# House Price Data Analysis
## Group Members
XiaoMing 1234
XiaoQiang 2341
## Overview
We will process the hourse price data to analyze the factors that are
related to house price and present the visualizing plots to users.
In particular, we will show the house price distribution, compute the
average price of different neighborhood and different hourse style.
Finally we will use machine learning technique to build a linear regression
model that can predict the price given the features. We will evaluate our
model’s performance and use the feature coefficients to analyze the impact
of different features on house price.
## Data
We will get the data from Kaggle website, see reference.
## Milestones
– 9/30 Read the data, compute the house price distribution and show the plot.
– 10/6 Compute the average price of different neighborhood and different hourse style
and show the plot.
– 10/13 Finish building the linear regression model, evaluate its performance and analyze the
impact of different features on house price through the learned feature coefficients.
– 10/20 Finish the report.
## Programming Language
We will use python for this project.
## References
https://www.kaggle.com/c/house-prices-advanced-regression-techniques