https://github.com/yashksaini-coder/cattle-weight-prediction

https://github.com/yashksaini-coder/cattle-weight-prediction

Last synced: 4 months ago
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• Host: GitHub
• URL: https://github.com/yashksaini-coder/cattle-weight-prediction
• Owner: yashksaini-coder
• Created: 2025-01-29T10:21:18.000Z (5 months ago)
• Default Branch: master
• Last Pushed: 2025-02-06T10:07:36.000Z (5 months ago)
• Last Synced: 2025-02-06T11:23:52.676Z (5 months ago)
• Language: HTML
• Size: 21.1 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Based on the Analysis done so far, the best model so far is **Ridge Regression**, based on the following:  

#### **📊 Model Performance Comparison**

| Model              | R² Score (Higher is Better) | RMSE (Lower is Better) | MAE (Lower is Better) |

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

| **Ridge Regression** | **0.0488**                 | **17.3417**             | **13.2005**             |

| Lasso Regression   | 0.0330                    | 17.4846                | 13.3260                |

| Linear Regression  | 0.0314                    | 17.4994                | 13.4688                |

| Random Forest      | 0.0256                    | 17.5517                | 13.6623                |

| Decision Tree      | **-0.2325** (Worst)        | 19.7395 (Worst)        | 14.7901 (Worst)        |

| XGBoost           | -0.1224                    | 18.8373                | 14.6421                |

##### **Why is Ridge Regression the Best?**

✅ **Highest R² Score** (0.0488) → It explains the most variance in the data  

✅ **Lowest RMSE** (17.3417) → It has the least error in predictions  

✅ **Lowest MAE** (13.2005) → It has the least average absolute error  

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### 🔥 **Final Results**  

#### **Final Tuned Model Performance**

| Metric         | Score  |  

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

| **Best Alpha** | **0.001**  |  

| **R² Score**   | **0.9876** ✅ (Very High) |  

| **RMSE**       | **1.9826** ✅ (Low Error) |  

| **MAE**        | **1.5273** ✅ (Very Low) |  

📊 **Key Takeaways:**  

✅ **R² Score of 0.9876** → Model explains **98.76%** of variance in the data!  

✅ **RMSE is just 1.98** → Very accurate predictions!  

✅ **MAE is 1.52** → Low absolute errors, indicating precise estimations!  

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