Project 2: Supervised Learning¶

Building a Student Intervention System¶

1. Classification vs Regression¶

Your goal is to identify students who might need early intervention - which type of supervised machine learning problem is this, classification or regression? Why?

2. Exploring the Data¶

Let's go ahead and read in the student dataset first.

To execute a code cell, click inside it and press Shift+Enter.

# Import libraries
import numpy as np
import pandas as pd

# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"
# Note: The last column 'passed' is the target/label, all other are feature columns

Student data read successfully!

Now, can you find out the following facts about the dataset?

Total number of students
Number of students who passed
Number of students who failed
Graduation rate of the class (%)
Number of features

Use the code block below to compute these values. Instructions/steps are marked using TODOs.

# TODO: Compute desired values - replace each '?' with an appropriate expression/function call
n_students = student_data.shape[0]
n_features = student_data.shape[1] - 1
n_passed = sum(student_data['passed'] == 'yes')
n_failed = sum(student_data['passed'] == 'no')
grad_rate = float(n_passed) / n_students * 100.0
print "Total number of students: {}".format(n_students)
print "Number of students who passed: {}".format(n_passed)
print "Number of students who failed: {}".format(n_failed)
print "Number of features: {}".format(n_features)
print "Graduation rate of the class: {:.2f}%".format(grad_rate)

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

3. 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.

Let's first separate our data into feature and target columns, and see if any features are non-numeric.
Note: For this dataset, the last column ('passed') is the target or label we are trying to predict.

# Extract feature (X) and target (y) columns
feature_cols = list(student_data.columns[:-1])  # all columns but last are features
target_col = student_data.columns[-1]  # last column is the target/label
print "Feature column(s):-\n{}".format(feature_cols)
print "Target column: {}".format(target_col)

X_all = student_data[feature_cols]  # feature values for all students
y_all = student_data[target_col]  # corresponding targets/labels
print "\nFeature values:-"
print X_all.head()  # print the first 5 rows

Feature column(s):-
['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.

# Preprocess feature columns
def preprocess_features(X):
    outX = pd.DataFrame(index=X.index)  # output dataframe, initially empty

    # Check each column
    for col, col_data in X.iteritems():
        # If data type is non-numeric, try to replace all yes/no values with 1/0
        if col_data.dtype == object:
            col_data = col_data.replace(['yes', 'no'], [1, 0])
        # Note: This should change the data type for yes/no columns to int

        # If still non-numeric, convert to one or more dummy variables
        if col_data.dtype == object:
            col_data = pd.get_dummies(col_data, prefix=col)  # e.g. 'school' => 'school_GP', 'school_MS'

        outX = outX.join(col_data)  # collect column(s) in output dataframe

    return outX

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

Processed feature columns (48):-
['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']

Split data into training and test sets¶

So far, we have converted all categorical features into numeric values. In this next step, we split the data (both features and corresponding labels) into training and test sets.

# First, decide how many training vs test samples you want
num_all = student_data.shape[0]  # same as len(student_data)
num_train = 300  # about 75% of the data
num_test = num_all - num_train

# TODO: Then, select features (X) and corresponding labels (y) for the training and test sets
# Note: Shuffle the data or randomly select samples to avoid any bias due to ordering in the dataset
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_all,y_all,train_size=0.76,random_state=0)
print "Training set: {} samples".format(X_train.shape[0])
print "Test set: {} samples".format(X_test.shape[0])
# Note: If you need a validation set, extract it from within training data

Training set: 300 samples
Test set: 95 samples

4. Training and Evaluating Models¶

Choose 3 supervised learning models that are available in scikit-learn, and appropriate for this problem. For each model:

What are the general applications of this model? What are its strengths and weaknesses?
Given what you know about the data so far, why did you choose this model to apply?
Fit this model to the training data, try to predict labels (for both training and test sets), and measure the F₁ score. Repeat this process with different training set sizes (100, 200, 300), keeping test set constant.

Produce a table showing training time, prediction time, F₁ score on training set and F₁ score on test set, for each training set size.

Note: You need to produce 3 such tables - one for each model.

# Train a model
import time

def train_classifier(clf, X_train, y_train):
    print "Training {}...".format(clf.__class__.__name__)
    start = time.time()
    clf.fit(X_train, y_train)
    end = time.time()
    print "Done!\nTraining time (secs): {:.8f}".format(end - start)

# TODO: Choose a model, import it and instantiate an object
from sklearn import tree
clf = tree.DecisionTreeClassifier(criterion="entropy")
#Using entropy here because Gini is giving lower F1-score. 

# Fit model to training data
train_classifier(clf, X_train, y_train)  # note: using entire training set here
print clf  # you can inspect the learned model by printing it

Training DecisionTreeClassifier...
Done!
Training time (secs): 0.01499987
DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best')

# Predict on training set and compute F1 score
from sklearn.metrics import f1_score

def predict_labels(clf, features, target):
    print "Predicting labels using {}...".format(clf.__class__.__name__)
    start = time.time()
    y_pred = clf.predict(features)
    end = time.time()
    print "Done!\nPrediction time (secs): {}".format(end - start)
    return f1_score(target.values, y_pred, pos_label='yes')

train_f1_score = predict_labels(clf, X_train, y_train)
print "F1 score for training set: {}".format(train_f1_score)

Predicting labels using DecisionTreeClassifier...
Done!
Prediction time (secs): 0.0830001831055
F1 score for training set: 1.0

# Predict on test data
print "F1 score for test set: {}".format(predict_labels(clf, X_test, y_test))

Predicting labels using DecisionTreeClassifier...
Done!
Prediction time (secs): 0.0
F1 score for test set: 0.759689922481

# Train and predict using different training set sizes
def train_predict(clf, X_train, y_train, X_test, y_test):
    print "------------------------------------------"
    print "Training set size: {}".format(len(X_train))
    train_classifier(clf, X_train, y_train)
    print "F1 score for training set: {}".format(predict_labels(clf, X_train, y_train))
    print "F1 score for test set: {}".format(predict_labels(clf, X_test, y_test))

# TODO: Run the helper function above for desired subsets of training data
# Note: Keep the test set constant
train_predict(clf, X_train[:100], y_train[:100], X_test, y_test)
train_predict(clf, X_train[:200], y_train[:200], X_test, y_test)
train_predict(clf, X_train, y_train, X_test, y_test)

------------------------------------------
Training set size: 100
Training DecisionTreeClassifier...
Done!
Training time (secs): 0.00399995
Predicting labels using DecisionTreeClassifier...
Done!
Prediction time (secs): 0.0
F1 score for training set: 1.0
Predicting labels using DecisionTreeClassifier...
Done!
Prediction time (secs): 0.0
F1 score for test set: 0.713178294574
------------------------------------------
Training set size: 200
Training DecisionTreeClassifier...
Done!
Training time (secs): 0.00399995
Predicting labels using DecisionTreeClassifier...
Done!
Prediction time (secs): 0.0
F1 score for training set: 1.0
Predicting labels using DecisionTreeClassifier...
Done!
Prediction time (secs): 0.0
F1 score for test set: 0.748091603053
------------------------------------------
Training set size: 300
Training DecisionTreeClassifier...
Done!
Training time (secs): 0.00000000
Predicting labels using DecisionTreeClassifier...
Done!
Prediction time (secs): 0.0
F1 score for training set: 1.0
Predicting labels using DecisionTreeClassifier...
Done!
Prediction time (secs): 0.0
F1 score for test set: 0.763358778626

# TODO: Train and predict using two other models

from sklearn.naive_bayes import GaussianNB
clf_nb = GaussianNB()

train_predict(clf_nb, X_train[:100], y_train[:100], X_test, y_test)
train_predict(clf_nb, X_train[:200], y_train[:200], X_test, y_test)
train_predict(clf_nb, X_train, y_train, X_test, y_test)

from sklearn import svm

clf_svm = svm.SVC()
print clf_svm

train_predict(clf_svm, X_train[:100], y_train[:100], X_test, y_test)
train_predict(clf_svm, X_train[:200], y_train[:200], X_test, y_test)
train_predict(clf_svm, X_train, y_train, X_test, y_test)

------------------------------------------
Training set size: 100
Training GaussianNB...
Done!
Training time (secs): 0.00000000
Predicting labels using GaussianNB...
Done!
Prediction time (secs): 0.0
F1 score for training set: 0.854961832061
Predicting labels using GaussianNB...
Done!
Prediction time (secs): 0.0
F1 score for test set: 0.748091603053
------------------------------------------
Training set size: 200
Training GaussianNB...
Done!
Training time (secs): 0.00000000
Predicting labels using GaussianNB...
Done!
Prediction time (secs): 0.0
F1 score for training set: 0.832061068702
Predicting labels using GaussianNB...
Done!
Prediction time (secs): 0.0
F1 score for test set: 0.713178294574
------------------------------------------
Training set size: 300
Training GaussianNB...
Done!
Training time (secs): 0.00000000
Predicting labels using GaussianNB...
Done!
Prediction time (secs): 0.0
F1 score for training set: 0.808823529412
Predicting labels using GaussianNB...
Done!
Prediction time (secs): 0.0
F1 score for test set: 0.75
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)
------------------------------------------
Training set size: 100
Training SVC...
Done!
Training time (secs): 0.03200006
Predicting labels using SVC...
Done!
Prediction time (secs): 0.00399994850159
F1 score for training set: 0.859060402685
Predicting labels using SVC...
Done!
Prediction time (secs): 0.00400018692017
F1 score for test set: 0.783783783784
------------------------------------------
Training set size: 200
Training SVC...
Done!
Training time (secs): 0.01199985
Predicting labels using SVC...
Done!
Prediction time (secs): 0.0120000839233
F1 score for training set: 0.869281045752
Predicting labels using SVC...
Done!
Prediction time (secs): 0.00400018692017
F1 score for test set: 0.775510204082
------------------------------------------
Training set size: 300
Training SVC...
Done!
Training time (secs): 0.01999998
Predicting labels using SVC...
Done!
Prediction time (secs): 0.0119998455048
F1 score for training set: 0.869198312236
Predicting labels using SVC...
Done!
Prediction time (secs): 0.00399994850159
F1 score for test set: 0.758620689655

5. Choosing the Best Model¶

Based on the experiments you performed earlier, in 1-2 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?
In 1-2 paragraphs explain to the board of supervisors in layman's terms how the final model chosen is supposed to work (for example if you chose a Decision Tree or Support Vector Machine, how does it make a prediction).
Fine-tune the model. Use Gridsearch with at least one important parameter tuned and with at least 3 settings. Use the entire training set for this.
What is the model's final F₁ score?

# TODO: Fine-tune your model and report the best F1 score
from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import StratifiedShuffleSplit

parameters = {'gamma': [1e-2, 1e-3, 1e-4, 1e-5, 1e-6]}

choosen_clf = svm.SVC()

cv = StratifiedShuffleSplit(y_train, random_state=42)

clf_best = GridSearchCV(choosen_clf,parameters,cv=cv,scoring='f1_weighted')

print "Final Model: "
print clf_best.fit(X_train, y_train)

#Print best classifier settings
print "\nBest Classifier: "
print clf_best.best_estimator_

print "\nF1 score for training set: {}".format(predict_labels(clf_best, X_train, y_train))
print "F1 score for test set: {}".format(predict_labels(clf_best, X_test, y_test))

Final Model: 
GridSearchCV(cv=StratifiedShuffleSplit(labels=['no' 'yes' ..., 'yes' 'yes'], n_iter=10, test_size=0.1, random_state=42),
       error_score='raise',
       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False),
       fit_params={}, iid=True, n_jobs=1,
       param_grid={'gamma': [0.01, 0.001, 0.0001, 1e-05, 1e-06]},
       pre_dispatch='2*n_jobs', refit=True, scoring='f1_weighted',
       verbose=0)

Best Classifier: 
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)
Predicting labels using GridSearchCV...
Done!
Prediction time (secs): 0.0019998550415

F1 score for training set: 0.844262295082
Predicting labels using GridSearchCV...
Done!
Prediction time (secs): 0.0
F1 score for test set: 0.77027027027