The objective of this project is to estimate obesity levels using Machine Learning and Artificial Neural Networks (ANN) with a focus on Long Short-Term Memory (LSTM) algorithm. By leveraging LSTM's ability to capture sequential data patterns, we aim to create a predictive model that utilizes relevant health and lifestyle features to classify individuals into different obesity levels. This approach not only provides accurate estimations but also aids in early intervention and personalized healthcare recommendations. Our goal is to contribute to proactive obesity management through data-driven insights, ultimately promoting healthier lifestyles and reducing obesity-related health risks.
Approach for estimating obesity levels using Machine Learning and Artificial Neural Networks (ANN) with the integration of Long Short-Term Memory (LSTM) and AdaBoosting algorithms. We collected extensive health data, including demographics, dietary habits, and physical activity levels, to develop a robust predictive model. LSTM is employed to capture temporal dependencies in the data, while ANN optimizes feature representation. AdaBoosting enhances model performance by combining multiple weak learners. Our results demonstrate the effectiveness of this hybrid model in accurately estimating obesity levels, offering a promising tool for early intervention and personalized health recommendations
Keywords: - Obesity dataset, LSTM, ANN, Ad boosting etc.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
H/W CONFIGURATION:
Processor - I3/Intel Processor
Hard Disk - 160GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
RAM - 8GB
S/W CONFIGURATION:
Operating System : Windows 7/8/10
Server side Script : HTML, CSS, Bootstrap & JS
Programming Language : Python
Libraries : Flask, Pandas, Mysql.connector, Os, Smtplib, Numpy
IDE/Workbench : PyCharm
Technology : Python 3.6+
Server Deployment : Xampp Server