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.

Fall 2020 - Computational Medicine - course project

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

Logistic regression model build on lead score data to score leads on the basis of their probability of conversion.

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).

Linear Regression, how number of features affect outcome

Predict the attrition (Yes/No) of employees, identify factors significantly impacting it, and finally state recommendations on how to mitigate the attrition.

CSCI 54900 INTELLIGENT SYSTEMS PROJECT

Case Study for Churn Modelling in a NGO

King County House Sales

I showcase that I have broad set of skills regarding machine learning algorithms since I use Logistic Regression, XGBoost and Neural Networks in this project. Especially that I have a good understanding regarding neural networks and the Keras library.

First project implementing Logistic Regression

The goal of this project is to develop a predictive model that accurately detects failures in the Air Pressure System (APS) of heavy Scania trucks. The APS is responsible for generating pressurized air used in various functions of the truck, such as braking and gear changes. By detecting failures in the APS system.

Delved into advanced techniques to enhance ML performance during the uOttawa 2023 ML course. This repository offers Python implementations of Naïve Bayes (NB) and K-Nearest Neighbor (KNN) classifiers on the MCS dataset.