The primary objective of this project is to predict weather using ML in remote areas having no access to internet, live satellite imagery. The model has been trained on weather dataset collected in India from past 20 years. This project has been completely written in Jupyter notebook and even the code profiling has been done in the same.

Instructions to run this project are:-

1. There will be many folders like Predicting Temperature, Predicting Humidity, Predicting Precipitation. Inside each of these folders there will be a ipynb file. Run that file sequentially. Basically each of the train and save multiple ML Model for that specific weather parameter e.g. temperature.

2. All the trained models are stored in Trained Models folder. Excluding Random Forest we have stored everythingf as these files were too large (exceeding file limit for github).

3. Go to the main ipynb file. Run this file sequentially, also you will need to enter certain weather parameters presently like tempertaure, humidity. Actually for the actual project, there was an Arduino equipped with sensors to do the same.

4. All the prediction will be given out with graphs, min max temp and properly dated predictions.

Hope you like my Project! Mayank Goel