This repository is for me to get familar with the implementation part of machine learning algorithms. Here the program predicts the most suitable crop from the targets specified based on the features.
Targets : rice, maize, chickpea, kidneybeans, pigeonpeas, mothbeans, mungbean, blackgram, lentil, pomegranate, banana, mango, grapes, watermelon, muskmelon, apple, orange, papaya, coconut, cotton, jute and coffee
Features : N, P, K, temperature, Humidity, pH and rainfall.
This is also for me to get a brief idea for my first semester maths project which is on precision farming -> https://github.com/ronakdudhani/CropRecommender .
Reference :
https://www.askpython.com/python/normal-distribution
https://www.kaggle.com/patelris/crop-prediction-analysis-w-classification
https://www.kaggle.com/atharvaingle/crop-recommendation-dataset
https://www.youtube.com/c/joshstarmer
Also i have managed to make a mini naive bayes algorithm from scratch here.