The second episode of the Crash Machine Learning - Regression of Supervised Learning + Data Processing (Practical Part)!

import pandas as pd

import as plt

import seaborn as sns

data_path=r'D:\Machine Learning\Dataset: Cost of Living in Country\Cost_of_Living_Index_by_Country_2024.csv'

df=pd.read_csv(data_path)

print(())

print(().sum())

print(())

(bins=20,figsize=(12,10),color='blue')

plt.tight_layout() #Adjust the sub-picture parameters to fill the entire image area

()

numeric_df = df.select_dtypes(include=['float64', 'int64'])

corr_matrix=numeric_df.corr()

(corr_matrix,annot=True,cmap='coolwarm')

('Correlation Matrix')

()

top_10_countries=(10)

(figsize=(12,6))

(x='Country',y='Cost of Living Index',data=top_10_countries)

(rotation=90)#Rotate x-axis label

('Top 10 Countries by Cost of Living Index')

()

(figsize=(10,6))

(x='Cost of Living Index',y='Rent Index',data=df)#Add title and tag

('Cost of Living Index vs Rent Index')

()

(figsize=(10,6))

numeric1_df = df.select_dtypes(include=['float64', 'int64'])

(numeric1_df.corr(),annot=True,cmap='coolwarm')

('Correlation Heatmap')

()