Shinchan2301/machine-learning-practice

Machine Learning practice notebooks covering EDA, Linear Regression, Multiple Linear Regression, Polynomial Regression, and Algerian Forest Fire regression. # Machine Learning Practice This repository contains my machine learning practice notebooks. ## Projects Included ### 1. Simple Linear Regression Folder: `01_simple_linear_regression` Concepts covered: - Data loading - Train-test split - Simple Linear Regression - Prediction - Model evaluation ### 2. Multiple Linear Regression Folder: `02_multiple_linear_regression` Concepts covered: - Multiple independent variables - Train-test split - Model training - Model evaluation ### 3. Polynomial Regression Folder: `03_polynomial_regression` Concepts covered: - Polynomial features - Linear vs Polynomial Regression - Model fitting - Prediction visualization Note: Dataset may be created inside the notebook. ### 4. Algerian Forest Fire Project Folder: `04_algerian_forest_fire_project` Concepts covered: - Exploratory Data Analysis - Data cleaning - Feature analysis - Regression model training - Model evaluation ## Tools Used - Python - Pandas - NumPy - Matplotlib - Seaborn - Scikit-learn - Google Colab - Jupyter Notebook ## Author Pavan Pramod Bhurke