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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r setup, include = FALSE}
# The README markdown generate by this rmd file, please edit the content within this R markdown.
knitr::opts_chunk$set(
collapse = TRUE,
message = FALSE,
error = FALSE,
warning = FALSE,
fig.path = "plot/",
fig.align = "center",
echo = FALSE
)
```
# Melbourne House Price Prediction
This repo is the summary for the work of Melbourne house price prediction.
### Report
Under [`report`](https://github.com/Jiaying-Wu/Melbourne-House-Price-Prediction/tree/master/report) folder, contain source code for reporting.
* [`report/README.Rmd`](https://github.com/Jiaying-Wu/Melbourne-House-Price-Prediction/blob/master/report/README.Rmd): R markdown to provide data insight of Melbourne house price data.
Further report detail under [report](https://github.com/Jiaying-Wu/Melbourne-House-Price-Prediction/tree/master/report) folder.
### Data Processing
Under [`process`](https://github.com/Jiaying-Wu/Melbourne-House-Price-Prediction/tree/master/process) folder, contain source code for data processing.
* [`process/spilt_train_data.R`](https://github.com/Jiaying-Wu/Grocery-Sales-Forecasting/blob/master/process/spilt_train_data.R): R Script to split training data at local train set and local test set under 85/15 ratio.
Further processing detail under [process](https://github.com/Jiaying-Wu/Melbourne-House-Price-Prediction/tree/master/process) folder.
### Data Modeling
Under [`model`](https://github.com/Jiaying-Wu/Melbourne-House-Price-Prediction/tree/master/model) folder, contain source code of models to predict Melbourne house price:
* [`model/model_decision_tree.R`](https://github.com/Jiaying-Wu/Melbourne-House-Price-Prediction/blob/master/model/model_decision_tree.R): R Script of Decision Tree Model.
* [`model/model_random_forest.R`](https://github.com/Jiaying-Wu/Melbourne-House-Price-Prediction/blob/master/model/model_random_forest.R): R Script of Random Forest Model.
* [`model/model_gbm.R`](https://github.com/Jiaying-Wu/Melbourne-House-Price-Prediction/blob/master/model/model_gbm.R): R Script of Gradient Boosting Model.
Further modeling detail under [model](https://github.com/Jiaying-Wu/Melbourne-House-Price-Prediction/tree/master/model) folder.
### Data Source
Data source from Monash University, access the data from this competition [vitticeps](https://www.kaggle.com/c/vitticeps/data) in Kaggle.
14 Features in this dataset:
1. `id`: unique id for property.
2. `suburb`: suburb location of property.
3. `result`: `S` indicates property sold; `SP` - property sold prior; `PI` - property passed in; `PN` - sold prior not disclosed; `SN` - sold not disclosed; `NB` - no bid; `VB` - vendor bid; `o res` - other residential; `w` - withdrawn prior to auction.
4. `rating`
5. `nbeds`: number of bedrooms.
6. `property_type`: `h` = house, `t` = townhouse, `u` = unit/apartment.
7. `day`: day of the month of auction.
8. `month`: month of auction.
9. `year`: year of auction.
10. `nvisits`: How many people came to open houses.
11. `ncars`: Number of parking places.
12. `nbaths`: Number of bathrooms.
13. `land_size`: Size of the lot, in sq m, units will be 0.
14. `house_size`: Internal size of property in sq m.