Veride Eksik Değerler (Missing Values) Nedir?

📰 Medium · Data Science

Learn to handle missing values in data science by understanding the concept and applying three main strategies to manage them effectively.

beginner Published 21 Apr 2026

Action Steps

Identify missing values in a dataset using tools like Pandas in Python
Apply the three main strategies to handle missing values: deletion, imputation, and interpolation
Use techniques like mean, median, or mode imputation to fill missing values
Evaluate the effectiveness of each strategy and choose the best approach for the specific dataset
Implement data validation and cleaning pipelines to prevent missing values in future datasets

Who Needs to Know This

Data scientists and analysts can benefit from this knowledge to improve the quality of their data and make more accurate predictions.

Key Insight

💡 Missing values can significantly impact the accuracy of predictions, and handling them effectively is crucial for data-driven decision making

Key Takeaways

Learn to handle missing values in data science by understanding the concept and applying three main strategies to manage them effectively.

Full Article

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------data_science-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 pro

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Veride Eksik Değerler (Missing Values) Nedir?

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