## Random Forest Classifier Scikit-learn Titanic Example

Here’s a quick example of how to leverage [Scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) on JetML.

##### Workflow Setup

1. Add the following python packages to your workflow configuration.

```python
    scikit-learn
    pandas
dtreeviz
    ```

1. Add the following Bash command

```python
    apt-get install -y graphviz
    ```

1. Run the workflow.
2. 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
```

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

```
Load titanic dataset
```

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

```python
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
```

```
Encode string characters for scikit learn
```

```python
full_df = pd.get_dummies(passenger_dataframe)
```

```
Let's choose which features to use
```

```python
features = ['Sex_female','Sex_male','Fare','Age','Pclass']
```  
```python
X = full_df.loc[:, features]
```  
```python
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
```

```python
y = passenger_dataframe.Survived
```

```python
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
```

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

```
Inspect first tree
```

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

```
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.
```

### [converted by JetML](/content/site-root.html)

[download original notebook .ipynb file](https://raw.githubusercontent.com/jetml/Stable-Diffusion-Weather-App/main/weather-stable-diffusion.ipynb)

[Download Jupyter Notebook](/content/wp-content/uploads/2023/01/titanic-reduced.ipynb)