Forest Cover Type Prediction

We need predict the forest cover type (the predominant kind of tree cover) from strictly cartographic variables (as opposed to remotely sensed data). The actual forest cover type for a given 30 x 30 meter cell was determined from US Forest Service (USFS) Region 2 Resource Information System data.

Dataset url: [Kaggle](https://www.kaggle.com/competitions/forest-cover-type-prediction/data)

Built With

• Python
• FastAPI
• Machine learning
• Docker
• Mongodb

🌐 Infrastructure Required.

1. AWS S3
2. AWS EC2
3. AWS ECR
4. Git Actions
5. Terraform

Snippets

FlowChart

Data Understanding

The dataset used to predict stroke is a dataset from Kaggle. This dataset has been used to predict student performance with different model algorithms. This dataset has:

• 581012 samples or rows
• 55 features or columns
• 1 target column (Cover_Type).

💻 How to setup:

Creating conda environment

conda create -p venv python==3.8 -y

activate conda environment

conda activate ./venv

Install requirements

pip install -r requirements.txt

Export the environment variable

export AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>

export AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY>

export AWS_DEFAULT_REGION=<AWS_DEFAULT_REGION>

export MONGODB_URL="mongodb+srv://<username>:<password>@ineuron-ai-projects.7eh1w4s.mongodb.net/?retryWrites=true&w=majority"

Run the live server using uvicorn

python app.py

To launch ui

http://127.0.0.1:5000/

🏭 Industrial Use-cases

1. Scientists can predict future wild fires & hence can save flora and fona.
2. Fire Rating Systems can be developed.

Languages & Libraries Used