MachineLearning-boston-housing

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MachineLearning-boston-housing

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Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools, Identified the best price that a client can sell their house utilizing machine learning.

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// repository documentation

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# Project 1: Model Evaluation & Validation
## Predicting Boston Housing Prices

The Boston housing market is highly competitive, and we want to be the best real estate agent in the area. To compete with our peers, we decide to leverage a few basic machine learning concepts to assist us and a client with finding the best selling price for their home. Luckily, we’ve come across the Boston Housing data set which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Our task is to build an optimal model based on a statistical analysis with the tools available. This model will then used to estimate the best selling price for our client’s home.

### Install

This project requires **Python 2.7** and the following Python libraries installed:

- [NumPy](http://www.numpy.org/)
- [matplotlib](http://matplotlib.org/)
- [scikit-learn](http://scikit-learn.org/stable/)

### Data

The dataset used in this project is included with the scikit-learn library ([`sklearn.datasets.load_boston`](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html#sklearn.datasets.load_boston)). We do not have to download it separately. We can find more information on this dataset from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Housing) page.

###Deliverables:
* [code](./visuals.py) 
* [report](./boston_housing.ipynb)
* [report (html version)](./boston_housing.html)

MachineLearning-boston-housing

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