Update README.md

README.md

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----
-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+task_categories:
+- tabular-classification
+tags:
+- biology
+- medical
+- genomics
+- genetics
+- bioinformatics
+pretty_name: Genetic Variant Pathogenicity Classification
+size_categories:
+- 10K<n<100K
+---
+
+# Genetic Variant Pathogenicity Dataset
+
+## Dataset Description
+This dataset contains annotated genetic variants (mutations) designed for tabular binary classification tasks. The objective is to predict whether a given genetic variant is **Pathogenic** (disease-causing) or **Benign** (harmless) based on a rich set of bioinformatics annotations, evolutionary conservation scores, and functional prediction tools.
+
+- **Task:** Binary Classification
+- **Target Column:** `Pathologic/Benign`
+
+## Dataset Structure
+
+The dataset is pre-split into `train` and `test` sets, making it ready for immediate machine learning modeling. The class distribution is highly balanced.
+
+| Split | Number of Rows | Benign Count | Pathogenic Count |
+|-------|----------------|--------------|------------------|
+| Train | 7,856          | 3,940        | 3,916            |
+| Test  | 4,910          | 2,460        | 2,450            |
+
+## Key Features
+
+The dataset consists of 69 columns. While it includes extensive biological data, some of the most critical feature categories include:
+
+* **Variant Identifiers:** `Chrom`, `Position`, `Ref Base`, `Alt Base`, `Gene`
+* **Molecular Consequences:** `Sequence Ontology` (e.g., *missense_variant*), `cDNA change`, `Protein Change`
+* **Population Frequencies:** Allele frequencies from the 1000 Genomes Project and ESP6500.
+* **Functional Prediction Scores:** `CADD Exome Score`, `PolyPhen-2`, `SIFT`, `REVEL Score`
+* **Conservation Scores:** `GERP++`, `PhyloP`, `SiPhy`
+* **Target Label:** `Pathologic/Benign` (Values: "Benign" or "Pathogenic")
+
+## Usage
+
+You can easily load and explore this dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the dataset (replace 'your-username' with your actual Hugging Face username)
+dataset = load_dataset("your-username/genetic-variant-pathogenicity")
+
+# View the dataset structure
+print(dataset)
+
+# Convert the train split to a Pandas DataFrame for easy manipulation
+df_train = dataset['train'].to_pandas()
+print(df_train['Pathologic/Benign'].value_counts())