Min-Max Normalization Practice Problem

This data science coding problem helps you practice Feature Scaling & Transformation, min-max normalization, and implementation skills. Read the problem statement, write your solution, and strengthen your understanding of Feature Scaling & Transformation.

• Problem ID: 121
• Problem key: 121-min-max-normalization
• URL: https://datacrack.app/solve/121-min-max-normalization
• Difficulty: easy
• Topic: Feature Scaling & Transformation
• Module: Data Cleaning

Problem Statement

# Min-Max Normalization

### 🎯 Goal
Normalize numeric columns to a [0, 1] range using Min-Max scaling.

### 💻 Task
Implement `min_max_normalize(data)` that:
1. Converts the input dictionary to a DataFrame
2. Applies the Min-Max formula to each numeric column: `(x - min) / (max - min)`
3. Returns the normalized DataFrame rounded to 2 decimal places

---

### 📥 Input
- `data`: A dictionary where keys are column names and values are lists of numeric data

### 📤 Output
- A pandas DataFrame with all numeric columns scaled to [0, 1], rounded to 2 decimals

---

### 🧩 Starter Code

```python
import pandas as pd
import numpy as np

def min_max_normalize(data):
    """
    Normalize numeric columns to [0, 1] using Min-Max scaling.

    Args:
        data (dict): Input data as dictionary (from JSON)

    Returns:
        pd.DataFrame: DataFrame with normalized values rounded to 2 decimals
    """
    # TODO: Convert the input dictionary to a DataFrame
    # TODO: Apply (x - min) / (max - min) to each numeric column
    # TODO: Round the result to 2 decimal places and return
    pass
```

---

### 💡 Examples

**Example 1:** Single column
```python
data = {"A": [1.0, 2.0, 3.0, 4.0, 5.0]}
min_max_normalize(data)
```
```
      A
0  0.00
1  0.25
2  0.50
3  0.75
4  1.00
```

**Example 2:** Multiple columns
```python
data = {"X": [10.0, 20.0, 30.0], "Y": [100.0, 200.0, 300.0]}
min_max_normalize(data)
```
```
     X    Y
0  0.0  0.0
1  0.5  0.5
2  1.0  1.0
```

**Example 3:** Including negative values
```python
data = {"A": [0.0, 50.0, 100.0], "B": [-10.0, 0.0, 10.0]}
min_max_normalize(data)
```
```
     A    B
0  0.0  0.0
1  0.5  0.5
2  1.0  1.0
```

Starter Code

import pandas as pd
import numpy as np

def min_max_normalize(data):
    """
    Normalize numeric columns to [0, 1] using Min-Max scaling.

    Args:
        data (dict): Input data as dictionary (from JSON)

    Returns:
        pd.DataFrame: DataFrame with normalized values rounded to 2 decimals
    """
    # TODO: Convert the input dictionary to a DataFrame
    # TODO: Apply (x - min) / (max - min) to each numeric column
    # TODO: Round the result to 2 decimal places and return
    pass

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