student_intervention.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Engineer Nanodegree\n",
    "## Supervised Learning\n",
    "## Project 2: Building a Student Intervention System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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!\n",
    "\n",
    "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.  \n",
    "\n",
    ">**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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 - Classification vs. Regression\n",
    "*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?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: ** Classification, because we are not predicting a continuous valued output. Rather, we are predicting a boolean (i.e. categorical) value for each student to decide whether they pass or fail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Data\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student data read successfully!\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "# Read student data\n",
    "# type DataFrame\n",
    "student_data = pd.read_csv(\"student-data.csv\")\n",
    "print \"Student data read successfully!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Data Exploration\n",
    "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",
    "- The total number of students, `n_students`.\n",
    "- The total number of features for each student, `n_features`.\n",
    "- The number of those students who passed, `n_passed`.\n",
    "- The number of those students who failed, `n_failed`.\n",
    "- The graduation rate of the class, `grad_rate`, in percent (%).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of students: 395\n",
      "Number of features: 30\n",
      "Number of students who passed: 265\n",
      "Number of students who failed: 130\n",
      "Graduation rate of the class: 67.09%\n"
     ]
    }
   ],
   "source": [
    "# Calculate number of students (i.e. rows)\n",
    "n_students = student_data.shape[0]\n",
    "\n",
    "# Calculate number of features (i.e. columns)\n",
    "# subtract a column to not account for 'passed', which is our target column\n",
    "n_features = student_data.shape[1] - 1\n",
    "\n",
    "# Calculate passing students\n",
    "# get row dimension after student_data is filtered by students who passed\n",
    "n_passed = student_data[student_data['passed'] == 'yes'].shape[0]\n",
    "\n",
    "# Calculate failing students\n",
    "n_failed = n_students - n_passed\n",
    "\n",
    "# Calculate graduation rate\n",
    "grad_rate = 100 * float(n_passed) / n_students\n",
    "\n",
    "# Print the results\n",
    "print \"Total number of students: {}\".format(n_students)\n",
    "print \"Number of features: {}\".format(n_features)\n",
    "print \"Number of students who passed: {}\".format(n_passed)\n",
    "print \"Number of students who failed: {}\".format(n_failed)\n",
    "print \"Graduation rate of the class: {:.2f}%\".format(grad_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Data\n",
    "In this section, we will prepare the data for modeling, training and testing.\n",
    "\n",
    "### Identify feature and target columns\n",
    "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.\n",
    "\n",
    "Run the code cell below to separate the student data into feature and target columns to see if any features are non-numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature columns:\n",
      "['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']\n",
      "\n",
      "Target column: passed\n",
      "\n",
      "Feature values:\n",
      "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  \\\n",
      "0     GP   F   18       U     GT3       A     4     4  at_home   teacher   \n",
      "1     GP   F   17       U     GT3       T     1     1  at_home     other   \n",
      "2     GP   F   15       U     LE3       T     1     1  at_home     other   \n",
      "3     GP   F   15       U     GT3       T     4     2   health  services   \n",
      "4     GP   F   16       U     GT3       T     3     3    other     other   \n",
      "\n",
      "    ...    higher internet  romantic  famrel  freetime goout Dalc Walc health  \\\n",
      "0   ...       yes       no        no       4         3     4    1    1      3   \n",
      "1   ...       yes      yes        no       5         3     3    1    1      3   \n",
      "2   ...       yes      yes        no       4         3     2    2    3      3   \n",
      "3   ...       yes      yes       yes       3         2     2    1    1      5   \n",
      "4   ...       yes       no        no       4         3     2    1    2      5   \n",
      "\n",
      "  absences  \n",
      "0        6  \n",
      "1        4  \n",
      "2       10  \n",
      "3        2  \n",
      "4        4  \n",
      "\n",
      "[5 rows x 30 columns]\n",
      "\n",
      "Target values:\n",
      "0     no\n",
      "1     no\n",
      "2    yes\n",
      "3    yes\n",
      "4    yes\n",
      "Name: passed, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Extract feature columns\n",
    "feature_cols = list(student_data.columns[:-1])\n",
    "\n",
    "# Extract target column 'passed'\n",
    "target_col = student_data.columns[-1] \n",
    "\n",
    "# Show the list of columns\n",
    "print \"Feature columns:\\n{}\".format(feature_cols)\n",
    "print \"\\nTarget column: {}\".format(target_col)\n",
    "\n",
    "# Separate the data into feature data and target data (X_all and y_all, respectively)\n",
    "X_all = student_data[feature_cols]\n",
    "y_all = student_data[target_col]\n",
    "\n",
    "# Show the feature information by printing the first five rows\n",
    "print \"\\nFeature values:\"\n",
    "print X_all.head()\n",
    "print \"\\nTarget values:\"\n",
    "print y_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess Feature Columns\n",
    "\n",
    "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.\n",
    "\n",
    "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.\n",
    "\n",
    "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_dummies#pandas.get_dummies) function to perform this transformation. Run the code cell below to perform the preprocessing routine discussed in this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed feature columns (48 total features):\n",
      "['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']\n"
     ]
    }
   ],
   "source": [
    "def preprocess_features(X):\n",
    "    ''' Preprocesses the student data and converts non-numeric binary variables into\n",
    "        binary (0/1) variables. Converts categorical variables into dummy variables. '''\n",
    "    \n",
    "    # Initialize new output DataFrame\n",
    "    output = pd.DataFrame(index = X.index)\n",
    "\n",
    "    # Investigate each feature column for the data\n",
    "    for col, col_data in X.iteritems():\n",
    "        \n",
    "        # If data type is non-numeric, replace all yes/no values with 1/0\n",
    "        if col_data.dtype == object:\n",
    "            col_data = col_data.replace(['yes', 'no'], [1, 0])\n",
    "\n",
    "        # If data type is categorical, convert to dummy variables\n",
    "        if col_data.dtype == object:\n",
    "            # Example: 'school' => 'school_GP' and 'school_MS'\n",
    "            col_data = pd.get_dummies(col_data, prefix = col)  \n",
    "        \n",
    "        # Collect the revised columns\n",
    "        output = output.join(col_data)\n",
    "    \n",
    "    return output\n",
    "\n",
    "X_all = preprocess_features(X_all)\n",
    "print \"Processed feature columns ({} total features):\\n{}\".format(len(X_all.columns), list(X_all.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Training and Testing Data Split\n",
    "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:\n",
    "- Randomly shuffle and split the data (`X_all`, `y_all`) into training and testing subsets.\n",
    "  - Use 300 training points (approximately 75%) and 95 testing points (approximately 25%).\n",
    "  - Set a `random_state` for the function(s) you use, if provided.\n",
    "  - Store the results in `X_train`, `X_test`, `y_train`, and `y_test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 300 samples.\n",
      "Testing set has 95 samples.\n"
     ]
    }
   ],
   "source": [
    "# Import any additional functionality you may need here\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# Set the number of training points\n",
    "num_train = 300\n",
    "\n",
    "# Set the number of testing points\n",
    "num_test = X_all.shape[0] - num_train\n",
    "\n",
    "# Shuffle and split the dataset into the number of training and testing points above\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size = 0.24, random_state = 1)\n",
    "\n",
    "# Show the results of the split\n",
    "print \"Training set has {} samples.\".format(X_train.shape[0])\n",
    "print \"Testing set has {} samples.\".format(X_test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Evaluating Models\n",
    "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 F<sub>1</sub> score. You will need to produce three tables (one for each model) that shows the training set size, training time, prediction time, F<sub>1</sub> score on the training set, and F<sub>1</sub> score on the testing set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2 - Model Application\n",
    "*List three supervised learning models that are appropriate for this problem. What are the general applications of each model? What are their strengths and weaknesses? Given what you know about the data, why did you choose these models to be applied?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: ** (1) Support Vector Machine / SVM. General applications of SVMs include a set of supervised learning methods used for classification, regression and outliers detection. SVMs are appropriate for this problem because they can be used for classification. Major strengths of SVMs include strong performance in high-dimensional spaces and versatility due to their different kernel functions. A major weakness of SVMs is that they perform poorly when the number of features is much greater than the number of samples. However, I chose SVM because this is not the case with our student dataset, where the number of features is 48 and the number of samples is 395 (i.e. # features << # samples). (2) Decision Tree Classifier. The general application of a decision tree is a non-parametric supervised learning method used for classification and regression. Since decision trees can be used for classification, they are appropriate for this problem. Strengths of decision trees include being easy to interpret and visualize, requiring little data preperation (e.g. no data normalization), and being able to predict in logarithmic time in the number of data points used to train the tree. Weaknesses of decision trees include instability due to small variations in the data that might result in a completely different tree being generated, being \"hard\" (i.e. NP Complete) to learn an optimal decision tree, and difficulty learning concepts such as XOR, parity or multiplexer problems. Given that our training set contains 300 students, I chose a decision tree because it can predict in logarithmic time (quicker than both SVM and Naive Bayes). (3) Gaussian Naive Bayes. General applications include a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. A strength of Naive Bayes is that it requires a small amount of training data to estimate the necessary parameters. A weakness of Naive Bayes is that it makes strong feature independence assumptions, which can throw the predictions off if some of the features actually depend upon each other. I chose Naive Bayes for our dataset because it appears that our features are independent from each other, which may lead to Naive Bayes performing well given its strong independence assumptions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup\n",
    "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:\n",
    "- `train_classifier` - takes as input a classifier and training data and fits the classifier to the data.\n",
    "- `predict_labels` - takes as input a fit classifier, features, and a target labeling and makes predictions using the F<sub>1</sub> score.\n",
    "- `train_predict` - takes as input a classifier, and the training and testing data, and performs `train_clasifier` and `predict_labels`.\n",
    " - This function will report the F<sub>1</sub> score for both the training and testing data separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def train_classifier(clf, X_train, y_train):\n",
    "    ''' Fits a classifier to the training data. '''\n",
    "    \n",
    "    # Start the clock, train the classifier, then stop the clock\n",
    "    start = time()\n",
    "    clf.fit(X_train, y_train)\n",
    "    end = time()\n",
    "    \n",
    "    # Print the results\n",
    "    print \"Trained model in {:.4f} seconds\".format(end - start)\n",
    "\n",
    "    \n",
    "def predict_labels(clf, features, target):\n",
    "    ''' Makes predictions using a fit classifier based on F1 score. '''\n",
    "    \n",
    "    # Start the clock, make predictions, then stop the clock\n",
    "    start = time()\n",
    "    y_pred = clf.predict(features)\n",
    "    end = time()\n",
    "    \n",
    "    # Print and return results\n",
    "    print \"Made predictions in {:.4f} seconds.\".format(end - start)\n",
    "    return f1_score(target.values, y_pred, pos_label='yes')\n",
    "\n",
    "\n",
    "def train_predict(clf, X_train, y_train, X_test, y_test):\n",
    "    ''' Train and predict using a classifer based on F1 score. '''\n",
    "    \n",
    "    # Indicate the classifier and the training set size\n",
    "    print \"Training a {} using a training set size of {}. . .\".format(clf.__class__.__name__, len(X_train))\n",
    "    \n",
    "    # Train the classifier\n",
    "    train_classifier(clf, X_train, y_train)\n",
    "    \n",
    "    # Print the results of prediction for both training and testing\n",
    "    print \"F1 score for training set: {:.4f}.\".format(predict_labels(clf, X_train, y_train))\n",
    "    print \"F1 score for test set: {:.4f}.\".format(predict_labels(clf, X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Model Performance Metrics\n",
    "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:\n",
    "- Import the three supervised learning models you've discussed in the previous section.\n",
    "- Initialize the three models and store them in `clf_A`, `clf_B`, and `clf_C`.\n",
    " - Use a `random_state` for each model you use, if provided.\n",
    " - **Note:** Use the default settings for each model — you will tune one specific model in a later section.\n",
    "- Create the different training set sizes to be used to train each model.\n",
    " - *Do not reshuffle and resplit the data! The new training points should be drawn from `X_train` and `y_train`.*\n",
    "- Fit each model with each training set size and make predictions on the test set (9 in total).  \n",
    "**Note:** Three tables are provided after the following code cell which can be used to store your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SVC: \n",
      "\n",
      "Training a SVC using a training set size of 100. . .\n",
      "Trained model in 0.0059 seconds\n",
      "Made predictions in 0.0015 seconds.\n",
      "F1 score for training set: 0.8591.\n",
      "Made predictions in 0.0010 seconds.\n",
      "F1 score for test set: 0.8333.\n",
      "Training a SVC using a training set size of 200. . .\n",
      "Trained model in 0.0038 seconds\n",
      "Made predictions in 0.0027 seconds.\n",
      "F1 score for training set: 0.8581.\n",
      "Made predictions in 0.0013 seconds.\n",
      "F1 score for test set: 0.8408.\n",
      "Training a SVC using a training set size of 300. . .\n",
      "Trained model in 0.0086 seconds\n",
      "Made predictions in 0.0053 seconds.\n",
      "F1 score for training set: 0.8584.\n",
      "Made predictions in 0.0018 seconds.\n",
      "F1 score for test set: 0.8462.\n",
      "\n",
      "DecisionTreeClassifier: \n",
      "\n",
      "Training a DecisionTreeClassifier using a training set size of 100. . .\n",
      "Trained model in 0.0039 seconds\n",
      "Made predictions in 0.0006 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for test set: 0.6833.\n",
      "Training a DecisionTreeClassifier using a training set size of 200. . .\n",
      "Trained model in 0.0020 seconds\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for test set: 0.7704.\n",
      "Training a DecisionTreeClassifier using a training set size of 300. . .\n",
      "Trained model in 0.0029 seconds\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for training set: 1.0000.\n",
      "Made predictions in 0.0002 seconds.\n",
      "F1 score for test set: 0.6880.\n",
      "\n",
      "GaussianNB: \n",
      "\n",
      "Training a GaussianNB using a training set size of 100. . .\n",
      "Trained model in 0.0008 seconds\n",
      "Made predictions in 0.0007 seconds.\n",
      "F1 score for training set: 0.8346.\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for test set: 0.7402.\n",
      "Training a GaussianNB using a training set size of 200. . .\n",
      "Trained model in 0.0010 seconds\n",
      "Made predictions in 0.0004 seconds.\n",
      "F1 score for training set: 0.7879.\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for test set: 0.6446.\n",
      "Training a GaussianNB using a training set size of 300. . .\n",
      "Trained model in 0.0009 seconds\n",
      "Made predictions in 0.0005 seconds.\n",
      "F1 score for training set: 0.7921.\n",
      "Made predictions in 0.0003 seconds.\n",
      "F1 score for test set: 0.6720.\n"
     ]
    }
   ],
   "source": [
    "# Import the three supervised learning models from sklearn\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import tree\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "#from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# Initialize the three models\n",
    "clf_A = SVC(random_state = 1)\n",
    "clf_B = tree.DecisionTreeClassifier(random_state = 2)\n",
    "clf_C = GaussianNB()\n",
    "#clf_D = MLPClassifier(random_state = 3)\n",
    "\n",
    "# Set up the training set sizes\n",
    "X_train_100 = X_train.head(100)\n",
    "y_train_100 = y_train.head(100)\n",
    "\n",
    "X_train_200 = X_train.head(200)\n",
    "y_train_200 = y_train.head(200)\n",
    "\n",
    "X_train_300 = X_train\n",
    "y_train_300 = y_train\n",
    "\n",
    "# Execute the 'train_predict' function for each classifier and each training set size\n",
    "# train_predict(clf, X_train, y_train, X_test, y_test)\n",
    "\n",
    "# loop through models, then through train sizes\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    print \"\\n{}: \\n\".format(clf.__class__.__name__)\n",
    "    for n in [100, 200, 300]:\n",
    "        train_predict(clf, X_train[:n], y_train[:n], X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tabular Results\n",
    "Edit the cell below to see how a table can be designed in [Markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet#tables). You can record your results from above in the tables provided."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Classifer 1 - SVM**  \n",
    "\n",
    "| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |\n",
    "| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |\n",
    "| 100               |   0.0025 s    |       0.0010 s      | 0.8356   | 0.7838                |\n",
    "| 200               |       0.0044 s         |    0.0018 s   |   0.8562  |      0.7895           |\n",
    "| 300               |          0.0095 s   |    0.0024 s    |     0.8589             |   0.7733      |\n",
    "\n",
    "** Classifer 2 - Decision Tree**  \n",
    "\n",
    "| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |\n",
    "| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |\n",
    "| 100               |       0.009 s |    0.0002 s  |  1.0000                |   0.6783  |\n",
    "| 200               |     0.0027 s   |    0.0002 s  |  1.0000            |       0.6552        |\n",
    "| 300               |     0.0028 s  |     0.0003 s  |  1.0000          |     0.6724     |\n",
    "\n",
    "** Classifer 3 - Gaussian Naive Bayes**  \n",
    "\n",
    "| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |\n",
    "| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |\n",
    "| 100               |        0.0008 s  |       0.0010 s      |       0.8120     |       0.7368          |\n",
    "| 200               |        0.0016 s  |       0.0005 s      |       0.8357     |       0.7879      |\n",
    "| 300               |        0.0014 s  |       0.0004 s      |       0.7990     |       0.7442     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing the Best Model\n",
    "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 F<sub>1</sub> score. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3 - Chosing the Best Model\n",
    "*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?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: ** I choose SVM to be the best model. SVM is generally the most appropriate for our data since the number of samples is much larger than the number of features. While SVM costed the most time since it performed the slowest in both training and prediction, this is not an issue since we are not limited in training and prediction time in this situation. Perhaps most importantly, SVM generated the highest F1 score on the test set which means it has the highest prediction accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4 - Model in Layman's Terms\n",
    "*In one to two paragraphs, explain to the board of directors in layman's terms how the final model chosen is supposed to work. For example if you've chosen to use a decision tree or a support vector machine, how does the model go about making a prediction?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: ** The Support Vector Machine (SVM) takes a set of data points (called a training set) of two or more categories (called classes) as input and outputs a boundary line that best seperates them; think of this as figuring out where to lay a straight stick (i.e. line) between red and blue marbles (i.e. data points of two different categories) to seperate them. An SVM chooses between multiple possible lines by finding the line that is the most centered between the data points. If it is not easy to draw a straight line between our data points, an SVM can mess up a few points try its best; in the marble analogy, this means some blue marbles may be on the red side of the stick or vice versa. Also, if the data points of different categories cannot be seperated by a line, an SVM can transform the data points in such a way that a line can be drawn between them (or get very close by trying its best). The SVM then uses this boundary line to make a prediction (i.e. to classify) by plotting a test point and observing which side of the line that point falls on; in the marble analogy, an SVM will predict that the test point is blue if it falls on the blue side of the stick, and red if it falls on the red side of the stick."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Model Tuning\n",
    "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:\n",
    "- Import [`sklearn.grid_search.gridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html) and [`sklearn.metrics.make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html).\n",
    "- Create a dictionary of parameters you wish to tune for the chosen model.\n",
    " - Example: `parameters = {'parameter' : [list of values]}`.\n",
    "- Initialize the classifier you've chosen and store it in `clf`.\n",
    "- Create the F<sub>1</sub> scoring function using `make_scorer` and store it in `f1_scorer`.\n",
    " - Set the `pos_label` parameter to the correct value!\n",
    "- Perform grid search on the classifier `clf` using `f1_scorer` as the scoring method, and store it in `grid_obj`.\n",
    "- Fit the grid search object to the training data (`X_train`, `y_train`), and store it in `grid_obj`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Made predictions in 0.0066 seconds.\n",
      "Tuned model has a training F1 score of 0.9542.\n",
      "Made predictions in 0.0031 seconds.\n",
      "Tuned model has a testing F1 score of 0.8535.\n"
     ]
    }
   ],
   "source": [
    "# Import 'GridSearchCV', 'make_scorer', and Normalizer\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.preprocessing import Normalizer\n",
    "\n",
    "# normalize features so values are consistent\n",
    "normer = Normalizer()\n",
    "X_train = normer.fit_transform(X_train)\n",
    "X_test = normer.transform(X_test)\n",
    "\n",
    "# Create the parameters list you wish to tune\n",
    "parameters = [\n",
    "   {'C': [pow(2, c) for c in range (-5, 11)],\n",
    "    'gamma': [pow(2, g) for g in range (-5, 11)],\n",
    "    #'coef0':[coef for coef in range(0, 10)],\n",
    "    #'degree': [d for d in range(1, 5)],\n",
    "    #'kernel': ['linear', 'rbf', 'sigmoid']}\n",
    "    'kernel': ['rbf']}\n",
    " ]\n",
    "\n",
    "# Initialize the classifier\n",
    "clf = SVC(random_state = 1)\n",
    "\n",
    "# Make an f1 scoring function using 'make_scorer' \n",
    "f1_scorer = make_scorer(f1_score, pos_label = 'yes')\n",
    "\n",
    "# Perform grid search on the classifie\"r using the f1_scorer as the scoring method\n",
    "grid_obj = GridSearchCV(clf, parameters, scoring = f1_scorer)\n",
    "\n",
    "# Fit the grid search object to the training data and find the optimal parameters\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Get the estimator\n",
    "clf = grid_obj.best_estimator_\n",
    "\n",
    "# Report the final F1 score for training and testing after parameter tuning\n",
    "print \"Tuned model has a training F1 score of {:.4f}.\".format(predict_labels(clf, X_train, y_train))\n",
    "print \"Tuned model has a testing F1 score of {:.4f}.\".format(predict_labels(clf, X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5 - Final F<sub>1</sub> Score\n",
    "*What is the final model's F<sub>1</sub> score for training and testing? How does that score compare to the untuned model?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: ** The final model's F1 scores for training and testing are 0.9542 and 0.8535, respectively. The untuned model's F1 scores for training and testing are 0.8589 and 0.7733, respectively. The final model's F1 score for both training and testing are higher than the untuned model which indicates that not only is it fitting the training data better, but it is also making better predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **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  \n",
    "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
  }
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