This project involves an Exploratory Data Analysis (EDA) on the Summer Olympics dataset, aiming to uncover valuable insights and trends within the historical data. The analysis covers a wide range of aspects related to athlete performances, event details, and other relevant factors.
Note: PDF file of the notebook is here.
The dataset used for this analysis includes historical data from Olympics events, containing information such as athlete details, event specifics, and medal outcomes. There are 2 csv files:
- Athlete_Events.csv : Olympics data from 1896 to 2016.
- NOC_regions.csv : For correct NOC of each team.
- Jupyter Notebook
- Pandas, NumPy, Matplotlib, Seaborn, Wordcloud
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Data Loading and Cleaning:
- Loading the dataset into a Pandas DataFrame.
- Cleaning the data by handling missing values and ensuring data consistency.
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Exploratory Data Analysis:
- Analyzing trends in the number of participating countries over the years.
- Investigating medal distributions and identifying countries with the most successes.
- Exploring athlete demographics and performance patterns.
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Data Visualization:
- Creating visualizations, including bar charts, heatmaps, and line plots, to represent key insights.
- Visualizing the geographical distribution of medal-winning countries.
- India's take in the olympics.
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Conclusion
- Summarized the insights drawn from the extensive analysis.
- Covered topics such as historic trends, country and gender-wise analysis, and athlete trends.
- The comprehensive analysis provides a holistic view of the Olympics dataset, revealing intriguing patterns and stories behind the numbers. 🌐🥇