Machine Learning Projects

This repository contains a collection of machine learning projects and implementations. The goal is to practice building models, understand algorithms, and apply ML techniques to real-world datasets.

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Contents

• Linear Regression – Predicting numerical outcomes from input features.
• Logistic Regression – Classification tasks such as predicting customer churn.
• Decision Trees & Random Forests – Tree-based models for classification and regression.
• K-Nearest Neighbors (KNN) – Instance-based learning for classification.
• Support Vector Machines (SVM) – Margin-based classification.
• Clustering (K-Means, Hierarchical) – Unsupervised learning for segmentation.
• Neural Networks (Basic) – Simple deep learning models.

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Tools & Libraries

• Python
• NumPy, Pandas – Data handling
• Matplotlib, Seaborn – Visualization
• Scikit-learn – ML algorithms and evaluation metrics
• TensorFlow / Keras (Optional) – For deep learning models

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Features

• Data preprocessing (handling missing values, normalization, encoding)
• Training & testing ML models
• Model evaluation (accuracy, precision, recall, F1-score, ROC)
• Hyperparameter tuning
• Visualization of results

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Example Workflows

1. Load dataset
2. Clean and preprocess data
3. Train/test split
4. Apply machine learning algorithm
5. Evaluate performance
6. Visualize results

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