# Data Handling and Preprocessing 

This session provides an introduction to **data preprocessing in Python** using the **pandas** library, tailored to medical research and clinical datasets. Participants will work with **synthetic patient data** that includes demographics, admission and discharge information, vital signs, and lab values.

## Learning Objectives

- Understand the role of data preprocessing in clinical research and machine learning.  
- Get familiar with **pandas** as the core library for handling tabular data in Python.  
- Practice working with structured medical datasets containing typical data quality issues.

## Topics Covered

### Introduction to pandas
- **DataFrames** and **Series**  
- Basic exploration and manipulation of tabular data

### Data Cleaning
- Merging patient and lab datasets
- Correcting data types  
- Detecting and handling implausible values
- Handling missing values
- Removing duplicates  

### Data Transformation
- Encoding categorical variables  
- Feature Engineering
- Normalization and Scaling

Slides used for this session can be downloaded as PDF [here](Data Handling and Preprocessing.pdf)
