Machine Learning Engineer Nanodegree

Supervised Learning

Project: 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:

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 [131]:
# 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 [132]:
#print student_data.columns.values

          
# TODO: Calculate number of students
n_students = len(student_data)

# TODO: Calculate number of features
n_features = len(student_data.columns.values)

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

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

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

# 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: 31
Number of students who passed: 265
Number of students who failed: 130
Graduation rate of the class: 0.67%

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 [133]:
# 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 [134]:
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 [135]:
# 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 = None
X_test = None
y_train = None
y_test = None"""

X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=1-float(num_train)/float(n_students), random_state=7)

# 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:

model real-world application strength weakness why it's a good candidate
Logistic Regression Cancer prediction based on patient characteristics predictions on small datasets small number of features with this can be efficient and fast When data contains features with complexity, unless the features are carefully selected and finetuned this may suffer from under / over - fitting This model may be suitable since the expected output is categorical and binary on top
Gaussian Naive Bayes say automatic sorting of incoming e-mail into different categories Simple model yet works in many complex real-world situations and requires a small number of training data Ideal for features with no relationship with each other It's simplicity is its power
k-Nearest Neighbor Applications that call for pattern recognition to determine outcome (Including recognizing faces of people in pictures) Simplest and yet effective (facial recognition !!) It is instance-based and lazy learning. It is sensitive to the local structure of the data With 48 total features and 300 observations, k-NN will certainly be manageable computationally

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 [136]:
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)
    
    # Print the results of prediction for both training and testing
    print "In training phase:"
    print "F1 score for training set: {:.4f}.".format(predict_labels(clf, X_train, y_train))
    print "In testing phase:"
    print "F1 score 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 [137]:
# TODO: Import the three supervised learning models from sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB

# TODO: Initialize the three models
seed=7
clf_A = LogisticRegression(random_state=seed)
clf_B = GaussianNB()
clf_C = KNeighborsClassifier()

# TODO: Set up the training set sizes
X_train_100 = X_all[:100]
y_train_100 = y_all[:100]

X_train_200 = X_all[:200]
y_train_200 = y_all[:200]

X_train_300 = X_all[:300]
y_train_300 = y_all[:300]

#X_test and y_test fixed at 95 earlier
# TODO: Execute the 'train_predict' function for each classifier and each training set size
sample_list = [[X_train_100,y_train_100],[X_train_200,y_train_200],[X_train_300,y_train_300]]

from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt

for clf in [clf_A, clf_B, clf_C]:
    clf_name = clf.__class__.__name__
    
    for sample in sample_list:
        list_sample = list(sample)
        X_train, y_train = list_sample[0],list_sample[1]
        train_predict(clf, X_train, y_train, X_test, y_test)


Training a LogisticRegression using a training set size of 100. . .
Trained model in 0.0030 seconds
In training phase:
Made predictions in 0.0020 seconds.
F1 score for training set: 0.9200.
In testing phase:
Made predictions in 0.0010 seconds.
F1 score for test set: 0.7945.
Training a LogisticRegression using a training set size of 200. . .
Trained model in 0.0040 seconds
In training phase:
Made predictions in 0.0010 seconds.
F1 score for training set: 0.8414.
In testing phase:
Made predictions in 0.0010 seconds.
F1 score for test set: 0.8227.
Training a LogisticRegression using a training set size of 300. . .
Trained model in 0.0050 seconds
In training phase:
Made predictions in 0.0020 seconds.
F1 score for training set: 0.8486.
In testing phase:
Made predictions in 0.0010 seconds.
F1 score for test set: 0.8472.
Training a GaussianNB using a training set size of 100. . .
Trained model in 0.0020 seconds
In training phase:
Made predictions in 0.0020 seconds.
F1 score for training set: 0.4536.
In testing phase:
Made predictions in 0.0010 seconds.
F1 score for test set: 0.2118.
Training a GaussianNB using a training set size of 200. . .
Trained model in 0.0020 seconds
In training phase:
Made predictions in 0.0020 seconds.
F1 score for training set: 0.8071.
In testing phase:
Made predictions in 0.0030 seconds.
F1 score for test set: 0.7914.
Training a GaussianNB using a training set size of 300. . .
Trained model in 0.0020 seconds
In training phase:
Made predictions in 0.0020 seconds.
F1 score for training set: 0.8246.
In testing phase:
Made predictions in 0.0020 seconds.
F1 score for test set: 0.8322.
Training a KNeighborsClassifier using a training set size of 100. . .
Trained model in 0.0000 seconds
In training phase:
Made predictions in 0.0000 seconds.
F1 score for training set: 0.8750.
In testing phase:
Made predictions in 0.0000 seconds.
F1 score for test set: 0.8027.
Training a KNeighborsClassifier using a training set size of 200. . .
Trained model in 0.0160 seconds
In training phase:
Made predictions in 0.0080 seconds.
F1 score for training set: 0.8600.
In testing phase:
Made predictions in 0.0050 seconds.
F1 score for test set: 0.8442.
Training a KNeighborsClassifier using a training set size of 300. . .
Trained model in 0.0020 seconds
In training phase:
Made predictions in 0.0130 seconds.
F1 score for training set: 0.8597.
In testing phase:
Made predictions in 0.0050 seconds.
F1 score for test set: 0.8212.

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 - LogisticRegression

Training Set Size Training Time Prediction Time (training) F1 Score (train) Prediction Time (test) F1 Score (test)
100 0.0030 0.0020 0.9200 0.0030 0.7945
200 0.0050 0.0010 0.8414 0.0020 0.8227
300 0.0050 0.0010 0.8486 0.0010 0.8472

Classifer 2 - GaussianNB

Training Set Size Training Time Prediction Time (training) F1 Score (train) Prediction Time (test) F1 Score (test)
100 0.0020 0.0020 0.4536 0.0010 0.2118
200 0.0020 0.0020 0.8071 0.0020 0.7914
300 0.0030 0.0020 0.8246 0.0010 0.8322

Classifer 3 - KNeighbors

Training Set Size Training Time Prediction Time (training) F1 Score (train) Prediction Time (test) F1 Score (test)
100 0.0010 0.0030 0.8750 0.0030 0.8027
200 0.0030 0.0070 0.860 0.0000 0.8442
300 0.0000 0.0170 0.8597 0.0060 0.8212

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?


In [138]:
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt

model = clf_A
cm = confusion_matrix(y_test.values, model.predict(X_test))
    # view with a heatmap
fig = plt.figure()
sns.heatmap(cm, annot=True, cmap='Blues', xticklabels=['no', 'yes'], yticklabels=['no', 'yes'])
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.title('Confusion matrix for:\n{}'.format(model.__class__.__name__));
plt.show()


Answer:

Based on the results we have got from the data-set we have and tests that we have conducted on three different models, it is advisable that we use Logistic Regression as our model of choice. F1-score reflects the accuracy of the model, After getting trained on limited training data-set of 100, our model has scored F1-score value of 0.7945, which is the second best of the three model at that sample size! With larger sample sizes such as 200 and 300 the F1 score is 0.8227 and 0.8472 which is in the same neighborhood. The testing time which matters for real time predictions has gone down from 0.0030 to 0.0020 to 0.0010 seconds as it learnt from larger training sample of 100 to 200 to 300

Contrast this with Gaussian Naive Bayes model which, at smaller sample size it started out with a poor F1-score of 0.2118 and then went on to 0.7914 and 0.8322 with the model sie. Contrast this with K-NearestNeighbors model, which started at respectable 0.8027 and went on to 0.8442,0.8212 respectively as model size increased to 300 and 300. Now models like Gaussian Naive Bayes and K-NearestNeighbors can be faster but they also require larger amount of training data. Our class sizes are not getting larger so I do not expect the training data-set to be significantly larger. We have a corpus of data over the years that is possibly larger (in thousands of observations going back to several decades) but a large portion of that data is irrelevant because the conditions of last decade do not appy to today's paradigm and hence last couple of years of class data is all that matters to us. With that limited relevant training sample data of say 300 we will have predictions with an accuracy of 0.8472 (the best out of the three models at that sample size) at 0.0010 seconds (as good as the best for 95 predictions form that size of training data)

In light of these observations we can all agree that Logistic Regression model will serve our requirements best

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:

Now that we have agreed on priciple that Logistics regression is our model of choice, let me explan how it works. Logistics regression works with odds. What are the odds that a student will pass (or fail). It depends upon what we knwo of the this student. It also depends on what have we learnt about students from the last class. We have 395 student records that show 265 have passed. Our model needs to learn. It needs to figure out odds of a new student graduating from given the fact that he / she will have a certain GPA, will have studied in a certain middle school, is a boy or a girl of age from 15 to 22, with a mother with education ranging from no education to higher education or father with education ranging from no education to higher educatio. See the picture of a cube below: It is am imaginary space for a model that considers middle school (one edge of the cube), boy or a girl (second edge of the cube) and say mother's education (the third edge of the cube). This model will read the data and place students that pass as blue ballsan the ones that fail as red balls See a plane that separates the red and the blue balls. Our smart model has figured out where that plane should be, what its shape should be and what it's angle should be to best separate the red and the blue balls. It did that in what we call as the training phase after it read the data and analyzed it.

We need to know how well will the model perform when it has to figure out graduating potential of a new student. This is the purpose of the 95 data records that we held back. In the second phase that we call as validation phase we ask our model to figure out its guess without disclosing the graduation status of these 95 students. Do these students pass or fail? We know what their record is. So we know if our model's prediction is accurate or not.

We also want to know its reliability / dependability. We don't want our model to falsely identify some student to have a potential of graduating when the student is not or falsely identify some student as not likely to pass when the student reall is a "A-grade" student. We also don't want someone exactly matching attributes of a known student who passed to be identified as a student un-liekly to pass and vice-a-versa. See below a 2 X 2 matrix where one side is student who passed (yes/no) and the other side is student predicted to pass (yes/no)

The way to read this figure is that our model predicts a student to pass 61 times out of 74. If a student is truly graduation material it correctly recognizes the fact 61 times out of 70. If falsely identifies them as failures 9 times out of 70 students that pass. This is a good model !

We have a lot of of attributes of the students that we collect. These are what we call features like:

  • school : student's school (binary: "GP" or "MS")
  • sex : student's sex (binary: "F" female or "M" male)
  • age : student's age (numeric: from 15 to 22)
  • address : student's home address type (binary: "U" urban or "R" rural)
  • famsize : family size (binary: "LE3" less or equal to 3 or "GT3" greater than 3)
  • Pstatus : parent's cohabitation status (binary: "T" living together or "A" apart)
  • Medu : mother's education (numeric: 0 none, 1 primary education (4th grade), 2 5th to 9th grade, 3 secondary education or 4 * higher education)
  • Fedu : father's education (numeric: 0 none, 1 primary education (4th grade), 2 5th to 9th grade, 3 secondary education or 4 * higher education)
  • Mjob : mother's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
  • Fjob : father's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
  • reason : reason to choose this school (nominal: close to "home", school "reputation", "course" preference or "other")
  • guardian : student's guardian (nominal: "mother", "father" or "other")
  • traveltime : home to school travel time (numeric: 1 <15 min., 2 15 to 30 min., 3 30 min. to 1 hour, or 4 >1 hour)
  • studytime : weekly study time (numeric: 1 <2 hours, 2 2 to 5 hours, 3 5 to 10 hours, or 4 >10 hours)
  • failures : number of past class failures (numeric: n if 1<=n<3, else 4)
  • schoolsup : extra educational support (binary: yes or no)
  • famsup : family educational support (binary: yes or no)
  • paid : extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
  • activities : extra-curricular activities (binary: yes or no)
  • nursery : attended nursery school (binary: yes or no)
  • higher : wants to take higher education (binary: yes or no)
  • internet : Internet access at home (binary: yes or no)
  • romantic : with a romantic relationship (binary: yes or no)
  • famrel : quality of family relationships (numeric: from 1 very bad to 5 excellent)
  • freetime : free time after school (numeric: from 1 very low to 5 very high)
  • goout : going out with friends (numeric: from 1 very low to 5 very high)
  • Dalc : workday alcohol consumption (numeric: from 1 very low to 5 very high)
  • Walc : weekend alcohol consumption (numeric: from 1 very low to 5 very high)
  • health : current health status (numeric: from 1 very bad to 5 very good)
  • absences : number of school absences (numeric: from 0 to 93)
  • passed : did the student pass the final exam (binary: yes or no)

There is a cost associated with collecting this data, storing it. Not all of it may be relevant nor is all of it likely to be useful. These are the edges that you saw in the example of our hypotetical cube.

In the course of our project we will decide how many edges does our model really need. It is best to not have too many since all of them are not likely ot be important.We can afford to not use some them without significant loss in accuracy at the same time make our prediction process faster. Once we have finalized these specific attributes (or features as we call them), we can feed to our model just the right attributesof a new student and it will tell us it's prediction whether the student will pgraduate or not

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 [139]:
# 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
param_grid = {'solver': ['sag', 'lbfgs', 'newton-cg'],
              'C': [0.01, 0.1, 1.0, 10.0, 100.0, 1000.0]}

# TODO: Initialize the classifier
model = LogisticRegression(random_state=7)


# TODO: Make an f1 scoring function using 'make_scorer' 

scorer = make_scorer(f1_score, pos_label='yes')


# TODO: Perform grid search on the classifier using the f1_scorer as the scoring method
#grid = GridSearchCV(estimator=model, param_grid=parameters, cv=ssscv, scoring=f1_scorer)
grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scorer)

# TODO: Fit the grid search object to the training data and find the optimal parameters

grid_fit = grid.fit(X_train, y_train)

# Get the estimator
best_clf = grid_fit.best_estimator_

predictions = (clf.fit(X_train, y_train)).predict(X_test)
best_predictions = best_clf.predict(X_test)

# 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 0.0220 seconds.
Tuned model has a training F1 score of 0.8597.
Made predictions in 0.0000 seconds.
Tuned model has a testing F1 score of 0.8212.

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: Our model is Logistic Regression: Before tuning for train data size of 300 and test data size of 95: The train F1 score was:0.8486 and the test F1 score was: 0.8472

After tuning for train data size of 300 and test data size of 95: (so that we are doing an apple to apple comparison) The train F1 score is:0.8597 and the test F1 score was: 0.8212

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.