Untitled12.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt


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bank=pd.read_csv('bank-full.csv')


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bank.head()


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bank.shape


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sns.histplot(bank['age'])
plt.show()


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bank.describe()


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bank['job'].value_counts(),bank.shape


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bank['marital'].value_counts(),bank.shape


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bank['education'].value_counts(),bank.shape


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bank['job'].value_counts().keys()


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bank['job'].value_counts().values


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plt.figure(figsize=(10,5))
plt.bar(list(bank['job'].value_counts().keys()[0:5]),list(bank['job'].value_counts()[0:5]),color=["blue","red","orange","yellow","green"])
plt.show()


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bank['marital'].value_counts().keys()


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bank['marital'].value_counts().values


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plt.bar(list(bank['marital'].value_counts().keys()),list(bank['marital'].value_counts().values),color=["red","green","orange"])
plt.show()                                                             


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bank.head()


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bank['education'].value_counts()


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bank['education'].value_counts().keys()


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bank['education'].value_counts().values


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plt.bar(list(bank['education'].value_counts().keys()),list(bank['education'].value_counts().values),color=["yellow","green","orange","red"])
plt.show()


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bank['default'].value_counts()


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bank['default'].value_counts().keys()


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bank['default'].value_counts().values


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plt.bar(list(bank['default'].value_counts().keys()),list(bank['default'].value_counts().values),color=["red","green"])
plt.show()


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sns.histplot(bank['balance'])


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sns.displot(bank['balance'])


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plt.figure(figsize=(10,5))
plt.hist(bank['balance'],bins=5)
plt.show()


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bank.head()


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bank['loan'].value_counts()


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bank['loan'].value_counts().keys()


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bank['loan'].value_counts().values


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plt.bar(list(bank['loan'].value_counts().keys()),list(bank['loan'].value_counts().values),color=["red","green"])
plt.show()


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x=bank[['age']]
y=bank[['balance']] 


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from sklearn.model_selection import train_test_split


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x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)


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from sklearn.ensemble import RandomForestRegressor


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rfg=RandomForestRegressor()


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rfg.fit(x_train,y_train)


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y_pred=rfg.predict(x_test)


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y_test.head(),y_pred[0:5]


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from sklearn.metrics import mean_squared_error


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mean_squared_error(y_test,y_pred)


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