A Boston Housing Price prediction model using linear regression involves the following steps:
1.Data Collection: You would need to collect data on the housing prices in Boston along with other relevant features such as the size of the house, the number of rooms, the neighborhood, etc.
2.Data Cleaning: Clean and preprocess the data to handle missing values and outliers.
3.Feature Engineering: Create new features from the existing ones that can better capture the relationship between the dependent and independent variables.
4.Split the Data: Split the data into training and testing sets to evaluate the performance of the model.
5.Train the Model: Fit a linear regression model on the training data and make predictions on the test data.
6.Evaluate the Model: The R-squared and adjusted R-squared metrics can be used to determine the goodness of fit of the model. R-squared measures the proportion of variance in the dependent variable that is explained by the independent variables in the model. The adjusted R-squared takes into account the number of independent variables in the model and adjusts the R-squared value accordingly. A higher R-squared and adjusted R-squared value indicates a better fit of the model to the data.
7.Make Predictions: Finally, you can use the trained model to make predictions on new data points and evaluate its performance.
TECHNOLOGIES USED:
1.Python
2.Numpy and Pandas for data cleaning
3.Matplotlib for data visualization
4.Sklearn for model building
5.Jupyter notebook