student_intervention.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Engineer Nanodegree\n",
    "## Supervised Learning\n",
    "## Project: 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: **\n",
    "\n",
    "This problem is a classification problem because we're dealing with non-continuous values (discrete) of which there are two: needs intervention or doesn't need intervention. In terms of the data set, we assume that a student who didn't graduate needed intervention and vice versa for a student that did graduate so no changing of the data set need occur."
   ]
  },
  {
   "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",
    "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,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of students: 395\n",
      "Number of features: 30\n",
      "Number of students who passed: 265.0\n",
      "Number of students who failed: 130.0\n",
      "Graduation rate of the class: 67.09%\n"
     ]
    }
   ],
   "source": [
    "# TODO: Calculate number of students\n",
    "n_students = len(student_data.index)\n",
    "\n",
    "# TODO: Calculate number of features\n",
    "n_features = len(student_data.columns)-1 # subtract 1 because it's our target column i.e. not a feature\n",
    "\n",
    "# TODO: Calculate passing students\n",
    "counts = student_data[\"passed\"].value_counts()\n",
    "\n",
    "n_passed = float(counts[\"yes\"])\n",
    "\n",
    "# TODO: Calculate failing students\n",
    "n_failed = float(counts[\"no\"])\n",
    "\n",
    "# TODO: Calculate graduation rate\n",
    "\n",
    "grad_rate = (n_passed/n_students)*100\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"
     ]
    }
   ],
   "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()"
   ]
  },
  {
   "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": [
    "# TODO: Import any additional functionality you may need here\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# TODO: 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",
    "# TODO: 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=num_test, train_size=num_train, random_state=42)\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.\n",
    "\n",
    "**The following supervised learning models are currently available in** [`scikit-learn`](http://scikit-learn.org/stable/supervised_learning.html) **that you may choose from:**\n",
    "- Gaussian Naive Bayes (GaussianNB)\n",
    "- Decision Trees\n",
    "- Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)\n",
    "- K-Nearest Neighbors (KNeighbors)\n",
    "- Stochastic Gradient Descent (SGDC)\n",
    "- Support Vector Machines (SVM)\n",
    "- Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2 - Model Application\n",
    "*List three supervised learning models that are appropriate for this problem. For each model chosen*\n",
    "- 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!)* \n",
    "- What are the strengths of the model; when does it perform well? \n",
    "- What are the weaknesses of the model; when does it perform poorly?\n",
    "- What makes this model a good candidate for the problem, given what you know about the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: **\n",
    "\n",
    "**SVM**\n",
    "\n",
    "SVMs are a very popular classifier, especially for things like text classification. Additionally, it's been used in various computational biology settings where it can achieve high success rates in identifying certain proteins. In general, SVMs are really effective when there is a large feature set i.e. highly dimensional data. When there is a memory/performance requirement, SVMs stand out in their efficiency as well. There are not too many drawbacks of SVMs, but they tend to not perform super well in cases where the number of features is greater than the amount of samples. Although, most algorithsm do not do well in this case. It is also somewhat of a black box in that it is hard to communicate its effectiveness. For this given problem, we have a large number of features and small dataset so SVMs are one of the go to methods for classification of this type of problem. SVMs work in a single pass and is well suited to static data sets like this one.\n",
    "\n",
    "**Logistic Regression**\n",
    "\n",
    "Logistic Regression is a useful classifier that has many applications in classification tasks such as predicting political affiliations based on voter data. Being able to accurately predict who an undecided voter will vote for is a valuable classification problem that logistic regression can handle due to the presumably large amount of binomial feautures present in these datasets.<sup>[1](https://arxiv.org/pdf/1311.7326.pdf)</sup> Logistic Regression is well suited towards these problems where there is a categorical output (needs intervention, doesn't need intervention) so it seems to be an appropriate model for this problem given that we have various predictor features and the categorical output. There are some drawbacks when dealing with highly correlated features as it is assumed that these data are i.i.d. and when it's not, it can lead to some interesting intepretations due to its probabilistic nature. Also, why use logisitc regression when you can use an SVM? \n",
    "\n",
    "**Random Forest**\n",
    "\n",
    "Random Forests are a popular ensemble method of multiple decision trees that avoid some problems inherent to decision trees such as tendency to overfit. As far as ensemble methods go, Random Forests are fairly popular and have seen applications in such areas as image/movement recognition in Xbox Kinect.<sup>[2](https://www.microsoft.com/en-us/research/publication/real-time-human-pose-recognition-in-parts-from-a-single-depth-image/)</sup> Random Forests, like singular decision trees, is adept at handling large feature spaces which is what we have in this student intervention problem. Although better at adressing overfitting, noisy data can still cause the RF to overfit and hamper the accuracy on testing sets. Regardless, this ensemble method seems like an interesting one to investigate on this data set. "
   ]
  },
  {
   "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": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training a SVC using a training set size of 100. . .\n",
      "Trained model in 0.0045 seconds\n",
      "Made predictions in 0.0008 seconds.\n",
      "F1 score for training set: 0.8772.\n",
      "Made predictions in 0.0005 seconds.\n",
      "F1 score for test set: 0.7843.\n",
      "Training a SVC using a training set size of 200. . .\n",
      "Trained model in 0.0026 seconds\n",
      "Made predictions in 0.0018 seconds.\n",
      "F1 score for training set: 0.8190.\n",
      "Made predictions in 0.0009 seconds.\n",
      "F1 score for test set: 0.7742.\n",
      "Training a SVC using a training set size of 300. . .\n",
      "Trained model in 0.0052 seconds\n",
      "Made predictions in 0.0032 seconds.\n",
      "F1 score for training set: 0.8761.\n",
      "Made predictions in 0.0010 seconds.\n",
      "F1 score for test set: 0.7838.\n",
      "Training a LogisticRegression using a training set size of 100. . .\n",
      "Trained model in 0.0007 seconds\n",
      "Made predictions in 0.0001 seconds.\n",
      "F1 score for training set: 0.9200.\n",
      "Made predictions in 0.0001 seconds.\n",
      "F1 score for test set: 0.7194.\n",
      "Training a LogisticRegression using a training set size of 200. . .\n",
      "Trained model in 0.0012 seconds\n",
      "Made predictions in 0.0001 seconds.\n",
      "F1 score for training set: 0.8197.\n",
      "Made predictions in 0.0001 seconds.\n",
      "F1 score for test set: 0.6957.\n",
      "Training a LogisticRegression using a training set size of 300. . .\n",
      "Trained model in 0.0018 seconds\n",
      "Made predictions in 0.0001 seconds.\n",
      "F1 score for training set: 0.8468.\n",
      "Made predictions in 0.0001 seconds.\n",
      "F1 score for test set: 0.8060.\n",
      "Training a RandomForestClassifier using a training set size of 100. . .\n",
      "Trained model in 0.0114 seconds\n",
      "Made predictions in 0.0006 seconds.\n",
      "F1 score for training set: 0.9934.\n",
      "Made predictions in 0.0005 seconds.\n",
      "F1 score for test set: 0.7727.\n",
      "Training a RandomForestClassifier using a training set size of 200. . .\n",
      "Trained model in 0.0116 seconds\n",
      "Made predictions in 0.0006 seconds.\n",
      "F1 score for training set: 0.9928.\n",
      "Made predictions in 0.0006 seconds.\n",
      "F1 score for test set: 0.6508.\n",
      "Training a RandomForestClassifier using a training set size of 300. . .\n",
      "Trained model in 0.0136 seconds\n",
      "Made predictions in 0.0008 seconds.\n",
      "F1 score for training set: 0.9951.\n",
      "Made predictions in 0.0006 seconds.\n",
      "F1 score for test set: 0.8092.\n"
     ]
    }
   ],
   "source": [
    "# TODO: Import the three supervised learning models from sklearn\n",
    "from sklearn import svm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# TODO: Initialize the three models\n",
    "rs = 42\n",
    "\n",
    "clf_A = svm.SVC(random_state=rs)\n",
    "clf_B = LogisticRegression(random_state=rs)\n",
    "clf_C = RandomForestClassifier(random_state=rs)\n",
    "\n",
    "# TODO: Set up the training set sizes\n",
    "X_train_100 = X_all.head(100)\n",
    "y_train_100 = y_all.head(100)\n",
    "\n",
    "X_train_200 = X_all.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",
    "\n",
    "# TODO: 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",
    "# SVMs\n",
    "train_predict(clf_A, X_train_100, y_train_100, X_test, y_test)\n",
    "train_predict(clf_A, X_train_200, y_train_200, X_test, y_test)\n",
    "train_predict(clf_A, X_train, y_train, X_test, y_test)\n",
    "\n",
    "# Logistic Regression\n",
    "train_predict(clf_B, X_train_100, y_train_100, X_test, y_test)\n",
    "train_predict(clf_B, X_train_200, y_train_200, X_test, y_test)\n",
    "train_predict(clf_B, X_train, y_train, X_test, y_test)\n",
    "\n",
    "# Ensemble\n",
    "train_predict(clf_C, X_train_100, y_train_100, X_test, y_test)\n",
    "train_predict(clf_C, X_train_200, y_train_200, X_test, y_test)\n",
    "train_predict(clf_C, X_train, y_train, X_test, y_test)\n"
   ]
  },
  {
   "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 - ?**  \n",
    "\n",
    "| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |\n",
    "| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |\n",
    "| 100               |        0.0018           |         0.0007         |     0.8772       |     0.7843      |\n",
    "| 200               |        0.0031           |         0.0012         |     0.8190       |     0.7742      |\n",
    "| 300               |        0.0046           |         0.0017         |     0.8761       |     0.7838      |\n",
    "\n",
    "** Classifer 2 - ?**  \n",
    "\n",
    "| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |\n",
    "| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |\n",
    "| 100               |        0.0015           |         0.0002         |     0.9200       |     0.7194      |\n",
    "| 200               |        0.0023           |         0.0002         |     0.8197       |     0.6957      |\n",
    "| 300               |        0.0029           |         0.0002         |     0.8468       |     0.8060      |\n",
    "\n",
    "** Classifer 3 - ?**  \n",
    "\n",
    "| Training Set Size | Training Time | Prediction Time (test) | F1 Score (train) | F1 Score (test) |\n",
    "| :---------------: | :---------------------: | :--------------------: | :--------------: | :-------------: |\n",
    "| 100               |        0.0328           |         0.0056         |     0.9934       |     0.7727      |\n",
    "| 200               |        0.0253           |         0.0051         |     0.9928       |     0.6508      |\n",
    "| 300               |        0.0257           |         0.0056         |     0.9951       |     0.8092      |"
   ]
  },
  {
   "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 - Choosing 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: **\n",
    "\n",
    "Based on the expirements run, I believe that while any of these models will work well enough, Logistic Regression is the opitmal model to continue to explore. All the models tested had great performance as far as time goes. This is expected due to the small dataset. Given that time/efficiency is not a factor in this case, the most important metric is soley the F1 score. The experiments revealed some unique insights when comparing F1 scores of each of the 3 classifiers tested: SVMs, Logistic Regression, and Random Forest. The F1 score on the full test set for Logistic Regression and Random Forest is approximately .81 compared to max F1 of .78 for SVMs. Now, there does not seem to be a  statisitical signficance in their difference, so choosing any of these 3 models is not a bad idea. If they're statistically similar, then why choose Logistic Regression? Well if we arbitrarily narrow down the results to Logistic Regression and Random Forests because of their slightly higher F1 test score, we can then observe the F1 score on the training set. Comparing the F1 score for the training data reveals that the Random Forest has an extremely high F1 score over .99 which indicates overfitting compared to the .82-92 range for the Logisitic Regression. This is something worth considering if we are to consider how extensible the interpretation of results are if given additional data points. So, all else equal, I decided to use Logisitic Regression."
   ]
  },
  {
   "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. 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.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: **\n",
    "\n",
    "For the final model, we are using something called a Logistic Regression model. Essentially what this allows us to do is approach problems where we have a binary decision, in this case: intervene or not intervene. In order to make this decision, this algorithm uses a large set of input variables known as \"features\" for things like sex, age, internet access, etc. We can \"train\" this algorithm by splitting the data we know about the students into a training set and test set which allows us to validate how well the algorithm can predict a student's need for intervention. The algorithm learns how to best predict a student's need for intervention by \"learning\" from the training set through calculating some probabilites of the input features and if they correlate to a student passing or not passing. Using a combination of these probabilities, it generates a decision based on whichever is the higher probability between the student passing or not passing. "
   ]
  },
  {
   "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/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).\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": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Made predictions in 0.0001 seconds.\n",
      "Tuned model has a training F1 score of 0.8377.\n",
      "Made predictions in 0.0001 seconds.\n",
      "Tuned model has a testing F1 score of 0.7914.\n"
     ]
    }
   ],
   "source": [
    "# TODO: Import 'GridSearchCV' and 'make_scorer'\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "# TODO: Create the parameters list you wish to tune\n",
    "parameters = {'max_iter':[100,150,200],'C':[0.50,1.0,1.5,2.0],'solver':['liblinear','newton-cg','sag','lbfgs']}\n",
    "\n",
    "# TODO: Initialize the classifier\n",
    "clf = LogisticRegression()\n",
    "\n",
    "# TODO: Make an f1 scoring function using 'make_scorer' \n",
    "f1_scorer = make_scorer(f1_score, pos_label='yes')\n",
    "\n",
    "# TODO: Perform grid search on the classifier using the f1_scorer as the scoring method\n",
    "grid_obj = GridSearchCV(clf, parameters, scoring=f1_scorer)\n",
    "\n",
    "# TODO: 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: **\n",
    "\n",
    "The fine tuned F1 testing score is approximately the same as the untuned model score (~.80) and the same for the training F1 score as well (~0.84). From this, there is no strong statistical significance in the difference to suggest the parameters beyond the defaults really do much. Unfortunately, the only way to know for sure is with more data given how smsll the available data set is."
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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