Predicting Boston House Prices
Project Description You want to be the best real estate agent out there. In order to compete with other agents in your area, you decide to use machine learning. You are going to use various statistical analysis tools to build the best model to predict the value of a given house. Your task is to find the best price your client can sell their house at. The best guess from a model is one that best generalizes the data.
Data Columns -
- CRIM per capita crime rate by town
- ZN proportion of residential land zoned for lots over 25,000 sq.ft.
- INDUS proportion of non-retail business acres per town
- CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
- NOX nitric oxides concentration (parts per 10 million)
- RM average number of rooms per dwelling
- AGE proportion of owner-occupied units built prior to 1940
- DIS weighted distances to five Boston employment centres
- RAD index of accessibility to radial highways
- TAX full-value property-tax rate per $10,000
- PTRATIO pupil-teacher ratio by town
- B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
- LSTAT % lower status of the population
- MEDV Median value of owner-occupied homes in $1000's
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent