Validation (like Recursive Feature Elimination for SHAP) of (multiclass) classifiers & regressors and data used to develop them.

A Linear Regression model to predict the car prices for the U.S market to help a new entrant understand important pricing variables in the U.S automobile industry. A highly comprehensive analysis with detailed explanation of all steps; data cleaning, exploration, visualization, feature selection, model building, evaluation & MLR assumptions vali…

using Drebin dataset to distinguish between malwares and not malwares

Engaged in research to help improve to boost text sentiment analysis using facial features from video using machine learning.

Feature selection package of the mlr3 ecosystem.

Through this research, we are able to model a student’s final grade in a particular subject and link it directly to certain relevant features that influence the outcome. We use the C5.0 decision tree technique to model the data.

Tumor prediction from microarray data using 10 machine learning classifiers. Feature extraction from microarray data using various feature extraction algorithms.

HR Analytics Dataset

Bike Sharing in Washington D.C.

The classification goal is to predict whether the client will subscribe (1/0) to a term deposit (variable y).

Forecast stocks using machine learning algorithms. This is the final project for Data Science 3580.

Fall 2020 - Computational Medicine - course project

Heart Attack Prediction by implementing Feature Selection such as SelectKBest & Recursive Feature Elimination

Feature-Engg

A Jupyter Notebook with the analysis and prediction of Final Grades (Pass/Fail) for students of mechatronics engineering in several mechanic courses.

Predicting survival of passengers for titanic dataset using RF and a NN

Used CDC dataset for heart attack detection in patients. Balanced the dataset using SMOTE and Borderline SMOTE and used feature selection and machine learning to create different models and compared them based on metrics such as F-1 score, ROC AUC, MCC, and accuracy.