{"id":23657845,"url":"https://github.com/3rd-son/car-mileage-model","last_synced_at":"2026-04-10T17:02:49.040Z","repository":{"id":175395858,"uuid":"653849907","full_name":"3rd-Son/Car-Mileage-Model","owner":"3rd-Son","description":"EDA and Model that predicts the mpg of cars","archived":false,"fork":false,"pushed_at":"2023-06-15T14:41:23.000Z","size":754,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-28T21:58:11.747Z","etag":null,"topics":["eda","jupyter-notebook","linear-regression","machine-learning","matplotlib","numpy","pandas","python","seaborn","sklearn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/3rd-Son.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-06-14T21:55:15.000Z","updated_at":"2023-08-22T06:19:51.000Z","dependencies_parsed_at":"2023-08-17T14:06:38.136Z","dependency_job_id":null,"html_url":"https://github.com/3rd-Son/Car-Mileage-Model","commit_stats":null,"previous_names":["vic3sax/car-mileage-model","thridson/car-mileage-model","3rd-son/car-mileage-model"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/3rd-Son%2FCar-Mileage-Model","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/3rd-Son%2FCar-Mileage-Model/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/3rd-Son%2FCar-Mileage-Model/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/3rd-Son%2FCar-Mileage-Model/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/3rd-Son","download_url":"https://codeload.github.com/3rd-Son/Car-Mileage-Model/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239635531,"owners_count":19672190,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["eda","jupyter-notebook","linear-regression","machine-learning","matplotlib","numpy","pandas","python","seaborn","sklearn"],"created_at":"2024-12-28T21:58:29.515Z","updated_at":"2025-11-26T06:30:16.273Z","avatar_url":"https://github.com/3rd-Son.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003c!DOCTYPE html\u003e\n\u003chtml\u003e\n\u003chead\u003e\n \n\u003c/head\u003e\n\u003cbody\u003e\n  \u003ch1\u003eAuto-Mpg Linear Regression Model\u003c/h1\u003e\n  \n  \u003ch2\u003eIntroduction\u003c/h2\u003e\n  \u003cp\u003e\n    This repository contains the code and data for a linear regression model that predicts the miles per gallon (mpg) of a car based on various features.\n  \u003c/p\u003e\n  \n  \u003ch2\u003eData\u003c/h2\u003e\n  \u003cp\u003e\n    The dataset used for training and evaluating the model is the \u003ca href=\"https://archive.ics.uci.edu/ml/datasets/auto+mpg\" target=\"_blank\" rel=\"noopener noreferrer\"\u003eAuto-Mpg Dataset\u003c/a\u003e from the UCI Machine Learning Repository. It consists of 398 instances with 9 attributes, including the target attribute (mpg). The dataset contains both continuous and discrete features such as cylinders, displacement, horsepower, weight, acceleration, model year, origin, and car name. You find the data at \u003ca href=\"https://www.kaggle.com/datasets/uciml/autompg-dataset\"\u003eMPG-Dataset\u003c/a\u003e\n\n  \u003c/p\u003e\n  \n  \u003ch2\u003eExploratory Data Analysis (EDA)\u003c/h2\u003e\n  \u003cp\u003e\n    Prior to building the model, an Exploratory Data Analysis (EDA) was performed on the dataset to gain insights and understand the relationships between the features and the target variable. Various visualizations and statistical analyses were conducted to identify patterns, correlations, and potential outliers in the data. See \u003ca href=\"https://github.com/Vic3sax/Car-Mileage-Model/blob/main/notebooks/visualization.ipynb\"\u003eEDA\u003c/a\u003e for the Exploratory Data Analysis.\n  \u003c/p\u003e\n  \n  \u003ch2\u003eModel Training\u003c/h2\u003e\n  \u003cp\u003e\n    The linear regression model was built using the scikit-learn library in Python. The features were preprocessed, including handling missing values in the \"horsepower\" attribute. The dataset was split into training and testing sets to evaluate the model's performance. Feature scaling and any necessary feature transformations were applied. \u003ca href=\"https://github.com/Vic3sax/Car-Mileage-Model/blob/main/models/model.ipynb\"\u003eModel.py\u003c/a\u003e \n  \u003c/p\u003e\n  \n  \u003ch2\u003eModel Evaluation\u003c/h2\u003e\n  \u003cp\u003e\n    The trained linear regression model was evaluated using various performance metrics such as mean squared error (MSE), mean absolute error (MAE), and R-squared (coefficient of determination). The model's performance on the test set was assessed to measure its accuracy in predicting the miles per gallon of cars.\n  \u003c/p\u003e\n  \n  \u003ch2\u003eUsage\u003c/h2\u003e\n  \u003cp\u003e\n    To use this model, follow the steps below:\n    \u003col\u003e\n      \u003cli\u003eClone the repository: \u003ccode\u003egit clone https://github.com/Vic3sax/Car-Mileage-Model.git\u003c/code\u003e\u003c/li\u003e\n      \u003cli\u003eNavigate to the project directory: \u003ccode\u003ecd Car-Mileage-Model\n\u003c/code\u003e\u003c/li\u003e\n      \u003cli\u003eInstall the required dependencies: \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e\u003c/li\u003e\n      \u003cli\u003eRun the prediction script: \u003ccode\u003epython predict_mpg.py\u003c/code\u003e\u003c/li\u003e\n    \u003c/ol\u003e\n  \u003c/p\u003e\n  \n  \u003ch2\u003eContributing\u003c/h2\u003e\n  \u003cp\u003e\n    Contributions to this project are welcome. If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.\n  \u003c/p\u003e\n  \n  \u003ch2\u003eLicense\u003c/h2\u003e\n  \u003cp\u003e\n    This project is licensed under the MIT License. See the \u003ca href=\"LICENSE\" target=\"_blank\" rel=\"noopener noreferrer\"\u003eLICENSE\u003c/a\u003e file for more details.\n  \u003c/p\u003e\n  \n  \u003ch2\u003eAcknowledgements\u003c/h2\u003e\n  \u003cp\u003e\n    The Auto-Mpg Dataset used in this project is available from the UCI Machine Learning Repository. Special thanks to the original contributors and maintainers of the dataset.\n  \u003c/p\u003e\n\u003c/body\u003e\n\u003c/html\u003e\n\nProject Organization\n------------\n\n    ├── LICENSE\n    ├── Makefile           \u003c- Makefile with commands like `make data` or `make train`\n    ├── README.md          \u003c- The top-level README for developers using this project.\n    ├── data\n    │   ├── external       \u003c- Data from third party sources.\n    │   ├── interim        \u003c- Intermediate data that has been transformed.\n    │   ├── processed      \u003c- The final, canonical data sets for modeling.\n    │   └── raw            \u003c- The original, immutable data dump.\n    │\n    ├── docs               \u003c- A default Sphinx project; see sphinx-doc.org for details\n    │\n    ├── models             \u003c- Trained and serialized models, model predictions, or model summaries\n    │\n    ├── notebooks          \u003c- Jupyter notebooks. Naming convention is a number (for ordering),\n    │                         the creator's initials, and a short `-` delimited description, e.g.\n    │                         `1.0-jqp-initial-data-exploration`.\n    │\n    ├── references         \u003c- Data dictionaries, manuals, and all other explanatory materials.\n    │\n    ├── reports            \u003c- Generated analysis as HTML, PDF, LaTeX, etc.\n    │   └── figures        \u003c- Generated graphics and figures to be used in reporting\n    │\n    ├── requirements.txt   \u003c- The requirements file for reproducing the analysis environment, e.g.\n    │                         generated with `pip freeze \u003e requirements.txt`\n    │\n    ├── setup.py           \u003c- makes project pip installable (pip install -e .) so src can be imported\n    ├── src                \u003c- Source code for use in this project.\n    │   ├── __init__.py    \u003c- Makes src a Python module\n    │   │\n    │   ├── data           \u003c- Scripts to download or generate data\n    │   │   └── make_dataset.py\n    │   │\n    │   ├── features       \u003c- Scripts to turn raw data into features for modeling\n    │   │   └── build_features.py\n    │   │\n    │   ├── models         \u003c- Scripts to train models and then use trained models to make\n    │   │   │                 predictions\n    │   │   ├── predict_model.py\n    │   │   └── train_model.py\n    │   │\n    │   └── visualization  \u003c- Scripts to create exploratory and results oriented visualizations\n    │       └── visualize.py\n    │\n    └── tox.ini            \u003c- tox file with settings for running tox; see tox.readthedocs.io\n\n\n--------\n\n\u003cp\u003e\u003csmall\u003eProject based on the \u003ca target=\"_blank\" href=\"https://drivendata.github.io/cookiecutter-data-science/\"\u003ecookiecutter data science project template\u003c/a\u003e. #cookiecutterdatascience\u003c/small\u003e\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F3rd-son%2Fcar-mileage-model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F3rd-son%2Fcar-mileage-model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F3rd-son%2Fcar-mileage-model/lists"}