Assignment #1: Exploratory Data Analysis and Regression Modeling

This repository contains the Python code and analysis for Assignment #1 in the "Machine Learning and Data Science" course (ENCS5341) at the Electrical and Computer Engineering Department.

Overview

The goal of this assignment is to perform an in-depth analysis of a dataset containing information about cars and fuel consumption. We will use Python for the solution, leveraging libraries such as Pandas for data manipulation and analysis, and NumPy for numerical operations.

Dataset

The dataset, cars.csv, is provided and contains information about various cars, including fuel consumption. The main tasks include:

1. Data Exploration:

   • Read the dataset and examine the number of features and examples.
   • Identify features with missing values and determine the number of missing values in each.

2. Data Imputation:

   • Fill missing values in each feature using appropriate imputation methods, such as mean, median, or mode.

3. Country-wise Fuel Economy:

   • Use a box plot to visualize fuel economy (mpg) for cars produced by different countries.

4. Distribution Analysis:

   • Determine which feature ('acceleration', 'horsepower', or 'mpg') has a distribution most similar to a Gaussian.
   • Support the answer with histograms of each feature.

5. Quantitative Measure:

   • Provide a quantitative measure supporting the distribution analysis.

6. Correlation Analysis:

   • Plot a scatter plot showing the correlation between 'horsepower' and 'mpg'.
   • Analyze whether the correlation is positive or negative.

7. Linear Regression:

   • Implement the closed-form solution of linear regression.
   • Learn a linear model to predict 'mpg' from 'horsepower' and visualize the learned line on the scatter plot.

8. Quadratic Regression:

   • Learn a quadratic function and visualize it.

9. Gradient Descent:

   • Implement the gradient descent algorithm for simple linear regression.