# ADAMS SoSe22 — Data

来源：https://www.kaggle.com/competitions/adams-sose22/data

本地 Meta Kaggle 快照；不是本次实时页面。

# Dataset Description

# Data partitioning

The data has been randomly partitioned into a training set and a test set. You should use the labeled training set for developing your models and validating their quality. Next, you should use the developed models to predict the property prices for the listings in the unlabeled test set.

To prevent overfitting, the test set is divided into a public and a private subset. The leaderboard score of your submission is computed on a private part of the test set. Once the competition is finished, you will see a private score of your submissions.


# File descriptions

The data consists of three `.csv` files containing different information:

- **train.csv**: training set with listing features, description and prices
- **test.csv**: test set with listing features and description
- **reviews.csv**: [optional] reviews for listings in both training and test set

# Data fields

Files `train.csv` and `test.csv` have the same columns except for the `price` variable:

- `listing_id`: unique property identifier
- `price`: **[target variable]** property price per night in US dollars
- `summary`: **[text]** listing summary
- `description:` **[text]** listing description
- `space`: **[text]** space description
- `neighborhood_overview`: **[text]** neighborhood description
- `picture_url`: **[optional]** URL to the property image

As indicated above, the text data is provided in several columns devoted to different topics. You will need to think whether to merge them into a single text or consider separately in order to improve your solution.

The remaining columns contain numeric/categorical features with the listing attributes such as location, host information, reviewer scores and more. You would need to perform some data preprocessing and think how to incorporate the tabular data into your modeling pipeline.


# Optional additional data

The data on property reviews in `reviews.csv` and property images in `picture_url` is optional. You may use it to further refine the quality of your solution but may also choose to ignore it.

### Reviews

Data dictionary for `reviews.csv`:

- `listing_id`: unique property identifier
- `review_id`: unique review identifier
- `reviewer_id`: unique reviewer identifier
- `comments`: **[text]** review

Reviews can be matched with `train.csv` by `listing_id`. You would need to think how to deal with the following challenges:

- reviews may come in different languages
- listings have a different number of reviews
- aggregating signal from multiple reviews


##  Images

The column `picture_url` in `train.csv` contains links to image properties that can be used to extract additional information. You would need to think how to deal with the following challenges:

- writing a script to loop through the URLs and download images
- incorporating signal from image data in your pipeline