Random Forest Classifier – Scikit-learn Titanic Example

Random Forest Classifier Scikit-learn Titanic Example

Here’s a quick example of how to leverage Scikit-learn on JetML.

Workflow Setup

Add the following python packages to your workflow configuration.

				
					scikit-learn
pandas
dtreeviz

Add the following Bash command

				
					apt-get install -y graphviz

Run the workflow.
Copy over the example code below into a notebook on the running workflow (download link at the bottom).

Example Notebook

Titanic Random Forest Classifier Example

Import libraries

from sklearn.ensemble import RandomForestClassifier
from sklearn import tree
import dtreeviz
import matplotlib.pyplot as plt
import pandas as pd

Load titanic dataset

passenger_dataframe = pd.read_csv('https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv')
passenger_dataframe.head()




  
    
      
      Survived
      Pclass
      Name
      Sex
      Age
      Siblings/Spouses Aboard
      Parents/Children Aboard
      Fare
    
  
  
    
      0
      0
      3
      Mr. Owen Harris Braund
      male
      22.0
      1
      0
      7.2500
    
    
      1
      1
      1
      Mrs. John Bradley (Florence Briggs Thayer) Cum...
      female
      38.0
      1
      0
      71.2833
    
    
      2
      1
      3
      Miss. Laina Heikkinen
      female
      26.0
      0
      0
      7.9250
    
    
      3
      1
      1
      Mrs. Jacques Heath (Lily May Peel) Futrelle
      female
      35.0
      1
      0
      53.1000
    
    
      4
      0
      3
      Mr. William Henry Allen
      male
      35.0
      0
      0
      8.0500

	Survived	Pclass	Name	Sex	Age	Siblings/Spouses Aboard	Fare
0	0	3	Mr. Owen Harris Braund	male	22.0	1	7.2500
1	1	1	Mrs. John Bradley (Florence Briggs Thayer) Cum...	female	38.0	1	71.2833
2	1	3	Miss. Laina Heikkinen	female	26.0	0	7.9250
3	1	1	Mrs. Jacques Heath (Lily May Peel) Futrelle	female	35.0	1	53.1000
4	0	3	Mr. William Henry Allen	male	35.0	0	8.0500

Encode string characters for scikit learn

full_df = pd.get_dummies(passenger_dataframe)
#full_df

Let's choose which features to use

features = ['Sex_female','Sex_male','Fare','Age','Pclass']
X = full_df.loc[:, features]
X.head()

	Sex_female	Sex_male	Fare	Age	Pclass
0	0	1	7.2500	22.0	3
1	1	0	71.2833	38.0	1
2	1	0	7.9250	26.0	3
3	1	0	53.1000	35.0	1
4	0	1	8.0500	35.0	3

Lets set if this person survived or not, this is what we'll predict

y = passenger_dataframe.Survived
y.head()

0    0
1    1
2    1
3    1
4    0
Name: Survived, dtype: int64

Let's create a random forest with 100 trees

rf_n_estimators = 100 #100 trees will be created
rf_depth= 4 #4 decision nodes per tree
model = RandomForestClassifier(n_estimators=rf_n_estimators, random_state=1, max_depth=rf_depth) #define the model
model.fit(X, y) #Train/fit the model, map the passenger's features (X) to the survival (y)
predicted = model.predict(X) #Use the model to predict 
passenger_dataframe['predict'] = predicted #add the new predictions to the initial passenger dataframe

Inspect first tree

viz = dtreeviz.model(model.estimators_[0], X, y, feature_names=X.columns,class_names=["died", "survived"], target_name="Target")
viz.view()

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findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.

Notes about this project

This was a simple example of how to run scikit-learn's Random Forest on JetML. Easily adapt this project to your own dataset and build out your full ML workflow.

Random Forest Classifier Scikit-learn Titanic Example

Workflow Setup

Example Notebook

Titanic Random Forest Classifier Example

Import libraries

Load titanic dataset

Encode string characters for scikit learn

Let's choose which features to use

Lets set if this person survived or not, this is what we'll predict

Let's create a random forest with 100 trees

Inspect first tree

Notes about this project

converted by JetML