Ch1 in Hands-On Machine Learning (Project Code)

The project aims to analyze the relationship between income and life satisfaction using two datasets: one from the OECD and another from the United Nations. It is a supervised learning problem because we have labeled data (known life satisfaction levels for countries). It's also a regression problem as we are predicting a continuous value (life satisfaction), not a categorical one (like 0 or 1). Finally, it's a model-based learning approach, assuming a mathematical equation (a linear relationship) connects income and satisfaction. The goal is to find the best parameters (θ0, θ1) for this equation.

The video highlights common ML challenges addressed in the project. These include 'non-representative data,' 'insufficient quantity of data,' 'overfitting' (where the model memorizes training data too well, failing on new data), and 'underfitting' (where the model is too simple to capture the underlying relationship). Chapter 1 emphasizes that machine learning involves letting computers learn from data rather than fixed rules, and data quality is paramount.

The code demonstrates data preparation, starting with transforming and merging datasets. This includes making the principal table 'wide' for easier analysis, renaming a column '2015' to 'GDP_per_capita,' and setting 'Country' as the index for effective merging. The purpose is to create a unified dataset where life satisfaction and income are associated with each country. Seven outlier countries (both rich with low satisfaction and poor with high satisfaction) are removed to keep the linear regression clear.

The video explains importing the 'os' library to interact with the operating system for folder management, ensuring compatibility across different OS (Windows, Linux, Mac). It outlines how the code sets up paths for saving and loading data. Crucially, it shows how to load the 'OECD' dataset using pandas, handling common data issues like commas in numbers ('thousands separator') and tab delimiters. It also explains encoding for European languages.

The code then focuses on cleaning and transforming the raw data to make it organized. It aggregates detailed data into 'total' values for broader comparisons and reshapes the dataset so that each country has a single row with relevant data (like life satisfaction and GDP per capita) in separate columns. This simplifies the data presentation, making it easier to work with. The initial rows of the processed data are displayed to show the improved structure.

The video details loading the GDP per capita data, similar to the life satisfaction data, handling delimiters and encoding. It then performs a merge operation between the life satisfaction and GDP datasets using 'Country' as the common index, resulting in a comprehensive dataset. This merged data is then sorted by wealth (from poorest to richest) to facilitate easier visualization and analysis of the relationship between wealth and life satisfaction.

The dataset is split into training and testing sets. Seven specific countries are chosen for the test set; these are carefully selected as they represent outliers (e.g., rich countries with low life satisfaction or poor countries with high life satisfaction). This selection ensures that the model's ability to generalize is properly evaluated, as these countries would make the initial linear regression plot misleading.

The video demonstrates how to plot the training data using a scatter plot, depicting income on the x-axis and life satisfaction on the y-axis. The plot shows a positive correlation, indicating that as income increases, so does life satisfaction. Annotations are added for specific countries (like Hungary and South Korea). The video then introduces a manual exploration of potential linear regression lines (red, green, blue) to visually understand how different slopes and intercepts affect the fit.

Using the 'sklearn' library, the Linear Regression model is applied to the training data. The model learns the optimal coefficients (θ0 and θ1) by minimizing the sum of squared distances between data points and the regression line through the Ordinary Least Squares method. The coefficients are printed, with an explanation for why θ1 is very small: a large change in income (dollars) leads to a small change in life satisfaction (on a 1-10 scale).

The trained Linear Regression model is then used to predict the life satisfaction for a new country, Cyprus, which was not part of the training data. Given Cyprus's GDP per capita, the model predicts a life satisfaction score of approximately 5.96. The prediction is visualized on the scatter plot, showing Cyprus's position relative to the established regression line.

As an alternative, the K-Nearest Neighbors (KNN) algorithm is introduced. For Cyprus, instead of fitting a line, KNN finds the three nearest data points (countries) to Cyprus based on income and averages their life satisfaction scores. This results in a predicted life satisfaction of approximately 5.76, demonstrating a different approach to prediction compared to linear regression. The concept of instance-based learning versus model-based learning is highlighted.

The video addresses the problem of overfitting, where a complex model (e.g., a high-degree polynomial regression) fits the training data perfectly but fails to generalize to new data. To mitigate this, the concept of regularization is introduced, employing a Ridge Regression model. This approach helps control the model's complexity by adding a penalty for large coefficients, making the model less sensitive to noise in the training data and improving its generalization capabilities. The final plot compares the original regression lines with the regularized one, showing an improved fit and better handling of the test data (the outlier countries discussed earlier).