Rainfall-Prediction-In-Australia-Deployment

• This repository consists of files required for end to end implementation of Rain Prediction in Australia Machine Learning Web App created with Flask on Heroku platform.

Problem statement:

A ML project with EDA and model that helps in predicting next-day rain by training classification models on the target variable RainTomorrow.

Dataset

You can find the dataset here.

Dependencies:

• Python
• Pandas
• Numpy
• Matplotlib
• Seaborn
• Scikit-Learn
• Catboost
• Tensorflow
• Flask

setup

To create a project from scratch use following steps - -

• Clone the repository : https://github.com/ni3choudhary/Rainfall-Prediction-In-Australia-Deployment.git
• Inside the project root directory, Create Python Virtual Environment using below command.

$ python3 -m venv venv

Activate Virtual Environment

$ .venv/bin/activate 
          OR
$ .\venv\Scripts\activate

Install Libraries using below command

$ pip install -r requirements.txt

• Run jupyter notebook to get the pickle file inside Model directory.

• Copy that pickle file and create a folder Model inside flask directory and paste the pickle file.

• Inside the flask directory run app.py on terminal to start local server.

$ python app.py

• If you want to view the deployed model, click on the following link: Deployed at: https://rainfall-prediction-australia.herokuapp.com/

• Please do ⭐ the repository, if it helped you in anyway.