Create README.md

README.md

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+---
+tags:
+- tabular-classification
+- sklearn
+- tensorflow
+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
+```
+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
+```
+new_data_transform = model_pipeline.transform(new_data)
+```
+
+### Predict using Neural Networks
+```
+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]]
+```