# Clothe Size Analyzing using Python in Machine Learning

• Clothe.csv
• CLOTHE SIZE.ipynb
• In this project, we analyze the clothing size, plot a graph between sizes and count and also plot heatmap using Python in Machine Learning.

Step:- 1

```import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt```
```CLL = pd.read_csv("Clothe.csv")
CLL```
`CLL.isna().sum()`
`CLL.describe()`
`CLL.info()`
```CLL['age'] = CLL['age'].fillna(CLL['age'].median())
CLL['height'] = CLL['height'].fillna(CLL['height'].median(```
`CLL.isna().sum()`

Step:- 2

Now Plotting histogram between Count and Predictor

```fig, axes = plt.subplots(1,3,figsize=(20,5))
fig.suptitle('Predictor')

# weight
sns.histplot(CLL['weight'], ax = axes)
axes.set_title('weight')

# age
sns.histplot(CLL['age'], ax = axes)
axes.set_title('age')

# height
sns.histplot(CLL['height'], ax = axes)
axes.set_title('height')```

Now Plotting histogram between Weight and Predictor

```fig, axes = plt.subplots(1,3,figsize=(20,5))
fig.suptitle('Predictor')

# weight
sns.boxplot(x = 'size',y = 'weight', data = CLL, ax = axes)
axes.set_title('weight')

# age
sns.boxplot(x = 'size',y = 'age', data = CLL, ax = axes)
axes.set_title('age')

# height
sns.boxplot(x = 'size',y = 'height', data = CLL, ax = axes)
axes.set_title('height')```

Step:- 3

`CLL['size'].value_counts()`

Here we see the most numbers of people use "M" Size and fewer numbers of people use "XXL"

`sns.countplot(x = 'size', data = CLL)`

Plotting graph between count and size

```CLL['size'] = CLL['size'].map({"XXS": 1,
"S": 2,
"M" : 3,
"L" : 4,
"XL" : 5,
"XXL" : 6,
"XXXL" : 7})

Mapping clothes size from strings to numeric

`sns.heatmap(CLL.corr(), annot=True)`

Now we Plot a heat map