Various Machine Learning Projects

CS760: Machine Learning

bagging and hyperparameter tuning on spam vs not spam dataset

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.

In this project I implemented decision tree, bagged tree, random forest and XGBoost for comparison of better MAE performance between Trees Algorithms.

Se aplica un decision tree/ bagging tree/ random forest para predecir un accidente cerebrovascular y observar la importancia de las variables predictoras. (Tidyverse y Tidymodels)

Goal Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio

Nonlinear Regression Models

Datascience hands on code

Machine Learning Library for Classification Tasks

Codes and slides of my Machine Learning lectures

In simple, a Loan (borrowing money from a bank) is the sum of money that you borrow from the bank or lending financial institution in order to meet needs. These needs could result from planned or unplanned events, and by borrowing, you incur a debt that you have to pay within the agreed duration on your contract.

Machine learning library for classification tasks

Building classification models to predict if a loan application is approved. Using under-sampling, bagging and boosting to tackle the problem of with unbalanced dataset

Regression Analysis - Toyota Corolla price prediction

This repository will help in understanding the basic concept of Random Forest algorithm and will also learn how to optimize the hyperparameters and prevent overfitting.

This project explains why and how are the Bagged Models better than the Complete Model. Bagged Model parameters have tighter confidence interval and a lower bias.

Use Random Forest to prepare a model on fraud data treating those who have taxable income <= 30000 as "Risky" and others are "Good"