readme: ---
  tags:
  - tabular-classification
  - sklearn
  dataset:
  - titanic
  widget:
    structuredData:
      PassengerId:
        - 1191
      Pclass:
        - 1
      Name:
        - Sherlock Holmes
      Sex:
        - male
      SibSp:
        - 0
      Parch:
        - 0
      Ticket:
        - C.A.29395
      Fare:
        - 12
      Cabin:
        - F44
      Embarked:
        - S
  ---
  
  ## Titanic (Survived/Not Survived) - Binary Classification
  
  ### How to use
  
  ```python
  from huggingface_hub import hf_hub_url, cached_download
  import joblib
  import pandas as pd
  import numpy as np
  from tensorflow.keras.models import load_model
  
  REPO_ID = 'danupurnomo/dummy-titanic'
  PIPELINE_FILENAME = 'final_pipeline.pkl'
  TF_FILENAME = 'titanic_model.h5'
  
  model_pipeline = joblib.load(cached_download(
      hf_hub_url(REPO_ID, PIPELINE_FILENAME)
  ))
  
  model_seq = load_model(cached_download(
      hf_hub_url(REPO_ID, TF_FILENAME)
  ))
  ```
  
  ### Example A New Data
  ```python
  new_data = {
      'PassengerId': 1191,
      'Pclass': 1, 
      'Name': 'Sherlock Holmes', 
      'Sex': 'male', 
      'Age': 30, 
      'SibSp': 0,
      'Parch': 0, 
      'Ticket': 'C.A.29395', 
      'Fare': 12, 
      'Cabin': 'F44', 
      'Embarked': 'S'
  }
  new_data = pd.DataFrame([new_data])
  ```
  
  ### Transform Inference-Set
  ```python
  new_data_transform = model_pipeline.transform(new_data)
  ```
  
  ### Predict using Neural Networks
  ```python
  y_pred_inf_single = model_seq.predict(new_data_transform)
  y_pred_inf_single = np.where(y_pred_inf_single >= 0.5, 1, 0)
  print('Result : ', y_pred_inf_single)
  # [[0]]
  ```