https://github.com/saifalibaig/crop-yield-prediction

🌾 A machine learning-based crop production prediction system using historical Indian agricultural data with advanced regression models and hyperparameter tuning.
https://github.com/saifalibaig/crop-yield-prediction

eda feature-encoding feature-selection kaggle-dataset lasso-regression linear-regression matplotlib-pyplot numpy onehot-encoding pandas python3 ridge-regression seaborn simpleimputer xgboost-regression

Last synced: 3 months ago
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🌾 A machine learning-based crop production prediction system using historical Indian agricultural data with advanced regression models and hyperparameter tuning.

• Host: GitHub
• URL: https://github.com/saifalibaig/crop-yield-prediction
• Owner: saifalibaig
• Created: 2025-08-06T08:11:12.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-08-06T09:39:58.000Z (4 months ago)
• Last Synced: 2025-08-06T10:28:13.606Z (4 months ago)
• Topics: eda, feature-encoding, feature-selection, kaggle-dataset, lasso-regression, linear-regression, matplotlib-pyplot, numpy, onehot-encoding, pandas, python3, ridge-regression, seaborn, simpleimputer, xgboost-regression
• Language: Jupyter Notebook
• Homepage:
• Size: 1.48 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🌾 Crop Yield Prediction using Machine Learning

## 📌 Project Overview

Agriculture is a key sector in when it comes to our country, and predicting crop production can help optimize **resource allocation, yield forecasting, and policy-making**. In this project, I used historical crop production data to build **predictive models** that estimate crop yield based on features like **State, District, Crop, Season, Area, and Year**.

Accurate crop prediction can help in **agriculture planning, resource optimization, and policy-making**.

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## 📊 Dataset

- **Source:** [Agriculture Crops Production in India - Kaggle](https://www.kaggle.com/datasets/pyatakov/india-agriculture-crop-production)

- **Rows:** 345,407  

- **Columns:** 10  

- **Target Variable:** `Yield`

### Key Features:

- `State` - Name of the state

- `District` - Name of the district

- `Season` - Season of cultivation

- `Crop` - Type of crop grown

- `Year`- Year of cultivation

- `Area` - Cultivation area (in hectares)

- `Production` - Total crop production 

- `Yield` - `Target Variable`

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## ✅ Project Workflow

1. **Dataset Overview**

   - Read Dataset

   - Display first 5 rows

   - Shape & Structure

2. **Exploratory Data Analysis (EDA)**

   - Null value analysis

   - Correlation heatmap of numerical features

   - ANOVA test for categorical columns

   - Production distribution analysis

   - Top crops by production

   - District-wise and season-wise crop production

3. **Data Preprocessing**

   - Imputation using SimpleImputer

   - Cardinality Calculation for Categorical Variables

   - One-hot encoding for low-cardinality variables

   - Frequency encoding for high-cardinality variables

4. **Model Development**

   - **Model 1:** Linear Regression (Baseline)

   - **Model 2:** Ridge Regression (with RandomizedSearchCV tuning)

   - **Model 3:** Lasso Regression (with RandomizedSearchCV tuning)

   - **Model 4:** XGBoost Regressor

     - Manual tuning (7 different parameter sets)

     - Hyperparameter tuning using `RandomizedSearchCV`

5. **Model Evaluation**

   - Metrics: `R²`, `RMSE`, `MSE`, `MAE`

   - Actual vs Predicted scatter plots for all models

6. **Feature Importance**

   - Extracted feature importance from XGBoost

---

## 🤖 Models Implemented & Performance

| Model                 | R² Score  | Observations |

|-----------------------|-----------|--------------|

| Linear Regression     | ~0.79    | Baseline |

| Ridge Regression      | ~0.79    | Similar to LR |

| Lasso Regression      | ~0.79    | Similar to Ridge |

| **XGBoost Regressor** | **0.945** | Best performing model |

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## 📈 Key Visualizations

- Correlation heatmap of numerical features

- Seasonal and state-wise crop production trends

- Top crops by production

- Actual vs Predicted plots for all models

- XGBoost feature importance chart

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## ✅ Conclusion

- **XGBoost Regressor** was the best-performing model with an R² score of **0.945** and the lowest RMSE.

- Regularization (Lasso/Ridge) did not significantly improve results compared to Linear Regression.

- Top 5 crops by production: **Coconut**, **Sugarcane**, **Rice**, **Wheat**, **Potato**.

- Highest production seen in states: **Kerela**, **Tamil Nadu**, **Karnataka**, **Andhara Pradesh**, **West Bemgal**.

-  Seasonal trend shows crops cultivated whole year leads in overall production while Kharif and Rabi come after it.

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## 🌍 Real-World Applications

- Assisting **farmers and policymakers** in making informed decisions on crop planning.

- Optimizing **resource allocation** based on expected production.

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## 🚀 Future Enhancements

- Incorporate **weather and soil data** for better accuracy.

- Deploy the model using **Flask, FastAPI, or Streamlit** for real-time prediction.

- Build a **dashboard for visualization and decision support**.

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