Crop Yield Predictions

🌟 Overview

Crop yield prediction is a critical challenge in agriculture. Accurate crop yield forecasts can:

• Address global food security challenges.
• Mitigate the impacts of climate change.
• Support effective agricultural risk management.

This project leverages supervised machine learning techniques to predict crop yields using various factors, such as weather conditions (rainfall, temperature), pesticide usage, and historical crop yield data.

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📂 Dataset

The dataset used for this project was sourced from Kaggle:
Crop Yield Prediction Dataset

This dataset provides essential features like weather data, pesticide use, and historical crop yields.

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⚙️ Tools & Technologies

• Language: Python
• Platform: Google Colab
• Libraries:
  • pandas - Data manipulation and analysis
  • scikit-learn - Machine learning algorithms
  • Other dependencies: numpy, matplotlib, etc.

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🚀 Features

• Preprocessing:

  • Data cleaning and handling missing values.
  • Feature engineering for better model performance.

• Machine Learning:

  • Implementation of supervised learning algorithms.
  • Model training, validation, and testing.

• Visualization:

  • Insights through plots and charts.

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📈 How to Run

1. Clone this repository:

   git clone https://github.com/your-username/crop-yield-predictions.git

2. Open the project in Google Colab.
3. Install the dependencies.
4. Load the dataset and execute the cells in the notebook to train and evaluate the model.

📝 Future Work

• Integrate additional features like soil type and crop type.
• Experiment with advanced models such as XGBoost and neural networks.
• Extend predictions to regional or global scales.