ML Mini Projects – Learning Portfolio by Diya M

This repository contains a collection of basic machine learning projects I worked on as part of a selection process for a college technical club. While I am no longer part of the selection process, I’ve retained these projects here to document my learning and growth in core ML concepts.

What This Repo Covers

These tasks focus on foundational machine learning workflows using structured data. I explored various algorithms and techniques, including:

• Linear Regression
• Logistic Regression
• Decision Trees
• Clustering (Unsupervised Learning)

Each project includes:

• 📄 The dataset used (in .csv format)
• 📓 A Jupyter Notebook with code and visualisations

Folder Structure

• linear_reg/

  • train.csv
  • test.csv
  • task2_0_linearregression.ipynb

• logistic_reg/

  • Freyja_Pumpkins-test.csv
  • Gotem_Pumpkins-train.csv
  • task2_1_logisticregression.ipynb

• decision_trees/

  • Threats (1).csv
  • task3_2_Decision_trees.ipynb

• clustering/

  • Clustering_Data.csv
  • task3_0_clustering.ipynb

• README.md

Tools & Libraries Used

• Python
• Jupyter Notebooks
• NumPy, Pandas
• Matplotlib, Seaborn
• Scikit-learn

Goals & Learning Outcomes

This repo marks my hands-on experience with machine learning. Through these mini-projects, I learned how to:

• Handle real-world CSV data
• Clean and preprocess datasets
• Build and evaluate ML models
• Visualise data insights and model performance