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https://github.com/rajveersinghcse/co2-emission-prediction

🌏My internship project on forecasting CO2 emissions by vehicle.
https://github.com/rajveersinghcse/co2-emission-prediction

co2-emission co2-emissions-prediction internship ml streamlit

Last synced: about 2 months ago
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🌏My internship project on forecasting CO2 emissions by vehicle.

Host: GitHub
URL: https://github.com/rajveersinghcse/co2-emission-prediction
Owner: rajveersinghcse
License: mit
Created: 2023-07-02T01:35:30.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-03-22T16:42:44.000Z (10 months ago)
Last Synced: 2024-03-22T18:04:03.678Z (10 months ago)
Topics: co2-emission, co2-emissions-prediction, internship, ml, streamlit
Language: Jupyter Notebook
Homepage:
Size: 10.6 MB
Stars: 3
Watchers: 1
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

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# CO2 Emissions Predictions by Cars

![Banner](https://github.com/rajveersinghcse/rajveersinghcse/blob/master/img/CO2_emissions.jpg)

Hey Folks,👨🏻‍💻

During my internship, I created a project titled "CO2 Emission Prediction by Cars." The goal was to predict how much carbon dioxide (CO2) a car would emit based on its data. I gathered information about different cars and their CO2 emissions. Using this data, I used advanced techniques to build a model that could accurately estimate CO2 emissions. This project not only showcased my skills in data analysis and machine learning but also aimed to contribute to understanding and reducing vehicle-related environmental impacts.

# Description of The Project:

Business Objective of the project

- The primary objective of the project is to develop a model that can accurately predict CO2 emissions based on different engine features of cars.
- The goal is to estimate the amount of CO2 a car will emit using the provided data.

# Description of The Data:

- The data used in the project was collected from the Canadian Government's Official [Website](https://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64#wb-auto-6).

## About Data 📈

### It includes the following attributes:

- Make: Car brand under study.
- Model: Specific model of the car.
- Vehicle_class: Car body type.
- Engine_size: Size of the car engine in liters.
- Cylinders: Number of cylinders.
- Transmission: Type of transmission (e.g., automatic, manual).
- Fuel_type: Type of fuel used by the car.
- Fuel_consumption_city: City fuel consumption ratings in liters per 100 kilometers.
- Fuel_consumption_hwy: Highway fuel consumption ratings in liters per 100 kilometers.
- Fuel_consumption_comb(l/100km): Combined fuel consumption rating (city and highway) in L/100 km.
- Fuel_consumption_comb(mpg): Combined fuel consumption rating in miles per gallon (mpg).
- Co2_emissions: Tailpipe emissions of carbon dioxide for combined city and highway driving, in grams per kilometer.

# Tools and Technologies:

#### The project was developed using various tools and technologies, including:
- Python programming language
- Libraries such as NumPy, Matplotlib, SciPy, scikit-learn, and Streamlit
- Linear Regression, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) models for prediction
- Deployment on the cloud using Streamlit

## How to install these libraries?
### You can install these libraries by using the command.

- You can install all the libraries in your system which I have used in my project by using only one command.
- You will need Python in your system to use this command. You can use this given link to install Python in your system : [Python](https://www.python.org/downloads/)
- After installation of Python, you need to run this command in your command prompt.

```bash
pip install -r requirements.txt
```
# Model Building:

- The project involved building and evaluating several machine learning models, including Linear Regression, Random Forest, KNN, and SVR.
- The Random Forest model yielded the highest accuracy among these models and was selected for deployment.
ModelBuilding

# Deployment:

- The project was deployed using Streamlit, allowing users to interact with the model and make predictions on CO2 emissions based on car engine features.
- The deployment version of the project can be accessed through a provided link :[Project](https://rajveersinghcse-co2emissionsprediction.streamlit.app/)

# Running the Project:

- To run the project locally, one can install the required libraries using the provided command in the command prompt. Use the below Streamlit command to launch the application.
```bash
streamlit run app.py
```

### The project not only demonstrates technical skills but also contributes to environmental awareness and sustainable practices. It effectively combines data analysis, machine learning, and software development to address real-world challenges.

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Enjoy Coding❤