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Decision Tree

The document outlines a data analysis process using the Titanic dataset, including data import, cleaning, and preprocessing steps. It highlights handling missing values, encoding categorical variables, and preparing data for machine learning. Finally, it demonstrates the use of a Decision Tree Classifier to predict survival outcomes based on selected features.

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Kavya Padarthi
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45 views2 pages

Decision Tree

The document outlines a data analysis process using the Titanic dataset, including data import, cleaning, and preprocessing steps. It highlights handling missing values, encoding categorical variables, and preparing data for machine learning. Finally, it demonstrates the use of a Decision Tree Classifier to predict survival outcomes based on selected features.

Uploaded by

Kavya Padarthi
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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In [72]: import numpy as np

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sn

In [74]: df = pd.read_csv('titanic.csv')
df

Out[74]: PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked

0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S

1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C

2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S

3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S

4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S

... ... ... ... ... ... ... ... ... ... ... ... ...

886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S

887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S

888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S

889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C

890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q

891 rows × 12 columns

In [76]: df.isnull()

Out[76]: PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked

0 False False False False False False False False False False True False

1 False False False False False False False False False False False False

2 False False False False False False False False False False True False

3 False False False False False False False False False False False False

4 False False False False False False False False False False True False

... ... ... ... ... ... ... ... ... ... ... ... ...

886 False False False False False False False False False False True False

887 False False False False False False False False False False False False

888 False False False False False True False False False False True False

889 False False False False False False False False False False False False

890 False False False False False False False False False False True False

891 rows × 12 columns

In [78]: df.isnull().sum()

Out[78]: PassengerId 0
Survived 0
Pclass 0
Name 0
Sex 0
Age 177
SibSp 0
Parch 0
Ticket 0
Fare 0
Cabin 687
Embarked 2
dtype: int64

In [80]: df1 = df.drop(['PassengerId', 'Name', 'SibSp', 'Parch', 'Ticket', 'Cabin', 'Embarked'], axis='columns')
df1

Out[80]: Survived Pclass Sex Age Fare

0 0 3 male 22.0 7.2500

1 1 1 female 38.0 71.2833

2 1 3 female 26.0 7.9250

3 1 1 female 35.0 53.1000

4 0 3 male 35.0 8.0500

... ... ... ... ... ...

886 0 2 male 27.0 13.0000

887 1 1 female 19.0 30.0000

888 0 3 female NaN 23.4500

889 1 1 male 26.0 30.0000

890 0 3 male 32.0 7.7500

891 rows × 5 columns

In [82]: df1.isnull().sum()

Out[82]: Survived 0
Pclass 0
Sex 0
Age 177
Fare 0
dtype: int64

In [84]: df['Age'].mode()

Out[84]: 0 24.0
Name: Age, dtype: float64

In [86]: df1.loc[:, 'Age'] = df1['Age'].fillna(df1['Age'].mode()[0])

In [88]: df1.isnull().sum()

Out[88]: Survived 0
Pclass 0
Sex 0
Age 0
Fare 0
dtype: int64

In [96]: df1.drop(columns=['Survived'], inplace=True)

In [100… target = df['Survived']


target

Out[100… 0 0
1 1
2 1
3 1
4 0
..
886 0
887 1
888 0
889 1
890 0
Name: Survived, Length: 891, dtype: int64

In [114… from sklearn.preprocessing import LabelEncoder


le_Sex = LabelEncoder()

In [126… df1['Sex_n'] = le_Sex.fit_transform(df1['Sex'])

In [128… df1

Out[128… Pclass Sex Age Fare age_n Sex_n

0 3 male 22.0 7.2500 28 1

1 1 female 38.0 71.2833 51 0

2 3 female 26.0 7.9250 34 0

3 1 female 35.0 53.1000 47 0

4 3 male 35.0 8.0500 47 1

... ... ... ... ... ... ...

886 2 male 27.0 13.0000 35 1

887 1 female 19.0 30.0000 24 0

888 3 female 24.0 23.4500 31 0

889 1 male 26.0 30.0000 34 1

890 3 male 32.0 7.7500 42 1

891 rows × 6 columns

In [132… df2 = df1.drop(['Sex','age_n'], axis='columns')


df2

Out[132… Pclass Age Fare Sex_n

0 3 22.0 7.2500 1

1 1 38.0 71.2833 0

2 3 26.0 7.9250 0

3 1 35.0 53.1000 0

4 3 35.0 8.0500 1

... ... ... ... ...

886 2 27.0 13.0000 1

887 1 19.0 30.0000 0

888 3 24.0 23.4500 0

889 1 26.0 30.0000 1

890 3 32.0 7.7500 1

891 rows × 4 columns

In [102… from sklearn import tree


from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

In [134… X_train, X_test, y_train, y_test = train_test_split(df2, target, test_size=0.3)

In [136… X_train

Out[136… Pclass Age Fare Sex_n

728 2 25.0 26.0000 1

229 3 24.0 25.4667 0

589 3 24.0 8.0500 1

37 3 21.0 8.0500 1

333 3 16.0 18.0000 1

... ... ... ... ...

359 3 24.0 7.8792 0

3 1 35.0 53.1000 0

180 3 24.0 69.5500 0

426 2 28.0 26.0000 0

756 3 28.0 7.7958 1

623 rows × 4 columns

In [138… y_train

Out[138… 728 0
229 0
589 0
37 0
333 0
..
359 1
3 1
180 0
426 1
756 0
Name: Survived, Length: 623, dtype: int64

In [140… classifier = DecisionTreeClassifier()

In [143… classifier.fit(X_train,y_train)

Out[143… ▾ DecisionTreeClassifier i ?

DecisionTreeClassifier()

In [145… y_pred = classifier.predict(X_test)

In [147… y_pred

Out[147… array([0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1,
0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1,
0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0,
1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0,
1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 1, 0, 1], dtype=int64)

In [149… classifier.score(df2,target)

Out[149… 0.9315375982042648

In [151… classifier.score(X_test,y_pred)

Out[151… 1.0

In [155… classifier.predict([[3,25,28,0]])# pclass(1,2,3) ,age , fare , sex -- 0 for female & 1 -- for male

C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names
warnings.warn(
Out[155… array([0], dtype=int64)

In [169… classifier.predict([[1,85,1200,1]])# pclass ,age , fare = money , sex -- 0 for female & 1 -- for male

C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names
warnings.warn(
Out[169… array([1], dtype=int64)

In [179… classifier.predict([[2,38,71000,0]])# pclass ,age , fare , sex -- 0 for female & 1 -- for male

C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names
warnings.warn(
Out[179… array([1], dtype=int64)
In [ ]:

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