{"id":21864102,"url":"https://github.com/blinksta1ker/co2-prediction","last_synced_at":"2026-04-09T07:39:52.458Z","repository":{"id":265032485,"uuid":"894616757","full_name":"BLinKSta1Ker/CO2-Prediction","owner":"BLinKSta1Ker","description":"EDA and Supervised ML model using Regression to predict Co2 Emissions from Vehicles","archived":false,"fork":false,"pushed_at":"2024-11-27T08:41:42.000Z","size":808,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-26T15:28:50.075Z","etag":null,"topics":["jupyter-notebook","machine-learning","numpy","pandas","python","regression","seaborn","sklearn","supervised-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BLinKSta1Ker.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-26T17:09:19.000Z","updated_at":"2024-11-27T08:41:46.000Z","dependencies_parsed_at":"2024-12-02T11:01:45.975Z","dependency_job_id":null,"html_url":"https://github.com/BLinKSta1Ker/CO2-Prediction","commit_stats":null,"previous_names":["blinksta1ker/co2-prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BLinKSta1Ker%2FCO2-Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BLinKSta1Ker%2FCO2-Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BLinKSta1Ker%2FCO2-Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BLinKSta1Ker%2FCO2-Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BLinKSta1Ker","download_url":"https://codeload.github.com/BLinKSta1Ker/CO2-Prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244868060,"owners_count":20523581,"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":["jupyter-notebook","machine-learning","numpy","pandas","python","regression","seaborn","sklearn","supervised-learning"],"created_at":"2024-11-28T04:07:27.763Z","updated_at":"2025-12-30T23:56:17.936Z","avatar_url":"https://github.com/BLinKSta1Ker.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Co2 Emissions Prediction\n\nThis project aims to predict Co2 emissions of vehicles using machine learning techniques. This model utilizes Supervise Machine Learning using Regression Model. \n\n## Features\n\n- **Data Preprocessing**: Handles missing values, scales numerical features, and encodes categorical variables.\n- **Data Visualization**: Provides insightful visualizations to explore relationships and distributions in the data.\n- **Machine Learning**: Utilizes a linear regression model to predict CO2 emissions.\n- **Evaluation**: Measures model performance using metrics like Mean Squared Error, R² Score, and Root Mean Squared Error.\n\n## Dataset\n\nThe dataset is assumed to contain details about vehicles, including:\n- Vehicle brand and model\n- Engine specifications\n- Fuel consumption\n- CO2 emissions (target variable)\n\n## Visualizations\n\n- **Distribution of Brands, Vehicle Classes, and Fuel Types**: Count plots to understand the dataset composition.\n- **CO2 Emissions by Features**: Boxplots showing variations in emissions by brand, vehicle class, and fuel type.\n- **Scatter Plots**: Relationships between CO2 emissions and engine size or fuel consumption.\n\n## Implementation\n\n### Libraries Used\n\n- `numpy`\n- `pandas`\n- `matplotlib`\n- `seaborn`\n- `scikit-learn`\n\n\n### Steps\n\n1. Load and clean the dataset.\n2. Analyze data using descriptive statistics and visualizations.\n3. Split the data into training and testing sets.\n4. Preprocess the data using pipelines.\n5. Train a linear regression model.\n6. Evaluate the model using performance metrics.\n7. Visualize actual vs predicted emissions and residuals.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblinksta1ker%2Fco2-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fblinksta1ker%2Fco2-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblinksta1ker%2Fco2-prediction/lists"}