Welcome to my repository of machine learning projects. This repository showcases my work in both supervised and unsupervised learning, demonstrating a variety of techniques and algorithms applied to real-world datasets.
- Supervised Learning Projects
- Unsupervised Learning Projects
- Getting Started
- Technologies Used
- Contributing
- License
In supervised learning, we aim to learn a mapping from inputs to outputs based on example input-output pairs. Here are some of the projects included:
- Regression Analysis: Projects focused on predicting continuous outcomes using linear regression, polynomial regression, and more.
- Classification Tasks: Implementations of various classification algorithms such as logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks to predict categorical outcomes.
- Time Series Forecasting: Applications of supervised learning techniques to forecast future values based on historical data, utilizing ARIMA, LSTM, and other models.
Unsupervised learning involves finding hidden patterns or intrinsic structures in input data. This section includes projects such as:
- Clustering: Exploration of clustering techniques like k-means, hierarchical clustering, and DBSCAN to group similar data points.
- Dimensionality Reduction: Application of PCA, t-SNE, and other techniques to reduce the number of variables under consideration.
- Anomaly Detection: Identification of outliers or anomalies in datasets using methods like isolation forests and one-class SVM.
To get started with these projects, clone the repository and explore the individual project folders. Each project includes its own README file with detailed instructions on how to run the code and explanations of the methodologies used.
git clone https://github.com/prasad-chavan1/Machine_Learningcd Machine-Learning