| ---
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| language: en
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| license: mit
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| tags:
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| - classification
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| - tabular
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| - titanic
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| - survival-prediction
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| - pytorch
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| datasets:
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| - titanic
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| metrics:
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| - accuracy
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| - f1
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| - precision
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| - recall
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| model-index:
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| - name: Fine_Tuning_Dataset
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| results:
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| - task:
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| type: tabular-classification
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| dataset:
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| name: Titanic
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| type: titanic
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| metrics:
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| - type: accuracy
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| value: 0.6111
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| - type: f1
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| value: 0.0
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| - type: precision
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| value: 0.0
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| - type: recall
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| value: 0.0
|
| ---
|
|
|
| # 🚢 Titanic Survival Classifier
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|
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| A lightweight MLP classifier wrapped in the Hugging Face `PreTrainedModel` interface,
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| trained to predict passenger survival on the Titanic dataset.
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|
|
| ## Model description
|
|
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| | Component | Detail |
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| |-----------|--------|
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| | Architecture | 4-layer MLP with BatchNorm, GELU, Dropout |
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| | Hidden dim | 128 |
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| | Input features | 13 engineered tabular features |
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| | Output | Binary (survived / not survived) |
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| | Parameters | ~12,578 |
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|
|
| ## Training details
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|
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| | Setting | Value |
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| |---------|-------|
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| | Optimizer | AdamW |
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| | Learning rate | 0.001 |
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| | Scheduler | Cosine annealing |
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| | Epochs | 30 |
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| | Batch size | 32 |
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| | Train / Val / Test split | 80 / 10 / 10 % |
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|
|
| ## Feature engineering
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|
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| Features used: `Pclass, Sex, Age, SibSp, Parch, Fare, Embarked, HasCabin, FamilySize, IsAlone, AgeBand, FareBand, Title`
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|
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| Key transformations applied:
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| - **Title extraction** from passenger names (Mr, Mrs, Miss, Master, Rare)
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| - **Age imputation** using median per title group
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| - **FamilySize** = SibSp + Parch + 1; **IsAlone** flag
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| - **HasCabin** binary flag
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| - **AgeBand** and **FareBand** discretisation
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| - StandardScaler normalisation (params saved in `scaler_params.json`)
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|
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| ## Test set performance
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|
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| | Metric | Score |
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| |--------|-------|
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| | Accuracy | 0.6111 |
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| | Precision | 0.0 |
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| | Recall | 0.0 |
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| | F1-Score | 0.0 |
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|
|
| ## How to use
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|
|
| ```python
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| import json, torch, numpy as np
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| from huggingface_hub import hf_hub_download
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| from transformers import PretrainedConfig, PreTrainedModel
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|
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| REPO = "Asimzaman19/Fine_Tuning_Dataset"
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|
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| # Load model
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| model = TitanicClassifier.from_pretrained(REPO)
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| model.eval()
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|
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| # Load scaler params
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| params_path = hf_hub_download(REPO, "scaler_params.json")
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| with open(params_path) as f:
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| sp = json.load(f)
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| mean = np.array(sp["mean"])
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| scale = np.array(sp["scale"])
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|
|
| # Prepare a sample (must match FEATURES order)
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| raw = np.array([[3, 1, 22, 1, 0, 7.25, 0, 0, 2, 0, 1, 0, 0]], dtype=np.float32)
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| scaled = ((raw - mean) / scale).astype(np.float32)
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|
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| with torch.no_grad():
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| logits = model(torch.tensor(scaled)).logits
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| pred = logits.argmax(-1).item()
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| prob = torch.softmax(logits, dim=-1)[0, 1].item()
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|
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| print(f"Survived: {bool(pred)} (prob={prob:.2%})")
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| ```
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|
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| ## Dataset
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|
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| The [Titanic dataset](https://www.kaggle.com/competitions/titanic) contains
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| information about 891 passengers including demographics, ticket class, and
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| fare — with the binary survival label as target.
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|
|
| ## Limitations
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|
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| - Trained on a small historical dataset (891 rows); performance may not
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| generalise beyond the Titanic domain.
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| - Features are hand-engineered; a more robust pipeline would use automated
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| feature selection.
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|
|
| ## License
|
|
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| MIT
|
|
|
