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---
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]]
``` |