🧠 Machine Learning Implementations

This repository contains my hands-on implementations of various Machine Learning algorithms using Google Colab. The projects are structured into three separate notebooks, each focusing on different aspects of ML.

📂 Project Structure

1. Classification_Magic - Supervised learning techniques for classification problems.
2. Seeds_Unsupervised - Clustering and dimensionality reduction techniques.
3. Bikes_Regression - Regression models using traditional and neural network approaches.

🚀 Implemented Algorithms

📌 Classification (Supervised Learning) - Classification_Magic.ipynb

• K-Nearest Neighbors (KNN)
• Naive Bayes
• Logistic Regression
• Support Vector Machine (SVM)
• Neural Networks (TensorFlow)

📌 Clustering & Dimensionality Reduction (Unsupervised Learning) - Seeds_Unsupervised.ipynb

• K-Means Clustering
• Principal Component Analysis (PCA)

📌 Regression (Supervised Learning) - Bikes_Regression.ipynb

• Linear Regression
• Linear Regression using a Single Neuron
• Regression Neural Network (TensorFlow)

🔧 Tools & Libraries

• Python 🐍
• TensorFlow
• Scikit-learn
• Pandas & NumPy
• Matplotlib & Seaborn
• Google Colab

📜 How to Use

1. Clone this repository:

   git clone https://github.com/MothilalShiva/Machine-Learning.git

Open the respective notebook in Google Colab. Run the cells to explore the implementations. 📌 Dataset Sources Publicly available datasets used in Google Colab. 📢 Connect with Me Feel free to check out my work and provide feedback! LinkedIn: https://www.linkedin.com/in/mothilal-shiva-41151b228/