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.

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📂 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

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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

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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

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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

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