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.

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts. They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to …

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

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).

Understanding and predicting the factors leading to employees leaving and finding relations between them. Also finding the importance of a feature according to ML models.

This project was done in the 4th semester of Btech data science. Took an online dataset about cars. Understood, cleaned, visualized and prepared our data for building a regression model. Using Recursive feature elimination and Backward selection as a method of feature selection we built a final model. Then tested the assumptions that we made abo…

The research aims to harness machine learning for predicting cardiovascular diseases based on numerous risk factors, addressing the high fatality rates associated with cardiovascular conditions.

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.

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.