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 (11 months ago)
• Default Branch: main
• Last Pushed: 2023-12-25T16:50:34.000Z (11 months ago)
• Last Synced: 2024-10-12T14:43:24.804Z (about 1 month 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

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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 |