Skip to content

README.md

Latest commit

 

History

History
41 lines (29 loc) · 2.59 KB

README.md

File metadata and controls

41 lines (29 loc) · 2.59 KB
Raw

Machine-Learning-Course-Projects

This repository contains my machine learning course assignments, completed during my university studies.
Each project is organized into its own directory and includes both the implementation code and a written report explaining the methodology, experiments, and results.

📂 Projects Overview

1. Campus Recruitment Placement Analysis

  • Goal: Analyze factors affecting campus recruitment outcomes.
  • Methods: Logistic Regression, Naive Bayes, Linear Discriminant Analysis (LDA).
  • Highlights: Explored the role of academic performance, employability skills, and work experience in predicting placement.
  • Dataset: Campus Recruitment Dataset

2. HR Dataset Classification – Kaggle Competition

  • Goal: Predict whether an employee will leave the company (left_company).
  • Methods: Compared multiple classification models for binary classification.
  • Highlights: Participated in a Kaggle competition hosted by the TA, benchmarking model performance.
  • Competition Link: ML4SBU HR Dataset Competition

3. Personality Pattern Clustering

  • Goal: Discover global patterns in personality traits using the Big Five (OCEAN) model.
  • Methods: K-Means, DBSCAN, Hierarchical Clustering (Ward linkage).
  • Highlights: Identified meaningful personality clusters across countries and demonstrated the use of clustering in psychological research.
  • Dataset: Big Five Personality Test Dataset

4. Uber and Lyft Dataset Analysis

  • Goal: Predict ride prices for Uber and Lyft in Boston, MA.
  • Methods: Linear Regression, Ridge, Lasso, ElasticNet with GridSearchCV and cross-validation.
  • Highlights: Achieved strong predictive performance with Ridge Regression (R² = 0.9376).
  • Dataset: Uber and Lyft Dataset – Boston, MA