:Machine Learning in Finding the Best Algorithm for Prediction of Fuel Efficiency in Automobile Industry
Using the dataset, which contains information on the MPG, cylinders, displacement, horsepower, weight, acceleration, model year, location of manufacture, and name of the car, the proposed work entails the development and implementation of a machine learning model. Based on the research that has already been done in the field, these characteristics have been recognized as key markers of fuel efficiency.
Methodology
Importing
Import necessary libraries for data analysis and machine learning.
Data Loading
Load relevant data for fuel efficiency prediction.
Data Preparation
Preprocess data to ensure it's clean and ready for analysis.
Data Cleaning
Remove irrelevant columns and handle data inconsistencies.
Exploratory Data Analysis
Analyze data distribution and visualize relationships between variables.
Replace Null Values in Horsepower
Address missing values in the "horsepower" column.
Feature Engineering
Identify and create new features for better predictions.
MPG Encoding
Encode the target variable "MPG" for regression analysis.
Preprocessing
Split data for training and testing and perform preprocessing.
Model Building
Implement and train various machine learning algorithms.
Results
Evaluate model performance and select the best-performing model for fuel efficiency prediction.
:Machine Learning in Finding the Best Algorithm for Prediction of Fuel Efficiency in Automobile Industry