R package for the simulation of the prior distribution of bayesian trees by Chipman et al. (1998).

Interactive Classification Tree (CART) for R

housing matching

Data Analysis and Decision Making Project using R

Various techniques applied for the prediction of median home value were- Generalized Linear Regression, Regression Tree, Generalized Additive Model and Neural Networks.

On creating synthetic, representative data from real, sensitive inputs

Nations Flags Classification & Clustering project. 🎏

HR Employee Attrition Data file is provided: To Build Neural Network and CART model on same:

Data Analytics Programs

Multivariate linear regression, CART and Random Forest dataset analysis

Build a model that will help them identify the potential customers who have a higher probability of purchasing the loan.

Develop a logistic regression model and a CART model to predict diabetes outcome

The objective of this exercise was to build a model using a Supervised learning technique to figure out profitable segments to target for cross-selling personal loans. A Pilot campaign data of 20000 customers was used which included several demographic and behavioral variables. The Model was further validated and a deployment strategy was recomm…

Initial text mining exercise was performed on a dataset of Shark tank episodes with 495 entrepreneurs making their pitch to VCs. Used that to build multiple models (CART, Random Forest, Logistic Regression) to predict keywords which have an impact on striking a deal.

Statistical analysis on calcium concentration during dialysis

The objective of the project is to create a machine learning model. We are doing a supervised learning and our aim is to do predictive analysis to predict median housing price.

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Here, the aim is to analyze the dataset and detect the fradulent transactions.

A data set of 30000 records and 24 variables containing information on defaults, demographic factors, credit data, delinquency, repayment and billed amounts of a credit card client in Taiwan from April 2005 to September 2005. The objective was to apply statistical, data visualization and Machine learning techniques (supervised and unsupervised) …

The objective of the project is to create a machine learning model. We are doing a supervised learning and our aim is to do predictive analysis to predict housing price.

The data at hand is of flight satisfaction survey along with the customer flight information, the task at hand is to build a model that predicts satisfaction/dissatisfaction given the various attributes