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WIL : Weekly I Learned 17.05.2024 WIL : Weekly I Learned 17.05.2024 FactsThis week, we learned about data preprocessing and visualization techniques. Starting today, we will embark on a new project. The chosen topic is service analysis using bank customer data.- Project Name: Bank Service Status Analysis and Improvement Project- Project Objective: The goal of this project is to understand the current state and issues of bank ser.. 2024. 5. 17.
Seaborn plot (Scatter, Hist, and Box) Today, I plan to practice again by using Seaborn to recreate the plots I made last time.  Seaborn is a Python package for data visualization, built on top of Matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics.Seaborn is used to visualize statistical data, particularly to explore relationships between variables. It offers a wide range of pl.. 2024. 5. 16.
Library (Pandas, Seaborn, Matplotlib, Numpy) - Iris Data When using Python, in addition to basic functions, we need to utilize various libraries. Especially, Pandas, Seaborn, Matplotlib, and Numpy are the most essential libraries when performing data analysis.  1. Pandas:   - Purpose: Pandas is used for data manipulation and analysis. It provides data structures to efficiently store and manipulate data. Its main data structures are Series (a one-dimen.. 2024. 5. 14.
WIL : Weekly I Learned 10.05.2024 WIL : Weekly I Learned 10.05.2024 FactsThis week, I learned about data preprocessing and visualization techniques. I learned how to manage missing data and sort desired data using Python. I utilized libraries necessary for data preprocessing in Python, such as Matplotlib, NumPy, Seaborn, and Pandas. I still need a lot of practice as I am not yet familiar with them. FeelingsThe learning pace has .. 2024. 5. 13.
The Pearson correlation coefficient The Pearson correlation coefficient is a statistical method used to measure the linear relationship between two variables. It typically describes how two variables move together. The Pearson correlation coefficient ranges from -1 to 1, and it is interpreted as follows:- Close to 1: Indicates a strong positive linear relationship between the two variables. As one variable increases, the other var.. 2024. 5. 13.
Data preprocessing Data preprocessing refers to the process of cleaning, transforming, and preparing data before it is analyzed. This process involves improving the quality of the data and transforming it into a suitable format for analysis, thereby enhancing the performance of analytical models. The main tasks involved in data preprocessing include:1. Data Cleaning: This involves removing noise from the data, suc.. 2024. 5. 10.

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