This repository contains resources and examples for detecting and handling null (missing) values in datasets using Python. It focuses on practical techniques to identify missing data, analyze its impact, and apply various strategies to clean and preprocess data for machine learning and data analysis tasks.
Handling missing data is a crucial step in any data science or machine learning pipeline. This project demonstrates:
- How to detect null or missing values in datasets
- Techniques to handle missing data such as removal, imputation, or transformation
- Use of dummy variables for categorical data preprocessing
- Practical examples using the Ames Housing dataset
The repository provides Jupyter Notebooks that walk through each step with clear explanations and code samples.
The project uses the Ames Housing dataset, a popular dataset for regression and data cleaning tasks. The dataset files included are:
Ames_all_numeric_dtype.csv— Dataset with all numeric featuresAmes_outliers_removed.csv— Dataset with outliers removedAmes_without_null.csv— Dataset with missing values removed
Additionally, the file Ames_Housing_Feature_Description.txt provides detailed descriptions of dataset features.
null_values.ipynb— Notebook demonstrating detection and handling of null valuesdummy_variables.ipynb— Notebook showing how to create and use dummy variables for categorical features- Several CSV files with different preprocessing stages of the Ames Housing dataset
- Feature description text file