Naive based.py

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

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import pandas as pd
df = pd.read_csv("titanic.csv")
df.head()


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df.drop(['PassengerId','Name','SibSp','Parch','Ticket','Cabin','Embarked'],axis='columns',inplace=True)
df.head()


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inputs = df.drop('Survived',axis='columns')
target = df.Survived


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dummies = pd.get_dummies(inputs.Sex)
dummies.head(3)


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inputs = pd.concat([inputs,dummies],axis='columns')
inputs.head(3)


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inputs.drop(['Sex','male'],axis='columns',inplace=True)
inputs.head(3)


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inputs.columns[inputs.isna().any()]


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inputs.Age[:10]


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inputs.Age = inputs.Age.fillna(inputs.Age.mean())
inputs.Age[:10]


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from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(inputs, target, test_size = 0.2)


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len(X_train)


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from sklearn.naive_bayes import GaussianNB
model = GaussianNB()


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model.fit(X_train, y_train)


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model.score(X_test, y_test)


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model.predict(X_test[:10])


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