In [2]:

import pandas as pd

In [3]:

toy_dataset = pd.read_csv('Churn Modeling.csv')
toy_dataset.head()

Out[3]:

	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
0	1	15634602	Hargrave	619	France	Female	42	2	0.00	1	1	1	101348.88	1
1	2	15647311	Hill	608	Spain	Female	41	1	83807.86	1	0	1	112542.58	0
2	3	15619304	Onio	502	France	Female	42	8	159660.80	3	1	0	113931.57	1
3	4	15701354	Boni	699	France	Female	39	1	0.00	2	0	0	93826.63	0
4	5	15737888	Mitchell	850	Spain	Female	43	2	125510.82	1	1	1	79084.10	0

exercise: Check that the probability of a person withdrawing their account using the law of total probability is the same as we calculated in the previous assignments

$P(A)=P(A|B)\times P(B)+P(A|B')\times P(B')$

With B= the person is Female.

In [ ]:

In [4]:

data_E = toy_dataset[(toy_dataset["Exited"]==1)]
data_FE = toy_dataset[(toy_dataset["Gender"] == "Female") & (toy_dataset["Exited"]==1)]
data_F= toy_dataset[toy_dataset["Gender"] == "Female"]
data_M= toy_dataset[toy_dataset["Gender"] == "Male"]
data_ME = toy_dataset[(toy_dataset["Gender"] == "Male") & (toy_dataset["Exited"]==1)]
PFE=data_FE.shape[0]/data_F.shape[0]
PME=data_ME.shape[0]/data_M.shape[0]
PFE*(data_F.shape[0]/toy_dataset.shape[0])+PME*(data_M.shape[0]/toy_dataset.shape[0]),data_E.shape[0]/toy_dataset.shape[0]

Out[4]:

(0.2037, 0.2037)

In [ ]:

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1.Law of Total Probability