{"id":15195398,"url":"https://github.com/trep48/crop-prediction","last_synced_at":"2026-03-05T19:04:22.206Z","repository":{"id":214075559,"uuid":"735633357","full_name":"Trep48/Crop-Prediction","owner":"Trep48","description":"Predicting crop using machine learning with Random Forest, SVM, Decision Tree, Gradient Boosting, and KNN algorithms.","archived":false,"fork":false,"pushed_at":"2023-12-25T16:50:34.000Z","size":8929,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-01T14:41:43.899Z","etag":null,"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"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Trep48.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2023-12-25T16:11:14.000Z","updated_at":"2023-12-25T17:00:00.000Z","dependencies_parsed_at":"2023-12-31T00:13:23.543Z","dependency_job_id":"27fd5531-1b5f-40e7-805f-e43276521eda","html_url":"https://github.com/Trep48/Crop-Prediction","commit_stats":null,"previous_names":["trep48/crop-prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Trep48%2FCrop-Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Trep48%2FCrop-Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Trep48%2FCrop-Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Trep48%2FCrop-Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Trep48","download_url":"https://codeload.github.com/Trep48/Crop-Prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241381519,"owners_count":19953749,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["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"],"created_at":"2024-09-27T23:23:03.942Z","updated_at":"2026-03-05T19:04:12.188Z","avatar_url":"https://github.com/Trep48.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Crop Prediction System using Machine Learning\n\nThis 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.\n\n## Team Members\n\n- [Tanvi kamanuri](https://www.linkedin.com/in/kamanuri-tanvi-35759a25b/)\n- [Yagna valkini](https://www.linkedin.com/in/yagna-valkini-suryadevara-b1929b217/)\n- [Sandeep Rajanla](https://www.linkedin.com/in/rvssm-sandeep/)\n- [Karthik](https://www.linkedin.com/in/karthik117a635/)\n\n## Dataset\n\nThe dataset used for this project contains the following features:\n\n- State_Name: Name of the state\n- Crop_Type: Type of crop\n- Crop: Specific crop name\n- N, P, K: Soil nutrient levels (in kg/ha)\n- pH: Soil pH level\n- Rainfall: Annual rainfall (in mm)\n- Temperature: Average temperature (in degrees Celsius)\n- Area_in_hectares: Cultivation area in hectares\n- Production_in_tons: Crop production in tons\n- Yield_ton_per_hec: Yield per hectare (target variable)\n\n## Algorithms\n\nWe have implemented the following five machine learning algorithms:\n\n1. Random Forest\n2. Support Vector Machine (SVM)\n3. Decision Tree\n4. Gradient Boosting\n5. K-Nearest Neighbors (KNN)\n\nExplore the Jupyter notebook `Crop_Prediction.ipynb` for data analysis and model training.\n\n## Results\n\nThe results of each algorithm can be found in the Jupyter notebook `Crop_Prediction.ipynb` file.\n\n### Training Results\n|Algorithm |\tDesicion Tree Classifier |\tRandom Forest Classifier |\tKNN |\tSVM |\tXGB |\n| --------- | ------------------------ | -------------------------- | --- | --- | --- |\n|train_accuracy|\t99.998748|\t99.998748|\t10.462074|\t97.717798|\t99.372801|\n|train_precision|\t99.998748|\t99.998748|\t1.756034|\t97.853954|\t99.3849|\ntrain_recall|\t99.998748|\t99.998748|\t10.462074|\t97.717798|\t99.372801|\ntrain_f1|\t99.998748|\t99.998748|\t2.9996|\t97.756293|\t99.37602|\n\n### Testing Results \n| Algorithm |\tDesicion Tree Classifier |\tRandom Forest Classifier\t| KNN |\tSVM\t| XGB |\n| --------- | ------------------------ | -------------------------- | --- | --- | --- |\ntest_accuracy |\t98.452679 |\t98.908363 |\t98.242364 |\t97.651477 |\t98.863295 |\ntest_precision |\t98.45159 |\t98.918539 |\t98.264676 |\t97.834533 |\t98.875949 |\ntest_recall |\t98.452679 |\t98.908363 |\t98.242364 |\t97.651477 |\t98.863295 |\ntest_f1 |\t98.451839 |\t98.911897 |\t98.2457 |\t97.698144 |\t98.867799 |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftrep48%2Fcrop-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftrep48%2Fcrop-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftrep48%2Fcrop-prediction/lists"}