Formula1
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Formula1 Grand Prix data analysis with interactive dashboards for the historic data from 1950-2017
Formula1 Grand Prix Analysis (1950 - 2017)
Formula 1 is one of most popular and highly enjoyed sport across the globe. Extreme precision, high accuracy and excellent team are the key constituents for a team to participate and win the grand prix. The moment anyone decides to act on their own, the team can either lose position or straight away get out of the competition. All the major teams like Scuderia Ferrari, McLaren, Redbull, Renault and numerous others compete for the world championship. As data science enthusiasts and huge fans of Formula1, we thought of analyzing the Formula1 dataset that we took from Kaggle which provided us with the open dataset that in turn provided us with numerous data attributes like Formula1 drivers, races, lap timings, seasons data, pitstop status and other related attributes.
Check out the Medium post for the complete analysis published in Towards Data Science here: https://towardsdatascience.com/formula-1-grand-prix-analysis-d05d73b1e79c
Tableau Analysis
Check out the entire analysis on Tableau interactive dashboards here: https://public.tableau.com/profile/chinmaynw#!/vizhome/Formula1Analysis/Sheet1
Gantt Chart
Gantt chart depicting history of Grand Prix in the period (1950 - 2017):
Discrete Line Chart
Line chart depicting Grand prix distribution across the year from January to December
Symbol Map
Symbol map depicting the distribution of race tracks across the world
Circle view
Circle chart describing the nationalities of F1 drivers with various colors
Bubble chart
Bubble chart depicting the number of rounds across the participating drivers in F1
Stacked Bar Chart
Stacked Bar chart describing the race status for grand prix for the participating drivers in F1
Contributors
References
[1] https://www.kaggle.com/cjgdev/formula-1-race-data-19502017
[2] https://www.behance.net/gallery/63599447/Formula-One-2018
Support
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License
This project is licensed under the MIT License. Feel free to create a Pull Request for adding implementations or suggesting new ideas to make the analysis more insightful