Prediction-of-Smoker-Status

Table of Contents

• Overview
• Data
• Project Workflow

Overview

This project revolves around predicting the smoker status of individuals based on bio-signals. The dataset, derived from a deep learning model trained on the Smoker Status Prediction using Bio-Signals dataset, consists of both training and test datasets. The primary objective is to predict the probability of positive smoking for individuals.

Data

Datasets are from Kaggle competition - Binary Prediction of Smoker Status using Bio-Signals https://www.kaggle.com/competitions/playground-series-s3e24/data

Project Workflow

• Data Visualization Overview: The project initiates with a comprehensive visualization overview of the dataset. This step aids in understanding the data's characteristics and identifying potential patterns.

• Model Building with XGBoost: The core of the project involves building a predictive model using the XGBoost algorithm. This model aims to predict the smoker status based on the bio-signals provided in the dataset.

• Evaluation Metrics:

  • Log Loss: The model's performance is evaluated using log loss, providing insights into the accuracy of probability predictions.
  • ROC AUC: Another crucial metric is the Receiver Operating Characteristic (ROC) Area Under the Curve (AUC), which measures the model's ability to distinguish between positive and negative cases.