README.md

metadata

title: 🍇 Blueberry Yield Regression
emoji: 🌾
colorFrom: indigo
colorTo: green
sdk: streamlit
app_file: app.py
pinned: true
license: mit
tags:
  - regression
  - machine-learning
  - streamlit
  - kaggle
  - agriculture

🍇 Blueberry Yield Prediction with Machine Learning

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.

📌 Project Type

• Supervised Learning
• Regression Problem

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🔍 Problem Description

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:

• Different species of pollinators (honeybee, bumblebee, osmia...)
• Environmental conditions (rainfall days, temperature ranges...)
• Fruit attributes (fruit mass, fruit set, seed count...)

🎯 Goal: Predict the yield (kg/ha) of blueberries based on input features.

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📊 Dataset Info

• train.csv: 15,289 samples with 18 features
• test.csv: same structure, no target
• No missing values, clean numerical data

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📈 What We Did (Pipeline Summary)

1. EDA (Exploratory Data Analysis)

   • Checked for missing values ✅
   • Analyzed feature distributions & target (yield)
   • Built correlation heatmaps — strongest positive correlations:
     • fruitmass, fruitset, seeds

2. Data Preprocessing

   • Removed id column
   • Standard feature selection based on correlation
   • No categorical encoding needed (all numerical)

3. Model Training

   • Model: RandomForestRegressor
   • Train-Test Split: 80/20
   • Results:
     • RMSE ≈ 573.8
     • R² Score ≈ 0.81 ✅

4. Test Prediction & Submission

   • Predictions made on test.csv
   • submission.csv generated for Kaggle submission

5. Streamlit App

   • Users input bee counts, rain days, and fruit measurements
   • Predicts blueberry yield in kg/ha
   • Uses trained model (rf_model.pkl) behind the scenes

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🚀 Try it Online

🌐 You can try this app live here:
Hugging Face Space Link

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🔮 What Could Be Improved?

Area	Suggestion
Feature Engineering	Create interaction terms, try log/ratio features
Model	Try LightGBM, XGBoost, or stacking
Tuning	GridSearchCV or Optuna for hyperparameter optimization
Visualization	Add interactive charts in Streamlit app
Real-World Data	Add satellite weather data, soil types, historical trends

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📁 Project Structure

📦 blueberry-yield-regression ├── app.py ├── rf_model.pkl ├── model_columns.pkl ├── requirements.txt ├── submission.csv └── README.md

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📜 License

MIT License – Free to use, modify and distribute.

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