Machine Learning Engineer Nanodegree

Supervised Learning

Project 2: Building a Student Intervention System

Welcome to the second project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with 'Implementation' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.

Question 1 - Classification vs. Regression

Your goal for this project is to identify students who might need early intervention before they fail to graduate. Which type of supervised learning problem is this, classification or regression? Why?

Answer:

It's a classification problem. Because, we need to classify students into two groups, whether they need intervention or they don't need intervention.

Exploring the Data

Run the code cell below to load necessary Python libraries and load the student data. Note that the last column from this dataset, 'passed', will be our target label (whether the student graduated or didn't graduate). All other columns are features about each student.


In [2]:
# Import libraries
import numpy as np
import pandas as pd
from time import time
from sklearn.metrics import f1_score

# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"


Student data read successfully!

Implementation: Data Exploration

Let's begin by investigating the dataset to determine how many students we have information on, and learn about the graduation rate among these students. In the code cell below, you will need to compute the following:

  • The total number of students, n_students.
  • The total number of features for each student, n_features.
  • The number of those students who passed, n_passed.
  • The number of those students who failed, n_failed.
  • The graduation rate of the class, grad_rate, in percent (%).

In [3]:
# TODO: Calculate number of students
n_students = len(student_data)

# TODO: Calculate number of features
n_features = len(student_data.columns[student_data.columns != 'passed'])

# TODO: Calculate passing students
n_passed = len(student_data[student_data['passed'] == 'yes'])

# TODO: Calculate failing students
n_failed = len(student_data[student_data['passed'] == 'no'])

# TODO: Calculate graduation rate
grad_rate = n_passed/float(n_students) * 100

# Print the results
print "Total number of students: {}".format(n_students)
print "Number of features: {}".format(n_features)
print "Number of students who passed: {}".format(n_passed)
print "Number of students who failed: {}".format(n_failed)
print "Graduation rate of the class: {:.2f}%".format(grad_rate)


Total number of students: 395
Number of features: 30
Number of students who passed: 265
Number of students who failed: 130
Graduation rate of the class: 67.09%

Preparing the Data

In this section, we will prepare the data for modeling, training and testing.

Identify feature and target columns

It is often the case that the data you obtain contains non-numeric features. This can be a problem, as most machine learning algorithms expect numeric data to perform computations with.

Run the code cell below to separate the student data into feature and target columns to see if any features are non-numeric.


In [4]:
# Extract feature columns
feature_cols = list(student_data.columns[:-1])

# Extract target column 'passed'
target_col = student_data.columns[-1] 

# Show the list of columns
print "Feature columns:\n{}".format(feature_cols)
print "\nTarget column: {}".format(target_col)

# Separate the data into feature data and target data (X_all and y_all, respectively)
X_all = student_data[feature_cols]
y_all = student_data[target_col]

# Show the feature information by printing the first five rows
print "\nFeature values:"
print X_all.head()


Feature columns:
['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu', 'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime', 'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery', 'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences']

Target column: passed

Feature values:
  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  \
0     GP   F   18       U     GT3       A     4     4  at_home   teacher   
1     GP   F   17       U     GT3       T     1     1  at_home     other   
2     GP   F   15       U     LE3       T     1     1  at_home     other   
3     GP   F   15       U     GT3       T     4     2   health  services   
4     GP   F   16       U     GT3       T     3     3    other     other   

    ...    higher internet  romantic  famrel  freetime goout Dalc Walc health  \
0   ...       yes       no        no       4         3     4    1    1      3   
1   ...       yes      yes        no       5         3     3    1    1      3   
2   ...       yes      yes        no       4         3     2    2    3      3   
3   ...       yes      yes       yes       3         2     2    1    1      5   
4   ...       yes       no        no       4         3     2    1    2      5   

  absences  
0        6  
1        4  
2       10  
3        2  
4        4  

[5 rows x 30 columns]

Preprocess Feature Columns

As you can see, there are several non-numeric columns that need to be converted! Many of them are simply yes/no, e.g. internet. These can be reasonably converted into 1/0 (binary) values.

Other columns, like Mjob and Fjob, have more than two values, and are known as categorical variables. The recommended way to handle such a column is to create as many columns as possible values (e.g. Fjob_teacher, Fjob_other, Fjob_services, etc.), and assign a 1 to one of them and 0 to all others.

These generated columns are sometimes called dummy variables, and we will use the pandas.get_dummies() function to perform this transformation. Run the code cell below to perform the preprocessing routine discussed in this section.


In [5]:
def preprocess_features(X):
    ''' Preprocesses the student data and converts non-numeric binary variables into
        binary (0/1) variables. Converts categorical variables into dummy variables. '''
    
    # Initialize new output DataFrame
    output = pd.DataFrame(index = X.index)

    # Investigate each feature column for the data
    for col, col_data in X.iteritems():
        
        # If data type is non-numeric, replace all yes/no values with 1/0
        if col_data.dtype == object:
            col_data = col_data.replace(['yes', 'no'], [1, 0])

        # If data type is categorical, convert to dummy variables
        if col_data.dtype == object:
            # Example: 'school' => 'school_GP' and 'school_MS'
            col_data = pd.get_dummies(col_data, prefix = col)  
        
        # Collect the revised columns
        output = output.join(col_data)
    
    return output

X_all = preprocess_features(X_all)
print "Processed feature columns ({} total features):\n{}".format(len(X_all.columns), list(X_all.columns))


Processed feature columns (48 total features):
['school_GP', 'school_MS', 'sex_F', 'sex_M', 'age', 'address_R', 'address_U', 'famsize_GT3', 'famsize_LE3', 'Pstatus_A', 'Pstatus_T', 'Medu', 'Fedu', 'Mjob_at_home', 'Mjob_health', 'Mjob_other', 'Mjob_services', 'Mjob_teacher', 'Fjob_at_home', 'Fjob_health', 'Fjob_other', 'Fjob_services', 'Fjob_teacher', 'reason_course', 'reason_home', 'reason_other', 'reason_reputation', 'guardian_father', 'guardian_mother', 'guardian_other', 'traveltime', 'studytime', 'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery', 'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences']

Implementation: Training and Testing Data Split

So far, we have converted all categorical features into numeric values. For the next step, we split the data (both features and corresponding labels) into training and test sets. In the following code cell below, you will need to implement the following:

  • Randomly shuffle and split the data (X_all, y_all) into training and testing subsets.
    • Use 300 training points (approximately 75%) and 95 testing points (approximately 25%).
    • Set a random_state for the function(s) you use, if provided.
    • Store the results in X_train, X_test, y_train, and y_test.

In [6]:
# TODO: Import any additional functionality you may need here
from sklearn.cross_validation import train_test_split

# TODO: Set the number of training points
num_train = 300

# Set the number of testing points
num_test = X_all.shape[0] - num_train

# TODO: Shuffle and split the dataset into the number of training and testing points above
X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, train_size=num_train, random_state=22)

# Show the results of the split
print "Training set has {} samples.".format(X_train.shape[0])
print "Testing set has {} samples.".format(X_test.shape[0])


Training set has 300 samples.
Testing set has 95 samples.

Training and Evaluating Models

In this section, you will choose 3 supervised learning models that are appropriate for this problem and available in scikit-learn. You will first discuss the reasoning behind choosing these three models by considering what you know about the data and each model's strengths and weaknesses. You will then fit the model to varying sizes of training data (100 data points, 200 data points, and 300 data points) and measure the F1 score. You will need to produce three tables (one for each model) that shows the training set size, training time, prediction time, F1 score on the training set, and F1 score on the testing set.

The following supervised learning models are currently available in scikit-learn that you may choose from:

  • Gaussian Naive Bayes (GaussianNB)
  • Decision Trees
  • Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)
  • K-Nearest Neighbors (KNeighbors)
  • Stochastic Gradient Descent (SGDC)
  • Support Vector Machines (SVM)
  • Logistic Regression

Question 2 - Model Application

List three supervised learning models that are appropriate for this problem. For each model chosen

  • Describe one real-world application in industry where the model can be applied. (You may need to do a small bit of research for this — give references!)
  • What are the strengths of the model; when does it perform well?
  • What are the weaknesses of the model; when does it perform poorly?
  • What makes this model a good candidate for the problem, given what you know about the data?

Answer:

I chose Support Vector Machines, AdaBoosting and Gaussian Naive Bayes algorithms.

Support Vector Classifier
  • General Applications
    • text (and hypertext) categorization such as email classification and web searching
    • image classification
    • bioinformatics (Protein classification, Cancer classification)
    • hand-written character recognition
  • Strengths

    • Avoids localization problems
    • Can use the kernel trick
    • Accurate with small & clean datasets
    • Effecient since it only uses a subset of the data
  • Weaknesses

    • Sensitive to noise (may need to drop some features that are causing noise)
    • It only considers two classes at a time
    • Can be painful to train with large datasets
  • Reasons for Choosing

    • SVMs can do well when with a small set as long as the number of data points is larger than the number of features being considered.
AdaBoost Classifier
  • General Applications
    • An AdaBoost classifier is to interpolot between many weak learners to create a more accurate model.
  • Strengths
    • Improves classification accuracy
    • Can be used with many different classifiers
    • Not prone to overfitting which is helpful given our dataset's limited size
  • Weaknesses
    • Effected by noisy data
    • Longer training time since boosting trains multiple instances of the estimator
  • Reasons for Choosing
    • In the case that the above 2 classifiers don't work well, we can use the AdaBoost classifier with SVC or simply go with the default estimator (Decision Trees).
Gaussian Naive Bayes
  • General Applications
    • Spam mail filter
    • Classifying emails based on content
  • Strengths
    • Easy and fast to use
    • Accurate if the models assumption holds up
  • Weaknesses
    • When assumptions don't hold and accuracy will be off. (Assumption being that the pressence of features are independent of each other.)
  • Reasons for Choosing
    • One could argue that in our use case, I felt like features are independent of each other, although they could be depenent.

Setup

Run the code cell below to initialize three helper functions which you can use for training and testing the three supervised learning models you've chosen above. The functions are as follows:

  • train_classifier - takes as input a classifier and training data and fits the classifier to the data.
  • predict_labels - takes as input a fit classifier, features, and a target labeling and makes predictions using the F1 score.
  • train_predict - takes as input a classifier, and the training and testing data, and performs train_clasifier and predict_labels.
    • This function will report the F1 score for both the training and testing data separately.

In [7]:
def train_classifier(clf, X_train, y_train):
    ''' Fits a classifier to the training data. '''
    
    # Start the clock, train the classifier, then stop the clock
    start = time()
    clf.fit(X_train, y_train)
    end = time()
    
    # Print the results
    print "Trained model in {:.4f} seconds".format(end - start)

    
def predict_labels(clf, features, target):
    ''' Makes predictions using a fit classifier based on F1 score. '''
    
    # Start the clock, make predictions, then stop the clock
    start = time()
    y_pred = clf.predict(features)
    end = time()
    
    # Print and return results
    print "Made predictions in {:.4f} seconds.".format(end - start)
    return f1_score(target.values, y_pred, pos_label='yes')


def train_predict(clf, X_train, y_train, X_test, y_test):
    ''' Train and predict using a classifer based on F1 score. '''
    
    # Indicate the classifier and the training set size
    print "Training a {} using a training set size of {}. . .".format(clf.__class__.__name__, len(X_train))
    
    # Train the classifier
    train_classifier(clf, X_train, y_train)
    # acc = accuracy_score(y_true, y_pred)
    # Print the results of prediction for both training and testing
    print "F1 score for training set: {:.4f}.\n".format(predict_labels(clf, X_train, y_train))
    print "F1 score for test set: {:.4f}.\n\n".format(predict_labels(clf, X_test, y_test))
    # print "accuracy for test set: {:.4f}.".format(predict_labels(clf, X_test, y_test))"

Implementation: Model Performance Metrics

With the predefined functions above, you will now import the three supervised learning models of your choice and run the train_predict function for each one. Remember that you will need to train and predict on each classifier for three different training set sizes: 100, 200, and 300. Hence, you should expect to have 9 different outputs below — 3 for each model using the varying training set sizes. In the following code cell, you will need to implement the following:

  • Import the three supervised learning models you've discussed in the previous section.
  • Initialize the three models and store them in clf_A, clf_B, and clf_C.
    • Use a random_state for each model you use, if provided.
    • Note: Use the default settings for each model — you will tune one specific model in a later section.
  • Create the different training set sizes to be used to train each model.
    • Do not reshuffle and resplit the data! The new training points should be drawn from X_train and y_train.
  • Fit each model with each training set size and make predictions on the test set (9 in total).
    Note: Three tables are provided after the following code cell which can be used to store your results.

In [9]:
# TODO: Import the three supervised learning models from sklearn
from sklearn import svm
from sklearn.ensemble import AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB


# TODO: Initialize the three models
clf_A = svm.SVC(random_state=24)
clf_B = AdaBoostClassifier(random_state=42)
clf_C = GaussianNB()

# TODO: Set up the training set sizes
X_train_100 = X_train[:100]
y_train_100 = y_train[:100]

X_train_200 = X_train[:200]
y_train_200 = y_train[:200]

X_train_300 = X_train
y_train_300 = y_train

# TODO: Execute the 'train_predict' function for each classifier and each training set size
train_predict(clf_A, X_train_100, y_train_100, X_test, y_test)
train_predict(clf_A, X_train_200, y_train_200, X_test, y_test)
train_predict(clf_A, X_train, y_train, X_test, y_test)

train_predict(clf_B, X_train_100, y_train_100, X_test, y_test)
train_predict(clf_B, X_train_200, y_train_200, X_test, y_test)
train_predict(clf_B, X_train, y_train, X_test, y_test)

train_predict(clf_C, X_train_100, y_train_100, X_test, y_test)
train_predict(clf_C, X_train_200, y_train_200, X_test, y_test)
train_predict(clf_C, X_train, y_train, X_test, y_test)


Training a SVC using a training set size of 100. . .
Trained model in 0.0019 seconds
Made predictions in 0.0010 seconds.
F1 score for training set: 0.8780.

Made predictions in 0.0012 seconds.
F1 score for test set: 0.7681.


Training a SVC using a training set size of 200. . .
Trained model in 0.0040 seconds
Made predictions in 0.0028 seconds.
F1 score for training set: 0.8849.

Made predictions in 0.0019 seconds.
F1 score for test set: 0.8514.


Training a SVC using a training set size of 300. . .
Trained model in 0.0070 seconds
Made predictions in 0.0050 seconds.
F1 score for training set: 0.8705.

Made predictions in 0.0016 seconds.
F1 score for test set: 0.8497.


Training a AdaBoostClassifier using a training set size of 100. . .
Trained model in 0.1115 seconds
Made predictions in 0.0045 seconds.
F1 score for training set: 0.9573.

Made predictions in 0.0044 seconds.
F1 score for test set: 0.6230.


Training a AdaBoostClassifier using a training set size of 200. . .
Trained model in 0.1216 seconds
Made predictions in 0.0054 seconds.
F1 score for training set: 0.8898.

Made predictions in 0.0046 seconds.
F1 score for test set: 0.8058.


Training a AdaBoostClassifier using a training set size of 300. . .
Trained model in 0.1351 seconds
Made predictions in 0.0072 seconds.
F1 score for training set: 0.8537.

Made predictions in 0.0046 seconds.
F1 score for test set: 0.7770.


Training a GaussianNB using a training set size of 100. . .
Trained model in 0.0012 seconds
Made predictions in 0.0005 seconds.
F1 score for training set: 0.2154.

Made predictions in 0.0003 seconds.
F1 score for test set: 0.2326.


Training a GaussianNB using a training set size of 200. . .
Trained model in 0.0012 seconds
Made predictions in 0.0005 seconds.
F1 score for training set: 0.7954.

Made predictions in 0.0004 seconds.
F1 score for test set: 0.6970.


Training a GaussianNB using a training set size of 300. . .
Trained model in 0.0011 seconds
Made predictions in 0.0007 seconds.
F1 score for training set: 0.7789.

Made predictions in 0.0004 seconds.
F1 score for test set: 0.7353.


Tabular Results

Edit the cell below to see how a table can be designed in Markdown. You can record your results from above in the tables provided.

Classifer 1 - SVM

Training Set Size Training Time Prediction Time (test) F1 Score (train) F1 Score (test)
100 0.0019 sec 0.0012 sec 0.8780 0.7681
200 0.0040 sec 0.0019 sec 0.8849 0.8514
300 0.0070 sec 0.0016 sec 0.8705 0.8497

Classifer 2 - AdaBoost

Training Set Size Training Time Prediction Time (test) F1 Score (train) F1 Score (test)
100 0.1115 sec 0.0044 sec 0.9573 0.6230
200 0.1216 sec 0.0046 sec 0.8898 0.8058
300 0.1351 sec 0.0046 sec 0.8537 0.7770

Classifer 3 - Gaussian NB

Training Set Size Training Time Prediction Time (test) F1 Score (train) F1 Score (test)
100 0.0012 sec 0.0003 sec 0.2154 0.2326
200 0.0012 sec 0.0004 sec 0.7954 0.6970
300 0.0011 sec 0.0004 sec 0.7789 0.7353

Choosing the Best Model

In this final section, you will choose from the three supervised learning models the best model to use on the student data. You will then perform a grid search optimization for the model over the entire training set (X_train and y_train) by tuning at least one parameter to improve upon the untuned model's F1 score.

Question 3 - Choosing the Best Model

Based on the experiments you performed earlier, in one to two paragraphs, explain to the board of supervisors what single model you chose as the best model. Which model is generally the most appropriate based on the available data, limited resources, cost, and performance?

Answer:

The chosen model for this project is one that implements a Support Vector Machine (SVM) algorithim.

Support Vector Machines can be used for Classification (labeling) or Regression (numeric) predications. Since we are trying to find out if an intervention is necessary or not, we will make use of specific type of SVMs called Support Vector Classifier (SVC) to get the results we are looking for.

The SVM takes data about past students (age, gender, family, etc), and uses them to create predictions about new student cases. These predictions are made by creating a function that draws a boundary between the students who graduated and those who did not. The boundary should be drawn so as to maximize the the space between itself and each of the classifications (graduation results), this space is called a "margin".

Often, though, it's not easy to draw a decision boundary in low dimensions (i.e. A line is not a good enough boundry), so the SVM separates the passing and failing students by turning the dimensions we're working with from a plane into a higher dimension such as a cube (see below). Once the SVM changes its way of looking at the data, it can then use a plane to seperate the data instead of just a line.

By using the technique mentioned above, our SVC algorithim predicts if a student will graduate or not with an acceptable accuracy.

Question 4 - Model in Layman's Terms

In one to two paragraphs, explain to the board of directors in layman's terms how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical or technical jargon, such as describing equations or discussing the algorithm implementation.

Answer:

The chosen model is called Support Vector Machine (SVM). This algorithim works by trying to split the data in the cleanest way. This means that SVMs try to draw a line between different outcomes (aka Classifications) from the training data (aka student records available) in order to learn to predict the outcomes of a new student case.

SVMs are a great choice when the resources are limited and the number of considerations (aka Features, ex: absences) in our data is relatively large. This is the case with in this project since the number of features is 40+ (after data processing) and the number of entries is 300+ which is relatively low. SVMs still however provide a quick training and prediction times compared to other algorithims that were considered.

Implementation: Model Tuning

Fine tune the chosen model. Use grid search (GridSearchCV) with at least one important parameter tuned with at least 3 different values. You will need to use the entire training set for this. In the code cell below, you will need to implement the following:

  • Import sklearn.grid_search.gridSearchCV and sklearn.metrics.make_scorer.
  • Create a dictionary of parameters you wish to tune for the chosen model.
    • Example: parameters = {'parameter' : [list of values]}.
  • Initialize the classifier you've chosen and store it in clf.
  • Create the F1 scoring function using make_scorer and store it in f1_scorer.
    • Set the pos_label parameter to the correct value!
  • Perform grid search on the classifier clf using f1_scorer as the scoring method, and store it in grid_obj.
  • Fit the grid search object to the training data (X_train, y_train), and store it in grid_obj.

In [10]:
# TODO: Import 'GridSearchCV' and 'make_scorer'
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import make_scorer
# TODO: Create the parameters list you wish to tune
parameters = {'kernel': ['linear', 'poly', 'rbf', 'sigmoid'], 'C': range(1, 11)}

# TODO: Initialize the classifier
clf = svm.SVC(random_state=24)

# TODO: Make an f1 scoring function using 'make_scorer' 
f1_scorer = make_scorer(score_func=f1_score, pos_label='yes')

# TODO: Perform grid search on the classifier using the f1_scorer as the scoring method
grid_obj = GridSearchCV(clf, param_grid=parameters, scoring=f1_scorer)

# TODO: Fit the grid search object to the training data and find the optimal parameters
grid_obj = grid_obj.fit(X_train, y_train)

# Get the estimator
clf = grid_obj.best_estimator_

# Report the final F1 score for training and testing after parameter tuning
print "Tuned model has a training F1 score of {:.4f}.".format(predict_labels(clf, X_train, y_train))
print "Tuned model has a testing F1 score of {:.4f}.".format(predict_labels(clf, X_test, y_test))


Made predictions in 0.0030 seconds.
Tuned model has a training F1 score of 0.7879.
Made predictions in 0.0012 seconds.
Tuned model has a testing F1 score of 0.8485.

Question 5 - Final F1 Score

What is the final model's F1 score for training and testing? How does that score compare to the untuned model?

Answer:

The final model's F1 score for training set is 0.7879, for testing set is 0.8485. F1 score for training set is less than that for untuned model and approximately the same for the testing set.

Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to
File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.