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What makes handling Categorical Variables important?Categorical variables - like colors, cities, or education levels - cannot be directly used in machine learning models since these models only understand numbers.Categorical variable encoding solves this problem by converting these text-based categories into numerical formats that machines can process.The challenge lies in choosing the right enc..

In this post, I want to talk about the difference between GBM(Gradient Boosting Machine) and XGBoost.  Reading the previous post will be helpful for your understanding: [Kaggle Extra Study] 14. Tree-based Ensemble ModelsTree-based Ensemble Models?LightGBM, XGBoost, CatBoost, Random Forest are all tree-based ensemble models.These algorithms are popular on Kaggle because of their high prediction p..

Tree-based Ensemble Models?LightGBM, XGBoost, CatBoost, Random Forest are all tree-based ensemble models.These algorithms are popular on Kaggle because of their high prediction performance, fast learning speed, and ability to handle various data types.They show particularly powerful performance in analyzing structured data."Tree-based": These algorithms all use decision trees as their basic buil..

Weight Initialization?When training a neural network, one of the things we should care about is where we should start on the loss function to reach the optimal solution more easily and quickly.When training a neural network, the method of determining the starting position on the loss function is called Model Initialization.In particular, weights account for the largest portion of the model's par..

What is Drop-out?Drop-out is one of the methods to reduce overfitting in neural networks.Drop-out is not the only method to avoid overfitting.There are various methods including regularization, and while weight reduction methods like regularization are simple to implement and can avoid overfitting to some extent, when neural network models become complex, it becomes difficult to cope with overfi..

Polars?Pandas library is widely used for data analysis and processing because it is flexible and convenient to use.However, Pandas has limitations in handling large-scale data due to processing speed and memory usage constraints when dealing with big data.To address this issues, Polars has emerged as a new data processing tool. Polars is implemented in Rust that offers great performance for proc..

Briefly About TabNetTabNet is a deep learning architecture developed by Google in 2019 for analyzing tabular data.In many competitions, including Kaggle, tree-based ensemble models like XGBoost and LightGBM usually dominate the leaderboards when it comes to tabular data analysis.While deep learning leads in analyzing unstructured data like images, text, and audio, tree-based methods have been wi..

Before we begin with the plots, you can find detailed information about data imputation from previous post: [Kaggle Extra Study] 7. Data ImputationWhile training machine learning models, it is necessary to preprocess missing values because it might greatly affect the performance of the model. Accordingly, there are various methods to deal with those missing values. Types of Missing Values:Missin..

We have to take different approach when dealing with time-series data. The fillna() method is used for imputing missing values in such problem.Basic Imputation Techniques:'ffill' or 'pad': Replace NaN values with last observed value'bfill' or 'backfill': Replace NaN values with next observed valueLinear Interpolation method1. Imputing using 'ffill' or 'pad'Code Example:city_day.fillna(method='ff..

While training machine learning models, it is necessary to preprocess missing values because it might greatly affect the performance of the model. Accordingly, there are various methods to deal with those missing values. Types of Missing Values:Missing completely at random (MCAR)Case when missing values occur independently of other variables, completely random in other words.No correlation with ..