text_plain_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

import warnings

import warnings
warnings.simplefilter('ignore')
plt.style.use('seaborn')

Data = pd.read_csv('/kaggle/input/country-dataset/Country_Dataset.csv')
df = pd.DataFrame(Data)
df

# Noise detection
df1 = pd.DataFrame(Data, columns = ['child_mort','exports','health','imports','income','inflation','life_expec','total_fer','gdpp'])
columns = ['child_mort','exports','health','imports','income','inflation','life_expec','total_fer','gdpp']
plt.figure(figsize=(15,20))
for i, col in enumerate (df1.columns):
    ax= plt.subplot(5,2,i+1)
    sns.boxplot(x= columns[i], data=df1, color='magenta' )
    plt.title(df1.columns[i])
    plt.tight_layout()
plt.show()

df1 = pd.DataFrame(Data, columns=['child_mort', 'exports', 'health', 'imports', 'income', 'inflation', 'life_expec', 'total_fer', 'gdpp'])
i = 1
for col in df1.columns:
    i = i + 1

plt.figure(figsize=(10, 5), dpi=100)
sns.heatmap(df1.corr(), annot=True, cmap='YlOrBr')
plt.suptitle(f'Correlation Between Features')
plt.tight_layout()
plt.show()