This repository contains the code and documentation for a data cleaning project focused on the Astronauts dataset. The goal of this project was to preprocess and clean the Astronauts dataset to ensure data quality and prepare it for further analysis or modeling tasks.
The Astronauts dataset is a collection of information about astronauts, including their personal details, educational background, military experience, space flights, and more. The dataset contains various data types, including numerical, categorical, and date/time data.
The data cleaning process involved the following steps:
Data Inspection: Initial exploration of the dataset to understand its structure, identify missing values, outliers, and potential data quality issues.
Handling Missing Values: Dealing with missing values by identifying the columns with missing values and applying appropriate strategies such as imputation or removal.
Data Type Conversion: Converting data types to ensure consistency and accuracy. This included converting columns representing dates to the datetime format, converting numeric columns to the appropriate numerical data types, and handling categorical columns.
Handling Outliers: Identifying and addressing outliers that could potentially impact statistical analysis or modeling results. Outliers were analyzed using statistical techniques and appropriate actions were taken based on the context and requirements of the analysis.
Data Standardization: Ensuring consistency and standardization of data by applying formatting or normalization techniques as needed. This step included standardizing date formats and transforming data to adhere to desired conventions.
Final Dataset: The resulting cleaned dataset, with missing values handled, outliers addressed, and appropriate data types assigned, is provided as the output of the data cleaning process.
Jupyter Notebook: The Jupyter Notebook contains the code and step-by-step data cleaning process, including data exploration, transformation, and cleaning operations.
Cleaned Dataset: The cleaned dataset in CSV format is provided, ready for further analysis or modeling.
The project relies on the following libraries and tools:
Python pandas numpy Usage The Jupyter Notebook can be executed to reproduce the data cleaning steps on the Astronauts dataset. Make sure to install the required dependencies before running the notebook.
Feel free to explore the code and adapt it to your specific needs. You can also contribute to the project by suggesting improvements, reporting issues, or submitting pull requests.