Scania-Truck-Failures-Prediction

Truck gif

Problem Statement:

The Air Pressure System (APS) is a critical component of a heavy-duty vehicle that uses compressed air to force a piston to provide pressure to the brake pads, slowing the vehicle down. The benefits of using an APS instead of a hydraulic system are the easy availability and long-term sustainability of natural air.
This is a Binary Classification problem, in which the affirmative class indicates that the failure was caused by a certain component of the APS, while the negative class indicates that the failure was caused by something else.

Project Demonstration

Check out the project demo at https://youtu.be/8IcTGZ6nDA0

Deployed app link

Check out the deployed app at https://shivamshinde123-scania-truck-failures-predicti-srcwebapp-rkeg3t.streamlit.app/

Data used

Get the data from https://archive-beta.ics.uci.edu/dataset/421/aps+failure+at+scania+trucks
APS Failure at Scania Trucks. (2017). UCI Machine Learning Repository.

Project Flow

Programming Languages Used

Python Libraries and tools Used

Run Locally

Clone the project

    git clone https://github.com/shivamshinde123/Scania-Truck-Failures-Prediction.git

Go to the project directory

    cd project-name

Create a conda environment

    conda create -n environment_name python=3.10

Activate the created conda environment

    conda activate environment_name

Install dependencies

  pip install -r requirements.txt

Load the data --> Preprocess the data --> Train the model --> Evaluate the model --> Plot the evaluations --> Testing code

  dvc repro

Make predictions using trained model

  streamlit run src/webapp.py

🚀 About Me

I'm an aspiring data scientist and a data analyst.

shivamshinde123 / Scania-Truck-Failures-Prediction

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