The given solutions in this project are only for reference purpose.
https://www.kaggle.com/vikrishnan/boston-house-prices
In this project, I have applied basic machine learning concepts on data collected for housing prices in Boston, Massachusetts area to predict the selling price of a new home. First, I have explored the data to obtain important features and descriptive statistics about the dataset. Next, I split the data into testing and training subsets, and determined a suitable performance metric for this problem. Then analyzed performance graphs for a learning algorithm with varying parameters and training set sizes. This enables me to pick the optimal model that best generalizes for unseen data. Finally, I have tested this optimal model on a new sample and compare the predicted selling price to your statistics.
This project is designed to get acquainted to working with datasets in Python and applying basic machine learning techniques using NumPy and Scikit-Learn. Before being expected to use many of the available algorithms in the sklearn library, it will be helpful to first practice analyzing and interpreting the performance of your model.
Things I have learned by completing 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.
The Boston housing market is highly competitive, and you want to be the best real estate agent in the area. To compete with your peers, you decide to leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home. Luckily, you've come across the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Your task is to build an optimal model based on a statistical analysis with the tools available. This model will then be used to estimate the best selling price for your clients' homes.
This project uses the following software and Python libraries:
You will also need to have software installed to run and execute a Jupyter Notebook.
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included.
This project contains three files:
Predicting Boston Housing Prices.ipynb: This is the main file where you will be performing your work on the project.housing.csv: The project dataset.visuals.py: This Python script provides supplementary visualizations for the project.
In the Terminal or Command Prompt, navigate to the folder containing the project files, and then use the command jupyter notebook Predicting Boston Housing Prices.ipynb to open up a browser window or tab to work with your notebook. Alternatively, you can use the command jupyter notebook or ipython notebook and navigate to the notebook file in the browser window that opens.