Data Visualization & Machine Learning - machine-learning

Question

Preprocess the data and see the results after and before preprocessing(Report as accuracy)
Draw the following charts:
Corelation chart Heatmap chart
Missing Values Heatmap chart
Line chart/ scatter chart for Country Vs Purchased, Age Vs Purchased and Salary Vs Purchased
Country Age Salary Purchased
France 44 72000 No
Spain 27 48000 Yes
Germany 30 54000 No
Spain 38 61000 No
Germany 40 Yes
France 35 58000 Yes
Spain 52000 No
France 48 79000 Yes
Germany 50 83000 No
France 37 Yes
France 18888 No
Spain 17 67890 Yes
Germany 12000 No
Spain 38 98888 No
Germany 50 Yes
France 35 58000 Yes
Spain 12345 No
France 23 Yes
Germany 55 78456 No
France 43215 Yes

Answer 1

Sometimes it's hard to understand from scatter plot like Country vs Purchased. Three country of your list somehow purhcased. It can be helpful to do heatmap here
import pandas as pd
from matplotlib import pyplot as plt
#read csv using panda
df = pd.read_csv('Data.csv')
copydf = df
#before data preprocessing
print(copydf)
#fill nan value with average of age and salary
df['Age'] = df['Age'].fillna(df['Age'].mean(axis=0))
df['Salary '] = df['Salary'].fillna(df['Salary'].mean(axis=0))
#after data preprocessing
print(df)
plt.figure(1)
# Country Vs Purchased
plt.subplot(221)
plt.scatter(df['Country'], df['Purchased'])
plt.title('Country vs Purchased')
plt.grid(True)
# Age Vs Purchased
plt.subplot(222)
plt.scatter(df['Age'], df['Purchased'])
plt.title('Age vs Purchased')
plt.grid(True)
# Salary Vs Purchased
plt.subplot(223)
plt.scatter(df['Salary'], df['Purchased'])
plt.title('Salary vs Purchased')
plt.grid(True)
plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.75,
wspace=0.5)
plt.show()