NanamYuk-ML

ML Part of Product Based Bangkit Capstone Project Team C22-PS316

Machine Learning Team

Name	Bangkit ID	Contacts
Muhammad Syah Zichrullah Habibie	M2322F2811	Github
Naufaldi Hafidhigbal	M2224W2072	Github

What we do?

We are making a crop recommendation system for recommending the top 5 crops to be grown based on the users city and environment.

Built with

• Python
• TensorFlow
• Flask
• OpenWeatherMap API
• Google Cloud Run
• Google Colab

Flowchart

API Endpoint

Click here ( Deployed using Google Cloud Run )

Endpoint

Endpoint	Method	Return
/predict	GET	JSON

Parameters

Parameter	Expected input	Explanation
city	str(city name)	-
soil	int(1, 2 or 3)	1: Pasir, 2: Lempung, 3: Liat
light	int(1 or 2)	1: Full sun, 2: Semi shade

Machine Learning Model

In our machine learning model we use a deep neural network with 4 layers, 1 layer for input with input_dim is 5 which will enter 5 data, namely temperature, humidity, rainfall, soil, and light, then 2 layers for the hidden layer, and the last 1 layer for the output, in the output layer we use softmax activation so that later we can take a sequence of 5 predictions with the highest accuracy. Here we can see after we train the model we get 33% loss and 89% accuracy, from this training result as you can see highest accuracy prediction and actual data are the same and then it will be followed up to second-highest accuracy to the fifth-highest accuracy

How to run this Flask app locally

• Clone this repo
• Open terminal and go to this project's root directory
• Create your own OpenWeatherMap API_KEY (*Notes)
• Create a config.py file and type api_key="your_api_key" then save
• Create and activate a python virtual environment if you want
• Type pip install -r requirements.txt
• Serve the Flask app by typing flask run
• It will run on http://127.0.0.1:5000