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Longevity

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Life Longevity Analysis

Overview

In this project, we analyze various lifestyle and health-related factors that influence life expectancy. Using real-world data, we explore how different habits and conditions either extend or reduce lifespan. This project provides valuable insights into longevity trends, making it a great addition to your data science portfolio.

Step 1: Understanding the Dataset

  • The dataset consists of various life expectancy factors, including social habits, diseases, and lifestyle choices.
  • Example factors: Smoking, Exercise, Diet, Mental Health, and Relationships.
  • Learning Opportunity: Develop data analysis skills by exploring health-related trends.
  • Alternative: You can integrate additional datasets from sources like WHO or CDC.

Step 2: Data Exploration

The dataset includes:

  • Health Factors: Smoking, alcohol consumption, obesity, meditation habits.
  • Social Factors: Marriage, relationships, and social interactions.
  • Medical Factors: Cancer, heart disease, mental illness impact on life expectancy.

Insights:

  • Discover how lifestyle choices impact longevity.
  • Evaluate the difference between urban and rural lifestyles.
  • Identify the most and least influential factors.

Step 3: Data Preparation and Analysis

To ensure accurate insights, we preprocess the dataset:

Preprocessing:

  • Clean missing or inconsistent values.
  • Normalize numerical features.
  • Convert categorical values for better analysis.

Analysis:

  • Use statistical methods to determine the most significant longevity factors.
  • Apply data visualization techniques to uncover hidden trends.
  • Conduct sentiment analysis on social factors.

Step 4: Data Visualization

Effective visualizations enhance the impact of findings.

Tools Used:

  • Matplotlib & Seaborn: For correlation and trend analysis.
  • GeoPandas & Folium: Mapping geographical longevity trends.
  • Word Clouds & Charts: Representing key longevity influencers.

Focus Areas:

  • Identify top life-extending habits.
  • Pinpoint high-risk behaviors reducing life expectancy.
  • Create dynamic graphs showcasing longevity trends across different demographics.

Conclusion

This project provides valuable insights into how lifestyle and health decisions influence longevity. By leveraging data science techniques, we explore ways to make informed choices for a longer, healthier life. Whether you’re a data enthusiast or a health-conscious individual, this project offers an exciting intersection of analytics and well-being.

Future Improvements

  • Integrate real-time health data.
  • Use machine learning models to predict life expectancy based on habits.
  • Expand datasets to include global demographic variations.