5 posts tagged with "Data Preprocessing"

Visualizing Missing Data: A Step-by-Step Guide​

Handling missing data is crucial in data analysis and machine learning. Visualizing missing data helps to understand the extent and pattern of missingness, which can inform the choice of strategies for dealing with it. In this guide, we'll use Python and the missingno library to visualize missing data in a dataset.

You can download the Jupiter notebook of the example Visualizing Missing Data

Handling missing values is a crucial step in preparing data for machine learning. This tutorial provides examples of how to manage missing values using Python, focusing on the Pandas library. We'll import the necessary libraries, read the data, and explore various methods to handle missing values.

You can check the full code in the Jupyter Notebook

The iloc indexer in pandas is a powerful tool for data selection, slicing, and manipulation, essential for preparing datasets for machine learning tasks. Here's a comprehensive guide to help you master iloc.

You can download the .ipynb file from here

Imagine you're a teacher and your students are working on a group project. One student is a math whiz, another excels at writing, and a third is a history buff. If you grade each section based on the individual's absolute strengths, the math whiz would dominate the score, even if the writing and history were excellent. This is similar to what can happen in machine learning with features (data points) on vastly different scales.

Machine learning (ML) has become a cornerstone of modern technology, driving advancements in various fields such as healthcare, finance, and transportation. To build effective ML models, it's essential to understand the three main steps in the machine learning process: Data Preprocessing, Modeling, and Evaluation. This article breaks down these steps, detailing the sub-steps involved and providing references for further reading and understanding.