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This project analyzes Nobel Prize winners using Python. It explores trends by category, gender, nationality, and time. Key insights include demographic patterns, most awarded categories, and the gender distribution of winners. Techniques used include data cleaning, aggregation, and anlysis with pandas.

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gabrielmazor/Nobel-prize-data-analysis

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Nobel Prize Winners Data Analysis

This project focuses on exploring and analyzing the history of Nobel Prize winners using Python. The dataset includes laureates from various categories like Physics, Chemistry, Medicine, Literature, Peace, and Economics.

Key Analysis:

•	Examined demographic trends by gender, nationality, and age.
•	Analyzed time-based trends across different decades and prize categories.
•	Identified frequently awarded categories and notable patterns in the data.
•	Studied gender representation and the distribution of winners by country.

Tools and Libraries:

•	Python: for data manipulation and analysis.
•	pandas: for data filtering, grouping, and aggregation.
•	Jupyter Notebook/VS Code: for writing and executing the code.

Project Objectives:

•	Practice data analysis techniques using Python.
•	Analyze trends and patterns in the Nobel Prize dataset.
•	Extract meaningful insights through data manipulation and exploration.

About

This project analyzes Nobel Prize winners using Python. It explores trends by category, gender, nationality, and time. Key insights include demographic patterns, most awarded categories, and the gender distribution of winners. Techniques used include data cleaning, aggregation, and anlysis with pandas.

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