Veride Eksik Değerler (Missing Values) Nedir?

📰 Medium · Python

Learn to handle missing values in data science with 3 key strategies: deletion, imputation, and interpolation, and apply them using Python

intermediate Published 21 Apr 2026

Action Steps

Identify missing values in a dataset using Python's pandas library
Apply the deletion strategy by removing rows or columns with missing values using pandas' drop() function
Use imputation methods such as mean, median, or mode to fill missing values with pandas' fillna() function
Implement interpolation techniques like linear or polynomial interpolation to estimate missing values
Evaluate the effectiveness of each strategy on model performance and choose the best approach

Who Needs to Know This

Data scientists and analysts can benefit from understanding how to manage missing values in datasets to improve model accuracy and reliability

Key Insight

💡 Missing values can significantly impact model accuracy, and choosing the right strategy to handle them is crucial

Key Takeaways

Learn to handle missing values in data science with 3 key strategies: deletion, imputation, and interpolation, and apply them using Python

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Title: Veride Eksik Değerler (Missing Values) Nedir?

URL Source: https://medium.com/huawei-student-developers-turkiye/veride-eksik-de%C4%9Ferler-missing-values-nedir-cb79159ff3fa?source=rss------python-5

Published Time: 2026-04-21T19:58:22Z

Markdown Content:
# Veride Eksik Değerler (Missing Values) Nedir? | by HSD Gedik University | Apr, 2026 | Medium

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# Veride Eksik Değerler (Missing Values) Nedir?

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![Image 3](https://miro.medium.com/v2/resize:fit:700/1*Qx-GLRhu_ydgN5TYbZNIPg.png)

[Figure 1](https://gemini.google.com/app?hl=tr): Missing Values

Veri biliminde bir projeye başlarken karşılaşılan en doğal durumlardan biri, veri setindeki her hücrenin dolu olmamasıdır. Teknik literatürde **Eksik Değerler (Missing Values)** olarak tanımlanan bu kavram, gözlemlerdeki bilgi kaybını veya bir değişkenin o anki temsil edilememe durumunu ifade eder.

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![Image 4](https://miro.medium.com/v2/resize:fit:700/1*1pip3ckOSiOH7e5PsHNaMA.png)

[Figure 1](https://miuul.com/): Örnek bir veri seti

Yukarıda verilen veri setinde görüldüğü üzere ‘NA’ yazılı değerler bulunmaktadır. Bu ifadeler, veri setinde o kısmın boş olduğu anlamına gelir. Eksik değerlerin çözümlenmesi, veri seti üzerinde işlem yaparken odaklanmamız gereken temel problemlerden biridir.

## Eksik Veri Nasıl Yönetilir?

Veri setindeki boşlukları (NA/NaN) doğru bir şekilde yönetmek için literatürde ve sektör uygulamalarında genel kabul görmüş **üç ana strateji** bulunmaktadır. Seçilecek yöntem, verinin doğasına ve eksikliğin kaynağına göre projenin

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Veride Eksik Değerler (Missing Values) Nedir?

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