Predicting House Prices with Machine Learning | Regression Models Explained Simply

Описание: In this video, I demonstrate my Machine Learning internship Task 1 project: House Price Prediction using Regression Models.

🏠 Project Goal: Build a machine learning model that can predict house prices based on features like area, number of bedrooms, bathrooms, and more.

🔍 What’s covered in the video:

Exploring and cleaning the housing dataset

Feature selection and preprocessing

Training multiple regression models:

Linear Regression

Ridge Regression

Lasso Regression

SGD Regressor

Evaluating model performance with R² Score and Mean Squared Error

Visualizing learning curves and feature importance (coefficients)

📊 This project is a practical demonstration of regression techniques and how machine learning can assist in real-world predictions.

💻 Tools used: Python, Scikit-learn, Pandas, Matplotlib, Seaborn

✅ Perfect for beginners learning regression and data science workflow from start to finish.

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