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

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

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…

Feature selection package of the mlr3 ecosystem.

using Drebin dataset to distinguish between malwares and not malwares

HR Analytics Dataset

This project showcases a Network Intrusion Detection System (NIDS) designed to bolster cybersecurity defenses against evolving threats

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

Evaluating machine learning methods for detecting sleep arousal, bachelor thesis by Jacob Stachowicz and Anton Ivarsson (2019)

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

King County House Sales

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

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

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.

Bike Sharing in Washington D.C.

Feature-Engg

To model the demand for shared bikes with the available independent variables

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.

This repository contains the notebook used for the Spring 2021 Kaggle Dengue Fever Prediction Competition. Placement was in the top 10% with a MAE of 24.86. Our best approach involved Random Forest Regression on a reduced featureset selected with Recursive Feature Elimination in combination with correlation with the target (number of dengue cases).