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https://github.com/nelsonkariuki/php-laravel-crud

This is a CRUD Application with Lavavel
https://github.com/nelsonkariuki/php-laravel-crud

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This is a CRUD Application with Lavavel

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README 

        

# Fuel Efficiency Prediction - ReadMe



## Project Title: Fuel Efficiency Prediction Using Vehicle Characteristics



### ORCID ID: 0009-0006-7325-8135



### Dataset: Vehicle Performance Data



# 1. Introduction

Fuel efficiency is a critical factor in the automotive industry, impacting environmental sustainability and consumer economics. The ability to predict a vehicle's fuel efficiency based on its characteristics informs both manufacturers and consumers about energy consumption and ecological impacts. This project aims to leverage vehicle performance data to develop predictive models that estimate fuel efficiency, measured in miles per gallon (MPG), based on various vehicle attributes.



# 2. Data Dictionary for Fuel Efficiency Prediction Dataset



## Data-Specific Information

The data-specific information for the Fuel Efficiency Prediction project is crucial for understanding the dataset's structure and its variables. While the data-specific details are described, the actual data dictionary or variable details are missing from the files. To address this gap, a comprehensive data dictionary is provided below. This dictionary outlines the key variables used in the analysis, including their definitions, data types, and significance in predicting fuel efficiency. Including this information enhances the dataset's usability and aids researchers in comprehending the relationship between vehicle characteristics and fuel efficiency metrics.



| **Column Name**  | **Description**                                                                 | **Data Type**       |

|-------------------|---------------------------------------------------------------------------------|----------------------|

| **MPG**           | Miles per gallon; the target variable representing fuel efficiency of the vehicle. | Continuous (float)   |

| **Cylinders**     | Number of cylinders in the engine, which can impact performance and fuel efficiency.  | Categorical (integer) |

| **Displacement**  | Engine displacement measured in cubic inches, indicating the engine size.        | Continuous (float)   |

| **Horsepower**    | Engine power output measured in horsepower, influencing the vehicle's performance. | Continuous (float)   |

| **Weight**        | Weight of the vehicle in pounds, which affects fuel consumption and efficiency.   | Continuous (float)   |

| **Acceleration**  | Time taken (in seconds) to accelerate from 0 to 60 mph, representing vehicle performance. | Continuous (float)   |

| **Model Year**    | The year the vehicle model was produced, which may correlate with advancements in technology and efficiency. | Categorical (integer) |

| **Origin**        | The country of origin of the vehicle (e.g., USA, Europe, Japan), which may influence design and fuel efficiency standards. | Categorical (string)  |



## Notes:

Continuous Variables: These are numerical values that can take any value within a range. They are useful for statistical analysis and modeling.



Categorical Variables: These are discrete values that represent categories or groups. They can be numeric or text-based and may require encoding for machine learning models.



# 3. Objectives

The primary objective of this research is to develop a robust predictive model for fuel efficiency using machine learning algorithms. The specific goals include:



•	Analyzing the relationship between vehicle characteristics and fuel efficiency.



•	Preprocessing the data to ensure quality and reliability.



•	Implementing various machine learning models to identify the most effective one for prediction.



•	Evaluating the model's performance using appropriate metrics.



# 4. Methodology

This study employs a combination of data preprocessing techniques, exploratory data analysis (EDA), and machine learning algorithms, including linear regression, decision trees, and ensemble methods. The models are evaluated based on their accuracy and ability to generalize to unseen data.



# 5. Expected Outcomes

The expected outcomes of this research include a predictive model that accurately estimates fuel efficiency based on vehicle characteristics. Additionally, the findings provide insights into the factors that significantly influence fuel efficiency, contributing to informed decision-making in the automotive sector.



# 6. Conclusion

This research project addresses a significant issue in the automotive industry by predicting fuel efficiency through vehicle characteristics. The results will be valuable for manufacturers and consumers alike, promoting energy-efficient vehicle choices and fostering a greater understanding of vehicle performance.



# References



https://ieeexplore.ieee.org/abstract/document/7480121



https://onlinelibrary.wiley.com/doi/10.1155/2020/9263605