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--- |
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annotations_creators: |
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- machine-generated |
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language_creators: |
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- found |
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language: |
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- en |
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license: apache-2.0 |
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multilinguality: monolingual |
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size_categories: 10K<n<100K |
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source_datasets: |
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- original |
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task_categories: |
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- regression |
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task_ids: |
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- dynamic-pricing |
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pretty_name: Dynamic Pricing Model |
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--- |
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# Model description |
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This is a regression model trained on the Dynamic Pricing Dataset. It was optimized using grid search with multiple hyperparameters. |
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## Intended uses & limitations |
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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. |
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**Intended Uses**: |
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- **Dynamic Pricing Analysis**: Helps optimize pricing strategies for ride-hailing platforms. |
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- **Demand Forecasting**: Supports business decisions by estimating cost trends based on ride-specific parameters. |
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**Limitations**: |
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- **Feature Dependence**: The model's accuracy is highly dependent on the input features provided. |
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- **Dataset Specificity**: Performance may degrade if applied to datasets with significantly different distributions. |
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- **Outlier Sensitivity**: Predictions can be affected by extreme values in the dataset. |
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## Training Procedure |
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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. |
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### Hyperparameters |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|------------------|---------| |
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| alpha | 1 | |
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| copy_X | True | |
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| fit_intercept | False | |
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| max_iter | 1000 | |
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| positive | False | |
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| precompute | False | |
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| random_state | | |
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| selection | cyclic | |
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| tol | 0.0001 | |
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| warm_start | False | |
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</details> |
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### Model Plot |
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<style>#sk-container-id-3 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: #000;--sklearn-color-text-muted: #666;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;} |
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}#sk-container-id-3 {color: var(--sklearn-color-text); |
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}#sk-container-id-3 pre {padding: 0; |
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}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px; |
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}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background); |
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}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative; |
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}#sk-container-id-3 div.sk-text-repr-fallback {display: none; |
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}div.sk-parallel-item, |
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div.sk-serial, |
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div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center; |
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}/* Parallel-specific style estimator block */#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1; |
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}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative; |
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}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column; |
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}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%; |
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}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%; |
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}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0; |
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}/* Serial-specific style estimator block */#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em; |
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}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is |
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clickable and can be expanded/collapsed. |
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- Pipeline and ColumnTransformer use this feature and define the default style |
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- Estimators will overwrite some part of the style using the `sk-estimator` class |
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*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-3 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background); |
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}/* Toggleable label */ |
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#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: flex;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;align-items: start;justify-content: space-between;gap: 0.5em; |
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}#sk-container-id-3 label.sk-toggleable__label .caption {font-size: 0.6rem;font-weight: lighter;color: var(--sklearn-color-text-muted); |
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}#sk-container-id-3 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon); |
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}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text); |
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}/* Toggleable content - dropdown */#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); |
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}#sk-container-id-3 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); |
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}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); |
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}#sk-container-id-3 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0); |
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}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto; |
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}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾"; |
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}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); |
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}#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2); |
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}/* Estimator-specific style *//* Colorize estimator box */ |
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#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); |
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}#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2); |
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}#sk-container-id-3 div.sk-label label.sk-toggleable__label, |
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#sk-container-id-3 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background); |
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}/* On hover, darken the color of the background */ |
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#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); |
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}/* Label box, darken color on hover, fitted */ |
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#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2); |
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}/* Estimator label */#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em; |
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}#sk-container-id-3 div.sk-label-container {text-align: center; |
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}/* Estimator-specific */ |
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#sk-container-id-3 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); |
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}#sk-container-id-3 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); |
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}/* on hover */ |
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#sk-container-id-3 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); |
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}#sk-container-id-3 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2); |
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}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link, |
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a:link.sk-estimator-doc-link, |
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a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 0.5em;text-align: center;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1); |
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}.sk-estimator-doc-link.fitted, |
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a:link.sk-estimator-doc-link.fitted, |
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a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); |
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}/* On hover */ |
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div.sk-estimator:hover .sk-estimator-doc-link:hover, |
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.sk-estimator-doc-link:hover, |
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div.sk-label-container:hover .sk-estimator-doc-link:hover, |
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.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
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}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover, |
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.sk-estimator-doc-link.fitted:hover, |
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div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover, |
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.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
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}/* Span, style for the box shown on hovering the info icon */ |
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.sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3); |
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}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3); |
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}.sk-estimator-doc-link:hover span {display: block; |
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}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-3 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid; |
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}#sk-container-id-3 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); |
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}/* On hover */ |
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#sk-container-id-3 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
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}#sk-container-id-3 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3); |
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} |
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</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Lasso(alpha=1, fit_intercept=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" checked><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Lasso</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.Lasso.html">?<span>Documentation for Lasso</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Lasso(alpha=1, fit_intercept=False)</pre></div> </div></div></div></div> |
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## Evaluation Results |
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The model achieved the following results on the test set: |
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- **Mean Absolute Error (MAE)**: 50.31928636001356 |
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- **R² Score**: 0.8854065597299239 |
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### Key Insights: |
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- Longer ride durations increase costs significantly, which may justify adding a surcharge for long-distance rides. |
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- Evening bookings reduce costs, potentially indicating lower demand during these hours. |
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- The model's accuracy is dependent on high-quality feature data. |
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Refer to the plots and tables for detailed performance insights. |
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### Model Coefficients |
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| Feature | Coefficient | |
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|---------|-------------| |
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| Number_of_Riders | -0.1398 | |
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| Number_of_Drivers | 0.4665 | |
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| Number_of_Past_Rides | -0.0033 | |
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| Average_Ratings | -0.0000 | |
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| Expected_Ride_Duration | 3.4973 | |
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| Location_Category_Suburban | 0.0000 | |
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| Location_Category_Urban | -0.0000 | |
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| Customer_Loyalty_Status_Regular | 0.0000 | |
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| Customer_Loyalty_Status_Silver | 0.0000 | |
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| Time_of_Booking_Evening | -2.4212 | |
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| Time_of_Booking_Morning | -0.0000 | |
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| Time_of_Booking_Night | 0.0000 | |
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| Vehicle_Type_Premium | 39.5754 | |
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### Regression Equation |
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Cost of Ride = *-0.1398* × *Number_of_Riders* + *0.4665* × *Number_of_Drivers* + *-0.0033* × *Number_of_Past_Rides* + *3.4973* × *Expected_Ride_Duration* + *-2.4212* × *Time_of_Booking_Evening* + *39.5754* × *Vehicle_Type_Premium* |
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### Actual vs Predicted |
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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. |
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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. |
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# How to Get Started with the Model |
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To use this model: |
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1. **Install Dependencies**: Ensure `scikit-learn` and `pandas` are installed in your environment. |
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2. **Load the Model**: Download the saved model file and load it using `joblib`: |
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```python |
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from joblib import load |
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model = load('best_model.joblib') |
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``` |
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3. **Prepare Input Features**: Create a DataFrame with the required input features in the same format as the training dataset. |
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4. **Make Predictions**: Use the `predict` method to generate predictions: |
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```python |
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predictions = model.predict(input_features) |
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``` |
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# Model Card Authors |
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This model card was written by **Pranav Sharma**. |
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# Model Card Contact |
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For inquiries or feedback, you can contact the author via **[GitHub](https://github.com/PranavSharma)**. |
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# Citation |
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If you use this model, please cite it as follows: |
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``` |
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@model{pranav_sharma_dynamic_pricing_model_2025, |
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author = {Pranav Sharma}, |
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title = {Dynamic Pricing Model}, |
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year = {2025}, |
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version = {1.0.0}, |
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url = {https://huggingface.co/PranavSharma/dynamic-pricing-model} |
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} |
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``` |
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