Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-classification
+language:
+- en
+size_categories:
+- 1K<n<10K
+tags:
+- binary-classification
+- tweets
+- natural-language-processing
+pretty_name: Disaster vs Non-Disaster Tweets
+---
+
+# Disaster Tweets Dataset For Binary Classification
+
+This dataset contains tweets classified as either disastrous (`label 1`) or not disastrous (`label 0`). It is designed to train and evaluate machine learning models for disaster-related tweet classification.
+
+## Files Included
+
+- `train.csv`: Contains **7,613** tweets with their respective labels.
+- `test.csv`: Contains **3,263** tweets without labels.
+
+## Columns
+
+Each CSV file contains the following columns:
+
+- `id` – Unique identifier for each tweet.
+- `keyword` – A keyword extracted from the tweet (may be blank).
+- `location` – The geographical location where the tweet was posted (may be blank).
+- `text` – The actual content of the tweet.
+- (`label` in `train.csv`) – Classification of the tweet:
+  - `1` → Disastrous
+  - `0` → Not Disastrous
+
+## Example Rows
+
+### `train.csv` (Sample Data)
+
+| id  | keyword |  location    | text                                                                                     | label |
+|-----|---------|--------------|------------------------------------------------------------------------------------------|-------|
+| 1   |         |              | Just happened a terrible car crash                                                       | 1     |
+| 2   |         |              | Heard about #earthquake in different cities, stay safe everyone!                         | 1     |
+| 3   |         |              | Forest fire spotted at the park. Geese are fleeing across the street!                    | 1     |
+| 10  |         |              | No I don’t like cold weather!                                                            | 0     |
+| 52  | ablaze  | Philadelphia | Crying out for more! Set me ablaze                                                       | 0     |
+
+### `test.csv` (Sample Data)
+
+| id  | keyword | location | text                                                                                    |
+|-----|---------|----------|-----------------------------------------------------------------------------------------|
+| 11  |         |          | Typhoon Soudelor kills 28 in China and Taiwan                                           |
+| 46  | ablaze  | London   | Birmingham Wholesale Market is ablaze! Fire breaks out at Birmingham's Wholesale Market |
+| 51  | ablaze  | NIGERIA  | Toke Makinwa’s marriage crisis sets Nigerian Twitter ablaze…                            |
+
+## Contributing
+
+If you would like to improve or expand the dataset, feel free to submit suggestions or contributions. Feedback is always welcome!