kasunUdayanga/Tea_yield_6_features
Tea Yield Prediction Dataset (6 Features) 📋 Quick Info Samples: 53,264 Features: 6 Task: Regression (predict tea yield) Type: Synthetic (realistic simulation) 🎯 Purpose Simple dataset for machine learning beginners to practice: Data preprocessing (missing values, outliers) Feature engineering Regression modeling Model evaluation 📊 Features # Feature Description Range 1 rainfall_mm Annual rainfall in mm 10-350 2… See the full description on the dataset page: https://huggingface.co/datasets/kasunUdayanga/Tea_yield_6_features.
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Tea Yield Prediction Dataset (6 Features)
📋 Quick Info
- Samples: 53,264
- Features: 6
- Task: Regression (predict tea yield)
- Type: Synthetic (realistic simulation)
🎯 Purpose
Simple dataset for machine learning beginners to practice:
- Data preprocessing (missing values, outliers)
- Feature engineering
- Regression modeling
- Model evaluation
📊 Features
🚀 Quick Start
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, r2_score
# Load data
df = pd.read_csv('tea_yield_6_features.csv')
# Handle missing values
df = df.fillna(df.median())
# Split data
X = df.drop('yield_kg_ha', axis=1)
y = df['yield_kg_ha']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Train model
model = RandomForestRegressor()
model.fit(X_train, y_train)
# Evaluate
predictions = model.predict(X_test)
print(f"R²: {r2_score(y_test, predictions):.3f}")
print(f"MAE: {mean_absolute_error(y_test, predictions):.2f}")