☕ Cafe Sales — Random Forest Regressor

A tuned Random Forest Regressor trained on cafe transaction data to predict Total Spent per transaction.

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📊 Dataset

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🧠 Features Used

Price Per Unit was intentionally dropped to avoid trivial prediction (Total Spent = Qty × PPU).

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⚙️ Best Hyperparameters (via RandomizedSearchCV, cv=5)

n_estimators      = 300
max_depth         = 10
min_samples_split = 10
min_samples_leaf  = 2
max_features      = 0.5

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📈 Evaluation Metrics (Test Set — 20%)

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🚀 Usage

import joblib
import pandas as pd
from huggingface_hub import hf_hub_download

# Model download karo
model_path = hf_hub_download(
    repo_id="rohansuyal/cafe-sales-rf-regressor",
    filename="rf_tuned_model.pkl"
)

# Load karo
model = joblib.load(model_path)

# Predict karo
sample = pd.DataFrame([{
    "Item": "Coffee",
    "Quantity": 2.0,
    "Payment Method": "Cash",
    "Location": "In-store",
    "Day_of_Week": 0,
    "Month": 6,
    "Is_Weekend": 0
}])

prediction = model.predict(sample)
print(f"Predicted Total Spent: {prediction[0]:.2f}")

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🛠️ Tech Stack

• Python 3.10
• scikit-learn — RandomForestRegressor, Pipeline, ColumnTransformer
• pandas / numpy — Data processing
• joblib — Model serialization

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📁 Files