Learning how to analyze imbalanced Data, implementing SMOTE and using unbalanced R package
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Updated
Feb 28, 2017 - R
Learning how to analyze imbalanced Data, implementing SMOTE and using unbalanced R package
Credit card fraud detection using machine learning techniques
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.
Predicting the churn of customers in a Telecom company using classification algorithms.
Comparing Machine Learning Algorithms for Credit Risk Analysis in Banking
This is a simple Imbalanced dataset handling problem where I have used Census Data
Data Science in the Banking Industry [Volume 1]
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?
Credit risk analysis using the LASSO, Random Forests and the SMOTE technique for balancing
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
Credit Card Fraud Detection
Predicting the churn of customers in a Telecom company using classification algorithms.
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.
CART and C4.5 decision trees, Synthetic Minority Over-sampling Techniques, and visualizations in R.
Machine Learning Project on Imbalanced Data in R
The machine learning project on UCI imbalanced data.
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