Nobel Prize Dataset Analysis Project

This project is a comprehensive analysis of the Nobel Prize dataset, focusing on data manipulation, filling missing values, and visualization. The analysis is done using Python, with libraries like pandas and matplotlib.

Project Overview

In this project, we explored the Nobel Prize dataset to gain insights into the distribution of prizes, trends over time, and the impact of socio-political factors on prize distribution. The dataset includes categories such as motivation, organization name, birth country, birth date, and prize categories.

Key Tasks

1. Data Cleaning:

   • Identified and handled missing values across several columns.
   • Filled missing values in the motivation and prize columns based on related entries, ensuring data consistency.

2. Data Manipulation:

   • Used logical conditions to fill missing entries in the organization_name, organization_city, and organization_country fields.
   • For Nobel laureates who passed away before receiving the prize, filled in organization details based on birth country and city.

3. Data Enrichment:

   • Populated empty birth_country fields using related organization_country data.
   • Used average birth dates to fill missing values, categorized by 20-year periods.

4. Data Visualization:

   • Created various visualizations to understand trends, such as prize distribution across countries and age groups.
   • Showcased the impact of historical events (like World War II) on the Nobel Prize distribution.

Insights

• Country Trends: Countries like the USA, UK, and Germany showed significant Nobel Prize achievements, with the USA experiencing a notable rise post-1940s.
• Impact of World Events: Nobel Prizes were not awarded during World War II, which is reflected as gaps in the dataset. The aftermath of wars and economic conditions also affected prize distributions.
• Gender and Age Analysis: Male laureates in the 60-90 age range were the most awarded, while younger female laureates were more common in fields like chemistry.

Sample Visualizations

Here are a few examples of visualizations created in this project:

• Nobel Prize Distribution by Country: A bar chart showing the top countries with the highest Nobel Prize counts.
• Age Analysis: Line charts depicting the age distribution of Nobel Prize winners by category and gender.
• Impact of World Wars: Visualization of Nobel Prize counts over time, highlighting the influence of significant historical events.

Conclusion

This project provides a detailed analysis of the Nobel Prize dataset, showing the influence of education, politics, and socio-economic conditions on prize distribution. It also highlights the changing trends in laureate demographics over time.

Requirements

To run the code in this project, you need the following Python packages:

• pandas
• matplotlib

How to Run

Clone this repository and navigate to the project directory. Run the Jupyter Notebook file to view the code and visualizations.

git clone <repository-url>
cd <repository-directory>
jupyter notebook Nobel_Prize_Analysis.ipynb