🚀 Deploying to Hugging Face Space: RidePricingInsightEngine

README.md

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+---
+title: RidePricingInsightEngine
+emoji: 🚗
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: "5.22.0"
+app_file: app.py
+pinned: false
+license: mit
+short_description: ML-powered ride fare prediction using regression
+---
+
+# Dynamic Ride Pricing Optimization
+
+## 🚀 Overview
+
+This project presents a machine learning-based solution for **ride fare prediction and optimization**, using a rich synthetic dataset of historical ride data. By analyzing real-time supply-demand conditions and customer attributes, the system aims to help ride-sharing platforms implement **data-driven dynamic pricing strategies**.
+
+## 📊 Dataset
+
+- **Source**: [Dynamic Pricing Dataset (Kaggle)](https://www.kaggle.com/datasets/arashnic/dynamic-pricing-dataset)
+- **Description**: A **synthetic dataset** designed for ride-sharing fare prediction. Features include:
+  - Number of Riders / Drivers
+  - Location Category
+  - Loyalty Status
+  - Number of Past Rides
+  - Ratings
+  - Booking Time
+  - Vehicle Type
+  - Expected Ride Duration
+  - Historical Cost of Ride
+
+## 🔍 Problem Statement
+
+The objective is to identify which features most influence ride fares and approximate pricing behavior through various **regression models**. This includes:
+- Finding key predictors for optimal fare setting.
+- Estimating price sensitivity across rider and supply characteristics.
+- Offering recommendations for **dynamic fare adjustments** in real time.
+
+## 🧠 Model & Features
+
+- **Regression-based modeling** (e.g., Linear, Ridge, XGBoost)
+- **Final model**: Huber Regressor with non-zero intercept — robust to outliers, encouraging sparse feature usage
+- **Feature selection pipeline** to reduce unnecessary variables
+- **Price approximation engine** that generalizes across different booking conditions
+- **Gradio-powered UI** for hands-on exploration
+
+## 🛠️ Tools and Libraries
+
+- **Python**
+- **Scikit-learn**, **XGBoost**
+- **Pandas**, **NumPy**
+- **Gradio** for app deployment
+- **Plotly** for interactive visualization
+
+## 🧪 How to Run Locally
+
+```bash
+git clone https://github.com/Sharma-Pranav/Portfolio.git
+cd Portfolio/projects/DynamicPricingOptimization
+pip install -r requirements.txt
+python app.py
+```
+
+## 📈 Results
+
+- Clear insights into **fare-driving features** like rider demand, loyalty, and time of day.
+- **Huber Regressor** outperformed others due to robustness and minimal feature reliance.
+- **Optimal pricing zones** visualized for strategic recommendations.
+- Portable deployment for teams to simulate and iterate pricing strategies.
+
+---
+
+> ✨ Developed by **Pranav Sharma** | 🚀 Hugging Face Space: [`RidePricingInsightEngine`](https://huggingface.co/spaces/PranavSharma/RidePricingInsightEngine)

app.py

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+import os
+import numpy as np
+import pandas as pd
+from joblib import dump
+import warnings
+from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet, HuberRegressor
+from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor
+from sklearn.utils.estimator_checks import check_estimator
+from sklearn.utils.metaestimators import available_if
+from sklearn.exceptions import NotFittedError
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.svm import SVR, LinearSVR
+from sklearn.tree import DecisionTreeRegressor
+# from xgboost import XGBRegressor
+# from lightgbm import LGBMRegressor
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.metrics import mean_absolute_error, r2_score
+import plotly.graph_objects as go
+from huggingface_hub import Repository, HfApi, DatasetCardData
+from skops.card import Card
+import pickle
+from pathlib import Path
+from tempfile import mkdtemp
+from skops import hub_utils
+from pathlib import Path
+from tempfile import mkdtemp
+from joblib import dump
+import pickle
+import pandas as pd
+
+# print(os.getcwd())
+# Initialize repository
+User = "PranavSharma"
+repo_name = "dynamic-pricing-model"
+repo_url = f"https://huggingface.co/{User}/{repo_name}"
+
+from skops.card import Card
+import gradio as gr
+
+# Suppress warnings for cleaner output
+warnings.filterwarnings("ignore")
+
+# Configuration variables for paths and parameters
+DATA_PATH = os.path.join("data", "dynamic_pricing.csv")
+
+# Utility function to check if a file exists
+def check_file_exists(file_path):
+    """
+    Check if a file exists at the given path.
+
+    Parameters
+    ----------
+    file_path : str
+        Path to the file.
+
+    Raises
+    ------
+    FileNotFoundError
+        If the file does not exist.
+    """
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"File not found: {file_path}")
+
+# Load and preprocess the dataset
+def load_data():
+    """
+    Load and preprocess the dataset by performing one-hot encoding
+    on categorical variables.
+
+    Returns
+    -------
+    tuple
+        A tuple containing the processed dataset and the list of boolean columns.
+    """
+    check_file_exists(DATA_PATH)
+    data = pd.read_csv(DATA_PATH)
+    data = data.sample(frac=1, random_state=42)  # Shuffle the data
+    categorical_columns = data.select_dtypes(include=["object"]).columns
+    data = pd.get_dummies(data, columns=categorical_columns, drop_first=True)
+    bool_columns = [col for col in data.columns if data[col].dropna().value_counts().index.isin([0, 1]).all()]
+    return data, bool_columns
+
+# Compute default values and feature types for Gradio inputs
+def compute_defaults_and_types(X, bool_columns):
+    defaults = {}
+    types = {}
+    for column in X.columns:
+        if column in bool_columns:
+            defaults[column] = 0
+            types[column] = "Categorical (One-hot)"
+        else:
+            defaults[column] = X[column].mean()
+            types[column] = "Numerical"
+    return defaults, types
+
+# Generate a scatter plot for Expected_Ride_Duration vs Historical_Cost_of_Ride
+def duration_vs_cost_plot(data):
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=data["Expected_Ride_Duration"],
+        y=data["Historical_Cost_of_Ride"],
+        mode="markers",
+        marker=dict(size=8, color="rgba(99, 110, 250, 0.7)", line=dict(width=1, color="rgba(99, 110, 250, 1)")),
+        name="Data Points"
+    ))
+    fig.update_layout(
+        title=dict(text="Expected Ride Duration vs Historical Ride Cost", font=dict(size=18)),
+        xaxis=dict(title="Expected Ride Duration (minutes)", gridcolor="lightgray"),
+        yaxis=dict(title="Historical Ride Cost ($)", gridcolor="lightgray"),
+        template="plotly_white"
+    )
+    return fig
+
+# Generate MAE and R² plots with GridSearchCV
+def performance_plots_with_gridsearch(results):
+    X_train = results["X_train"]
+    y_train = results["y_train"]
+    X_test = results["X_test"]
+    y_test = results["y_test"]
+    train_sizes = np.linspace(50, len(X_train), 10, dtype=int)
+
+    mae_scores = []
+    r2_scores = []
+
+    param_grid = {"alpha": np.logspace(-4, 0, 10)}
+
+    for train_size in train_sizes:
+        X_train_sub = X_train.iloc[:train_size]
+        y_train_sub = y_train.iloc[:train_size]
+
+        grid_search = GridSearchCV(
+            Lasso(fit_intercept=False),
+            param_grid,
+            scoring="neg_mean_absolute_error",
+            cv=5
+        )
+        grid_search.fit(X_train_sub, y_train_sub)
+        best_model = grid_search.best_estimator_
+
+        y_pred = best_model.predict(X_test)
+        mae_scores.append(mean_absolute_error(y_test, y_pred))
+        r2_scores.append(r2_score(y_test, y_pred))
+
+    mae_fig = go.Figure()
+    mae_fig.add_trace(go.Scatter(
+        x=train_sizes,
+        y=mae_scores,
+        mode="lines+markers",
+        marker=dict(size=6, color="blue"),
+        line=dict(width=2, color="blue"),
+        name="MAE"
+    ))
+    mae_fig.update_layout(
+        title="Effect of Training Size on MAE (with GridSearchCV)",
+        xaxis_title="Training Size",
+        yaxis_title="Mean Absolute Error (MAE)",
+        template="plotly_white"
+    )
+
+    r2_fig = go.Figure()
+    r2_fig.add_trace(go.Scatter(
+        x=train_sizes,
+        y=r2_scores,
+        mode="lines+markers",
+        marker=dict(size=6, color="green"),
+        line=dict(width=2, color="green"),
+        name="R²"
+    ))
+    r2_fig.update_layout(
+        title="Effect of Training Size on R² (with GridSearchCV)",
+        xaxis_title="Training Size",
+        yaxis_title="R² Score",
+        template="plotly_white"
+    )
+
+    return mae_fig, r2_fig
+
+# Generate coefficient progression plot with tracking
+# Generate coefficient progression plot with tracking
+def coefficients_progression_plot_with_tracking(results):
+    X_train = results["X_train"]
+    y_train = results["y_train"]
+    train_sizes = np.linspace(50, len(X_train), 10, dtype=int)
+
+    coefficients_progress = []
+    feature_names = results["feature_names"]
+
+    param_grid = {"alpha": np.logspace(-4, 0, 10)}
+
+    for train_size in train_sizes:
+        X_train_sub = X_train.iloc[:train_size]
+        y_train_sub = y_train.iloc[:train_size]
+
+        grid_search = GridSearchCV(
+            Lasso(fit_intercept=False),
+            param_grid,
+            scoring="neg_mean_absolute_error",
+            cv=5
+        )
+        grid_search.fit(X_train_sub, y_train_sub)
+        best_model = grid_search.best_estimator_
+
+        coefficients_progress.append(best_model.coef_)
+
+    coefficients_array = np.array(coefficients_progress)
+
+    fig = go.Figure()
+    for idx, feature in enumerate(feature_names):
+        fig.add_trace(go.Scatter(
+            x=train_sizes,
+            y=coefficients_array[:, idx],
+            mode="lines+markers",
+            name=feature,
+            line=dict(width=2),
+            marker=dict(size=6, opacity=0.8)
+        ))
+    fig.update_layout(
+        title="Coefficient Progression with Training Size (Tracking)",
+        xaxis_title="Training Size",
+        yaxis_title="Coefficient Value",
+        template="plotly_white",
+        height=700,  # Increased height for better vertical visibility
+        legend=dict(
+            orientation="h",  # Horizontal legend
+            y=-0.3,  # Position legend below the plot
+            x=0.5,
+            xanchor="center"
+        )
+    )
+    return fig
+
+
+# New function to evaluate multiple linear models using GridSearchCV
+def train_linear_models_with_gridsearch(X_train, y_train, X_test, y_test):
+    """
+    Train and evaluate multiple linear models using GridSearchCV and compare their performance.
+
+    Parameters
+    ----------
+    X_train : pd.DataFrame
+        Training feature set.
+    y_train : pd.Series
+        Training target variable.
+    X_test : pd.DataFrame
+        Testing feature set.
+    y_test : pd.Series
+        Testing target variable.
+
+    Returns
+    -------
+    dict
+        A dictionary containing the best model, its parameters, and performance metrics.
+    """
+    models = {
+        "Lasso": {
+            "model": Lasso(fit_intercept=False),
+            "param_grid": {"alpha": [0.001, 0.01, 0.1, 1]},
+        },
+        "Ridge": {
+            "model": Ridge(fit_intercept=False),
+            "param_grid": {"alpha": [0.001, 0.01, 0.1, 1]},
+        },
+        "ElasticNet": {
+            "model": ElasticNet(fit_intercept=False),
+            "param_grid": {
+                "alpha": [0.001, 0.01, 0.1, 1],
+                "l1_ratio": [0.2, 0.5, 0.8],
+            },
+        },
+        "LinearRegression": {
+            "model": LinearRegression(fit_intercept=False),
+            "param_grid": {},  # No hyperparameters for tuning
+        },
+        "HuberRegressor": {
+            "model": HuberRegressor(fit_intercept=False),
+            "param_grid": {"epsilon": [1.2, 1.5], "alpha": [0.001, 0.01]},
+        },
+        "KNeighborsRegressor": {
+            "model": KNeighborsRegressor(),
+            "param_grid": {"n_neighbors": [3, 5, 7], "weights": ["uniform", "distance"]},
+        },
+        "DecisionTreeRegressor": {
+            "model": DecisionTreeRegressor(),
+            "param_grid": {
+                "max_depth": [None, 10, 20],
+                "min_samples_split": [2, 5],
+                "min_samples_leaf": [1, 2],
+            },
+        },
+        "RandomForestRegressor": {
+            "model": RandomForestRegressor(random_state=42),
+            "param_grid": {
+                "n_estimators": [50, 100],
+                "max_depth": [10, 20, None],
+                "min_samples_split": [2, 5],
+            },
+        },
+        "GradientBoostingRegressor": {
+            "model": GradientBoostingRegressor(random_state=42),
+            "param_grid": {
+                "n_estimators": [50, 100],
+                "learning_rate": [0.05, 0.1],
+                "max_depth": [3, 5],
+            },
+        },
+        "AdaBoostRegressor": {
+            "model": AdaBoostRegressor(random_state=42),
+            "param_grid": {
+                "n_estimators": [50, 100],
+                "learning_rate": [0.05, 0.1],
+            },
+        },
+        "SVR": {
+            "model": SVR(),
+            "param_grid": {
+                "C": [0.1, 1],
+                "epsilon": [0.01, 0.1],
+                "kernel": ["linear", "rbf"],
+            },
+        },
+        "LinearSVR": {
+            "model": LinearSVR(random_state=42),
+            "param_grid": {"C": [0.1, 1]},
+        },
+    }
+
+    results = []
+    best_model = None
+    best_result = None
+    for name, config in models.items():
+        try:
+            grid_search = GridSearchCV(
+                config["model"],
+                config["param_grid"],
+                scoring="neg_mean_absolute_error",
+                cv=5
+            )
+            grid_search.fit(X_train, y_train)
+
+            # Predictions and evaluation
+            y_pred = grid_search.best_estimator_.predict(X_test)
+            mae = mean_absolute_error(y_test, y_pred)
+            r2 = r2_score(y_test, y_pred)
+
+            # Collect results
+            results.append({
+                "model": name,
+                "best_params": grid_search.best_params_,
+                "mae": mae,
+                "r2": r2,
+                "best_estimator": grid_search.best_estimator_,
+            })
+
+        except Exception as e:
+            print(f"Error training model {name}: {e}")
+
+    # Identify the best model based on MAE
+    if results:
+        best_result = min(results, key=lambda x: x["mae"])
+        best_model = best_result["best_estimator"]
+
+    return {
+        "results": results,
+        "best_model_name": best_result["model"] if best_result else None,
+        "best_model_metrics": best_result if best_result else None,
+        "best_model": best_model,  # Return the best model directly
+    }
+
+def train_model():
+    original_data = pd.read_csv(DATA_PATH)
+    data, bool_columns = load_data()
+    X = data.drop("Historical_Cost_of_Ride", axis=1)
+    y = data["Historical_Cost_of_Ride"]
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Get the best linear model and top results
+    linear_model_results = train_linear_models_with_gridsearch(X_train, y_train, X_test, y_test)
+    best_model_name = linear_model_results["best_model_name"]
+    best_model_metrics = linear_model_results["best_model_metrics"]
+    top_models = linear_model_results["results"]  # Get all models' results
+    best_model = linear_model_results["best_model"]
+    y_pred = best_model.predict(X_test)
+    mae = mean_absolute_error(y_test, y_pred)
+    r2 = r2_score(y_test, y_pred)
+
+    feature_names = X_train.columns
+    coefficients = best_model.coef_
+
+    useful_features = [(feature, coef) for feature, coef in zip(feature_names, coefficients) if coef != 0]
+    not_useful_features = [feature for feature, coef in zip(feature_names, coefficients) if coef == 0]
+
+    equation_terms = [f"*{coef:.4f}* × *{feature}*" for feature, coef in useful_features]
+    regression_equation = " + ".join(equation_terms)
+    regression_equation = "Cost of Ride = " + regression_equation
+
+    actual_vs_pred_plot = actual_vs_predicted_plot(y_test, y_pred)
+    useful_features_formatted = "\n".join(
+        [f"- {feature}: {coef:.4f}" for feature, coef in useful_features]
+    )
+    not_useful_features_formatted = "\n".join(
+        [f"- {feature}" for feature in not_useful_features]
+    )
+
+    default_values, types = compute_defaults_and_types(X_train, bool_columns)
+
+    scatter_plot = duration_vs_cost_plot(original_data)
+
+    # Generate a DataFrame for the top 10 models
+    top_models_sorted = sorted(top_models, key=lambda x: x['mae'])[:10]
+    top_models_df = pd.DataFrame.from_records(
+        [
+            {
+                "Rank": idx + 1,
+                "Model": result["model"],
+                "MAE": f"{result['mae']:.4f}",
+                "R²": f"{result['r2']:.4f}",
+                "Best Params": result["best_params"],
+            }
+            for idx, result in enumerate(top_models_sorted)
+        ]
+    )
+    top_models_html = top_models_df.to_html(index=False, border=0, classes="table table-striped")
+
+    return {
+        "X_train": X_train,
+        "y_train": y_train,
+        "X_test": X_test,
+        "y_test": y_test,
+        "y_pred": y_pred,
+        "feature_names": feature_names,
+        "coefficients": coefficients,
+        "mae": mae,
+        "r2": r2,
+        "best_model_name": best_model_name,
+        "best_model_metrics": best_model_metrics,
+        "best_model": best_model,
+        "regression_equation": regression_equation,
+        "scatter_plot": scatter_plot,
+        "useful_features": useful_features_formatted,
+        "not_useful_features": not_useful_features_formatted,
+        "top_models_html": top_models_html,  # Include HTML table here
+        "default_values": default_values,
+        "feature_types": types,
+        "original_data_html": original_data.head(3).to_html(classes="table table-striped"),
+        "original_data": original_data,
+        "actual_vs_predicted_plot": actual_vs_pred_plot
+        
+    }
+
+def process_features_with_values(feature_string):
+        """Cleans and splits the feature string, retaining both feature names and values."""
+        if not feature_string:
+            return []
+        feature_string = feature_string.strip()
+        formatted_features = []
+        for item in feature_string.split("-"):
+            if not item.strip():
+                continue
+            if item.strip().replace(".", "", 1).isdigit():  # Check if the item is a float
+                if formatted_features:
+                    formatted_features[-1] = formatted_features[-1].strip() + ": " + item.strip() + "\n"
+            else:
+                formatted_features.append(" ".join(item.split()) + "\n")  # Clean extra spaces and add
+        return formatted_features
+
+def process_features_without_values(feature_string):
+    """Cleans and splits the feature string, keeping only feature names."""
+    if not feature_string:
+        return []
+    feature_string = feature_string.strip()
+    return [
+        item.split(":")[0].strip() + "\n"  # Keep only the feature name before ":"
+        for item in feature_string.split("-")
+        if item.strip()
+    ]
+
+def actual_vs_predicted_plot(y_actual, y_pred):
+    """
+    Create a scatter plot for Actual vs Predicted values.
+
+    Parameters
+    ----------
+    y_actual : array-like
+        Actual target values.
+    y_pred : array-like
+        Predicted target values.
+
+    Returns
+    -------
+    go.Figure
+        A Plotly scatter plot.
+    """
+    fig = go.Figure()
+
+    # Add scatter points
+    fig.add_trace(go.Scatter(
+        x=y_actual,
+        y=y_pred,
+        mode="markers",
+        marker=dict(size=8, color="rgba(99, 110, 250, 0.7)", line=dict(width=1)),
+        name="Actual vs Predicted"
+    ))
+
+    # Add ideal reference line
+    min_val = min(min(y_actual), min(y_pred))
+    max_val = max(max(y_actual), max(y_pred))
+    fig.add_trace(go.Scatter(
+        x=[min_val, max_val],
+        y=[min_val, max_val],
+        mode="lines",
+        line=dict(dash="dash", color="gray"),
+        name="Ideal Line"
+    ))
+
+    # Update layout
+    fig.update_layout(
+        title="Actual vs Predicted Values",
+        xaxis_title="Actual Values",
+        yaxis_title="Predicted Values",
+        template="plotly_white"
+    )
+    fig.add_annotation(
+        x=max_val,
+        y=max_val,
+        text="Ideal Line (y=x)",
+        showarrow=True,
+        arrowhead=2
+    )
+    return fig
+
+
+def train_model_button():
+    """
+    Train the model and return all relevant outputs for display.
+    Save a model card documenting the results using skops 0.10.0.
+    Push the model and card to Hugging Face Hub.
+    """
+
+    # Train the model and get the results
+    comprehensive_interface.trained_model = train_model()
+    results = comprehensive_interface.trained_model
+
+    # Extract results
+    mae = results["mae"]
+    r2 = results["r2"]
+    scatter_plot = results["scatter_plot"]
+    regression_equation = results["regression_equation"]
+    coefficients = results["coefficients"]  # NumPy array of coefficient values
+    feature_names = results["feature_names"]  # Ensure feature names are provided
+    coefficients_plot = coefficients_progression_plot_with_tracking(results)
+    mae_plot, r2_plot = performance_plots_with_gridsearch(results)
+    original_data_html = results["original_data_html"]
+    original_data = results["original_data"]
+    actual_vs_pred_plot = results["actual_vs_predicted_plot"]
+
+    feature_importance_text = (
+        f"### Useful Features:\n"
+        + "".join(
+            [
+                f"- {feature}: {coef:.4f} "
+                f"(e.g., a unit increase in {feature} affects the cost by ${coef:.2f})\n"
+                for feature, coef in zip(
+                    results["useful_features"].splitlines(), 
+                    [float(line.split(":")[1]) for line in results["useful_features"].splitlines()]
+                )
+            ]
+        )
+        + "\n\n### Non-Useful Features:\n"
+        + "".join([f"- {feature}\n" for feature in results["not_useful_features"].splitlines()])
+    )
+
+    # Save the best model using joblib
+    model_path = "best_model.joblib"
+    dump(results["best_model"], model_path)
+
+    # Initialize a temporary repository
+    local_repo = mkdtemp(prefix="skops-")
+
+    # Save the model as a pickle file
+    pkl_name = "best_model.pkl"
+    with open(pkl_name, mode="wb") as f:
+        pickle.dump(results["best_model"], f)
+
+    # Initialize repository for Hugging Face Hub
+    hub_utils.init(
+        model=pkl_name,
+        requirements=["scikit-learn"],
+        dst=local_repo,
+        task="tabular-regression",
+        data=original_data,
+    )
+
+    # Prepare coefficients table
+    coefficients_text = ""#"### Model Coefficients:\n\n"
+    coefficients_text += "| Feature | Coefficient |\n|---------|-------------|\n"
+    coefficients_text += "\n".join(
+        [f"| {feature} | {value:.4f} |" for feature, value in zip(feature_names, coefficients)]
+    )
+
+    # Prepare hyperparameters
+    hyperparameters = results["best_model"].get_params()
+
+    hyperparameters_text = "### Hyperparameters:\n\n"
+    hyperparameters_text += "\n".join([f"- {param}: {value}" for param, value in hyperparameters.items()])
+
+    # Convert Plotly plot to an inline image for Markdown
+    actual_vs_pred_plot_path = Path(local_repo) / "actual_vs_predicted.png"
+    actual_vs_pred_plot.write_image(str(actual_vs_pred_plot_path), format="png", scale=2)
+
+    # Embed image in Markdown with a description
+    actual_vs_pred_plot_md = (
+        #"### Actual vs Predicted Plot\n\n"
+        "The following plot shows the relationship between the actual and predicted values. "
+        "The closer the points are to the diagonal line, the better the predictions. "
+        "The dashed line represents the ideal case where predictions perfectly match the actual values.\n\n"
+        "![Actual vs Predicted Plot](actual_vs_predicted.png)"
+    )
+
+    # Create and save the model card
+    metadata = DatasetCardData(
+        language=["en"],
+        license="apache-2.0",
+        annotations_creators=["machine-generated"],
+        language_creators=["found"],
+        multilinguality="monolingual",
+        size_categories="10K<n<100K",
+        source_datasets=["original"],
+        task_categories=["regression"],
+        task_ids=["dynamic-pricing"],
+        pretty_name="Dynamic Pricing Model",
+    )
+    card = Card(model=pkl_name, metadata=metadata)
+    model_description = (
+        "This is a regression model trained on the Dynamic Pricing Dataset. "
+        "It was optimized using grid search with multiple hyperparameters."
+    )
+    card.add(
+    **{
+        "Model description": model_description,
+        "Model description/Intended uses & limitations": (
+            "This regression model is designed to predict the cost of rides based on various features such as expected ride duration, "
+            "number of drivers, and time of booking.\n\n"
+            "**Intended Uses**:\n"
+            "- **Dynamic Pricing Analysis**: Helps optimize pricing strategies for ride-hailing platforms.\n"
+            "- **Demand Forecasting**: Supports business decisions by estimating cost trends based on ride-specific parameters.\n\n"
+            "**Limitations**:\n"
+            "- **Feature Dependence**: The model's accuracy is highly dependent on the input features provided.\n"
+            "- **Dataset Specificity**: Performance may degrade if applied to datasets with significantly different distributions.\n"
+            "- **Outlier Sensitivity**: Predictions can be affected by extreme values in the dataset."
+        ),
+        "Model description/Training Procedure": "The model was trained using grid search to optimize hyperparameters. Cross-validation (5-fold) was performed to ensure robust evaluation. The best model was selected based on the lowest Mean Absolute Error (MAE) on the validation set.",
+        #"Hyperparameters": hyperparameters_text,
+        "Model description/Evaluation Results/Model Coefficients": coefficients_text,
+        "Model description/Evaluation Results/Regression Equation": regression_equation,
+        "Model description/Evaluation Results/Actual vs Predicted": (
+            actual_vs_pred_plot_md + "\n\n"
+            "The scatter plot above shows the predicted values against the actual values. The dashed line represents the ideal predictions "
+            "where the predicted values are equal to the actual values."
+        ),
+        "Model description/Evaluation Results": (
+            "The model achieved the following results on the test set:\n"
+            f"- **Mean Absolute Error (MAE)**: {mae}\n"
+            f"- **R² Score**: {r2}\n\n"
+            "### Key Insights:\n"
+            "- Longer ride durations increase costs significantly, which may justify adding a surcharge for long-distance rides.\n"
+            "- Evening bookings reduce costs, potentially indicating lower demand during these hours.\n"
+            "- The model's accuracy is dependent on high-quality feature data.\n"
+
+            "\nRefer to the plots and tables for detailed performance insights."
+        ),
+        "How to Get Started with the Model": (
+            "To use this model:\n"
+            "1. **Install Dependencies**: Ensure `scikit-learn` and `pandas` are installed in your environment.\n"
+            "2. **Load the Model**: Download the saved model file and load it using `joblib`:\n"
+            "   ```python\n"
+            "   from joblib import load\n"
+            "   model = load('best_model.joblib')\n"
+            "   ```\n"
+            "3. **Prepare Input Features**: Create a DataFrame with the required input features in the same format as the training dataset.\n"
+            "4. **Make Predictions**: Use the `predict` method to generate predictions:\n"
+            "   ```python\n"
+            "   predictions = model.predict(input_features)\n"
+            "   ```"
+        ),
+        "Model Card Authors": "This model card was written by **Pranav Sharma**.",
+        "Model Card Contact": "For inquiries or feedback, you can contact the author via **[GitHub](https://github.com/PranavSharma)**.",
+        "Citation": (
+            "If you use this model, please cite it as follows:\n"
+            "```\n"
+            "@model{pranav_sharma_dynamic_pricing_model_2025,\n"
+            "  author       = {Pranav Sharma},\n"
+            "  title        = {Dynamic Pricing Model},\n"
+            "  year         = {2025},\n"
+            "  version      = {1.0.0},\n"
+            "  url          = {https://huggingface.co/PranavSharma/dynamic-pricing-model}\n"
+            "}\n"
+            "```"
+        ),
+    }
+)
+
+
+    card_path = Path(local_repo) / "README.md"
+    card.save(card_path)
+    print("Model card saved as README.md")
+
+    # Push model and card to Hugging Face Hub
+    try:
+        hub_utils.push(
+            repo_id=f"{User}/{repo_name}",
+            source=local_repo,
+            commit_message="Pushing model and README files to the repo!",
+            create_remote=True,
+        )
+        print("Model and card pushed to Hugging Face Hub.")
+    except Exception as e:
+        print(f"Failed to push to Hugging Face Hub: {e}")
+
+    # Return outputs for display in Gradio
+    return (
+        "Model trained successfully and pushed to Hugging Face Hub!",
+        scatter_plot,
+        regression_equation,
+        mae_plot,
+        r2_plot,
+        coefficients_plot,
+        actual_vs_pred_plot,  # New output added
+        results["top_models_html"],
+        original_data_html,
+        feature_importance_text,
+    )
+
+
+
+# Updated prediction functionality to ensure other outputs are consistent
+def use_trained_model_button(*inputs):
+    """
+    Use the existing trained model for predictions and return relevant outputs.
+    """
+    if "trained_model" not in comprehensive_interface.__dict__:
+        return "No trained model found. Please train the model first.", None, None, None, None, None, None, None, None
+
+    results = comprehensive_interface.trained_model
+
+    if any(inputs):
+        user_inputs = list(inputs)
+        try:
+            custom_prediction = results["best_model"].predict([user_inputs])[0]
+            prediction_result = f"Custom Prediction: {custom_prediction:.2f}"
+        except NotFittedError:
+            prediction_result = "Trained model is not properly fitted. Please train the model again."
+    else:
+        prediction_result = "No custom input provided."
+
+    scatter_plot = results["scatter_plot"]
+    regression_equation = results["regression_equation"]
+    coefficients_plot = coefficients_progression_plot_with_tracking(results)
+    mae_plot, r2_plot = performance_plots_with_gridsearch(results)
+    original_data_html = results["original_data_html"]
+    top_models_html = results["top_models_html"]
+    feature_importance = (
+        f"### Useful Features:\n {results['useful_features']}\n\n"
+        f"### Non-Useful Features:\n {results['not_useful_features']}"
+    )
+
+    return (
+        prediction_result,
+        scatter_plot,
+        regression_equation,
+        mae_plot,
+        r2_plot,
+        coefficients_plot,
+        f"<h3>Top 10 Models</h3>{top_models_html}",
+        f"<h3>Original Dataset</h3>{original_data_html}",
+        feature_importance,
+    )
+
+# Comprehensive interface function
+def comprehensive_interface(*inputs):
+    if "trained_model" not in comprehensive_interface.__dict__:
+        comprehensive_interface.trained_model = train_model()
+
+    results = comprehensive_interface.trained_model
+    scatter_plot = results["scatter_plot"]
+    regression_equation = results["regression_equation"]
+    coefficients_plot = coefficients_progression_plot_with_tracking(results)
+    mae_plot, r2_plot = performance_plots_with_gridsearch(results)
+    original_data_html = results["original_data_html"]
+    top_models_html = results["top_models_html"]
+
+    # Ensure useful and non-useful features are properly formatted
+    useful_features = results.get("useful_features", "")
+    not_useful_features = results.get("not_useful_features", "")
+
+    # Process useful features (retain values) and non-useful features (omit values)
+    useful_features = process_features_with_values("".join(useful_features))
+    not_useful_features = process_features_without_values("".join(not_useful_features))
+
+    # Create feature importance display
+    feature_importance = (
+        f"### Useful Features:\n " + "".join(useful_features) + "\n\n"
+        f"### Non-Useful Features:\n " + "".join(not_useful_features)
+    )
+
+    # Prediction logic
+    if any(inputs):
+        user_inputs = list(inputs)
+        custom_prediction = results["best_model"].predict([user_inputs])[0]
+        prediction_result = f"Custom Prediction: {custom_prediction:.2f}"
+    else:
+        prediction_result = "No custom input provided."
+
+    return (
+        prediction_result,  # Return only the prediction for the prediction output
+        scatter_plot,
+        regression_equation,
+        mae_plot,
+        r2_plot,
+        coefficients_plot,
+        f"<h3>Top 10 Models</h3>{top_models_html}",
+        f"<h3>Original Dataset</h3>{original_data_html}",
+        feature_importance,  # Include feature importance in the outputs
+    )
+
+# Generate Gradio inputs dynamically
+def generate_gradio_inputs():
+    results = train_model()
+    inputs = []
+    for feature, default in results["default_values"].items():
+        feature_type = results["feature_types"][feature]
+        inputs.append(gr.Number(label=f"{feature} ({feature_type}, e.g., {default})", value=default))
+    return inputs
+# Layout with proper updates for all outputs
+with gr.Blocks() as demo:
+    gr.Markdown("# Dynamic Pricing Model - Comprehensive Analysis")
+    gr.Markdown(
+        "Train a range of regression models, view metrics, selection of best models, coefficients, and make custom predictions."
+    )
+
+    # Outputs Section (Top)
+    with gr.Row():
+        with gr.Column():
+            scatter_plot_output = gr.Plot(label="Scatter Plot")
+            original_data_output = gr.HTML(label="Original Dataset")
+            top_models_output = gr.HTML(label="Top 10 Models")
+        with gr.Column():
+            actual_vs_predicted_output = gr.Plot(label="Actual vs Predicted Plot")
+            mae_plot_output = gr.Plot(label="MAE Plot")
+            r2_plot_output = gr.Plot(label="R² Plot")
+            
+        with gr.Column():
+            coeff_plot_output = gr.Plot(label="Coefficient Progression")
+            regression_eq_output = gr.Textbox(label="Regression Equation")
+            output_feat_importance = gr.Textbox(label="Feature Importance (Useful vs Non-Useful)")
+
+    # Inputs Section
+    gr.Markdown("### Input Features")
+    inputs = generate_gradio_inputs()
+    with gr.Row():
+        input_fields = [input for input in inputs]
+    with gr.Row():
+        train_button = gr.Button("Train Model")
+        predict_button = gr.Button("Use Trained Model for Prediction")
+
+    # Predictions Section (Below Inputs)
+    with gr.Row():
+        prediction_output = gr.Textbox(label="Result")
+
+    # Connect training button
+    train_button.click(
+    fn=train_model_button,
+    inputs=[],
+    outputs=[
+        prediction_output,
+        scatter_plot_output,
+        regression_eq_output,
+        mae_plot_output,
+        r2_plot_output,
+        coeff_plot_output,
+        actual_vs_predicted_output,  # New output
+        top_models_output,
+        original_data_output,
+        output_feat_importance,
+    ],
+    )
+
+    # Connect prediction button
+    predict_button.click(
+        fn=use_trained_model_button,
+        inputs=input_fields,
+        outputs=[
+            prediction_output,
+            scatter_plot_output,
+            regression_eq_output,
+            mae_plot_output,
+            r2_plot_output,
+            coeff_plot_output,
+            top_models_output,
+            original_data_output,
+            output_feat_importance,
+        ],
+    )
+
+demo.launch()

create_repo.py

@@ -0,0 +1,7 @@
+from huggingface_hub import HfApi
+
+repo_name = "dynamic-pricing-model"
+api = HfApi()
+
+# Create a new repository
+api.create_repo(repo_id=repo_name, repo_type="model", exist_ok=False)

data/dynamic_pricing.csv

@@ -0,0 +1,1001 @@
+Number_of_Riders,Number_of_Drivers,Location_Category,Customer_Loyalty_Status,Number_of_Past_Rides,Average_Ratings,Time_of_Booking,Vehicle_Type,Expected_Ride_Duration,Historical_Cost_of_Ride
+90,45,Urban,Silver,13,4.47,Night,Premium,90,284.25727302188227
+58,39,Suburban,Silver,72,4.06,Evening,Economy,43,173.87475274940766
+42,31,Rural,Silver,0,3.99,Afternoon,Premium,76,329.7954689622466
+89,28,Rural,Regular,67,4.31,Afternoon,Premium,134,470.2012317513198
+78,22,Rural,Regular,74,3.77,Afternoon,Economy,149,579.6814223640141
+59,35,Urban,Silver,83,3.51,Night,Economy,128,339.9553606047221
+93,43,Suburban,Regular,44,4.41,Afternoon,Premium,16,104.06154127359153
+62,39,Rural,Gold,83,3.59,Afternoon,Premium,47,235.81186355604996
+79,14,Rural,Silver,71,3.74,Evening,Economy,128,501.4125174833056
+42,6,Rural,Silver,21,3.85,Night,Premium,128,398.99336457061025
+86,17,Urban,Regular,99,4.69,Morning,Economy,167,669.2986264658781
+60,38,Rural,Gold,15,4.14,Evening,Premium,144,414.9901047325251
+24,8,Rural,Gold,50,4.12,Morning,Economy,164,490.41072171830416
+36,24,Suburban,Silver,88,4.22,Night,Premium,83,296.24287599192223
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+46,14,Suburban,Silver,21,4.71,Afternoon,Economy,15,47.31430863361691
+33,7,Suburban,Gold,49,4.31,Evening,Premium,22,129.8082629093964
+31,7,Suburban,Silver,24,4.82,Evening,Premium,87,382.9481103198511
+96,45,Urban,Regular,6,4.95,Afternoon,Premium,11,82.77279318243978
+22,11,Suburban,Silver,79,4.48,Night,Economy,15,64.07117255500656
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+24,12,Urban,Regular,10,3.6,Evening,Premium,45,212.16652497375455
+48,19,Suburban,Gold,72,3.81,Night,Premium,65,235.65813370551408
+56,13,Rural,Regular,36,4.81,Evening,Economy,162,406.5574980533908
+72,33,Rural,Gold,66,4.39,Morning,Premium,30,182.4744270596517
+97,75,Urban,Silver,76,4.7,Night,Economy,158,652.617297300081
+62,33,Suburban,Regular,39,3.91,Evening,Premium,129,403.22709836941993
+57,38,Suburban,Silver,54,4.1,Night,Premium,131,537.7848594528473
+25,7,Suburban,Silver,13,3.99,Morning,Premium,81,286.13145256368455
+75,56,Suburban,Gold,94,4.79,Evening,Premium,153,485.0546633356366
+39,9,Urban,Gold,39,4.64,Afternoon,Premium,48,171.0859835160851
+35,24,Suburban,Gold,73,3.76,Evening,Premium,105,485.8784537318511
+68,32,Suburban,Regular,34,3.58,Morning,Economy,77,294.7328864777875
+96,43,Rural,Silver,97,3.68,Morning,Premium,91,392.83872040801793
+28,11,Suburban,Gold,52,4.33,Afternoon,Premium,94,456.1809467866996
+25,15,Rural,Gold,65,4.88,Evening,Premium,101,502.52932078734773
+28,6,Suburban,Regular,2,4.29,Morning,Economy,89,244.21766165291973
+73,17,Rural,Regular,14,4.81,Evening,Premium,170,634.7265312250814
+36,18,Urban,Silver,88,3.73,Night,Premium,92,345.40676830595487
+56,10,Urban,Silver,55,4.5,Night,Economy,45,151.15212964816465
+97,81,Suburban,Regular,52,4.56,Evening,Economy,125,530.7599938912294
+63,9,Rural,Regular,89,3.87,Afternoon,Premium,166,482.85016419959805
+78,33,Urban,Silver,35,4.46,Morning,Premium,116,355.9595074862448
+29,10,Suburban,Regular,25,3.77,Afternoon,Economy,91,303.9609626071891
+73,33,Suburban,Silver,60,3.88,Night,Premium,161,514.8771742414517
+94,29,Urban,Regular,67,3.59,Evening,Premium,96,361.13235495172165
+54,28,Suburban,Silver,83,4.49,Night,Economy,32,80.26246462184758
+67,6,Rural,Gold,15,3.53,Night,Economy,123,420.6239113101238
+23,8,Urban,Silver,27,4.73,Afternoon,Economy,117,520.8926706123274
+70,14,Urban,Silver,16,4.3,Morning,Economy,53,183.07579104191225
+20,10,Rural,Silver,70,4.92,Evening,Economy,65,190.13746358777752
+95,84,Suburban,Gold,18,3.86,Afternoon,Premium,112,522.3021028701461
+43,16,Urban,Silver,40,4.89,Night,Premium,106,410.6863449474928
+97,55,Suburban,Silver,94,4.62,Evening,Premium,150,584.9958711291002
+32,20,Rural,Silver,44,3.55,Night,Economy,68,301.62262644973293
+95,25,Suburban,Regular,72,4.16,Morning,Premium,128,413.5870840445325
+47,15,Urban,Gold,14,4.84,Night,Economy,89,273.11810461931896
+25,11,Rural,Silver,33,3.85,Night,Economy,94,247.1745626508575
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+59,39,Suburban,Gold,49,3.78,Morning,Premium,98,295.79438015283176
+42,20,Urban,Silver,88,4.64,Night,Premium,29,145.0698091748401
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+40,29,Urban,Regular,72,4.3,Afternoon,Economy,147,624.9860100296204
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+72,43,Suburban,Gold,28,4.87,Afternoon,Economy,132,443.25815535245397
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+56,15,Urban,Gold,34,4.72,Night,Premium,78,313.9950046053331
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+77,25,Rural,Regular,70,4.27,Afternoon,Premium,52,260.34417984860374
+27,6,Urban,Gold,2,4.42,Night,Economy,86,320.30170917475425
+74,34,Rural,Regular,18,4.66,Morning,Premium,140,454.49212153190336
+92,69,Urban,Silver,88,4.69,Afternoon,Economy,18,45.11502370436019
+77,34,Rural,Gold,33,4.13,Night,Premium,48,223.66211429318122
+66,6,Rural,Regular,23,4.2,Evening,Economy,45,173.1577542620492
+77,39,Urban,Regular,23,4.29,Morning,Economy,167,592.240499627505
+35,16,Urban,Regular,31,4.75,Afternoon,Premium,113,379.3078104185831
+48,38,Rural,Gold,40,3.7,Evening,Premium,80,360.71117031918743
+61,18,Rural,Gold,84,3.93,Evening,Premium,58,203.49261837786295
+71,21,Suburban,Silver,82,4.88,Afternoon,Premium,126,553.5451307734787
+95,7,Rural,Gold,40,4.68,Evening,Economy,95,283.46644254307614
+76,44,Suburban,Gold,20,3.56,Afternoon,Premium,105,435.14534513079855
+32,13,Suburban,Regular,21,4.64,Afternoon,Economy,156,685.7681264991488
+49,30,Rural,Silver,57,4.83,Evening,Economy,154,630.8608868741742
+38,17,Urban,Silver,92,4.41,Afternoon,Premium,138,546.1568747653598
+92,35,Suburban,Regular,46,4.72,Morning,Economy,45,115.35700254441713
+40,29,Suburban,Silver,26,4.26,Night,Economy,152,404.0498951423794
+55,42,Suburban,Silver,24,4.49,Afternoon,Economy,45,156.03832988580584
+58,34,Rural,Gold,19,4.55,Morning,Premium,131,501.26295506529306
+34,5,Urban,Regular,26,4.85,Evening,Premium,152,562.4369764527329
+70,47,Suburban,Silver,3,3.66,Morning,Premium,53,216.80247508698307
+51,24,Rural,Gold,25,4.68,Afternoon,Premium,16,120.09069016935676
+88,56,Rural,Regular,11,4.36,Afternoon,Economy,60,221.1576532597614
+25,8,Suburban,Silver,83,3.53,Evening,Economy,121,492.7012709061221
+34,19,Rural,Regular,51,4.2,Night,Premium,166,754.2344800100251
+40,15,Urban,Regular,90,3.62,Night,Premium,42,211.52772882563164
+26,7,Suburban,Gold,58,4.91,Afternoon,Economy,161,460.75927538572495
+81,42,Suburban,Regular,88,4.14,Afternoon,Economy,47,164.10747354764283
+80,56,Suburban,Gold,63,4.57,Night,Premium,43,213.6962129956236
+64,19,Rural,Gold,86,4.68,Night,Premium,171,732.9893961300692
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+84,49,Urban,Regular,32,4.25,Night,Premium,53,192.8798062051633
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+77,46,Suburban,Regular,97,4.2,Afternoon,Premium,25,150.53929481479082
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+81,16,Suburban,Regular,73,4.89,Night,Economy,125,494.1401793052489
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+43,9,Rural,Silver,2,4.12,Morning,Economy,126,436.5576476487414
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+51,5,Urban,Gold,0,4.59,Afternoon,Premium,92,320.8576215627125
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+96,29,Urban,Regular,84,3.65,Night,Economy,176,758.5015334295354
+94,59,Suburban,Regular,84,4.38,Morning,Premium,175,665.7295341217325
+74,32,Suburban,Regular,2,4.7,Evening,Premium,38,171.06636292972274
+91,13,Rural,Silver,32,3.6,Morning,Premium,160,647.3526381087524
+69,12,Suburban,Regular,42,4.34,Afternoon,Premium,158,608.1282399929386
+51,20,Rural,Silver,57,3.68,Night,Economy,67,246.92292493661097
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+92,34,Suburban,Regular,98,3.99,Night,Economy,140,396.63875270925445
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+56,14,Suburban,Silver,30,3.67,Night,Economy,48,202.9454568505992
+43,26,Rural,Regular,59,4.91,Afternoon,Economy,175,731.7816872483901
+86,48,Urban,Silver,37,4.15,Night,Economy,84,216.05054809363546
+61,34,Urban,Gold,82,3.99,Night,Premium,10,83.86835620425342
+50,36,Suburban,Gold,18,4.61,Night,Economy,145,572.2420492964254
+73,34,Urban,Regular,95,4.31,Morning,Premium,21,118.8070474355678
+76,7,Urban,Gold,76,4.35,Morning,Economy,72,245.8935711917164
+87,41,Urban,Regular,87,4.36,Night,Economy,87,387.1117403297744
+53,33,Rural,Regular,40,3.8,Morning,Premium,139,507.9505746530285
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+91,30,Suburban,Gold,34,3.69,Afternoon,Economy,96,424.6120578300381
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+94,42,Rural,Regular,0,4.23,Morning,Premium,36,185.311838106628
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+97,18,Suburban,Regular,53,4.25,Night,Premium,26,126.4771352873347
+47,17,Rural,Silver,7,4.8,Morning,Economy,173,609.5038276051239
+39,28,Urban,Silver,72,3.76,Afternoon,Premium,87,287.1624375675698
+50,39,Suburban,Regular,94,3.81,Evening,Premium,139,406.32480651562713
+35,17,Urban,Regular,78,4.4,Evening,Premium,61,249.03711265544786
+28,8,Suburban,Regular,69,3.86,Evening,Premium,175,620.9016915662245
+51,32,Rural,Regular,98,4.44,Morning,Premium,51,267.0607016773296
+54,12,Suburban,Silver,84,4.98,Evening,Premium,172,715.7547930992089
+65,10,Urban,Regular,77,4.36,Afternoon,Economy,170,430.385487919734
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+46,9,Suburban,Silver,67,4.22,Evening,Premium,47,243.5250631593867
+73,50,Urban,Regular,7,4.22,Night,Economy,45,176.2144067384048
+86,49,Urban,Regular,99,3.51,Night,Economy,130,471.4796320940979
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+54,35,Suburban,Silver,66,4.4,Night,Economy,77,234.0714535105622
+53,26,Urban,Gold,54,4.6,Night,Premium,52,184.02445130071476
+81,34,Urban,Silver,98,3.55,Afternoon,Premium,164,554.8869659163038
+45,20,Urban,Regular,80,4.73,Morning,Economy,63,192.61977241404603
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+49,8,Urban,Regular,1,4.06,Afternoon,Premium,157,694.4247148756359
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+83,50,Rural,Regular,7,3.66,Evening,Premium,100,376.3000600585162
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+79,23,Urban,Gold,88,3.88,Night,Economy,164,492.11345835908634
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+35,14,Urban,Silver,74,5.0,Night,Premium,175,727.8809447636283
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+38,25,Urban,Regular,2,4.24,Morning,Premium,128,452.4258268814717
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+91,64,Suburban,Regular,26,4.56,Morning,Premium,96,420.2245689053783
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+83,59,Urban,Gold,8,4.85,Morning,Economy,31,119.0485543701777
+48,23,Rural,Gold,0,4.46,Afternoon,Economy,174,472.56954081275165
+65,19,Suburban,Silver,24,4.59,Evening,Economy,96,334.6493506052282
+79,29,Suburban,Silver,0,4.86,Morning,Economy,127,406.08552566041345
+100,22,Suburban,Silver,45,4.23,Night,Premium,141,551.365137615676
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+88,55,Rural,Regular,56,4.0,Night,Premium,26,159.60691863182956
+76,34,Suburban,Silver,10,4.92,Morning,Economy,103,359.8010509571677
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+87,26,Rural,Gold,63,4.2,Morning,Premium,118,485.683239513509
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+28,15,Rural,Gold,90,4.25,Night,Premium,177,511.92939099380595
+97,7,Urban,Silver,22,3.74,Afternoon,Premium,147,441.74670095471794
+48,12,Urban,Silver,51,4.63,Night,Premium,32,175.5180543386476
+70,37,Urban,Silver,10,3.65,Night,Premium,168,658.5834276725151
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+43,33,Rural,Gold,16,4.01,Morning,Premium,159,630.4284012724294
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+83,43,Suburban,Regular,20,3.59,Morning,Economy,63,267.28219711727195
+50,29,Urban,Regular,2,4.81,Morning,Economy,143,518.9547629602604
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+84,24,Suburban,Regular,39,4.08,Afternoon,Economy,86,349.4432162449335
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+77,30,Urban,Regular,99,4.7,Evening,Premium,179,689.7483154802385
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+75,24,Rural,Regular,44,3.79,Night,Premium,72,307.621949405299
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+55,21,Urban,Regular,20,3.56,Night,Premium,134,519.469492374694
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+34,8,Suburban,Silver,51,4.09,Afternoon,Premium,130,388.1122298927073
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+55,19,Urban,Regular,64,4.34,Morning,Premium,129,555.0976877626003
+93,67,Urban,Silver,21,4.88,Night,Premium,87,417.91314129158997
+94,69,Urban,Silver,86,4.12,Morning,Premium,18,97.80093621671637
+70,60,Urban,Regular,57,4.05,Afternoon,Premium,43,193.08911199878557
+36,25,Suburban,Regular,61,3.64,Night,Economy,132,492.4640116460618
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+77,15,Suburban,Regular,47,4.47,Morning,Economy,66,228.64349787316
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+64,25,Suburban,Silver,85,4.46,Afternoon,Premium,37,168.61019430479575
+57,42,Rural,Silver,36,3.59,Afternoon,Premium,167,587.2581373063812
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+69,56,Urban,Regular,73,4.02,Night,Premium,116,352.3474982568331
+67,54,Suburban,Regular,53,3.67,Morning,Economy,110,415.3075369486595
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+68,30,Urban,Regular,28,4.69,Night,Premium,134,581.1514565564086
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+81,35,Suburban,Regular,45,4.11,Night,Economy,174,708.8801421484817
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+87,48,Suburban,Silver,95,4.73,Morning,Economy,162,453.2541298779704
+56,37,Suburban,Silver,96,3.83,Afternoon,Premium,130,412.5848379812361
+75,32,Urban,Silver,20,3.69,Evening,Premium,96,440.4442377359072
+99,54,Rural,Silver,96,3.77,Afternoon,Premium,61,241.64071137767894
+33,16,Rural,Regular,81,4.08,Afternoon,Premium,50,201.29253189374313
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+55,19,Suburban,Silver,74,3.51,Morning,Economy,173,702.9134159653358
+93,51,Rural,Silver,12,4.98,Morning,Premium,90,431.42166025583447
+54,35,Urban,Silver,11,4.49,Afternoon,Premium,96,466.8626810645626
+34,20,Urban,Gold,21,4.15,Morning,Premium,158,445.1263611668488
+86,18,Suburban,Silver,61,4.3,Afternoon,Premium,126,441.6378571357946
+85,27,Suburban,Gold,8,4.11,Night,Premium,157,669.5210116683382
+74,5,Suburban,Regular,80,3.63,Night,Economy,134,354.3852115042604
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+51,27,Urban,Gold,86,3.82,Evening,Premium,152,472.21573527678265
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+49,14,Suburban,Silver,55,4.43,Morning,Premium,131,633.4966221375331
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+34,24,Urban,Silver,3,4.41,Morning,Economy,97,242.75171700488073
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+77,43,Suburban,Regular,73,4.07,Afternoon,Premium,131,505.54671645728143
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+88,15,Urban,Regular,39,4.51,Afternoon,Economy,137,598.7750233051204
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+30,7,Rural,Regular,85,3.74,Evening,Economy,44,184.11208970364285
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+27,5,Rural,Gold,2,4.3,Evening,Economy,22,60.98181721983856
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+32,15,Urban,Gold,67,3.76,Evening,Premium,23,132.12301879164062
+76,8,Urban,Gold,40,4.68,Afternoon,Economy,157,561.5796822292014
+21,9,Suburban,Regular,38,4.54,Morning,Economy,179,546.3787898746884
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+92,66,Rural,Silver,26,3.56,Evening,Economy,19,62.06928479024478
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+67,20,Urban,Regular,42,4.13,Evening,Economy,68,243.22861759627588
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+46,22,Urban,Regular,9,4.33,Morning,Economy,128,522.7974318549136
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+39,12,Urban,Regular,70,4.02,Morning,Premium,66,314.46800852423786
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+69,51,Urban,Regular,75,3.57,Night,Premium,136,470.18271352951473
+69,47,Urban,Regular,65,4.22,Night,Economy,18,53.5391040086766
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+84,74,Suburban,Regular,7,4.4,Afternoon,Premium,101,396.3698341114785
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+60,48,Suburban,Regular,15,4.06,Morning,Economy,78,339.1055825696459
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+91,61,Rural,Silver,86,4.44,Morning,Premium,129,567.5392184514062
+91,60,Rural,Gold,2,3.53,Evening,Premium,107,492.17901322228107
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+61,47,Suburban,Silver,100,4.58,Night,Premium,157,658.3040013013618
+57,40,Urban,Regular,78,3.62,Afternoon,Economy,171,764.5953413909641
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+64,45,Rural,Gold,22,3.96,Night,Premium,118,404.6018875393623
+91,29,Urban,Regular,82,3.73,Afternoon,Premium,90,446.81702579708536
+91,53,Suburban,Gold,67,4.56,Night,Economy,50,158.1067231747087
+69,37,Suburban,Regular,47,4.39,Evening,Premium,60,284.2362895344896
+71,61,Suburban,Regular,79,3.85,Afternoon,Economy,147,591.2989763287625
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+100,32,Suburban,Regular,67,3.94,Evening,Premium,162,581.1370845965885
+65,51,Suburban,Gold,97,4.81,Night,Premium,144,588.1367698544636
+73,38,Urban,Regular,91,4.23,Evening,Premium,86,400.2673330229722
+56,25,Suburban,Regular,87,3.73,Evening,Premium,172,575.4102708388258
+75,55,Rural,Silver,80,4.7,Morning,Economy,69,190.9788526811355
+97,21,Urban,Regular,43,3.79,Afternoon,Premium,106,398.1593839328215
+94,71,Rural,Regular,48,4.73,Night,Premium,169,770.8733244527833
+53,22,Urban,Silver,28,4.99,Afternoon,Premium,64,211.0820229741896
+81,8,Rural,Silver,78,4.8,Afternoon,Premium,141,618.4288567281086
+47,34,Rural,Silver,87,3.53,Morning,Premium,170,739.2271199769044
+80,48,Suburban,Regular,13,4.44,Night,Economy,35,128.7410738814351
+75,55,Rural,Gold,54,4.73,Night,Premium,145,584.4917299915722
+89,12,Urban,Silver,42,4.73,Afternoon,Economy,102,328.31398217312767
+30,13,Rural,Gold,8,4.1,Afternoon,Economy,94,274.68037544392655
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+94,76,Suburban,Gold,26,4.7,Morning,Premium,94,405.317954516569
+53,28,Urban,Gold,6,3.67,Evening,Economy,178,775.7916851510323
+29,7,Suburban,Regular,68,3.98,Morning,Economy,114,289.1296809487768
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+75,42,Suburban,Silver,38,4.25,Afternoon,Economy,146,479.11995337350106
+74,53,Suburban,Regular,60,3.93,Night,Economy,162,676.4404324115253
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+40,20,Rural,Silver,45,4.42,Evening,Premium,130,445.4842486108808
+79,11,Urban,Regular,1,4.35,Night,Premium,45,210.25675967194184
+98,87,Urban,Gold,87,4.28,Afternoon,Premium,170,603.8458024755097
+95,76,Urban,Regular,74,4.39,Night,Economy,118,464.9915827255092
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+38,22,Suburban,Regular,18,4.17,Night,Premium,121,450.5387531372676
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+59,24,Suburban,Regular,40,4.0,Afternoon,Premium,147,631.6312970255759
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+30,6,Rural,Regular,93,3.81,Evening,Economy,129,505.92196882791006
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+84,55,Suburban,Regular,1,4.47,Afternoon,Economy,146,487.3991192361553
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+22,5,Urban,Regular,75,4.39,Night,Premium,19,128.0167601925525
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+48,18,Rural,Silver,60,4.47,Afternoon,Premium,124,524.1655595085831
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+63,48,Rural,Regular,88,4.09,Morning,Economy,70,212.74075193712682
+44,24,Urban,Regular,83,3.61,Night,Economy,13,48.928952535474146
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+50,40,Rural,Silver,61,4.7,Evening,Premium,73,319.11098294212263
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+90,54,Rural,Regular,1,3.59,Afternoon,Premium,87,427.8571629452071
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+99,22,Suburban,Silver,7,4.4,Morning,Economy,39,131.44358716419623
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+50,27,Suburban,Gold,100,4.22,Morning,Premium,173,685.6696935694298
+89,8,Urban,Gold,40,3.59,Morning,Premium,151,528.0024896243917
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+71,41,Urban,Gold,31,3.81,Afternoon,Premium,140,614.63671317069
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+36,16,Urban,Regular,82,4.62,Afternoon,Premium,72,253.4912438007839
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+56,27,Suburban,Silver,62,4.74,Evening,Premium,129,435.71545381085645
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+94,57,Suburban,Regular,13,4.04,Afternoon,Economy,60,205.49575958337198
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+53,40,Urban,Regular,13,3.79,Night,Economy,118,318.2115758092651
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+44,9,Suburban,Regular,54,4.2,Morning,Premium,13,91.67748868757226
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+49,30,Urban,Regular,7,3.97,Afternoon,Premium,77,248.17033364227854
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+62,45,Rural,Regular,87,4.74,Afternoon,Premium,20,108.6023564641184
+69,7,Urban,Regular,51,4.56,Afternoon,Premium,103,462.66304412150464
+32,14,Suburban,Silver,80,3.56,Afternoon,Premium,80,254.9510771161675
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+97,58,Urban,Gold,1,4.11,Morning,Premium,83,383.07490640970946
+87,77,Urban,Gold,12,3.56,Night,Premium,11,97.78450586126135
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+100,85,Suburban,Silver,59,4.46,Evening,Economy,110,329.4837131421355
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+52,9,Suburban,Regular,24,4.19,Morning,Economy,102,366.7282586383836
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deploy.py

@@ -0,0 +1,11 @@
+import os
+from huggingface_hub import Repository
+
+repo = Repository(
+    local_dir='.',
+    clone_from='PranavSharma/RidePricingInsightEngine',
+    use_auth_token=os.getenv('HUGGINGFACE_TOKEN')
+)
+repo.git_add(auto_lfs_track=True)
+repo.git_commit(commit_message='Automated deployment')
+repo.git_push()

requirements.txt

@@ -0,0 +1,7 @@
+gradio
+scikit-learn
+pandas
+numpy
+skops
+plotly
+kaleido