Coders Packet

Clothe Size Analyzing using Python in Machine Learning

By Subhojit Jalal

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

First, we upload the necessary libraries then we upload the dataset

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[0])
axes[0].set_title('weight')

# age
sns.histplot(CLL['age'], ax = axes[1])
axes[1].set_title('age')

# height
sns.histplot(CLL['height'], ax = axes[2])
axes[2].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[0])
axes[0].set_title('weight')

# age
sns.boxplot(x = 'size',y = 'age', data = CLL, ax = axes[1])
axes[1].set_title('age')

# height
sns.boxplot(x = 'size',y = 'height', data = CLL, ax = axes[2])
axes[2].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})
CLL.head()

Mapping clothes size from strings to numeric

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

Now we Plot a heat map

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