Pinned Repositories
Classification-of-SUV-Buyers
Performed Classification on non-linearly separable datasets of SUV Buyers. Modeled all the classification techniques available to find the best algorithm that classify whether a person will buy a SUV or not. Used k-Fold Validation for all the techniques. Model Accuracy on test set are: Logistic Regression-89.00% KNN- 93.00% SVM-90.00% Kernel SVM 93.00%
Computer-Aided-Modelling-and-Analysis-of-Offset-Slider-Crank-Mechanism
Modelled the offset-slider crank mechanism and performed Kinematic & Dynamic analysis along with joint clearance analysis in Adams software and validated with theoretical results to an accuracy of 99.71%
Computer-Vision-Tutorial
Finite-Element-Analysis-of-Solid-Mechanics-Problem
Three Solid mechanics problems were simulated in ABAQUS and obtained results were compared either with analytical or published results
High-performance-scientific-computing
Machine-Learning-Model-for-Vehicle-Classification
Mall-Customer-Clusters
Python code to clusters the different type of customers in a mall in separate categories. Methods used are K-Means Clustering and Hierarchical Clustering
Prediction-of-Flight-Delays
Modeled Logistic regression from scratch to predict the delay of flights.
VUMAT-Implementation-in-ABAQUS-Explicit
Developed a finite deformation VUMAT in Fortran to implement von-Mises plasticity for an elastic-isotropic hardening material and then validated the results of the code with ABAQUS inbuilt model using Standard test cases
Jalaj-G's Repositories
Jalaj-G/VUMAT-Implementation-in-ABAQUS-Explicit
Developed a finite deformation VUMAT in Fortran to implement von-Mises plasticity for an elastic-isotropic hardening material and then validated the results of the code with ABAQUS inbuilt model using Standard test cases
Jalaj-G/Classification-of-SUV-Buyers
Performed Classification on non-linearly separable datasets of SUV Buyers. Modeled all the classification techniques available to find the best algorithm that classify whether a person will buy a SUV or not. Used k-Fold Validation for all the techniques. Model Accuracy on test set are: Logistic Regression-89.00% KNN- 93.00% SVM-90.00% Kernel SVM 93.00%
Jalaj-G/Computer-Aided-Modelling-and-Analysis-of-Offset-Slider-Crank-Mechanism
Modelled the offset-slider crank mechanism and performed Kinematic & Dynamic analysis along with joint clearance analysis in Adams software and validated with theoretical results to an accuracy of 99.71%
Jalaj-G/Computer-Vision-Tutorial
Jalaj-G/Finite-Element-Analysis-of-Solid-Mechanics-Problem
Three Solid mechanics problems were simulated in ABAQUS and obtained results were compared either with analytical or published results
Jalaj-G/High-performance-scientific-computing
Jalaj-G/Machine-Learning-Model-for-Vehicle-Classification
Jalaj-G/Mall-Customer-Clusters
Python code to clusters the different type of customers in a mall in separate categories. Methods used are K-Means Clustering and Hierarchical Clustering
Jalaj-G/Prediction-of-Flight-Delays
Modeled Logistic regression from scratch to predict the delay of flights.