Last updated Unknown

Imagine that you are a data scientist at a global health organization. The organization wants to understand the key factors affecting life expectancy across different countries and to develop a model that can predict life expectancy based on these factors. By doing so, the organization aims to identify areas of intervention and resource allocation to improve life expectancy in countries with lower health indicators.

Life Expectancy WHO

1. Data Exploration: Analyze the dataset to gain insights into the features affecting life expectancy.
2. Data Cleaning and Preprocessing: Handle missing values, outliers, and other data quality issues.
3. Feature Selection: Identify the most relevant features for predicting life expectancy.
4. Model Building and Evaluation: Train a machine learning model to predict life expectancy based on other features in the dataset.
5. Insights and Limitations: Reflect on the insights gained from the model and limitations of the data and model.

• Explore the dataset: What patterns, correlations, or trends do you observe among the features?
• Identify the distribution of life expectancy and other key features.
• Check for missing values and decide how to handle them. Are there any features with a significant amount of missing data? How would you address this?
• Consider socioeconomic factors (e.g., GDP, schooling, income composition of resources) and health factors (e.g., adult mortality, infant deaths, HIV/AIDS prevalence) and discuss how they might relate to life expectancy.

• Handle missing values: Explain your approach to handling missing data. Will you drop, impute, or use other techniques?
• Handle categorical variables: The “Status” column might be a categorical variable (e.g., “Developed” or “Developing”). Convert it into a format suitable for machine learning if necessary.
• Consider normalizing or scaling numerical features: Consider whether scaling is necessary for any of the features.

• Explain why you would include or exclude certain features in your model.

• Build several machine learning model (e.g., linear regression, decision tree, or any other model of your choice) to predict life expectancy. Compare the performance and advantages of different models.
• Train and test your model. Evaluate its performance using metrics such as Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE).

• Based on your model, what factors seem to have the most influence on life expectancy?
• Discuss any limitations of your model and dataset.
• If the organization wanted to improve life expectancy based on your findings, what areas would you suggest they focus on?

• Discuss the ethical implications of using such data to make predictions and decisions. Are there potential biases in the data?
• How could the organization responsibly use your findings to make real-world decisions?

All code and analysis should be kept in a single well formatted notebook.