\n","metadata":{}},{"cell_type":"markdown","source":"# I. Introduction:\n\nKaggle, the popular platform for data science competitions, has been a thriving hub for data enthusiasts, researchers, and professionals alike. In this essay, we will delve into one specific Kaggle competition, \"Chess ratings - Elo versus the Rest of the World,\" and explore various aspects of the competition, including its launch date, write-ups, and valuable insights gained from the analysis.\n","metadata":{}},{"cell_type":"markdown","source":"## **[What is Kaggle?](https://www.youtube.com/watch?v=TNzDMOg_zsw)**\n\nKaggle is an online platform for data scientists and machine learning enthusiasts. It hosts competitions where participants solve data science problems by developing predictive models. Companies provide datasets and problem statements, and participants analyze the data, engineer features, and build machine learning models. They submit their code and predictions, and Kaggle evaluates their performance. Kaggle also provides a collaborative environment for sharing datasets, discussing ideas, and learning from others. It has become a popular platform for showcasing skills, learning, and competing for prizes, making it valuable for beginners and professionals in the field.\n","metadata":{}},{"cell_type":"code","source":"from IPython.display import HTML\n\nHTML('')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-07-04T02:54:38.228489Z","iopub.execute_input":"2023-07-04T02:54:38.229050Z","iopub.status.idle":"2023-07-04T02:54:38.278627Z","shell.execute_reply.started":"2023-07-04T02:54:38.229006Z","shell.execute_reply":"2023-07-04T02:54:38.277328Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## **[History Background](https://en.wikipedia.org/wiki/Kaggle#:~:text=Kaggle%20was%20founded%20by%20Anthony,that%20they%20were%20acquiring%20Kaggle.)**\n\nKaggle, founded in 2010, is an online platform that hosts [data science](https://en.wikipedia.org/wiki/Data_science) and [machine learning](https://en.wikipedia.org/wiki/Machine_learning) competitions. It was started by Anthony Goldbloom and Ben Hamner. Initially, Kaggle aimed to connect data scientists with organizations that had complex data problems to solve.\n\nIn its early years, Kaggle gained traction by offering a platform where companies could pose challenges and offer prizes to data scientists who could develop the most effective solutions. This approach attracted a community of talented data scientists who competed to win recognition and cash prizes.\n\nAs Kaggle grew in popularity, it expanded its offerings beyond competitions. It introduced features like public datasets, where users could share and access various datasets, and kernels, which allowed users to publish and share their data analyses and code.\n\nIn 2017, [Google](https://en.wikipedia.org/wiki/Google_LLC) acquired Kaggle, which further enhanced the platform's resources and reach. This acquisition brought Kaggle under the umbrella of Google Cloud, allowing it to leverage Google's infrastructure and resources.\n\nOver the years, Kaggle has evolved into a thriving community of data scientists, researchers, and machine learning enthusiasts. It has become a go-to platform for individuals and organizations seeking data-driven solutions, offering opportunities to collaborate, learn, and showcase skills in a competitive environment.\n\nToday, Kaggle continues to host a wide range of competitions covering diverse domains such as computer vision, natural language processing, and time series analysis. It remains a hub for data science practitioners to access datasets, exchange knowledge, and push the boundaries of data-driven innovation.\n","metadata":{}},{"cell_type":"markdown","source":"### [**Why Google Bought Kaggle**](https://www.data-mania.com/blog/google-bought-kaggle/#:~:text=Why%20Google%20Bought%20Kaggle,-Google%20seeks%20to&text=Google%20wants%20to%20make%20AI,to%20become%20machine%20learning%20experts.)\n\n\nGoogle wants to make AI open to an ever-expanding network of application developers. (Think: βAI-Liteβ β Developers can use and deploy machine learning algorithms in their applications without having to become machine learning experts.)\n\n\n\n\n### [**Google buys Australia's Kaggle**](https://www.afr.com/technology/google-buys-australias-kaggle-20170309-gutzx3)\n\nAustralian Financial Review\n\nMar 9, 2017 β Kaggle, the data science platform set up by former government economist Anthony Goldbloom in his Sydney bedroom, has been bought by American technology giant Google in an estimated multi-million dollar deal.\n\nKaggle hosts online competitions for data scientists and machine learning experts to develop algorithms to solve complex problems for businesses, including by mining untapped data that firms don't know how to use effectively.\n\n","metadata":{}},{"cell_type":"markdown","source":"## [**What is Kaggle platform?**](https://datascientest.com/en/kaggle-all-about-this-platform#:~:text=Kaggle%20is%20a%20web%20platform,achieve%20all%20data%20science%20advancements.)\n\nKaggle is an online platform that provides a community and infrastructure for data science and machine learning competitions, collaborative projects, and educational resources. It was founded in 2010 and acquired by Google in 2017.\n\nKaggle hosts a wide range of data-related challenges where participants compete to develop the best predictive models or solutions for specific problems. These challenges cover various domains, such as computer vision, natural language processing, tabular data analysis, and more. Participants can submit their code and models, and Kaggle evaluates their performance based on predefined evaluation metrics.\n\nIn addition to competitions, Kaggle also offers a platform for data science collaboration. Users can create and join teams to work on projects together, sharing code, insights, and datasets. This collaborative environment fosters knowledge sharing and enables individuals to learn from others and enhance their data science skills.\n\nKaggle provides access to datasets hosted on its platform, allowing users to explore and analyze data from a wide range of domains. It also offers a cloud-based workbench called Kaggle Kernels, which allows users to write and execute code in a Jupyter Notebook environment without the need for any local setup.\n\nThe platform has a strong community aspect, with forums, discussions, and the ability to vote and comment on kernels and competition entries. Kaggle also features a leaderboard, where participants can track their progress and compare their performance with others.\n\nOverall, Kaggle serves as a hub for data scientists, machine learning practitioners, and enthusiasts, providing a collaborative and competitive environment to learn, showcase skills, and solve real-world data problems.\n","metadata":{}},{"cell_type":"markdown","source":"## **Some courses offer by kaggle:**\n\nKaggle offers a variety of courses and learning resources to help individuals enhance their data science and machine learning skills. Some of the courses available on Kaggle at that time included:\n\n1. Python: This course covers the fundamentals of Python programming language, which is widely used in data analysis and machine learning.\n\n2. Intro to Machine Learning: This course provides an introduction to the basics of machine learning, including supervised and unsupervised learning algorithms.\n\n3. Data Visualization: This course focuses on data visualization techniques using Python libraries such as Matplotlib and Seaborn to effectively communicate insights.\n\n4. Pandas: Pandas is a popular library for data manipulation and analysis in Python. This course teaches the essentials of working with Pandas for data preprocessing and analysis.\n\n5. SQL: SQL is a language used for managing and manipulating relational databases. The Kaggle course on SQL covers the basics of SQL syntax and querying databases.\n\n6. Deep Learning: This course delves into the field of deep learning, covering neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).\n\nIt's important to note that Kaggle continually updates and expands its course offerings, so there may be additional courses available beyond what I have mentioned. It's recommended to visit the Kaggle website to explore the most up-to-date list of courses and learning resources they offer.","metadata":{}},{"cell_type":"markdown","source":"### **How long do Kaggle courses take?**\n\nbetween 1 and 7 hours\nKaggle, a data scientist company and subsidiary of Google, offers 12 free micro-courses designed to improve data science skills. Each course is between 1 and 7 hours and is comprised of a few lessons each.\n","metadata":{}},{"cell_type":"markdown","source":"## **Exploring the Thriving Community and Achievements of Kaggle: A Data Science Powerhouse**\n\nKaggle, a thriving data science platform, boasts an impressive user base of 14,072,909 accounts, highlighting its popularity and wide reach. It all began in April 2010 when the first Kaggle competition was launched, marking the inception of a dynamic and competitive environment.\n\nSince then, Kaggle has hosted a remarkable total of 5,596 competitions, encouraging participants to develop innovative solutions and predictive models. This extensive competition history has witnessed an incredible 12,614,127 submissions, averaging approximately 2,254 submissions per competition. This reflects the platform's vibrant and active community.\n\nAmong these competitions, there have been different types, including 239 featured competitions, 153 research competitions, 116 playground competitions, and 5,053 community competitions. This diverse range of contest categories provides opportunities for individuals with varied interests and expertise to engage and contribute.\n\nAs a testament to its impact, Kaggle has awarded cash prizes in 365 competitions, totaling an impressive $18,261,227 in prize money. This not only fuels competition but also recognizes and rewards the exceptional talent and ingenuity within the Kaggle community.\n\nKaggle's influence extends beyond competitions, as it offers a vast repository of public datasets contributed by 95,665 authors. With 239,089 datasets available, this equates to an average of 2.5 datasets per author. Similarly, the platform boasts a rich collection of public notebooks, with 931,226 contributed by 285,252 authors, averaging 3.3 notebooks per author. This vast wealth of shared knowledge empowers users to learn, collaborate, and build upon existing work.\n\nThe community-driven aspect of Kaggle is evident in the 334,667 topics posted and the impressive 1,963,299 comments made. On average, there are 5.9 comments per topic, fostering discussions, knowledge exchange, and a supportive environment for individuals to grow and thrive.\n\nThese statistics showcase the vibrant and bustling nature of Kaggle, where millions of data enthusiasts, researchers, and practitioners come together to tackle real-world problems, share insights, and drive innovation in the field of data science.\n","metadata":{}},{"cell_type":"markdown","source":"## **The history of Kaggle Grandmasters**\n","metadata":{}},{"cell_type":"markdown","source":"### **How many grandmasters are on Kaggle?**\n\nThere are 441 kaggle Grandmasters. Out of these there are 7 quadruple Grandmasters, 12 triple Grandmasters, and 35 double Grandmasters. There are 2,400 kaggle Masters.","metadata":{}},{"cell_type":"markdown","source":"### **List of Top 5 Kaggle Grandmasters**\n\n[Kaggle Rankings](https://www.kaggle.com/rankings)\n\n### **Datasets**\n1. The Devastator\n2. Larxel\n3. Sourav Banerjee\n4. Marilia Prata\n5. SJ\n\n### **Notebooks**\n1. Chris Deotte\n2. Marilia Prata\n3. Alexis Cook\n4. Jeremy Howard\n5. AmbrosM\n\n### **Competition**\n1. bestfitting\n2. Psi\n3. Dieter\n4. Chris Deotte\n5. Qishen Ha\n\n### **Discussion**\n1. Chris Deotte\n2. Marilia Prata\n3. Ravi Ramakrishnan\n4. The Devastator\n5. hengck23","metadata":{}},{"cell_type":"markdown","source":"### **Who is the Kaggle triple grandmaster?**\n\n**[Shivam Bansal](https://www.kaggle.com/shivamb)** is a Kaggle Triple Grandmaster and a 5 times winner of Kaggle's Analytics and Data Science for Good Competitions. Competiton Master - Highest Rank #1 (datasets), #2 (Notebooks) - Winner: 5 Kaggle Competitions. He is also the winner of several other offline and online competitions. He holds a master's degree from the National University of Singapore and was selected as the Valedictorian.\n\n","metadata":{}},{"cell_type":"markdown","source":"### **Who is the youngest Kaggle grandmaster?**\n\n[**Peiyuan Liao**](https://www.kaggle.com/alexanderliao) is the youngest Chinese Kaggle Competitions Grandmaster and ranks 28th with 7 gold medals to his name.Jan 19, 2021\n\n\n\n**[Ref:Kaggle Grandmaster Series | Exclusive Interview with Peiyuan Liao](https://www.analyticsvidhya.com/blog/2021/01/kaggle-grandmaster-series-exclusive-interview-with-kaggle-competitions-grandmaster-peiyuan-liao-rank-28/#:~:text=Peiyuan%20is%20the%20youngest%20Chinese,gold%20medals%20to%20his%20name.)**","metadata":{}},{"cell_type":"markdown","source":"## [**What is Kaggle competition?**](https://www.kaggle.com/docs/competitions?utm_medium=youtube&utm_source=channel&utm_campaign=yt-comps)\n\nA Kaggle competition is a data science or machine learning challenge on the Kaggle platform. Participants solve a specific problem using a provided dataset. They perform tasks like data exploration, cleaning, feature engineering, and building models. The goal is to achieve high performance on an evaluation metric.\n\nCompetitions have rules, deadlines, and training/test datasets. Participants submit predictions or code, and Kaggle ranks and scores submissions. Competitions vary in duration, complexity, and prizes, covering topics like image recognition, natural language processing, and more.\nParticipating offers a valuable learning experience, exposing participants to real-world challenges, collaboration, and learning from top performers' techniques.\n","metadata":{}},{"cell_type":"markdown","source":"## [**How to Enter a Kaggle Competition**](https://www.youtube.com/watch?v=GJBOMWpLpTQ&t=70s)","metadata":{}},{"cell_type":"code","source":"from IPython.display import HTML\n\nHTML('')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-07-04T02:55:25.062365Z","iopub.execute_input":"2023-07-04T02:55:25.063046Z","iopub.status.idle":"2023-07-04T02:55:25.073716Z","shell.execute_reply.started":"2023-07-04T02:55:25.062986Z","shell.execute_reply":"2023-07-04T02:55:25.072354Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **Kaggle competitions hold significant importance in the data science community due to several reasons:**\n\n1.** Skill Development:** Kaggle competitions provide a platform for data scientists to sharpen their skills and gain hands-on experience with real-world datasets and challenges. By participating in these competitions, individuals can enhance their data analysis, modeling, and problem-solving abilities, thereby improving their proficiency in the field of data science.\n\n2. **Learning Opportunities:** Kaggle competitions offer a wealth of learning opportunities. Participants can explore diverse problem domains, novel datasets, and innovative techniques employed by fellow data scientists. This exposure enables continuous learning and keeps participants updated with the latest advancements and best practices in the field.\n\n3. **Collaboration and Networking:** Kaggle competitions foster a collaborative environment where participants can engage with a global community of data scientists, researchers, and industry professionals. This collaborative atmosphere encourages knowledge sharing, idea exchange, and teamwork. Through forums, discussions, and team formation, participants can build professional connections and expand their network within the data science community.\n\n4. **Practical Application:** Kaggle competitions often involve solving real-world problems faced by organizations and industries. By participating in these competitions, data scientists can apply their skills and knowledge to address practical challenges, gaining experience in tackling complex problems with real impact. This practical application bridges the gap between theoretical knowledge and its implementation in real-world scenarios.\n\n5. **Benchmarking and Evaluation:** Kaggle competitions provide a standardized evaluation framework, typically with leaderboards and evaluation metrics. This allows participants to compare their models' performance with others and benchmark their approaches. The competitive aspect motivates participants to strive for better results and encourages innovation and the development of novel techniques.\n\n6. **Visibility and Recognition:** Kaggle competitions offer participants the opportunity to showcase their skills and expertise to a global audience. Top performers and winners in Kaggle competitions often gain recognition for their achievements, attracting attention from potential employers, industry experts, and the data science community. This visibility can lead to career opportunities, consulting projects, and collaborations.\n\n7. **Advancement of Data Science:** Kaggle competitions contribute to the advancement of data science as a field. Through these competitions, new algorithms, methodologies, and techniques are developed, pushing the boundaries of what is possible in data analysis and modeling. The insights and solutions generated during Kaggle competitions have the potential to drive progress and create impact in various industries and domains.\n\nIn summary, Kaggle competitions play a crucial role in the data science community by providing avenues for skill development, learning, collaboration, practical application, benchmarking, visibility, and the advancement of the field. These competitions inspire data scientists to push their limits, foster innovation, and contribute to solving real-world problems, ultimately driving the growth and development of data science as a discipline.","metadata":{}},{"cell_type":"markdown","source":"## **\"Unlocking Success: Beginner's Guide to Winning Kaggle Competitions\"**\n\nA beginner has the potential to win a Kaggle competition. While Kaggle competitions can be highly competitive, there have been instances where beginners or participants with limited experience have achieved impressive results. Winning a Kaggle competition depends on various factors, including the complexity of the competition task, the quality of the data, the participants' problem-solving skills, domain knowledge, and the techniques and models employed.\n\nEven as a beginner, you can increase your chances of success by approaching competitions with a strategic mindset and a strong learning orientation. Here are a few tips:\n\n1. **Gain foundational knowledge:** Familiarize yourself with the basics of data science, machine learning algorithms, and relevant programming languages such as Python.\n\n2. **Learn from others:** Study and analyze the approaches and solutions of top-performing competitors in previous competitions. Kaggle provides public notebooks from previous competitions, which can serve as valuable learning resources.\n\n3. **Collaborate and seek feedback:** Join Kaggle forums and communities to engage with other participants, seek feedback on your work, and learn from their experiences.\n\n4. **Focus on data preprocessing and feature engineering:** A significant portion of a successful competition solution lies in effectively preprocessing the data and engineering informative features that capture the underlying patterns.\n\n5. **Experiment and iterate:** Continuously improve your models by experimenting with different algorithms, techniques, and hyperparameters. Use cross-validation techniques to evaluate and fine-tune your models.\n\n6. **Leverage ensemble methods:** Ensemble techniques, such as combining multiple models or using model stacking, can often boost performance and increase your chances of success.\n\n7. **Stay updated and participate consistently:** Kaggle hosts a wide range of competitions, so participating regularly will help you gain experience, learn new techniques, and adapt to different problem domains.\n\nRemember, winning a Kaggle competition as a beginner might be challenging, but it's not impossible. With dedication, perseverance, a willingness to learn, and an analytical approach, you can make progress and potentially achieve success in Kaggle competitions.","metadata":{}},{"cell_type":"markdown","source":"## **What is a kaggle leaderbord?**\n\nOn Kaggle, a leaderboard is a feature that tracks and displays the performance of participants in a competition. It serves as a ranking system that shows how well participants' models or solutions are performing compared to others.\n\nWhen participants submit their predictions or solutions to a competition on Kaggle, their results are evaluated based on predefined evaluation metrics specific to the competition's task. These metrics can include accuracy, precision, recall, mean squared error, or other appropriate measures depending on the problem at hand.\n\nThe leaderboard displays the scores or rankings of participants based on their performance on the evaluation metric. Typically, the leaderboard is sorted in descending order, with the highest-scoring participant or team at the top. As the competition progresses and participants make improvements to their models or solutions, their scores may change, leading to shifts in their positions on the leaderboard.\n\nThe leaderboard is visible to all participants and the Kaggle community, fostering a competitive environment where participants can track their progress, compare their performance with others, and strive to improve their rankings. It serves as a benchmark for measuring the quality and effectiveness of different approaches and provides a clear indication of the current leaders in the competition.\n\nThe leaderboard is an integral part of Kaggle competitions, as it not only motivates participants to continually refine their models but also allows them to learn from the strategies and techniques employed by top performers. It adds excitement and a sense of achievement to the competition experience, driving innovation and pushing the boundaries of data science.","metadata":{}},{"cell_type":"markdown","source":"## **What is the highest prize on Kaggle?**\n\nThe 1 million dollar going towards this year's competition will be the most ever given out as a prize on the site. The first place winner will receive 500,000 dollar with second and third getting 200,000 dollar and 100,000 dollar respectively. The next seven contenders by rank will each receive 25,000 dollar.Jan 13, 2017\n\n[Kaggle hosting $1M competition to improve lung cancer detection with ...](https://techcrunch.com/2017/01/12/kaggle-hosting-1m-competition-to-improve-lung-cancer-detection-with-machine-learning/#:~:text=The%20%241%20million%20going%20towards,rank%20will%20each%20receive%20%2425%2C000.)","metadata":{}},{"cell_type":"markdown","source":"\n### **Kaggle awards medals in competitions, datasets, notebooks, and discussions**\n\nKaggle awards medals in competitions, datasets, notebooks, and discussions to recognize and highlight exceptional contributions from the community. The number of medals awarded varies based on different criteria:\n\n* Competition Medals: Medals are given to top performers in competitions. The number of medals awarded depends on the competition size. However, InClass, playground, and getting started competitions do not grant medals.\n\n* Dataset Medals: Medals are awarded to popular public datasets based on the number of upvotes received. Bronze medals are given for 5 votes, silver medals for 20 votes, and gold medals for 50 votes. This recognition encourages the sharing of high-quality and valuable datasets.\n\n* Notebook Medals: Medals are granted to popular notebooks based on the number of upvotes received. Similar to dataset medals, bronze, silver, and gold medals are awarded for 5, 20, and 50 votes respectively. This highlights notebooks that have been well-received by the community, indicating their quality and usefulness.\n\n* Discussion Medals: Medals are given to popular topics and comments based on the net votes received (upvotes minus downvotes). Bronze, silver, and gold medals are awarded for 1, 5, and 10 votes respectively. This encourages engaging discussions and recognizes valuable contributions to the community.\n\nThese medals serve as a way to acknowledge and celebrate the efforts and achievements of Kaggle community members in various aspects of the platform. They not only provide recognition but also help users discover high-quality content and contributions that have gained significant community support.\n","metadata":{}},{"cell_type":"markdown","source":"## **[Getting Started on Kaggle](https://www.youtube.com/playlist?list=PLqFaTIg4myu8gbDh6oBl7XRYNBlthpDEW)**\n\n1. Visit www.kaggle.com and open account.\n2. Go to the \"Chess ratings - \"Elo versus the Rest of the World\"[webpage](https://www.kaggle.com/competitions/chess) .\n3. Review competition details - objectives, deadline,data,evaluation,submission,rules, etc.\n","metadata":{}},{"cell_type":"code","source":"from IPython.display import HTML\n\nHTML('')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-07-04T02:55:46.880952Z","iopub.execute_input":"2023-07-04T02:55:46.882058Z","iopub.status.idle":"2023-07-04T02:55:46.894241Z","shell.execute_reply.started":"2023-07-04T02:55:46.881951Z","shell.execute_reply":"2023-07-04T02:55:46.892353Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* **Overview of the specific Kaggle competition** - [\"Chess ratings - Elo versus the Rest of the World\"](https://www.kaggle.com/competitions/chess)","metadata":{}},{"cell_type":"markdown","source":"Overview\n\nThe Elo rating system was invented half a century ago by Hungarian-born physicist and chess master Arpad Elo. It is the most famous technique for rating chess players and is used throughout the chess world. It has been applied to many other contests as well, including other board games, sports, and video games. However, it has never really been demonstrated that the Elo approach to calculating chess ratings is superior. Elo's formula was derived theoretically, in an era without large amounts of historical data or significant computing power. With the benefit of powerful computers and large game databases, we can easily investigate approaches that might do better than Elo at predicting chess results.\n\nThere are several alternatives to the Elo approach. Professor Mark Glickman developed the Glicko and Glicko-2 systems, which extend the Elo system by introducing additional parameters to represent the reliability and volatility of player ratings. Ken Thompson uses a linearly weighted average of a player's last 100 results to calculate a weighted performance rating. Jeff Sonas (who put together this competition) developed Chessmetrics ratings to maximize predictive power. More details are available on the hints page.\n\nWe want to see if somebody out there can do even better. Competitors train their rating systems using a training dataset of over 65,000 recent results for 8,631 top players. Participants then use their method to predict the outcome of a further 7,809 games.\n\nAlong with the opportunity to help shape the future of chess ratings, the top ten entries win the following prizes (assuming they share their methodology).\n1. Fritz DVD autographed by world champions Viswanathan Anand, Garry Kasparov, Anatoly Karpov and Viktor Korchnoi (see image)\n2. ChessBase 10 Starter Package\n3. Big Database 2010\n4. Kasparov: How to play the Queen's Gambit\n5. Anand - My Career (Vol 1 and 2)\n6. Kramnik - My Path to the Top\n7-10. $50 Amazon.com voucher\nPrizes 1-6 have been donated by Chessbase.\n\nUpdate: The team Elo Benchmark (see the leaderboard), uses the Elo rating system.\n\nUpdate 2: Thanks to Chessbase, we've got some new prizes up for grabs.\n\nUpdate 18 October: Some of the rules of this competition have not been explcitly stated, notably that:\n1. participants are restricted to two entries per day;\n2. the public leaderboard is calculated based on 20 per cent of the test dataset. The final standings are calculated based on the remaining 80 per cent of the test dataset; and\n3. the final standings are calculated based on participants' best (rather than last) entry. The best entry is the entry that scores highest on the 80 per cent of the test dataset that is used to calculate the final standings (which may different from the best entry on the public leaderboard).","metadata":{}},{"cell_type":"markdown","source":"* **Purpose of the essay:** Exploring various aspects of the competition and its impact","metadata":{}},{"cell_type":"markdown","source":"# II. Competition Launch Date:\n\nThe \"Chess ratings - Elo versus the Rest of the World\" competition was launched on August 3, 2010. This competitive event aimed to explore the Elo rating system's effectiveness and compare it with alternative approaches in predicting chess player ratings. The competition attracted participants from diverse backgrounds, all eager to showcase their data science skills and contribute to the realm of chess analytics.","metadata":{}},{"cell_type":"markdown","source":"# III. Number of Write-ups per Competition:\n\nAnalyzing the competition write-ups provides us with valuable insights into the participants' contributions and the overall engagement with the competition. The write-ups not only serve as a platform for participants to share their methodologies and findings but also foster a collaborative environment for knowledge exchange. Upon analyzing the dataset, we find that the competition received multiple write-ups from participants.\n\n# IV. Earliest and Latest Write-up Dates:\n\nThe earliest write-up in the dataset dates back to November 18, 2010, demonstrating the longevity and enduring interest in the competition. The latest write-up, on the other hand, was documented on November 23, 2010, showcasing the continued engagement of participants even after the competition's initial conclusion. This span of write-up dates highlights the enduring impact and ongoing discussions surrounding the competition.\n\n# V. Most Common Write-up Titles:\n\nAmong the plethora of write-ups submitted by participants, several titles stood out as the most common. These titles offer a glimpse into the diverse perspectives and approaches taken by participants to tackle the challenges posed by the competition. Some of the most frequently encountered write-up titles include \"Released: my Source Code and Analysis,\" \"6th place (UriB) by Uri Blass,\" \"7th place - littlefish,\" \"3rd place: Chessmetrics - Variant,\" and \"2nd place: TrueSkill Through Time.\" These titles highlight the exceptional work and innovative solutions produced by participants.\n\n# VI. Average Word Count per Write-up:\n\nAnalyzing the write-ups further, we explore the average word count per write-up, providing insights into the depth and complexity of the analyses performed. Based on our analysis, the average word count per write-up in the \"Chess ratings - Elo versus the Rest of the World\" competition is 531.9488. This statistic indicates the participants' dedication to documenting their methodologies, explaining their insights, and showcasing their findings in a comprehensive manner.\n\n# VII. Word Cloud Visualization of Write-up Titles:\n\nTo gain a visual representation of the most common write-up titles, we employed a word cloud visualization technique. The word cloud provides an intuitive and visually appealing representation of the frequency of different write-up titles. The generated word cloud showcases the prominence of certain titles, visually emphasizing their significance in the competition.\n\n# VIII. Conclusion:\n\nIn conclusion, the \"Chess ratings - Elo versus the Rest of the World\" Kaggle competition proved to be a fascinating endeavor, attracting participants from diverse backgrounds and fostering a collaborative environment for data science enthusiasts. Through the analysis of launch dates, write-ups, and valuable insights gained, we gain a deeper understanding of the competition's impact and the remarkable contributions made by its participants. The competition's enduring legacy is evidenced by the diverse range of write-ups, spanning a significant timeframe, and the valuable knowledge shared among participants.\n\nKaggle competitions, such as the one explored in this essay, continue to push the boundaries of data science, inspiring innovation, and driving advancements in various domains. They serve as a testament to the power of collaboration, critical thinking, and data-driven problem-solving. The \"Chess ratings - Elo versus the Rest of the World\" competition stands as a shining example of the transformative potential of Kaggle competitions and the remarkable insights that can be gained through data analysis.\n\nBy participating in such competitions, individuals not only have the opportunity to showcase their skills but also contribute to the collective body of knowledge, advancing the field of data science and inspiring future generations of data enthusiasts. Kaggle competitions truly represent a remarkable platform for exploration, collaboration, and growth in the exciting world of data science.","metadata":{}},{"cell_type":"markdown","source":"# Reference: \n\nFollow Kaggle online:\n\n* Visit the WEBSITE: http://www.kaggle.com/?utm_medium=you...\n* Like Kaggle on FACEBOOK: http://www.facebook.com/kaggle?utm_me...\n* Follow Kaggle on TWITTER: http://twitter.com/kaggle?utm_medium=...\n* Check out our BLOG: http://blog.kaggle.com/?utm_medium=yo...\n* Connect with us on LINKEDIN: http://www.linkedin.com/company/kaggl...\n\nAdvance your data science skills:\n* Take our free online courses: http://www.kaggle.com/learn/overview?...\n* Get started with Kaggle Kernels: http://www.kaggle.com/docs/kernels?ut...\n* Download clean datasets from Kaggle: http://www.kaggle.com/docs/datasets?u...\n* Sign up for a Kaggle Competition: http://www.kaggle.com/docs/competitio...\n* Explore the Kaggle Public API: http://www.kaggle.com/docs/api?utm_me...\n\n* [kaggle-Wikipedia](https://en.wikipedia.org/wiki/Kaggle#:~:text=Kaggle%20was%20founded%20by%20Anthony,that%20they%20were%20acquiring%20Kaggle.)\n* [\"Chess ratings - Elo versus the Rest of the World\"](https://www.kaggle.com/competitions/chess)\n* Jeff Moser has a C# implementation of Elo and TrueSkill on [Github](http://github.com/moserware/Skills). has posted a Java implementation of Glicko on [Github](http://gist.github.com/539141).\n\n* [Kaggle in Numbers by: CARL MCBRIDE ELLIS](https://www.kaggle.com/code/carlmcbrideellis/kaggle-in-numbers)\n* [Google buys Australia's Kaggle](https://www.afr.com/technology/google-buys-australias-kaggle-20170309-gutzx3)\n\n* [Kaggle Rankings](https://www.kaggle.com/rankings)\n* [How to get medals on Kaggle?](https://www.kaggle.com/general/237659)\n","metadata":{}},{"cell_type":"markdown","source":"# **Import the required libraries**","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nfrom wordcloud import WordCloud\nimport matplotlib.pyplot as plt\nimport plotly.express as px","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:56:08.355802Z","iopub.execute_input":"2023-07-04T02:56:08.358219Z","iopub.status.idle":"2023-07-04T02:56:09.302784Z","shell.execute_reply.started":"2023-07-04T02:56:08.358129Z","shell.execute_reply":"2023-07-04T02:56:09.300841Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Load the dataset**","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-07-04T02:56:15.820782Z","iopub.execute_input":"2023-07-04T02:56:15.821382Z","iopub.status.idle":"2023-07-04T02:56:15.834982Z","shell.execute_reply.started":"2023-07-04T02:56:15.821338Z","shell.execute_reply":"2023-07-04T02:56:15.833729Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Read the dataset into a pandas DataFrame.\nessay_df = pd.read_csv(\"/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv\")\nsubmission_df = pd.read_csv(\"/kaggle/input/2023-kaggle-ai-report/sample_submission.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:56:25.522365Z","iopub.execute_input":"2023-07-04T02:56:25.523001Z","iopub.status.idle":"2023-07-04T02:56:25.995344Z","shell.execute_reply.started":"2023-07-04T02:56:25.522951Z","shell.execute_reply":"2023-07-04T02:56:25.993341Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Examine the dataset structure and variables\nessay_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:56:30.809914Z","iopub.execute_input":"2023-07-04T02:56:30.810654Z","iopub.status.idle":"2023-07-04T02:56:30.863700Z","shell.execute_reply.started":"2023-07-04T02:56:30.810600Z","shell.execute_reply":"2023-07-04T02:56:30.862032Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Drop rows with missing writeups\nessay_df = essay_df.dropna(subset=['Writeup'])","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:56:35.562605Z","iopub.execute_input":"2023-07-04T02:56:35.563140Z","iopub.status.idle":"2023-07-04T02:56:35.583667Z","shell.execute_reply.started":"2023-07-04T02:56:35.563100Z","shell.execute_reply":"2023-07-04T02:56:35.581925Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(essay_df.info())","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:56:41.207705Z","iopub.execute_input":"2023-07-04T02:56:41.208381Z","iopub.status.idle":"2023-07-04T02:56:41.247716Z","shell.execute_reply.started":"2023-07-04T02:56:41.208336Z","shell.execute_reply":"2023-07-04T02:56:41.245241Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Perform descriptive statistics on the dataset\nessay_df.describe()","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:56:46.411869Z","iopub.execute_input":"2023-07-04T02:56:46.412398Z","iopub.status.idle":"2023-07-04T02:56:46.483315Z","shell.execute_reply.started":"2023-07-04T02:56:46.412355Z","shell.execute_reply":"2023-07-04T02:56:46.481424Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Competition Launch Date Analysis:**\n\n* Total number of competitions\n* Earliest competition launch date\n* Latest competition launch date","metadata":{}},{"cell_type":"code","source":"# Competition Launch Date Analysis\ntotal_competitions = len(essay_df['Competition URL'].unique())\nearliest_launch_date = essay_df['Competition Launch Date'].min()\nlatest_launch_date = essay_df['Competition Launch Date'].max()\n\n# Print the results\nprint(\"Competition Launch Date Analysis:\")\nprint(\"Total number of competitions:\", total_competitions)\nprint(\"Earliest launch date:\", earliest_launch_date)\nprint(\"Latest launch date:\", latest_launch_date)","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:56:55.216971Z","iopub.execute_input":"2023-07-04T02:56:55.217635Z","iopub.status.idle":"2023-07-04T02:56:55.233436Z","shell.execute_reply.started":"2023-07-04T02:56:55.217583Z","shell.execute_reply":"2023-07-04T02:56:55.231974Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Competition Analysis:**\n\n* Most common competition titles.\n* Competition with the most write-ups.\n* Number of write-ups per competition.","metadata":{}},{"cell_type":"code","source":"# Competition Analysis\nmost_common_titles = essay_df['Title of Competition'].value_counts().head(5)\ncompetition_with_most_writeups = essay_df['Title of Competition'].mode()[0]\nwriteups_per_competition = essay_df['Title of Competition'].value_counts()\n\n# Number of write-ups per competition\nfig = px.bar(writeups_per_competition,\n x=writeups_per_competition.index,\n y=writeups_per_competition.values,\n labels={'x': 'Competition', 'y': 'Number of Write-ups'},\n title='Number of Write-ups per Competition')\n\nfig.update_layout(xaxis_tickangle=-45, \n xaxis_title_font=dict(size=14),\n yaxis_title_font=dict(size=14),\n plot_bgcolor='rgba(0,0,0,0)', # Transparent background\n paper_bgcolor='rgba(0,0,0,0)', # Transparent background\n font_family='Arial',\n font_color='#333333')\n\nfig.show()\n\n# Print the results\nprint(\"Competition Analysis:\")\nprint(\"Most common competition titles:\")\nprint(most_common_titles)\nprint(\"Competition with the most write-ups:\", competition_with_most_writeups)\nprint(\"Number of write-ups per competition:\")\nprint(writeups_per_competition)\n","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:57:00.663223Z","iopub.execute_input":"2023-07-04T02:57:00.663799Z","iopub.status.idle":"2023-07-04T02:57:03.524686Z","shell.execute_reply.started":"2023-07-04T02:57:00.663756Z","shell.execute_reply":"2023-07-04T02:57:03.522621Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Write-up Analysis:**\n\n* Earliest write-up date\n* Latest write-up date\n* Most common write-up titles\n* Average word count per write-up\n* Word cloud visualization of write-up titles","metadata":{}},{"cell_type":"code","source":"# Write-up Analysis\nearliest_writeup_date = essay_df['Date of Writeup'].min()\nlatest_writeup_date = essay_df['Date of Writeup'].max()\nmost_common_writeup_titles = essay_df['Title of Writeup'].value_counts().head(5)\naverage_word_count = essay_df['Writeup'].apply(lambda x: len(str(x).split()) if pd.notnull(x) else 0).mean()\n\n# Word Cloud Visualization\nwordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(essay_df['Title of Writeup'].value_counts())\n\n# Display Word Cloud\nplt.figure(figsize=(10, 6))\nplt.imshow(wordcloud, interpolation='bilinear')\nplt.axis('off')\nplt.show()\n\n# Print the results\nprint(\"Write-up Analysis:\")\nprint(\"Earliest write-up date:\", earliest_writeup_date)\nprint(\"Latest write-up date:\", latest_writeup_date)\nprint(\"Most common write-up titles:\")\nprint(most_common_writeup_titles)\nprint(\"Average word count per write-up:\", average_word_count)","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:57:15.937065Z","iopub.execute_input":"2023-07-04T02:57:15.937601Z","iopub.status.idle":"2023-07-04T02:57:17.033635Z","shell.execute_reply.started":"2023-07-04T02:57:15.937565Z","shell.execute_reply":"2023-07-04T02:57:17.032130Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Calculate average word count per write-up\nessay_df['Word Count'] = essay_df['Writeup'].apply(lambda x: len(str(x).split()))\naverage_word_count = essay_df['Word Count'].mean()\n\n# Print the average word count\nprint(\"The average word count per write-up in the 'Chess ratings - Elo versus the Rest of the World' competition is:\", average_word_count)\n\nimport plotly.graph_objects as go\nfrom wordcloud import WordCloud\n\n# Generate word frequencies\nword_frequencies = essay_df['Writeup'].str.split(expand=True).stack().value_counts()\n\n# Create a word cloud\nwordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(word_frequencies)\n\n# Create a figure\nfig = go.Figure(data=go.Image(z=wordcloud.to_array()))\n\n# Customize the layout\nfig.update_layout(\n title=\"Word Cloud of Write-ups\",\n xaxis=dict(visible=False),\n yaxis=dict(visible=False),\n)\n\n# Show the plot\nfig.show()\n\n","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:57:25.435071Z","iopub.execute_input":"2023-07-04T02:57:25.435561Z","iopub.status.idle":"2023-07-04T02:57:32.084778Z","shell.execute_reply.started":"2023-07-04T02:57:25.435527Z","shell.execute_reply":"2023-07-04T02:57:32.081901Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Convert the relevant columns to the appropriate data types.\nessay_df['Competition Launch Date'] = pd.to_datetime(essay_df['Competition Launch Date'])\nessay_df['Date of Writeup'] = pd.to_datetime(essay_df['Date of Writeup'])\n","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:57:43.276634Z","iopub.execute_input":"2023-07-04T02:57:43.277170Z","iopub.status.idle":"2023-07-04T02:57:43.730467Z","shell.execute_reply.started":"2023-07-04T02:57:43.277128Z","shell.execute_reply":"2023-07-04T02:57:43.729324Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Calculate the total number of competitions launched\ntotal_competitions = essay_df.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:57:48.615578Z","iopub.execute_input":"2023-07-04T02:57:48.616201Z","iopub.status.idle":"2023-07-04T02:57:48.624872Z","shell.execute_reply.started":"2023-07-04T02:57:48.616155Z","shell.execute_reply":"2023-07-04T02:57:48.622991Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Create a bar plot to visualize the number of competitions launched per year.\n\ncompetition_counts = essay_df['Competition Launch Date'].dt.year.value_counts().sort_index()\n\nfig = px.bar(x=competition_counts.index, y=competition_counts.values,\n labels={\"x\": \"Year\", \"y\": \"Number of Competitions Launched\"},\n title=\"Number of Competitions Launched Per Year\",\n width=800, height=400)\nfig.update_layout(showlegend=False)\nfig.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:57:54.954163Z","iopub.execute_input":"2023-07-04T02:57:54.954750Z","iopub.status.idle":"2023-07-04T02:57:55.053735Z","shell.execute_reply.started":"2023-07-04T02:57:54.954702Z","shell.execute_reply":"2023-07-04T02:57:55.051430Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Calculate the average number of writeups per competition\navg_writeups_per_competition = essay_df.shape[0] / total_competitions\n","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:58:04.543052Z","iopub.execute_input":"2023-07-04T02:58:04.543599Z","iopub.status.idle":"2023-07-04T02:58:04.552509Z","shell.execute_reply.started":"2023-07-04T02:58:04.543562Z","shell.execute_reply":"2023-07-04T02:58:04.549384Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Display the total number of competitions and the average number of writeups per competition.\nprint(\"Total Competitions Launched:\", total_competitions)\nprint(\"Average Writeups per Competition:\", avg_writeups_per_competition)\n","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:58:10.536930Z","iopub.execute_input":"2023-07-04T02:58:10.537597Z","iopub.status.idle":"2023-07-04T02:58:10.546182Z","shell.execute_reply.started":"2023-07-04T02:58:10.537556Z","shell.execute_reply":"2023-07-04T02:58:10.544275Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In this analysis, we read the dataset, convert the necessary columns to datetime format, calculate the total number of competitions launched, create a bar plot to visualize the number of competitions launched per year, and calculate the average number of writeups per competition. Finally, we display the total number of competitions and the average number of writeups per competition.","metadata":{}},{"cell_type":"code","source":"import json\nimport pandas as pd\n\ndata = []\nwith open('/kaggle/input/2023-kaggle-ai-report/arxiv_metadata_20230510.json', 'r') as f:\n for line in f:\n data_item = json.loads(line)\n data.append(data_item)\n\ndf = pd.DataFrame([item['title'] for item in data], columns=['Title'])\nprint(df)\n","metadata":{"execution":{"iopub.status.busy":"2023-07-04T02:58:16.240286Z","iopub.execute_input":"2023-07-04T02:58:16.240790Z","iopub.status.idle":"2023-07-04T03:00:52.983842Z","shell.execute_reply.started":"2023-07-04T02:58:16.240752Z","shell.execute_reply":"2023-07-04T03:00:52.981841Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Data Distribution:**\n\n* Calculates the total number of records in the dataset.\n* Determines the number of unique titles.\n","metadata":{}},{"cell_type":"code","source":"# Data Distribution\ntotal_records = len(df)\nunique_titles = len(df['Title'].unique())\n\n# Print the results\nprint(\"Data Distribution:\")\nprint(\"Total records:\", total_records)\nprint(\"Unique titles:\", unique_titles)\n","metadata":{"execution":{"iopub.status.busy":"2023-07-04T03:01:00.070794Z","iopub.execute_input":"2023-07-04T03:01:00.072693Z","iopub.status.idle":"2023-07-04T03:01:03.892676Z","shell.execute_reply.started":"2023-07-04T03:01:00.072622Z","shell.execute_reply":"2023-07-04T03:01:03.890850Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Word Frequency:**\n\n* Counts the frequency of each word in the titles.\n* Selects the most common words based on the frequency.\n* Prints the most common words in the titles.\n","metadata":{}},{"cell_type":"code","source":"# Word Frequency\nword_freq = df['Title'].str.split(expand=True).stack().value_counts()\nmost_common_words = word_freq[:10]\n\n# Word Cloud Visualization\nwordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(word_freq)\n\n# Display Word Cloud\nplt.figure(figsize=(10, 6))\nplt.imshow(wordcloud, interpolation='bilinear')\nplt.axis('off')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-04T03:01:10.408605Z","iopub.execute_input":"2023-07-04T03:01:10.409222Z","iopub.status.idle":"2023-07-04T03:02:46.620266Z","shell.execute_reply.started":"2023-07-04T03:01:10.409170Z","shell.execute_reply":"2023-07-04T03:02:46.616967Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Text Length:**\n\n* Computes the length of each title.\n* Creates a histogram to visualize the distribution of title lengths.\n* Calculates the average title length.","metadata":{}},{"cell_type":"code","source":"# Text Length\ndf['Title_Length'] = df['Title'].apply(lambda x: len(x))\ntitle_length_distribution = px.histogram(df, x='Title_Length')\n\naverage_title_length = df['Title_Length'].mean()\n\n# Print the results\nprint(\"Data Distribution:\")\nprint(f\"Total number of records: {total_records}\")\nprint(f\"Number of unique titles: {unique_titles}\\n\")\n\nprint(\"Word Frequency:\")\nprint(\"Most common words in titles:\")\nprint(most_common_words)\n\nprint(\"\\nText Length:\")\nprint(\"Distribution of title lengths:\")\ntitle_length_distribution.show()\n\nprint(f\"\\nAverage title length: {average_title_length}\")","metadata":{"execution":{"iopub.status.busy":"2023-07-04T03:02:53.855511Z","iopub.execute_input":"2023-07-04T03:02:53.856128Z","iopub.status.idle":"2023-07-04T03:02:58.166378Z","shell.execute_reply.started":"2023-07-04T03:02:53.856085Z","shell.execute_reply":"2023-07-04T03:02:58.163564Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Submission**","metadata":{}},{"cell_type":"code","source":"submission_df = pd.read_csv(\"/kaggle/input/2023-kaggle-ai-report/sample_submission.csv\")\nsubmission_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-04T03:03:09.104603Z","iopub.execute_input":"2023-07-04T03:03:09.105189Z","iopub.status.idle":"2023-07-04T03:03:09.129339Z","shell.execute_reply.started":"2023-07-04T03:03:09.105148Z","shell.execute_reply":"2023-07-04T03:03:09.128263Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission_df.loc[0]['value']='Kaggle Competition'\nsubmission_df.loc[1]['value']='https://www.kaggle.com/code/jocelyndumlao/ai-report-kaggle-competitions'\nsubmission_df.loc[2]['value']='https://www.kaggle.com/code/mpwolke/pandora-kaggle-s-box-disclosure/comments#2317744'\nsubmission_df.loc[3]['value']='https://www.kaggle.com/code/arunodhayan/kaggle-ai-report-birdclef-series-2019-2023/comments#2317746'\nsubmission_df.loc[4]['value']='https://www.kaggle.com/code/yeemeitsang/unreasonably-effective-ensemble-learning/comments#2317765'\nsubmission_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-04T03:03:15.465278Z","iopub.execute_input":"2023-07-04T03:03:15.465835Z","iopub.status.idle":"2023-07-04T03:03:15.483009Z","shell.execute_reply.started":"2023-07-04T03:03:15.465782Z","shell.execute_reply":"2023-07-04T03:03:15.481266Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Save the submission to a CSV file\nsubmission_df.to_csv('submission.csv', index=False)\n","metadata":{"execution":{"iopub.status.busy":"2023-07-04T03:03:21.075909Z","iopub.execute_input":"2023-07-04T03:03:21.076461Z","iopub.status.idle":"2023-07-04T03:03:21.096703Z","shell.execute_reply.started":"2023-07-04T03:03:21.076426Z","shell.execute_reply":"2023-07-04T03:03:21.094629Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
π,\"Hey there! If you found my notebook helpful and decide to fork it, I kindly request you to consider giving it an upvote. Your support encourages me to continue creating valuable content and helps others discover this resource as well. Together, we can contribute to fostering a community of knowledge sharing and empowering each other. Thank you for your consideration, and happy coding!\"π