Boston-House-Pricing-Prediction

This Project Has Been Confirmed As Successful By A Udacity Reviewer.

What I Did

In this project, I applied basic machine learning concepts on data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home. I first used the NumPy libary to analyze the data to obtain important features and descriptive statistics about the dataset. Next, I split the data into testing and training subsets, and determine a suitable performance metric for this problem. I analyzed performance graphs for a learning algorithm with varying parameters and training set sizes. Finally, I tested this model on a new sample and compare the predicted selling price to my statistics. The result was less than one standard deviation away from the mean.

What I Learned

From this project I was acquainted to working with datasets in Python and applying basic machine learning techniques using NumPy and Scikit-Learn.

Things I learned from this project:

• How to use NumPy to investigate the latent features of a dataset.
• How to analyze various learning performance plots for variance and bias.
• How to determine the best-guess model for predictions from unseen data.
• How to evaluate a model’s performance on unseen data using previous data.
• Model fitting, data train & test split, cross-validation, & parameter optimization with grid search.