<jupyter_start><jupyter_text>Visualisation<jupyter_code>import seaborn as sns
import pandas as pd
df=pd.read_csv("E:/bank.csv")
df
from sklearn.preprocessing  import LabelEncoder
al=LabelEncoder()
df['contact']=al.fit_transform(df['contact'])
df['housing']=al.fit_transform(df['housing'])
df['loan']=al.transform(df['loan'])
df['default']=al.fit_transform(df['default'])
x=df.iloc[:,0:16].values
y=df.iloc[:,16:17].values
x
y
a=np.ravel(x)
a
b=np.ravel(y)
b
plt.subplot(2,2,1)
plt.plot(data['age'],data['deposit'],b,color="red")
plt.xlabel("Age")
plt.ylabel("Deposit")
plt.subplot(2,2,4)
plt.plot(data['balance'],data['deposit'],b,color="green")
plt.xlabel("balance")
plt.ylabel("Deposit")


import seaborn as sns
plt.bar(df['contact'],df['deposit'],color="green")
sns.countplot(df.education,data=df)
sns.heatmap(df.corr())<jupyter_output><empty_output>
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