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---
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title: "🍇 Blueberry Yield Regression"
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emoji: 🌾
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colorFrom: indigo
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colorTo: green
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sdk: streamlit
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app_file: app.py
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pinned: true
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license: mit
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tags:
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- regression
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- machine-learning
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- streamlit
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- kaggle
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- agriculture
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---
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# 🍇 Blueberry Yield Prediction with Machine Learning
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This project is a complete machine learning pipeline that predicts the **yield of wild blueberries** using various environmental and biological features such as pollinator counts, rainfall, and fruit measurements.
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## 📌 Project Type
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- Supervised Learning
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- Regression Problem
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---
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## 🔍 Problem Description
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Predicting agricultural yield is a crucial component in planning, sustainability, and food economics. The dataset used in this project comes from the **Kaggle Playground Series S3E14** competition and contains information on:
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- Different species of pollinators (honeybee, bumblebee, osmia...)
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- Environmental conditions (rainfall days, temperature ranges...)
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- Fruit attributes (fruit mass, fruit set, seed count...)
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🎯 **Goal**: Predict the `yield` (kg/ha) of blueberries based on input features.
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---
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## 📊 Dataset Info
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- `train.csv`: 15,289 samples with 18 features
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- `test.csv`: same structure, no target
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- No missing values, clean numerical data
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---
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## 📈 What We Did (Pipeline Summary)
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1. **EDA (Exploratory Data Analysis)**
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- Checked for missing values ✅
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- Analyzed feature distributions & target (`yield`)
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- Built correlation heatmaps — strongest positive correlations:
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- `fruitmass`, `fruitset`, `seeds`
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2. **Data Preprocessing**
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- Removed `id` column
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- Standard feature selection based on correlation
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- No categorical encoding needed (all numerical)
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3. **Model Training**
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- Model: `RandomForestRegressor`
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- Train-Test Split: 80/20
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- **Results**:
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- RMSE ≈ **573.8**
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- R² Score ≈ **0.81** ✅
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4. **Test Prediction & Submission**
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- Predictions made on `test.csv`
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- `submission.csv` generated for Kaggle submission
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5. **Streamlit App**
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- Users input bee counts, rain days, and fruit measurements
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- Predicts blueberry yield in kg/ha
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- Uses trained model (`rf_model.pkl`) behind the scenes
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---
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## 🚀 Try it Online
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🌐 You can try this app live here:
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[Hugging Face Space Link](https://huggingface.co/spaces/yazodi/blueberry-yield-regression-app)
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---
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## 🔮 What Could Be Improved?
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| Area | Suggestion |
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|------|------------|
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| Feature Engineering | Create interaction terms, try log/ratio features |
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| Model | Try LightGBM, XGBoost, or stacking |
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| Tuning | GridSearchCV or Optuna for hyperparameter optimization |
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| Visualization | Add interactive charts in Streamlit app |
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| Real-World Data | Add satellite weather data, soil types, historical trends |
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---
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## 📁 Project Structure
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📦 blueberry-yield-regression
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├── app.py
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├── rf_model.pkl
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├── model_columns.pkl
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├── requirements.txt
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├── submission.csv
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└── README.md
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---
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## 📜 License
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MIT License – Free to use, modify and distribute.
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--- |
