Project Summary
Nobel Prizes is an international award given by the Nobel Foundation in Stockholm, Sweden, based on the fortune of Alfred Nobel, Swedish inventor. The recipient gets a gold medal showing Alfred Nobel who established the prize. It’s awarded to scientists, scholars etc. The first Nobel Prize was awarded in 1901. A person or organisation awarded the Nobel Prize is called Noble Laureate. The word “laureate” refers to being signified by the laurel wreath. Laurel wreaths were awarded to victors as a sign of honor, in ancient Greece. This dataset includes a record for every individual or organization that has received the Nobel Prize award since 1901 to 2016.
Project Overview: Utilized Python to analyze and visualize the patterns and trends among Nobel Prize winners over time using Python. The analysis aims to provide insights into award distributions by categories, countries, and demographics, as well as identify any significant trends or anomalies in the Nobel Prize data.
- DELIVERABLES
- Developed Python scripts to preprocess and clean Nobel Prize winners' data, addressing missing values and ensuring data accuracy.
- Conducted exploratory data analysis (EDA) to reveal trends and patterns in Nobel Prize awards over time.
- Created visualizations, including line charts and bar plots, to illustrate changes in the number of awards by year and category.
- Performed descriptive statistics to summarize key metrics, such as mean and median number of awards per year and per category.
- Utilized count plots to visualize the distribution of Nobel Prizes across different categories, highlighting any trends or shifts.
- Developed correlation analysis to explore relationships between award categories and recipient nationalities.
- Demonstrated proficiency in Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, and GeoPandas for data manipulation and visualization.
- ANALYSIS IMPACT
- The analysis of Nobel Prize winners provides valuable insights into award distributions across categories and countries over time. By utilizing Python for data cleaning, statistical analysis, and visualization, this project highlights key trends, identifies anomalies, and illustrates the global impact of Nobel Prize awards.