Project Overview

This project is built around core Machine Learning concepts, focusing on developing intelligent systems capable of learning from data. It includes data preprocessing, algorithm selection, model training, and performance evaluation. The outcome is a functional ML-based solution that can be used for classification, prediction, or recommendation tasks.

• Data cleaning, normalization, and feature engineering

• Implementation of supervised algorithms like Decision Trees or SVM

• Use of unsupervised learning for pattern detection and clustering

• Model evaluation using accuracy, F1-score, and confusion matrix

• Application of cross-validation and parameter tuning

• Development using Python, scikit-learn, or TensorFlow

• Visualization of data trends using Matplotlib and Seaborn

• Deployment-ready model with real-world dataset

• Practical use-case: spam detection, fraud prediction, or sentiment analysis