Final project.py

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

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import numpy as np 
import pandas as pd 
import seaborn as sns                 
import matplotlib.pyplot as plt       
get_ipython().run_line_magic('matplotlib', 'inline')
import warnings                        
warnings.filterwarnings("ignore")


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import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))


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train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')


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train.columns


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test.columns


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print(train.shape) 
print(test.shape)


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print(train.dtypes)


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train['subscribed'].value_counts()


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train['subscribed'].value_counts(normalize=True)


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train['subscribed'].value_counts().plot.bar(color=['red','green'])


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sns.distplot(train["age"],color='green')


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train['job'].value_counts().plot.bar(color=['red', 'hotpink', 'green', 'blue', 'cyan','yellow','magenta'])


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train['default'].value_counts().plot.bar(color = ['blue','red'])


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print(pd.crosstab(train['job'],train['subscribed']))

job=pd.crosstab(train['job'],train['subscribed'])
job.div(job.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True, figsize=(8,8))
plt.xlabel('Job')
plt.ylabel('Percentage')


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print(pd.crosstab(train['default'],train['subscribed']))

default=pd.crosstab(train['default'],train['subscribed'])
default.div(default.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True, figsize=(8,8))
plt.xlabel('default')
plt.ylabel('Percentage')


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train['subscribed'].replace('no', 0,inplace=True)
train['subscribed'].replace('yes', 1,inplace=True)


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corr = train.corr()
mask = np.array(corr)
mask[np.tril_indices_from(mask)] = False
fig,ax= plt.subplots()
fig.set_size_inches(20,10)
sns.heatmap(corr, mask=mask,vmax=.9, square=True,annot=True, cmap="YlGnBu")


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train.isnull().sum() 


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target = train['subscribed']
train = train.drop('subscribed',1)


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train = pd.get_dummies(train)


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from sklearn.model_selection import train_test_split


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X_train, X_val, y_train, y_val = train_test_split(train, target, test_size = 0.2, random_state=12)


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from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score


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model = LogisticRegression()


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model.fit(X_train,y_train)


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prediction = model.predict(X_val)


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accuracy_score(y_val, prediction)


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from sklearn.tree import DecisionTreeClassifier


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clf = DecisionTreeClassifier(max_depth=4, random_state=0)


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clf.fit(X_train,y_train)


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predict = clf.predict(X_val)


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accuracy_score(y_val, predict)


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test = pd.get_dummies(test)


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test_prediction = clf.predict(test)


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submission = pd.DataFrame()


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submission['ID'] = test['ID']
submission['subscribed'] = test_prediction


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submission['subscribed'].replace(0,'no',inplace=True)
submission['subscribed'].replace(1,'yes',inplace=True)


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submission.to_csv('submission.csv', header=True, index=False)


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pip install plotting


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import seaborn as sns
import matplotlib.pyplot as plt


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get_ipython().system('pip install --upgrade pandas')


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pd.plot(kind="bar", x="age", y="marital")


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