piyusharora2003 / healthtrack---analysis

HealthTrack: Data Analysis - Regional vs. Regionless BMI

Introduction:

This repository delves into the analysis of Body Mass Index (BMI) data from two sources: regional data and regionless data. The regional data is assumed to be associated with specific geographical locations within the dataset, while the origin of the regionless data is unknown.

Data Source:

The data analyzed in this project originates from the HealthTrack GitHub repository "https://github.com/Piyusharora2003/HealthTrack". It's crucial to ensure the data's quality and representativeness for drawing reliable conclusions. The regionless data however is taken from kaggle

Analysis Objectives:

Identify trends and patterns: Compare average heights, weights, and BMIs between the regional and regionless data sets. Assess BMI classifications: Determine the predominant BMI categories (underweight, normal weight, overweight, obese) for each group.

Data Loading and Cleaning:

-[x] Load the data from the HealthTrack repository using appropriate libraries (e.g. pymono). -[x] Examine the data for missing values, outliers, or inconsistencies. Address any data quality issues through cleaning techniques (e.g., imputation, removal).

• []Exploratory Data Analysis (EDA): -[] Visualize the distribution of heights, weights, and BMIs using histograms, boxplots, or scatter plots. -[]Calculate descriptive statistics (mean, median, standard deviation) for each variable in both regional and regionless groups.

Statistical Testing:

-[]Conduct appropriate statistical tests (e.g., t-tests, ANOVAs) to determine if the observed differences in average BMI between the two groups are statistically significant. Results:

The analysis will present findings regarding:

Average heights, weights, and BMIs for regional and regionless data. The distribution of BMI categories within each group. Statistical significance of differences in BMI between the groups.

Further Exploration:

-[] Investigate correlations between BMI and other factors present in the dataset (e.g., age, occupation). Analyze regional variations within the regional data, if possible. Explore the impact of additional sociodemographic variables on BMI.

Disclaimer:

The analysis results and interpretations presented in this repository are based on the provided data and should be considered within the limitations of the information available. It's recommended to consult relevant healthcare professionals for personalized interpretations of BMI data.