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

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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

Flowchart

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

image 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

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

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