Missing Data Imputation with Maximum Likelihood Estimation

📌 Exploratory Analysis – Lab Assessment 2

This project demonstrates how to handle missing data using Maximum Likelihood Estimation (MLE) on the NHANES dataset (2015–2016).

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📂 File Structure

• Maximum_Likelihood_Estimation.ipynb → Jupyter Notebook containing the full analysis and imputation process.
• NHANES.csv → Raw dataset with missing values.
• NHANES_2015_2016_tidy_imputed.csv → Cleaned and imputed dataset after applying MLE.

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🔎 Workflow

1. Load & Explore Data

   • Import the NHANES dataset.
   • Perform exploratory data analysis (EDA) to identify missing values.

2. Imputation using Maximum Likelihood Estimation (MLE)

   • Apply statistical modeling for estimating missing values.
   • Validate assumptions and distributions.

3. Save Final Dataset

   • Export the imputed data into a tidy CSV format.

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⚙️ Requirements

Install the following Python libraries before running the notebook:

pip install pyampute missingno