> ## Content Index
> Fetch the complete content index at: https://www.mekeywhydoesmatter.com/llms.txt
> Use this file to discover other available public pages before exploring further.

# [DATA][ENG.] 1. Why EDA(Exporatory Data Analsis)?
- URL: https://www.mekeywhydoesmatter.com/data-eng-1-why-eda-exporatory-data-analsis/
- Published: 2026-08-30T15:18:11.000Z
- Updated: 2026-08-30T15:18:11.000Z
- Author: mekey
- Tags: Data

---

### **Taking Time to Understand the Data Before Using It**

When starting data analysis, many people immediately think about models.

However, in my experience, **about 70% of a model’s performance is already determined during EDA**.

EDA is *the process of understanding what kind of data this is, whether it can be trusted, and what questions can be asked of it*.

---

## **Why Is EDA Necessary?**

When analysis starts without EDA, the following problems often occur:

- Training a model without realizing the data has many missing values
- Using a column with constant values as a feature
- Training directly on data with distorted distributions

EDA prevents these mistakes early on.

EDA is closer to *having a conversation with the data* than to “analysis.”

---

## **Core Questions of EDA**

When doing EDA, I always start by asking the following questions.

---

### **1\. What does this data look like?**

- What does a row represent?
- What does each column mean?
- Is there a notion of time? (event time, snapshot time)

**Example:**

- row = one customer
- column = customer behavior / attributes

---

### **2\. Is the data sufficient?**

- What is the total number of samples?
- What is the class distribution?
- Is the data concentrated in a specific time period?

```
df.shape
df['label'].value_counts(normalize=True)
```

Here, we judge whether the data size is sufficient and whether class imbalance is severe.

---

### **3\. What about missing values and outliers?**

This is the first thing to check in EDA.

```
df.isnull().mean().sort_values(ascending=False)
```

- Why do some columns have many missing values?
- Are they “meaningful zeros” or truly “missing values”?
- Do missing values occur only in specific ranges?

Missing values are not just a problem—they are clues about the data generation process.

---

### **4\. Are the distributions reasonable?**

For numerical data, always inspect the distribution.

```
df['price'].hist(bins=50)
```

**Checkpoint:**

You must be able to trust the distribution before trusting the model.

---

### **5\. What are the relationships between variables?**

The essence of EDA lies in exploring relationships.

- Feature ↔ target relationships
- Feature ↔ feature correlations

```
df.corr(numeric_only=True)
```

Insights gained here include:

- Removing useless variables
- Discovering redundant features
- Finding signals that can be explained by simple rules

---

## **Visualization Is the Language of EDA**

EDA is a phase with more pictures than numbers.

Commonly used visualizations:

- Histograms (distribution)
- Box plots (outliers)
- Scatter plots (relationships)
- Time-series line charts

If you find it hard to explain EDA results in words, it likely means you don’t fully understand the data yet.

---

## **Practical EDA Checklist**

Below is an EDA checklist I use almost every time in practice:

- Understand the data schema
- Clarify the meaning of rows and columns
- Check missing value ratios
- Identify outliers
- Inspect target distribution
- Visualize key feature distributions
- Explore feature–target relationships
- Check for potential data leakage
- Verify whether time-based splits are possible

---

## **EDA Does Not End After One Pass**

One important point:

EDA is **not** something you do once before modeling and then forget.

- Create new features → EDA again
- Model performance is poor → EDA again
- Data changes → EDA again

**EDA ↔ Modeling ↔ EDA**

The more this cycle repeats, the better the analysis becomes.

---

## **Conclusion**

EDA is the stage that prevents the most mistakes.

If your model isn’t performing well,

before tuning hyperparameters, ask this question first:

> **“Do I truly understand this data?”**

EDA is the process that answers that question.