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README.md

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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]]
+```

config.json

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+{
+    "sklearn": {
+        "columns": [
+            "PassengerId",
+            "Pclass",
+            "Name",
+            "Sex",
+            "Age",
+            "SibSp",
+            "Parch",
+            "Ticket",
+            "Fare",
+            "Cabin",
+            "Embarked"
+        ],
+        "environment": [
+            "scikit-learn=1.1.1"
+        ],
+        "example_input": {
+            "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"
+            ]
+        },
+        "model": {
+            "file": "sklean_model.jpblib"
+        },
+        "task": "tabular-classificaation"
+    }
+}

final_pipeline.pkl

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+oid sha256:f19010fbf0a972ebd404d9c7c07ff768b6793b51fdeb30122b254617b50bea78
+size 3435

sklearn_model.joblib

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+oid sha256:24aaf13e7124bc8af8d319d2a093e611919eb8b7584aecbe1f4199715f9553ae
+size 4287

titanic_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:15f39eb8d2456faaa095259e186bb6f438698cc0c5a4324c08a4f6a5346d5213
+size 26368