Titanic_decisionTree.py

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

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import pandas as pd
df = pd.read_csv('/Users/kavya/Documents/Python_datascience/Titanic/train.csv')
df.head()


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df.describe()


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df1 = df.drop(['Name','Ticket','Cabin','Embarked','PassengerId','SibSp','Parch'],axis='columns')


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df1.describe()


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df1.dropna(inplace=True)
df1


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df1.describe()


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inputs = df1.drop('Survived',axis='columns')
target = df1['Survived']
inputs.head()


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target.head()


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inputs.describe()


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inputs.dtypes


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inputs.isnull().values.any()


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from sklearn.preprocessing import LabelEncoder


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lb = LabelEncoder()


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inputs['Sex_n'] = lb.fit_transform(inputs['Sex'])
inputs['Age_n'] = lb.fit_transform(inputs['Age'].astype('int64'))
inputs['Fare_n'] = lb.fit_transform(inputs['Fare'].astype('int64'))


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inputs.dtypes


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inputs.head()


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inputs_n=inputs.drop(['Sex','Age','Fare'],axis='columns')
inputs_n.head()


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from sklearn import tree


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model = tree.DecisionTreeClassifier()


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model.fit(inputs_n,target)


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model.score(inputs_n,target)


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model.predict([[1,0,22,4]])