This repository contains solutions to various machine learning tasks designed to apply and demonstrate different techniques and algorithms in Python using Jupyter Notebook.
The notebook in this repository includes the following machine-learning tasks:
- Data Preprocessing: Cleaning and preparing data for machine learning models, including handling missing values, normalization, and feature engineering.
- Supervised Learning: Implementation of various supervised learning algorithms such as Linear Regression, Decision Trees, and Support Vector Machines (SVM) for classification and regression tasks.
- Unsupervised Learning: Clustering using algorithms like K-Means and Hierarchical Clustering to find patterns and groupings in unlabeled data.
- Model Evaluation: Techniques to evaluate the performance of machine learning models, including cross-validation, confusion matrices, ROC curves, and precision-recall analysis.
- Advanced Techniques: Application of ensemble methods like Random Forests and Gradient Boosting to improve model performance.
- Deep Learning: Basics of neural networks, implementation of simple feedforward networks using libraries such as TensorFlow or PyTorch.
- Optimization and Hyperparameter Tuning: Use of Grid Search and Random Search for hyperparameter tuning to optimize machine learning models.
The solutions leverage Python libraries like NumPy, pandas, scikit-learn, and TensorFlow/PyTorch to implement and evaluate these models.