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https://github.com/trep48/crop-prediction
Predicting crop using machine learning with Random Forest, SVM, Decision Tree, Gradient Boosting, and KNN algorithms.
https://github.com/trep48/crop-prediction
anaconda anaconda-environment colab-notebook colaboratory crop croprecommendations decision-tree-classifier ipynb ipython-notebook jupyter-notebook jupyter-notebooks knn-algorithm machine-learning python python3 random-forest-classifier svm xgboost-algorithm
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Predicting crop using machine learning with Random Forest, SVM, Decision Tree, Gradient Boosting, and KNN algorithms.
- Host: GitHub
- URL: https://github.com/trep48/crop-prediction
- Owner: Trep48
- Created: 2023-12-25T16:11:14.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2023-12-25T16:50:34.000Z (12 months ago)
- Last Synced: 2024-10-12T14:43:24.804Z (2 months ago)
- Topics: anaconda, anaconda-environment, colab-notebook, colaboratory, crop, croprecommendations, decision-tree-classifier, ipynb, ipython-notebook, jupyter-notebook, jupyter-notebooks, knn-algorithm, machine-learning, python, python3, random-forest-classifier, svm, xgboost-algorithm
- Language: Jupyter Notebook
- Homepage:
- Size: 8.52 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Crop Prediction System using Machine Learning
This project, developed by a team of four individuals, aims to predict crop yields based on various features using machine learning. We employ five different algorithms to train the model and predict crop yields.
## Team Members
- [Tanvi kamanuri](https://www.linkedin.com/in/kamanuri-tanvi-35759a25b/)
- [Yagna valkini](https://www.linkedin.com/in/yagna-valkini-suryadevara-b1929b217/)
- [Sandeep Rajanla](https://www.linkedin.com/in/rvssm-sandeep/)
- [Karthik](https://www.linkedin.com/in/karthik117a635/)## Dataset
The dataset used for this project contains the following features:
- State_Name: Name of the state
- Crop_Type: Type of crop
- Crop: Specific crop name
- N, P, K: Soil nutrient levels (in kg/ha)
- pH: Soil pH level
- Rainfall: Annual rainfall (in mm)
- Temperature: Average temperature (in degrees Celsius)
- Area_in_hectares: Cultivation area in hectares
- Production_in_tons: Crop production in tons
- Yield_ton_per_hec: Yield per hectare (target variable)## Algorithms
We have implemented the following five machine learning algorithms:
1. Random Forest
2. Support Vector Machine (SVM)
3. Decision Tree
4. Gradient Boosting
5. K-Nearest Neighbors (KNN)Explore the Jupyter notebook `Crop_Prediction.ipynb` for data analysis and model training.
## Results
The results of each algorithm can be found in the Jupyter notebook `Crop_Prediction.ipynb` file.
### Training Results
|Algorithm | Desicion Tree Classifier | Random Forest Classifier | KNN | SVM | XGB |
| --------- | ------------------------ | -------------------------- | --- | --- | --- |
|train_accuracy| 99.998748| 99.998748| 10.462074| 97.717798| 99.372801|
|train_precision| 99.998748| 99.998748| 1.756034| 97.853954| 99.3849|
train_recall| 99.998748| 99.998748| 10.462074| 97.717798| 99.372801|
train_f1| 99.998748| 99.998748| 2.9996| 97.756293| 99.37602|### Testing Results
| Algorithm | Desicion Tree Classifier | Random Forest Classifier | KNN | SVM | XGB |
| --------- | ------------------------ | -------------------------- | --- | --- | --- |
test_accuracy | 98.452679 | 98.908363 | 98.242364 | 97.651477 | 98.863295 |
test_precision | 98.45159 | 98.918539 | 98.264676 | 97.834533 | 98.875949 |
test_recall | 98.452679 | 98.908363 | 98.242364 | 97.651477 | 98.863295 |
test_f1 | 98.451839 | 98.911897 | 98.2457 | 97.698144 | 98.867799 |