate accuracy and precision to determine which model works best for this dataset. The agenda of this project is to analyze the trend of customer churn from a wealth management company.
- Analyze customer churn trend
- Predict future churn
- Build predictive model based on best accuracy and precision score.
Detailed Report
- Language : Python
- Code Editor: Jupyter Notebook
- Libraries : pandas, numpy, keras, tensorflow, matplotlib, yellowbrick
- Machine Learning Algorithms : Logistic Regression, Gaussian Naive Bayes, Decision Tree, Random Forest, XGBClassifier
- Random Forest is the winner for building a predictive model for this dataset.
- Highest Precision score yielded: 95%
- Highest Accuracy score yielded: 86%