Feature Scaling Normalization Standardization in Data Mining In this article, I will discuss, Normalization – Standardization (Z-score scaling) Min-Max scaling Robust scaling Video Tutorial – Feature Scaling Normalization Standardization Click here to download the dataset titanic.csv file, which is used in this article for demonstration. First, we will import the required libraries like pandas, NumPy,
How to Detect and Handle Outliers – Feature Engineering and Feature Selection in Data Mining In this article, I will discuss, Detect outliers by an arbitrary boundary Detect outliers using Interquartile Ranges Rule Detect outliers using Mean and Standard Deviation Method Imputation of outliers with an arbitrary value Imputation of outliers with Mean, Median, Mode
How to Handle Missing Values – Feature Engineering and Feature Selection in Data Mining In this article, I will discuss, How to check the Missing values in the given dataset Listwise deletion – Deleting the missing values Arbitrary Value Imputation Mean/Median/Mode Imputation Random Imputation Video Tutorial – Missing Values in Data Mining Click here to
Introduction to Data Exploration – Feature Engineering and Feature Selection in Data Mining In this article, I will discuss, How to read the dataset? How to know the data types of columns? general Data Description Univariate analysis and Bi-Variate Analysis Video Tutorial – Data Exploration in Data Mining Click here to download the titanic.csv file,
Introduction to Data Preprocessing – Feature Engineering and Feature Selection in Data Mining In this article, I will discuss, Motivation for Data Preprocessing, Steps in Data Preprocessing Motivation for Data Preprocessing Real-world datasets are highly influenced by negative factors such as the presence of noise, missing values, redundancy, outliers, and inconsistencies. A low-quality dataset will
