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--- |
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language: en |
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license: mit |
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tags: |
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- agriculture |
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- regression |
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- crop-yield |
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- tea |
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datasets: |
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- synthetic-tea-yield |
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model-index: |
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- name: tea-yield-predictor |
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results: |
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- task: |
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type: regression |
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name: Tea Yield Prediction |
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metrics: |
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- type: r2_score |
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value: 0.6448 |
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- type: mae |
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value: 200.27 |
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- type: rmse |
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value: 254.21 |
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widget: |
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- example_title: Good Conditions Farm |
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rainfall_mm: 180 |
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temperature_avg: 24 |
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soil_ph: 5.5 |
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fertilizer_kg_ha: 400 |
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plant_age_years: 7 |
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altitude_m: 1200 |
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- example_title: Challenging Conditions Farm |
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rainfall_mm: 90 |
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temperature_avg: 28 |
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soil_ph: 4.8 |
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fertilizer_kg_ha: 250 |
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plant_age_years: 15 |
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altitude_m: 800 |
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--- |
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# Tea Yield Prediction Model 🌱 |
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## Model Description |
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This is a **Linear Regression** model that predicts tea crop yield (in kg/ha) using six key agricultural and environmental factors. The model was selected as the best performer among four algorithms tested, achieving an **R² score of 0.6448**. |
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### Key Features |
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- ✅ **Best performer** among Linear Regression, Decision Tree, Random Forest, and SVR |
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- ✅ **Simple & interpretable** linear model |
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- ✅ **Practical application** for agricultural planning |
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- ✅ **Ready-to-use** with minimal dependencies |
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## Model Performance |
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| Metric | Value | Description | |
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|--------|-------|-------------| |
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| **R² Score** | 0.6448 | Explains 64.48% of yield variance | |
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| **MAE** | 200.27 kg/ha | Average prediction error | |
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| **RMSE** | 254.21 kg/ha | Error with penalty for large mistakes | |
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| **Training Samples** | 47,536 | After preprocessing | |
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| **Features** | 6 | Agricultural/environmental factors | |
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## Input Features |
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| Feature | Type | Range | Description | |
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|---------|------|-------|-------------| |
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| `rainfall_mm` | float | 50-220 mm | Monthly rainfall | |
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| `temperature_avg` | float | 18-30°C | Average temperature | |
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| `soil_ph` | float | 4.5-6.0 | Soil pH level | |
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| `fertilizer_kg_ha` | float | 200-500 | Fertilizer application rate | |
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| `plant_age_years` | float | 2-25 | Age of tea plants | |
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| `altitude_m` | float | 500-2000 | Farm elevation | |
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## Quick Start |
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### Installation |
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```bash |
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pip install scikit-learn pandas joblib |
