Linear regression is a fundamental supervised learning algorithm in machine learning. It aims to establish a linear relationship between a dependent variable (target) and one or more independent variables (features). In the context of house price prediction, the dependent variable will be the house price, and the independent variables can be factors like the size of the house, number of bedrooms, location, etc.House Price Prediction with Linear Regression Involves Following Steps:

Dataset Collection: Gather historical house price data and corresponding features from platforms like Zillow or Kaggle. Data Preprocessing: Clean the data, handle missing values, and perform feature engineering, such as converting categorical variables to numerical representations. Splitting the Dataset: Divide the dataset into training and testing sets for model building and evaluation. Building the Model: Create a linear regression model to learn the relationships between features and house prices. Model Evaluation: Assess the model’s performance on the testing set using metrics like MSE or RMSE. Fine-tuning the Model: Adjust hyperparameters or try different algorithms to improve the model’s accuracy. Deployment and Prediction: Deploy the robust model into a real-world application for predicting house prices based on user inputs