---
language: en
license: mit
tags:
- agriculture
- regression
- crop-yield
- tea
datasets:
- synthetic-tea-yield
model-index:
- name: tea-yield-predictor
  results:
  - task:
      type: regression
      name: Tea Yield Prediction
    metrics:
    - type: r2_score
      value: 0.6448
    - type: mae
      value: 200.27
    - type: rmse
      value: 254.21
widget:
- example_title: Good Conditions Farm
  rainfall_mm: 180
  temperature_avg: 24
  soil_ph: 5.5
  fertilizer_kg_ha: 400
  plant_age_years: 7
  altitude_m: 1200
- example_title: Challenging Conditions Farm
  rainfall_mm: 90
  temperature_avg: 28
  soil_ph: 4.8
  fertilizer_kg_ha: 250
  plant_age_years: 15
  altitude_m: 800
---

# Tea Yield Prediction Model 🌱


## Model Description

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**.

### Key Features
- ✅ **Best performer** among Linear Regression, Decision Tree, Random Forest, and SVR
- ✅ **Simple & interpretable** linear model
- ✅ **Practical application** for agricultural planning
- ✅ **Ready-to-use** with minimal dependencies

## Model Performance

| Metric | Value | Description |
|--------|-------|-------------|
| **R² Score** | 0.6448 | Explains 64.48% of yield variance |
| **MAE** | 200.27 kg/ha | Average prediction error |
| **RMSE** | 254.21 kg/ha | Error with penalty for large mistakes |
| **Training Samples** | 47,536 | After preprocessing |
| **Features** | 6 | Agricultural/environmental factors |

## Input Features

| Feature | Type | Range | Description |
|---------|------|-------|-------------|
| `rainfall_mm` | float | 50-220 mm | Monthly rainfall |
| `temperature_avg` | float | 18-30°C | Average temperature |
| `soil_ph` | float | 4.5-6.0 | Soil pH level |
| `fertilizer_kg_ha` | float | 200-500 | Fertilizer application rate |
| `plant_age_years` | float | 2-25 | Age of tea plants |
| `altitude_m` | float | 500-2000 | Farm elevation |

## Quick Start

### Installation
```bash
pip install scikit-learn pandas joblib