smote meets uplift modeling

Comparing Machine Learning Algorithms for Credit Risk Analysis in Banking

Credit risk analysis using the LASSO, Random Forests and the SMOTE technique for balancing

Credit Card Fraud Detection

Used an ensemble learning approach to distinguish between T-helper and T-regulatory cells, known to be hard to differentiate.

The project involves deciding on the mode of transport that the employees prefer while commuting to office. For this, multiple models such as KNN, Naive Bayes, Logistic Regression have been created and explored to check their model performance metrics. Bagging and Boosting modelling procedures have also been applied to create the models.

Credit card fraud detection using machine learning techniques

Data Science in the Banking Industry [Volume 1]

Predicting the churn of customers in a Telecom company using classification algorithms.

Predicting the churn of customers in a Telecom company using classification algorithms.

Predict whether or not an employee will use Car as a mode of transport from given employee information about their mode of transport as well as their personal and professional details like age, salary, work exp. Also, which variables are a significant predictor behind this decision?

For a classification problem, when classes in the dependent variable are severely imbalanced (e.g. 90 yes, 10% no), training an efficient machine learning model becomes very difficult. However with SMOTE method, we can transform the data into a balaced form and train the model efficiently.

Machine Learning Project on Imbalanced Data in R

This is a simple Imbalanced dataset handling problem where I have used Census Data

CART and C4.5 decision trees, Synthetic Minority Over-sampling Techniques, and visualizations in R.

The machine learning project on UCI imbalanced data.

Learning how to analyze imbalanced Data, implementing SMOTE and using unbalanced R package

a predictive model to determine the income level for people in US. Imputed and manipulated large and high dimensional data using data.table in R. Performed SMOTE as the dataset is highly imbalanced. Developed naïve Bayes, XGBoost and SVM models for classification