Jeremy Howard’s Victorious Journey in the Predict Grant Applications Competition – Insights from the Kaggle Team in the Kaggle Blog

Jeremy Howard’s Victorious Journey in the Predict Grant Applications Competition – Insights from the Kaggle Team in the Kaggle Blog

Introduction:

Welcome to Kaggle’s Introduction to the Competition. Unfortunately, as an employee of Kaggle, I am not eligible to win any prizes. Therefore, the deserving winner of this competition is Quan Sun from team ‘student1’. Congratulations!

In approaching this competition, I began by analyzing the data using Excel pivottables. This allowed me to identify groups with high or low application success rates. Through this analysis, I discovered several strong predictors, such as application dates (New Year’s Day and Sundays being notable predictors), as well as the presence of null values in certain fields.

To further analyze the data, I utilized C# to normalize the data into Grants and Persons objects. This allowed me to construct a comprehensive dataset for modeling, incorporating various features such as categorical codes, number of applicants per person, relevant IDs, sponsorship details, and more.

Following the creation of these features, I employed a generalized version of the random forest algorithm to build a predictive model. This algorithm, while similar to a regular random forest, possesses certain enhancements that optimize its performance.

Prior to running the data through the model, I undertook preprocessing steps. This involved grouping small groups in categorical variables and replacing null values in continuous columns with binary predictors and the original column median. All preprocessing and modeling were conducted in C# using libraries I developed specifically for this competition. I intend to document and release these libraries, potentially refining them based on future competitions.

Stay tuned for more updates as the competition progresses.

Full Article: Jeremy Howard’s Victorious Journey in the Predict Grant Applications Competition – Insights from the Kaggle Team in the Kaggle Blog

Analyzed data reveals predictors for success in grant applications

In a recent competition held by Kaggle, Quan Sun (team ‘student1’) emerged as the winner. The author of this news report, who has recently joined Kaggle, shares their approach to analyzing the data in the competition.

Using Excel pivottables, the author looked for groups with high or low application success rates. This analysis led to the identification of several strong predictors. For instance, the date of application played a significant role, with new year’s day and applications processed on Sundays proving to be strong predictors. Additionally, null values in certain fields were found to be highly predictive.

To further analyze the data, the author used C# to normalize it into Grants and Persons objects. They then constructed a dataset for modeling, incorporating various features such as CatCode, NumPerPerson, PersonId, NumOnDate, AnyHasPhd, Country, Dept, DayOfWeek, and more. These features helped in predicting the success of grant applications.

The field names of the features provide insight into their meaning. For example, features starting with ‘Any’ denote whether any person attached to the grant possesses a specific attribute (e.g., ‘AnyHasPhd’). Additionally, the author considered predictors that focused on individual persons as well as the maximum values across all applicants (e.g., ‘APapers’ vs. ‘MaxAPapers’).

Once the features were prepared, the author employed a generalized version of the random forest algorithm to build a predictive model. They plan to provide a more detailed explanation of the algorithm in the future, noting that it is not vastly different from a regular random forest.

Before inputting the data into the model, the author pre-processed it by grouping small groups in categorical variables and substituting null values in continuous columns. For the latter, they utilized two new columns, one containing a binary predictor indicating if the continuous column was null, and the other preserving the original values but replacing nulls with the median. All pre-processing and modeling tasks were completed in C#, utilizing custom libraries developed by the author during the competition. They envision documenting and releasing these libraries in the future, potentially after refining them in subsequent competitions.

Overall, the author’s approach to analyzing the data in this competition yielded valuable insights into the predictors of success in grant applications.

Summary: Jeremy Howard’s Victorious Journey in the Predict Grant Applications Competition – Insights from the Kaggle Team in the Kaggle Blog

Kaggle recently announced the winner of their competition, with the prize going to Quan Sun. The writer shares their approach to the competition, which involved analyzing the data using Excel pivot tables. They discovered several strong predictors, such as the impact of specific dates and the significance of null values. Next, they normalized the data using C# and created a dataset for modeling. They utilized various features, including categorical codes, person IDs, and the presence of certain characteristics. The writer then applied a modified version of the random forest algorithm to build their model. They also conducted pre-processing tasks, such as grouping small categories and handling null values. The entire process, from data analysis to modeling, was conducted using C#. The writer aims to release their libraries developed during this competition after further refining them.

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