Adult Income Prediction using Machine Learning Pipeline

This project aims to develop a Machine Learning pipeline to predict income levels based on the Adult Income Census dataset. The pipeline includes various components such as a custom logging system, custom exception handling and deliverables ensuring robust and transparent operation throughout the ML workflow.

Dataset

Becker, B. & Kohavi, R. (1996). Adult [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5XW20.

Features

• Data Ingestion: Collecting and loading the dataset for processing.
• Exploratory Data Analysis (EDA): Analyzing the dataset to identify patterns, distributions, and correlations.
• Data Transformation: Preprocessing and transforming the data for model readiness.
• Model Training: Training various models to predict income levels.
• Model Evaluation: Evaluating model performance using appropriate metrics.
• Model Deployment: Deploying the model to a Flask server for making predictions on new data.
• Logger and Exception Handler: Custom logging and exception handling mechanisms are integrated to provide transparency, easy debugging, and robust error management across the pipeline.
• Artifacts/Deliverables: All key artifacts such as preprocessed data transformations, trained models, and important EDA insights are saved and versioned in the artifacts/ directory for reproducibility and further analysis. The project demonstrates end-to-end model development and deployment while leveraging custom logging and error handling for a production-ready solution.

Tech Stack

Virtual Environment Setup

Install Anaconda to manage project dependencies.

Open Anaconda Terminal, create a new envrionment with python 3.10 installed.

    conda create -p env/ python=3.10

Navigate to folder where env is located. Activate conda envrionment.

    conda activate env/

You should see the path of the enviroment on the leftmost hand side, indicating successfull activation.

Install Dependencies

Clone the project

  git clone https://github.com/Tryd3x/ml-pipeline.git

Go to the project directory

  cd ml-pipeline

Activate conda environment

    conda activate env/

Install dependencies using pip

  pip install -r "requirements.txt"

Start the Flask server

  python app.py

Inquiries

If you have any feedback, please reach out to us at htelegraphy@gmail.com