Machine Learning Practices🤓

Welcome to the machine learning practices repository! This repository contains four directories, each focusing on a specific machine learning practice.

Table of Contents

• Practice 1: Decision Trees and KNN
• Practice 2: Stroke Prediction and Insurance Cost Prediction
• Practice 3: Simple Neural Network Implementation and Training
• Practice 4: Implementing DBSCAN Algorithm and Clustering

Practice 1: Decision Trees and KNN

• Practice Number 1
• Goals: Implement and evaluate Decision Trees and K-Nearest Neighbors (KNN) algorithms. Explore hyperparameter tuning techniques and model evaluation.

Practice 2: Stroke Prediction and Insurance Cost Prediction

• Practice Number 2
• Goals: Predict stroke occurrence and insurance costs using Support Vector Classifier (SVC) and Linear Regression models. Handle missing values, feature scaling, and evaluate model performance.

Practice 3: Simple Neural Network Implementation and Training

• Practice Number 3

• Goals: Implement a simple neural network from scratch and train it using the backpropagation algorithm. Gain insights into the training process and parameter optimization.

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Practice 4: Implementing DBSCAN Algorithm and Clustering

• Practice Number 4
• Goals: Implement the DBSCAN algorithm for density-based clustering. Visualize clustering results and explore different hyperparameter combinations.

Feel free to explore each practice directory for detailed implementations, code, and results!😃