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.
Answer: It's a classification problem since students are basicaly catogorized to "need early intervention" or "doesn't need early intervention" subgroups
In [108]:
# 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!"
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:
n_students.n_features.n_passed.n_failed.grad_rate, in percent (%).
In [109]:
# TODO: Calculate number of students
n_students = len(student_data)
# TODO: Calculate number of features
n_features = len(student_data.columns)-1
# TODO: Calculate passing students
n_passed = student_data['passed'].value_counts()['yes']
# TODO: Calculate failing students
n_failed = student_data['passed'].value_counts()['no']
# TODO: Calculate graduation rate
grad_rate = float(n_passed)*100/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)
In this section, we will prepare the data for modeling, training and testing.
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 [70]:
# 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()
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 [76]:
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))
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:
X_all, y_all) into training and testing subsets.random_state for the function(s) you use, if provided.X_train, X_test, y_train, and y_test.
In [144]:
# TODO: Import any additional functionality you may need here
from sklearn import cross_validation
# 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 = cross_validation.train_test_split(X_all, y_all, stratify = y_all, test_size= 95,
random_state = 42 )
# 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])
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.
Answer:
Why following models?"
Since our problem is a supervised classification problem I have chosen these three models which relatively work good on this kind of problems
Decission Tree There are many application of Decision Trees algorithm in industry for example "Project Risk Management" softwares which are built by many companies like Salesforce are using this algorithm as part of their package for SWOT (Strengths, Weaknesses, Oppportunities, and Threats) Analysis. In general when there are some path of connected rulles or conditions (connected nodes and branches) which at the end they lead in a conclusion (Target Value) it is good to consider decision trees.
Pros:
Cons:
Reason to choose:
Because Decision Tree is easy to interpret and explain to non-professional users I tried this algorithm first.
SVM
SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputsas. Svm has a significant discriminative power for classification, especially in cases where sample sizes are small and a large number of variables are involved (high-dimensionality space). support vector machine algorithm, has demonstrated high performance in solving classification problems in many biomedical fields, especially in bioinformatics
Pros:
Cons:
Reason to choose:
Since there are relatively high number of features compared to data records and the fact that SVM works well with high dimentional data so I tried this algorithm too.
AdaBoost
AdaBoost or adaptive boosting is a ML meta-algorithem which basicaly tries to improve other algorithems performance. So Adaboost fit the data with a chosen algorithem which by default is Decision tree and repeats this process over and over again but in each round it diagnose the hard examples which are missclassified and put more focus on theme by changing theire weight. in general if train/evaluation time is not an issue and higher accuracy is desired in most cases adaboost outperforms to other algorithms however if time matters usually boosting approach is not an ideal choice. One of the first and succesfull application of this algorithm was 'optical character recognition'(OCR) problem on digital handwritten digits.
Pros:
Cons:
Reason to choose:
I got a better solution with SVM previously but since higher accuracy might be more important than the speed in this case I tried Adaboost to compare with the SVM.
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.
In [145]:
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 "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))
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:
clf_A, clf_B, and clf_C.random_state for each model you use, if provided.X_train and y_train.
In [146]:
# TODO: Import the three supervised learning models from sklearn
# from sklearn import model_A
# from sklearn import model_B
# from skearln import model_C
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import AdaBoostClassifier
# TODO: Initialize the three models
clf_A = DecisionTreeClassifier(random_state = 42)
clf_B = SVC(random_state = 42)
clf_C = AdaBoostClassifier(random_state=42)
# TODO: Execute the 'train_predict' function for each classifier and each training set size
# train_predict(clf, X_train, y_train, X_test, y_test)
for clf in [clf_A, clf_B, clf_C]:
print "\n{}: \n".format(clf.__class__.__name__)
for n in [100, 200, 300]:
train_predict(clf, X_train[:n], y_train[:n], X_test, y_test)
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 - Decission Tree
| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |
|---|---|---|---|---|
| 100 | 0.0010 | 0.0000 | 1.0 | 0.6452 |
| 200 | 0.0010 | 0.0000 | 1.0 | 0.7258 |
| 300 | 0.0020 | 0.0000 | 1.0 | 0.6838 |
Classifer 2 -SVM
| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |
|---|---|---|---|---|
| 100 | 0.0010 | 0.0000 | 0.8354 | 0.8025 |
| 200 | 0.0050 | 0.0010 | 0.8431 | 0.8105 |
| 300 | 0.0080 | 0.0020 | 0.8664 | 0.8052 |
Classifer 3 - Boosting
| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |
|---|---|---|---|---|
| 100 | 0.0960 | 0.0050 | 0.9778 | 0.6880 |
| 200 | 0.1260 | 0.0040 | 0.8905 | 0.7445 |
| 300 | 0.1220 | 0.0080 | 0.8565 | 0.7328 |
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.
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 experiments done and the results table above SVM model is giving the most accurate prediction in a reasonable trainig and testing time. Since this data set was small the time diffrents may not seem an issue for comparison but if we get more and more data then for sure it can cause some problems. By coparing Decision Tree model and SVM which both has almost same running time but SVM turn out to be more accurate. And also it is obvious from the results that Decision Tree is highly dependant on data and in terms of machine learning is overfitted because it gives a 100% accurate result on the trained data it shows that it was not able to generelize the prediction. Therefore my choice is SVM model.
Answer:
Support Vector Machine is a classification algorithm which simply means it classify data into diffrent categories (in our case we have two categories "passed" and "not passed"). But how does this algorithm work? Well, the main goal of this algorithm is to find a seperating vector which will group all the data. For simplicity lets assume that we only have 2 features and when we draw the scatter plot we see that the data are divided to 2 diffrent groups which we can seperate using a line, so in this case our goal is to find the maximum margin between these two group of data. As a result in the future when we have a new data point we simply predict the associated labele to it based on it's location regarding to line. Its good to mention that when we have more features the line changes to a plane which seperates data exactly the same way but in higher dimention.
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:
sklearn.grid_search.gridSearchCV and sklearn.metrics.make_scorer.parameters = {'parameter' : [list of values]}.clf.make_scorer and store it in f1_scorer.pos_label parameter to the correct value!clf using f1_scorer as the scoring method, and store it in grid_obj.X_train, y_train), and store it in grid_obj.
In [151]:
# TODO: Import 'GridSearchCV' and 'make_scorer'
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import make_scorer, f1_score
# TODO: Create the parameters list you wish to tune
parameters = {'C': range(1,10), 'random_state': [42], 'gamma': np.arange(0,1,0.1)}
# TODO: Initialize the classifier
clf = SVC()
# TODO: Make an f1 scoring function using 'make_scorer'
f1_scorer = make_scorer(f1_score, pos_label='yes')
# TODO: Perform grid search on the classifier using the f1_scorer as the scoring method
grid_obj = GridSearchCV(estimator=clf, scoring= f1_scorer, param_grid=parameters )
# 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))
Answer: The final F1 score for training is 0.8664 and for testing data is 0.8052 which indicate same score compare to untuned model.
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.