Boston Housing Price Prediction using Machine Learning

The project's goal is to create a model that can precisely forecast the costs of homes in Boston based on a variety of factors, including the number of rooms, crime rate, and distance to job hubs.

 Each sample in the project has 13 parameters, such as the crime rate, the percentage of residential land, the average number of rooms per home, and other socioeconomic indicators. The dataset for the project consists of 506 samples. A machine-learning model that can forecast Boston home prices was created using the dataset. 

To create the prediction model, the project makes use of a number of well-known machine-learning methods, including support vector machines, decision trees, random forests, and linear regression. The model's accuracy was assessed using a variety of performance indicators, including mean squared error (MSE), mean absolute error (MAE), and the R-squared (R2) score. 

The project also incorporates exploratory data analysis (EDA) to learn more about the dataset in addition to developing the prediction model. The EDA provides visuals to help users comprehend the distribution and connections between the various variables, such as histograms, scatter plots, and correlation matrices.

 The project offers a thorough explanation of the machine learning-based house price forecast method.