ashishpatel26

A Beginner's First Kaggle Month: From Titanic to the Leaderboardrendered video captioned A Beginner's First Kaggle Month: From Titanic to the Leaderboard What does a beginner's first Kaggle month look like? Follow Meera, a student at her dorm desk, as she opens the Kaggle Titanic competition for the first time and works toward her first leaderboard entry. In this video, you'll see how the first week of that first Kaggle month goes: Opening the Titanic competition and understanding the task: predicting who survived from passenger data such as age, sex and ticket class Reading other beginners' Titanic write-ups before writing code, including Stefan Lütschg's PyTorch attempt and Uros Zizek's SQL approach to pulling titles out of passenger names, without copying either Making a first set of predictions and saving them as a CSV in the format the competition asks for Submitting to Kaggle, getting a score, and seeing your entry placed on the leaderboard Noting what you submitted beside the result, so later attempts have something to compare against Meera is a fictional character, but every step she takes is one you can take yourself. The video follows the start of the month, from the first look at Titanic to the first leaderboard result. It does not promise a top ranking or a specific score. If you're searching for Kaggle for beginners, a Kaggle Titanic tutorial, how to start Kaggle, or what a first Kaggle competition involves, this is a walkthrough of the first steps and the first leaderboard submission. Credit: the Titanic accounts by Stefan Lütschg and Uros Zizek are mentioned as reading material and inspiration. Sources: GitHub - ashishpatel26/Titanic-Machine-Learning-from-Disaster: Start here if... You're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions. Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python and R basics · GitHub: ashishpatel26/Titanic-Machine-Learning-from-Disaster How I Solved the Kaggle’s Titanic ML Challenge Using Snowflake Classification Model | by Uros Zizek | Medium: medium.com Introduction to Kaggle and Scoring Top 7% in the Titanic Competition | Towards Data Science: towardsdatascience.com Kaggle Fundamentals: The Titanic Competition – Dataquest: dataquest.io

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