<jupyter_start><jupyter_text>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: ** The problem in question is a classification one, because we have a discrete/categorical set of outputs, in this case binary, that states if a student DO NEED(1) intervention or DO NOT NEED(0) intervention.  Exploring the DataRun 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.<jupyter_code># 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!"<jupyter_output>Student data read successfully!<jupyter_text>Implementation: Data ExplorationLet'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 (%).<jupyter_code>n_students = student_data.shape[0]
n_features = student_data.shape[1]  -1 #minus the target variable
n_passed = (student_data.passed=='yes').sum()
n_failed = (student_data.passed=='no').sum()
grad_rate = (student_data.passed=='yes').mean()*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)<jupyter_output>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%<jupyter_text>Preparing the DataIn this section, we will prepare the data for modeling, training and testing. Identify feature and target columnsIt 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.<jupyter_code># 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()<jupyter_output>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   ...       [...]<jupyter_text>Preprocess Feature ColumnsAs 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()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummiespandas.get_dummies) function to perform this transformation. Run the code cell below to perform the preprocessing routine discussed in this section.<jupyter_code>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))<jupyter_output>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']<jupyter_text>Implementation: Training and Testing Data SplitSo 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`.<jupyter_code># 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, test_size=num_test, random_state=42,stratify=y_all)

# 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])<jupyter_output>Training set has 300 samples.
Testing set has 95 samples.<jupyter_text>Training and Evaluating ModelsIn 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`](http://scikit-learn.org/stable/supervised_learning.html) **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: **Gradient Boosting, KNeighbors, Support Vector Machines- Gradient Boosting:     - This model has applications in Mobility data, by making predictions on people choice of transportation in daily decision making, which can be very useful in managing a smart city.     - The model is great for working on sparse data.    - Its weaknesses are its high memory consumption and evaluation speed.    - We have a somewhat sparse data set, resulted from our 'get_dummies' preprocessing.    - Sources: ['Gradient boosting machines, a tutorial'](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3885826/B1) and ['Smart City Mobility Application—Gradient Boosting Trees for Mobility rediction and Analysis Based on Crowdsourced Data'](http://www.mdpi.com/1424-8220/15/7/15974)    - KNeighbors:    - This model has a wide range of applications, from face detection in preprocessed data to medical diagnosis of cancer.    - Some strenghts are: the training is very fast, as the classifier is lazy and leaves the work for the prediction, and there is little assumptions about the data in the model.    - The model is very sensitive to irrelevant or redundant features, has slow runtime if has a large training set, and the curse of dimensionality is specially relevant in KNN, as it makes local approximations.    - As we do not have much domain knowledge to make assumptions about the data, KNN is a great option for learning.    - Sources: ['kNearest Neighbour Classifiers'](https://csiweb.ucd.ie/files/UCD-CSI-2007-4.pdf), ['Application of K-nearest neighbors algorithm on breast cancer diagnosis problem'](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2243774/)    - Support Vector Machines    - Applications range from text classification to face detection    - There is much space for expertise knowledge in the kernel function.    - Some disadvantages lie in the limitation of the dataset's size due to big (O(m^2) to O(m^3)) space complexity.    - As our dataset is small, the disadvantages do not affect us much.    - Sources: ['Core Vector Machines: Fast SVM Training on Very Large Data Sets'](http://machinelearning.wustl.edu/mlpapers/paper_files/TsangKC05.pdf),   SetupRun 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.<jupyter_code>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)
    try:
        return f1_score(target.values, y_pred, pos_label='yes')
    except:
        return f1_score(target, 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 "F1 score for training set: {:.4f}.".format(predict_labels(clf, X_train, y_train))
    print "F1 score for test set: {:.4f}.".format(predict_labels(clf, X_test, y_test))<jupyter_output><empty_output><jupyter_text>Implementation: Model Performance MetricsWith 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.<jupyter_code># TODO: Import the three supervised learning models from sklearn
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.cross_validation import train_test_split

# TODO: Initialize the three models
clf_A = GradientBoostingClassifier(random_state=17)
clf_B = KNeighborsClassifier()
clf_C = SVC(random_state=17)
# TODO: Set up the training set sizes

X_train_100, _, y_train_100, _ = train_test_split(X_train, y_train, train_size=100,random_state=42, stratify=y_train)

X_train_200, _, y_train_200, _ = train_test_split(X_train, y_train, train_size=200,random_state=42, stratify=y_train)

X_train_300, y_train_300 = X_train, 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_300, y_train_300, X_test,y_test)
print('\n')
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_300, y_train_300, X_test,y_test)
print('\n')
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_300, y_train_300, X_test,y_test)<jupyter_output>Training a GradientBoostingClassifier using a training set size of 100. . .
Trained model in 0.0571 seconds
Made predictions in 0.0007 seconds.
F1 score for training set: 1.0000.
Made predictions in 0.0006 seconds.
F1 score for test set: 0.7442.
Training a GradientBoostingClassifier using a training set size of 200. . .
Trained model in 0.0664 seconds
Made predictions in 0.0008 seconds.
F1 score for training set: 0.9889.
Made predictions in 0.0006 seconds.
F1 score for test set: 0.7556.
Training a GradientBoostingClassifier using a training set size of 300. . .
Trained model in 0.0842 seconds
Made predictions in 0.0011 seconds.
F1 score for training set: 0.9781.
Made predictions in 0.0006 seconds.
F1 score for test set: 0.7407.


Training a KNeighborsClassifier using a training set size of 100. . .
Trained model in 0.0006 seconds
Made predictions in 0.0027 seconds.
F1 score for training set: 0.8611.
Made predictions in 0.0025 seconds.
F1 score for test set: 0.7445.
Training a KNeighbor[...]<jupyter_text>Tabular ResultsEdit the cell below to see how a table can be designed in [Markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheettables). You can record your results from above in the tables provided.  Choosing the Best ModelIn 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.  ** Classifer 1 - GradientBoostingClassifier**  | Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) || :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: || 100               |            0.0554      |      0.0005        |       1.000       |         0.7519        || 200               |        0.0658          |      0.0005         |      0.9964      |        0.7591         || 300               |           0.0853    |        0.0007      |     0.9739   |     0.7463     |** Classifer 2 - KNeighborsClassifier**  | Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) || :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: || 100               |     0.0009 |        0.0021     |    0.8611   |      0.7445    || 200               |     0.0009 |        0.0030      |   0.8301        |    0.7785  || 300               |    0.0010    |       0.0034   |     0.8539       |     0.8138     |** Classifer 3 - SVC**  | Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) || :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: || 100               |     0.0017    |      0.0011      |   0.8777     |    0.8025    || 200               |     0.0042   |       0.0016        |     0.8679 |    0.8105      || 300               |   0.0088      |     0.0022          |    0.8761 |    0.8052      |  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: ** Based on the tables above, the best model to be applied is the KNeighborsClassifier(KNN). The time needed for testing is a little higher than SVC (expected for being a lazy classifier) but it needs less time for training and has the best score of the 3 models. The Gradient Boosting has the faster prediction time, but at the expense of a lower f1 score and a much slower training.  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 model we're going to use is one of the most straightfoward in machine learning. It works by determining a similarity measure between students ( which is inversely proportional to some chosen mathematical distance), computed from the data we have on them. That done, for any new student (having a set of features) we predict its need of intervention by looking at some of the most similar cases, and taking an average of their known outcomes. The number of cases inspected for this decision is somewhat flexible, just as the distance, and they depend on the problem. The intuition, however, is always the same: we expect the outcome of a given student to be similar to similar student's outcomes.  Implementation: Model TuningFine 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`](http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html) and [`sklearn.metrics.make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html).- 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`.<jupyter_code>from sklearn.grid_search import GridSearchCV 
from sklearn.metrics import make_scorer
from sklearn.metrics import f1_score
from sklearn.preprocessing import LabelBinarizer

# TODO: Create the parameters list you wish to tune
parameters = {'n_neighbors':range(1,8), 'metric':['minkowski','braycurtis']}

clf = KNeighborsClassifier()

f1_scorer = make_scorer(lambda yt,yp: f1_score(yt,yp,pos_label='yes'))

grid_obj = GridSearchCV(clf, parameters, scoring=f1_scorer,cv=8)

grid_obj = grid_obj.fit(X_train,y_train)

clf = grid_obj.best_estimator_
#clf = KNeighborsClassifier()
#clf.fit(X_train,y_train)

# 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))
clf<jupyter_output><empty_output>
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