Fuel Economy Prediction Model

This project aims to build a linear regression model to predict a vehicle's fuel efficiency, measured in miles per gallon (mpg), based on various vehicle attributes. The prediction model will be developed using Python, leveraging libraries such as pandas, scikit-learn (sklearn), and statsmodels. The dataset will be sourced from a CSV file containing relevant vehicle data.

Objectives

1. Data Preprocessing: Load and clean the dataset, addressing challenges such as inconsistent data formats and missing values.
2. Exploratory Data Analysis (EDA): Perform EDA to understand the relationships between features and the target variable (mpg).
3. Feature Selection and Engineering: Select and engineer features to improve model performance.
4. Model Development: Build and train a linear regression model using scikit-learn and statsmodels.
5. Model Evaluation: Assess the model's performance using appropriate metrics and validate its accuracy.

Data Source

The data for this project will be sourced from a CSV file containing vehicle attributes (cylinders, displacement, horsepower, weight, acceleration, model year, origin and car name)

Tools and Libraries

1. pandas: For data manipulation and preprocessing.
2. statsmodels, scikit-learn (sklearn): For building and evaluating the linear regression model.

Methodology

1. Data Loading and Cleaning:

Load the CSV file using pandas.

Inspect and clean the data to handle missing values and correct inconsistent data formats.

2. Exploratory Data Analysis (EDA):

Generate summary statistics and visualize the data distribution.

Examine correlations between features and the target variable (mpg).

3. Feature Selection and Engineering:

Select relevant features based on EDA insights.

Encode categorical variables when necessary.

Engineer new features if they can improve the model's predictive power.

4. Model Development:

Split the data into training and testing sets.

Train a linear regression model using scikit-learn and statsmodels.

Perform a detailed statistical analysis of the model

5. Model Evaluation:

Evaluate the model's performance using metrics such as Mean Squared Error (MSE) and R-squared on the training dataset.

Validate the model with cross-validation techniques.

Check model assumptions (Linearity, Independence, Normality, No multicollinearity, Equal variance).

6. Model Scoring:

Score the model's performance on the test dataset.

7. Ridge Regression:

Fit, evaluate, and score a Ridge Regression model as an alternative approach.

8. Model Selection:

Choose the final model based on performance metrics and assumptions checks.