Fuel Consumption Prediction

Overview

This machine learning project aims to predict fuel consumption based on various features and data points. The project leverages supervised learning techniques to build a predictive model that can estimate fuel consumption for vehicles.

Table of Contents

1. Getting Started

   • Prerequisites
   • Installation

2. Data

   • Data Source
   • Data Preprocessing

3. Exploratory Data Analysis (EDA)

4. Model Development

   • Feature Engineering
   • Model Selection
   • Model Training
   • Model Evaluation

5. Deployment

6. Usage

7. Contributing

8. License

Getting Started

Prerequisites

List any prerequisites or system requirements needed to run the project, such as Python version, libraries, or dependencies.

# Example:
$ pip install -r requirements.txt

Installation

Provide installation instructions, including how to clone the repository and set up the project environment.

# Clone the repository
$ git clone https://github.com/yourusername/fuel-consumption-prediction.git

# Change directory to project folder
$ cd fuel-consumption-prediction

# Create a virtual environment (optional but recommended)
$ python -m venv venv

# Activate the virtual environment
$ source venv/bin/activate  # On Windows, use 'venv\Scripts\activate'

# Install project dependencies
$ pip install -r requirements.txt

Data

Data Source

Explain where the dataset used for this project is sourced from. Provide a link to the dataset if available.

Data Preprocessing

Describe the steps taken to preprocess and clean the data. Include information about handling missing values, encoding categorical variables, and any other data transformations performed.

Exploratory Data Analysis (EDA)

Include visualizations and insights gained from exploring the dataset. EDA helps in understanding the data and identifying potential patterns or outliers.

Model Development

Feature Engineering

Detail the features used for model training. Explain how these features were selected or engineered.

Model Selection

Discuss the choice of machine learning algorithms and models considered for this project. Explain why a particular model was chosen.

Model Training

Provide instructions on how to train the machine learning model. Include code examples if necessary.

Model Evaluation

Explain how the model's performance is evaluated, including metrics used for assessment. Provide information on how well the model predicts fuel consumption.

Deployment

If the project includes a deployed model (e.g., a web application or API), describe how to deploy it.

Usage

Provide instructions on how to use the trained model for fuel consumption prediction. Include code examples if applicable.

Contributing

Explain how others can contribute to the project. Include guidelines for code contributions, bug reporting, and feature requests.

License

Specify the project's license and any terms or conditions for its use.

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