William's Data Science Blog

Tag: scikit-learn

  • Exploring the Impact of Alcohol Consumption on Student Grades with Gaussian Naive Bayes

    Exploring the Impact of Alcohol Consumption on Student Grades with Gaussian Naive Bayes

    William's Data Science Blog

    In today’s data-driven world, even seemingly straightforward questions can reveal surprising insights. In this post, I investigate whether students’ alcohol consumption habits bear any relationship to their final math grades. Using the Student Alcohol Consumption dataset from Kaggle, which contains survey responses on a myriad aspects of students’ lives—ranging from study habits and social factors to gender and alcohol use—I set out to determine if patterns exist that can predict academic performance.

    Dataset Overview

    The dataset originates from a survey of students enrolled in secondary school math and Portuguese courses. It includes rich, social, and academic information, such as:

    • Social and family background
    • Study habits and academic support
    • Alcohol consumption details during weekdays and weekends

    I focused on predicting the final math grade (denoted as G3 in the raw data) while probing how alcohol-related features, especially weekend consumption, might play a role in performance. The binary insight wasn’t just about whether students drank, but which drinking pattern might be more telling of their academic results.

    Data Preprocessing: Laying the Groundwork

    Before diving into modeling, the data needed some cleanup. Here’s how I systematically prepared the dataset for analysis:

    1. Loading the Data: I imported the CSV into a Pandas DataFrame for easy manipulation.
    2. Renaming Columns: Clarity matters. I renamed ambiguous columns for better readability (e.g., renaming walc to weekend_alcohol and dalc to weekday_alcohol).
    3. Label Encoding: Categorical data were converted to numeric representations using scikit-learn’s LabelEncoder, ensuring all features could be numerically processed.
    4. Reusable Code: I encapsulated the training and testing phases within a reusable function, which made it straightforward to test different feature combinations.

    Here’s are some snippets:

    In those cells:

    • I rename columns to make them more readable.
    • I instantiate a LabelEncoder object and encode a list of columns that have string values.
    • I add an absence category to normalize absence count a little due to how variable that data is.

    Experimenting With Gaussian Naive Bayes

    The heart of this exploration was to see how well a Gaussian Naive Bayes classifier could predict the final math grade based on different selections of features. Naive Bayes, while greatly valued for its simplicity and speed, operates under the assumption that features are independent—a condition that might not fully hold in educational data.

    Training and Evaluation Function

    To streamline the experiments, I wrote a function that:

    • Splits the data into training and testing sets.
    • Trains a GaussianNB model.
    • Evaluates accuracy on the test set.

    In that cell:

    • I create a function that:
      • Drops unwanted columns.
      • Runs 100 training cycles with the given data.
      • Captures the accuracy measured from each run and returns the average.

    Single and Two column sampling

    In those cells:

    • I get a list of all columns.
    • I create loop(s) over the column list and create a list of features to test.
    • I call my function to measure the the accuracy of the features at predicting student grades.

    Diving Into Feature Combinations

    I aimed to assess the predictive power by testing different combinations of features:

    1. All Columns: This gave the best accuracy of around 22%, yet it was clear that even the full spectrum of information struggled to make strong predictions.
    2. Handpicked Features: I manually selected features that I hypothesized might be influential. The resulting accuracy dipped below that of the full dataset.
    3. Individual Features: Evaluating each feature solo revealed that the column indicating whether students planned to pursue higher education yielded the highest individual accuracy—though still far lower than all features combined.
    4. Two-Feature Combinations: By testing all pairs, I noticed that combinations including weekend alcohol consumption appeared in the top 20 predictive pairs four times, including in both of the top two.
    5. Three-Feature Combinations: The trend became stronger—combinations featuring weekend alcohol consumption topped the list ten times and were present in each of the top three combinations!
    6. Four-Feature Combinations: Here, weekend alcohol consumption featured in the top 20 combination results even more robustly—15 times in total.

    These experiments showcased one noteworthy pattern: weekend alcohol consumption consistently emerged as a common denominator in the best-performing feature combinations, while weekday consumption rarely made an appearance.

    Analysis of the Findings

    Several key observations emerged from this series of experiments:

    • Predictive Accuracy: Even with the full set of features, the best accuracy reached was only around 22%. This underwhelming performance is indicative of the challenges posed by the dataset and the restrictive assumptions embedded within the Naive Bayes model.
    • Role of Alcohol Consumption: The repeated appearance of weekend alcohol consumption in high-ranking feature combinations suggests a potential association—it may capture lifestyle or social habits that indirectly correlate with academic performance. However, it is not a standalone predictor; rather, it seems to be relevant as part of a multifactorial interaction.
    • Model Limitations: The Gaussian Naive Bayes classifier assumes feature independence. The complexities inherent in student performance—where multiple social, educational, and psychological factors interact—likely violate this assumption, leading to lower predictive performance.

    Conclusion and Future Directions

    While the Gaussian Naive Bayes classifier provided some interesting insights, especially regarding the recurring presence of weekend alcohol consumption in influential feature combinations, its overall accuracy was modest. Predicting the final math grade, a multifaceted outcome influenced by numerous interdependent factors, appears too challenging for this simplistic probabilistic model.

    Next Steps:

    • Alternative Machine Learning Algorithms: Investigating other approaches like decision trees, random forests, support vector machines, or ensemble methods may yield better performance.
    • Enhanced Feature Engineering: Incorporating interaction terms or domain-specific features might help capture the complex relationships between social habits and academic outcomes.
    • Broader Data Explorations: Diving deeper into other factors—such as study habits, parental support, and extracurricular involvement—could provide additional clarity.

    Final Thoughts and Next Steps

    This journey reinforced the idea that while Naive Bayes is a great tool for its speed and interpretability, it might not be the best choice for all datasets. More sophisticated models and careful feature engineering are necessary when dealing with some datasets like student academic performance.

    The new Jupyter notebook can be found here in my GitHub.

    – William

  • Leveraging Scikit-Learn and Polars to Test a Naive Bayes Classifier

    Leveraging Scikit-Learn and Polars to Test a Naive Bayes Classifier

    William's Data Science Blog

    In today’s post, I use scikit-learn with the same sample dataset I used in the previous post. I need to use the LabelEncoder to encode the strings as numeric values and then the GaussianNB to train and testing a Gaussian Naive Bayes classifier model and to predict the class of an example record. While many tutorials use pandas, I use Polars for fast data manipulation alongside scikit-learn for model development.

    Understanding Our Data and Tools

    Remember that the dataset includes ‘features’ for height, weight, foot size. It also has a categorical field for gender. Because classifiers like Gaussian Naive Bayes require numeric inputs, I need to transform the string gender values into a numeric format.

    In my new Jupyter notebook I use two libraries:

    Scikit-Learn for its machine learning utilities. Specifically, LabelEncoder for encoding and GaussianNB for classification.

    Polars for fast, efficient DataFrame manipulations.

    Step 1: Encoding Categorical Variables

    The first step is to convert our categorical column (gender) to a numeric format using scikit-learn’s LabelEncoder. This conversion is vital because machine learning models generally can’t work directly with string labels.

    Below is the code from our first notebook cell:

    In that cell:

    • I instantiate a LabelEncoder object.
    • For every feature in columns_to_encode (in this case, just "gender"), I create a new Polars Series with the suffix "_num", containing the encoded numeric values.
    • Finally, I add these series as new columns to our original DataFrame.

    This ensures that our categorical data is transformed into a machine-friendly format, an also preserves the human-readable string values for future reference.

    Step 2: Mapping Encoded Values to Original Labels

    Once we’ve encoded the data, it’s important to retain the mapping between the original string values and their corresponding numeric codes. This mapping is particularly useful when you want to interpret or display the model’s predictions.

    The following code block demonstrates how to generate and view this mapping:

    In that cell:

    • I save the original "gender" column and its encoded counterpart "gender_num".
    • By grouping on "gender" and aggregating with the first encountered numeric value, I create a mapping from string labels to numerical codes.

    Step 3: Training and Testing the Gaussian Naive Bayes Classifier

    Now it’s time to build, train, and evaluate our model. I separate the features and target, split the data, and then initialize the classifier.

    In that cell:

    • Get the data to use in training: I drop the raw "gender" and its encoded version from the Dataframe (X) and save the encoded classification in (y).
    • Data Splitting: train_test_split is used to randomly partition the data into training and testing sets.
    • Model Training: A GaussianNB classifier is instantiated and trained on the training data using the fit() method.
    • Prediction and Evaluation: The model’s predictions on the test set (y_pred) are generated and compared against the true labels using accuracy_score. This gives us a quantitative measure of the model’s performance.

    Step 4: Classifying a New Record

    Now I can test it on the sample observation. Consider the following code snippet:

    In that cell:

    • Create Example Data: I define a new sample record (with features like height, weight, and foot size) and create a Polars DataFrame to hold this record.
    • Prediction: The classifier is then used to predict the gender (encoded as a number) for this new record.
    • Decoding: Use the gender_mapping to display the human-readable gender label corresponding to the model’s prediction.

    Final Thoughts and Next Steps

    This step-by-step notebook shows how to preprocess data, map categorical values, train a Gaussian Naive Bayes classifier, and test new data with the combination of Polars and scikit-learn.

    The new Jupyter notebook can be found here in my GitHub. If you follow the instructions in my previous post you can run this notebook for yourself.

    – William