Machine Learning – Supervised & Unsupervised Learning

This repository contains my learnings and practice work from the Skills4Future Program by Edunet Foundation, covering both Supervised Learning and Unsupervised Learning techniques in Machine Learning.

Tools & Technologies

• Language: Python
• Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
• Environment: Jupyter Notebook / Google Colab

Supervised Learning

Supervised learning is used when the dataset contains both input features and corresponding output labels.

Topics Learned:

• Linear Regression
• Logistic Regression
• Decision Trees & Random Forest
• Support Vector Machines (SVM)
• k-Nearest Neighbors (k-NN)
• Evaluation Metrics: Accuracy, Precision, Recall, F1-score, Confusion Matrix

Unsupervised Learning

Unsupervised learning is used when the dataset has no output labels, and the goal is to find hidden patterns.

Topics Learned:

• k-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Dimensionality Reduction
• Applications: Customer Segmentation, Data Compression

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Author: Rutuja