Machine-learning-projects

Machine learning assignments and projects from MLDE coursework at Lapland University of Applied Sciences.

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Projects

Exercise Project 1 – Linear Regression

Builds a linear regression model to predict continuous values. Covers data preprocessing, train/test splitting, model fitting, and evaluation using metrics like MSE and R² score.

Exercise Project 2 – Logistic Regression (Classification)

Uses logistic regression for binary and multi-class classification tasks. Includes feature scaling, confusion matrix analysis, and accuracy evaluation.

Exercise Project 3 – Support Vector Machine (SVM)

Applies SVM for classification problems. Explores different kernels (linear, RBF) and uses cross-validation to tune hyperparameters.

Exercise Project 4 – Decision Trees and Random Forest

Implements decision tree and random forest classifiers. Compares model performance and visualizes feature importance.

Exercise Project 5 – K-Nearest Neighbors (KNN)

Uses KNN for classification. Tests different values of K to find the best fit and evaluates model accuracy on test data.

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Tech Stack

• Python 3.x
• scikit-learn
• pandas
• numpy
• matplotlib
• seaborn
• Jupyter Notebook

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Setup

1. Clone the repo:

git clone https://github.com/SaadM-Codes/machine-learning-projects.git
cd machine-learning-projects

2. Install dependencies:

pip install -r requirements.txt

3. Launch Jupyter:

jupyter lab

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Author

Saad Mahmood
Machine Learning and Data Engineering Student
GitHub: @SaadM-Codes