Boston_Housing_Prediction
Model Evaluation and Validation
Project: Predicting Boston Housing Prices
Description
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.
Install
This project requires Python and the following Python libraries installed:
NumPy Pandas matplotlib scikit-learn We will also need to have software installed to run and execute a Jupyter Notebook
Data
The modified Boston housing dataset consists of 489 data points, with each datapoint having 3 features. This dataset is a modified version of the Boston Housing dataset found on the UCI Machine Learning Repository.
Features
RM: average number of rooms per dwelling LSTAT: percentage of population considered lower status PTRATIO: pupil-student ratio by town
Target Variable 4. MEDV: median value of owner-occupied homes