Tobacco Consumption Habits of UK Smoking Survey Data

Keywords: Demographic, Characteristics

Background

This study uses deep learning techniques to analyze UK Smoking Survey data and predict tobacco consumption habits based on demographic characteristics. The research provides insights for developing targeted interventions and policies to reduce tobacco use in the UK population. Selecting the most relevant demographic characteristics and designing appropriate preprocessing techniques for the data can be complex. Choosing the right set of features that effectively capture the relationship between demographics and tobacco consumption habits is essential. This study aims to develop a predictive model using deep learning techniques to analyze the UK Smoking Survey data. The model will explore the relationship between demographic characteristics and tobacco consumption habits, providing valuable insights for public health interventions and policies to reduce tobacco use in the UK population. Dropped unnecessary columns and scaled the data using a min max scaler. Model Sequential used and hidden layers passed through it. The sigmoid activation function is used because it has binary classes in the target variable. Accuracy gets 99% whereas loss is 0.0192. Test loss is 0.0166 and test accuracy is 0.99705.

Aim & Objectives

Aim
To predict the Survey of UK Smoking Data Using DL Techniques
Objective
Implement an ANN model to predict tobacco consumption habits based on UK Smoking Survey data.
Develop an ANN system that analyzes survey responses to forecast smoking behavior and trends.
Evaluate the ANN's ability to predict tobacco consumption habits, contributing to public health strategies and interventions.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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