Life Expectancy Model

Installation

Open a terminal and install the following dependencies

pip install scikit-learn
pip install streamlit
pip install pandas
pip install numpy
pip install seaborn
pip install matplotlib
pip install streamlit-navigation-bar

Once Dependencies are installed you can simply run the app by running:

streamlit run streamlit_main.py

Introduction

Life expectancy is influenced by a wide range of factors, including socioeconomic status, lifestyle behaviors, healthcare access, environmental conditions, and more. Therefore, it is important to understand these factors in order to design effective public health policies. Using a dataset provided by the World Health Organization, we will develop a comprehensive model to estimate life expectancy. Through various machine-learning techniques, we will be able to develop a model that is both easy to interpret as well as being accurate in predicting life expectancy

Project Road Map

https://github.com/liangtravis510/ECS-171-Project/milestones?state=closed

Collaboration

Nghi Dao - Team Leader, Writer, and Coder

• Organized deadlines and discussed project ideas
• Cleaned data and compiled/fitted the models
• wrote intro, experiment and evaluation, and literature review

Jeffrey Wang - Coder and Writer

• Coded data cleaning and data transformation
• Typed out the contents for the data transformation and data cleaning
• wrote sections on data cleaning, conclusions/discussion, and linear regression model

Adisak Sangiamputtakoon Coder and Writer

• Coded data transformation information and analysis
• Typed out content for data transformation

Baron Fung - Coder and Writer

• Coded the parts of EDA
• wrote the section on the linear regression model used
• Created video presentation and demonstrated demo

Travis Liang - Coder

• Created the initial EDA, which includes data types, missing data, and early plots
• Created graphs on data skewness and correlation matrix
• Create a website demonstrating the model