## About the dataset

This dataset consists of a `train.csv` file which contains the unique id `id` for each row, 6 `category` columns with suffix A to F, 9 `feature` columns with suffix A to I, 10 `composition` columns with suffix from A to J, `unit` column which is the unit of measurement of the result value and finally a `result` columns which is the measure of the quality of water as a numerical value to be predicted.

1. The `category` columns are various categorical features for a data point such as country of data collection, the site from which the data is collected, media of sample, etc.

2. The `feature` columns are the various demographic features that affect the pollution of water in a particular region such as population density, GDP, droughts in a region, literacy rate of students in a region, etc.

3. The `composition` columns are the compositions of various elements like paper, plastic wastes, cardboard, etc. in water.

4. The `unit` value is the unit of measurement in which the result value is measured.

5. The `result` value is a floating number that expresses the quality of water based on the various factors provided in the dataset.


### FAQ

**What files do I need?** 

You are required to use the `train.csv` file to read the tabular dataset provided and train your algorithms to predict the numerical value in `result` column in `train.csv` file.
 
**What am I predicting?**

You would be training your model based on the `train.csv` (training dataset) and then would be predicting the `result` value for each row in `test.csv` to create a submission. 

## Files

*   **train.csv** - the provided training data set.
*   **test.csv** - the provided testing data set.
*   **sample_submission.csv** - a sample submission file in the correct format.

## Columns

- `id`: The unique ID for each row.
- `categoryA` - `categoryF`: 6 category columns with suffix A to F.
- `featureA` - `featureI`: 9 feature columns with suffix A to I.
- `compositionA` - `compositionJ`: 10 composition columns with suffix A to J.
- `unit`: The unit of measurement for the result values.
- `result`: The measure for water quality (target variable).

The training dataset provided could be read using the `read_csv()` function in the pandas module.