Let’s talk about my story on becoming a Kaggle Expert in just one month of joining the platform and how I managed to bag multiple medals…
In this article, I am going to discuss with you my small milestone achievement of becoming a kaggle expert in the Dataset, Notebooks, and Discussion categories. This article is going to be a different one from the ones I generally write. This one is very special for me as it going to describe my journey on becoming a kaggle 3X-Expert ** and later **Master . I am going to talk about my journey towards becoming a Kaggle Expert in just one month and how anyone can achieve that level with just two things persistence ** and **dedication .
It was the time when I was studying at my college in the second year of the undergraduate program in Computer science. I was introduced by my friends to a new field of study which was Data Science and Machine Learning. In no time I was curious to know more about the domain and started exploring new things and technologies to disrupt Machine Learning. Soon, I came to know about something called Kaggle which everyone was talking about but didn’t know what was that? I tried exploring more about it and found that it’s really a cool platform where one can participate in ML competitions and share public notebooks and datasets. Moreover, discussion was also an amazing section that Kaggle where one can talk to amazing data scientists across the globe and know more about their learnings and ask any kind of public doubts or share knowledge. I became curious to know more and was eager to participate in the global events on Kaggle but I was just a newbie in this vast field and didn’t have any good experience with working on good projects.
Then I decided to build up my skill set and then dive in to Kaggle. I started working on improving my ML sills with time and as time passed I was learning more, participated in local and regional hackathons overtime, and made myself ready for Kaggle.
Then during the time of COVID-19 pandemic in I was forced to lockdown at my place with a lot of free time with me. Then I decided somewhere in the starting of mid of May 2020 that now I guess I’m ready to join Kaggle. I created my account on Kaggle on 3rd June 2020 and started exploring the platform in depth. I started discussing with people how to start with Kaggle in the discussions and the community helped me a lot. My first post in the discussion section was “Help me start with Kaggle!”. I gained a gold medal in that discussion in no time and that was just enough to give me that initial boost and push me towards learning and exploring more from the community support.
Soon I decided to write public notebooks and work on datasets. I was parallelly participating in Knowledge and Kudos Competitions. The first and classical competition I ever participated in was the “*Titanic: Machine Learning from Disaster” *which I should say is one of the best places to start for Kaggle competitions. I wrote a notebook and kept updating it over time and built a well-structured plan to get into a good position in that competition. Even after a lot of effort, I was not even in the top 10% of people and that was heartbreaking for me. Then I did some research and found that people release public notebooks in competitions and most of the top-level competitors use good public notebooks/datasets to ensemble and stack up and form better models. Wow! I was totally blown up that such a level of collaborative works is also possible. I then started reading other participant’s notebooks, their work and discussed it with the authors if I find something hard to understand. I was soon in the top 3% of participants in the competition and was very happy.
I then started giving more time to kaggle and soon within a month I was bagged with 3 Expert badges in Notebooks, Datasets, and Discussion categories. I knew these badges are just for our own excitement and does not define the level of knowledge and skills a data scientist has, but somewhere within I was very excited with my level of learning in just a very small time. I started working more on the Notebooks section took advice from top data scientists about my work and learned from my mistakes and kept working on writing good stuff, then in the next 2 months, I was a Notebook Master which was really a breathtaking feeling for me.
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