https://github.com/agungbudiwirawan/data_science_in_telco-data_cleansing

Data cleansing using python: handling missing data values, outliers, and standardized values.
https://github.com/agungbudiwirawan/data_science_in_telco-data_cleansing

data-analysis-python data-cleansing data-science pandas python

Last synced: 3 months ago
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Data cleansing using python: handling missing data values, outliers, and standardized values.

• Host: GitHub
• URL: https://github.com/agungbudiwirawan/data_science_in_telco-data_cleansing
• Owner: agungbudiwirawan
• Created: 2022-10-03T01:55:12.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-12-09T02:41:43.000Z (over 2 years ago)
• Last Synced: 2025-02-06T05:32:38.080Z (5 months ago)
• Topics: data-analysis-python, data-cleansing, data-science, pandas, python
• Language: Jupyter Notebook
• Homepage:
• Size: 259 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Data Science in Telco: Data Cleansing

### Overview

The goal of this project is to clean the data before it is processed. Data cleaning, such as handling duplication of values, overcoming missing values, overcoming outliers, and standardizing values.

### Library

- Pandas

- Matplotlib

### Algorithm

- Handling missing values by droping rows

- Handling missing values by filling them using the median

- Handling outliers by interquartile range

- Standardizing the value by replacing the value

### Certificate

![alt text](certificate.png)