Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+- tabular-regression
+tags:
+- agriculture
+- tea-yield
+- sri-lanka
+- machine-learning
+- regression
+dataset_info:
+  features:
+  - name: Rainfall_mm
+    dtype: float64
+    description: Annual rainfall in millimeters
+  - name: Avg_Temp_C
+    dtype: float64
+    description: Average annual temperature in Celsius
+  - name: Soil_pH
+    dtype: float64
+    description: Soil acidity/alkalinity (4.5-6.0 optimal for tea)
+  - name: Fertilizer_kg_per_hectare
+    dtype: float64
+    description: Fertilizer application rate in kg per hectare
+  - name: Sunshine_hours
+    dtype: float64
+    description: Average daily sunshine hours
+  - name: Altitude_m
+    dtype: float64
+    description: Elevation in meters above sea level
+  - name: Age_of_tea_plant_years
+    dtype: float64
+    description: Age of tea bushes in years
+  - name: Yield_kg_per_hectare
+    dtype: float64
+    description: Tea yield in kilograms per hectare (target variable)
+  - name: Season_Condition
+    dtype: int64
+    description: Synthetic season indicator (0=Normal, 1=Monsoon, 2=Drought)
+  configs:
+  - config_name: default
+    data_files:
+    - split: train
+      path: tea_yield_dataset_53264.csv
+---
+
+# Sri Lanka Tea Yield Prediction Dataset
+
+## 📋 Dataset Description
+
+A synthetic dataset for predicting tea yield in Sri Lanka based on agricultural and environmental factors. This dataset simulates real-world conditions for machine learning regression tasks.
+
+### **Dataset Summary**
+
+- **Size:** 53,264 samples × 10 features (including target)
+- **Type:** Tabular/Structured data
+- **Task:** Regression (predicting continuous tea yield)
+- **Domain:** Agriculture, Climate, Food Production
+
+### **Supported Tasks**
+
+- `tabular-regression`: Predicting tea yield (kg/hectare) based on environmental and agricultural factors
+- `feature-importance`: Understanding which factors most influence tea production
+- `outlier-detection`: Identifying unusual yield patterns
+
+### **Languages**
+
+English (feature names and descriptions)
+
+## 📊 Dataset Structure
+
+### **Data Fields**
+
+| Feature | Type | Range | Description |
+|---------|------|-------|-------------|
+| Rainfall_mm | float64 | 1500-3500 mm | Annual rainfall |
+| Avg_Temp_C | float64 | 18-28°C | Average temperature |
+| Soil_pH | float64 | 4.5-6.0 | Soil pH level |
+| Fertilizer_kg_per_hectare | float64 | 200-500 kg/ha | Fertilizer usage |
+| Sunshine_hours | float64 | 4-8 hours | Daily sunshine |
+| Altitude_m | float64 | 500-2000 m | Elevation |
+| Age_of_tea_plant_years | float64 | 3-30 years | Plant age |
+| Yield_kg_per_hectare | float64 | 300-7000 kg/ha | **Target variable** |
+| Season_Condition | int64 | 0,1,2 | Synthetic season indicator |
+
+### **Data Splits**
+
+The dataset is provided as a single file suitable for train/validation/test splitting (recommended: 70/15/15).
+
+## 🚀 Usage
+
+### **Loading with Hugging Face Datasets**
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("kasunUdayanga/Tea_Yield_Prediction")
+
+# Convert to pandas DataFrame
+df = dataset['train'].to_pandas()