finding_donors_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Engineer Nanodegree\n",
    "## Supervised Learning\n",
    "## Project: Finding Donors for *CharityML*"
   ]
  },
  {
   "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": [
    "## Getting Started\n",
    "\n",
    "In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations.  Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with.  While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. \n",
    "\n",
    "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income). The datset was donated by Ron Kohavi and Barry Becker, after being published in the article _\"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid\"_. You can find the article by Ron Kohavi [online](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf). The data we investigate here consists of small changes to the original dataset, such as removing the `'fnlwgt'` feature and records with missing or ill-formatted entries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Exploring the Data\n",
    "Run the code cell below to load necessary Python libraries and load the census data. Note that the last column from this dataset, `'income'`, will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   workclass education_level  education-num  marital-status  \\\n",
       "0   39   State-gov       Bachelors           13.0   Never-married   \n",
       "\n",
       "      occupation    relationship    race    sex  capital-gain  capital-loss  \\\n",
       "0   Adm-clerical   Not-in-family   White   Male        2174.0           0.0   \n",
       "\n",
       "   hours-per-week  native-country income  \n",
       "0            40.0   United-States  <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Import supplementary visualization code visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the Census dataset\n",
    "data = pd.read_csv(\"census.csv\")\n",
    "\n",
    "# Success - Display the first record\n",
    "display(data.head(n=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Data Exploration\n",
    "A cursory investigation of the dataset will determine how many individuals fit into either group, and will tell us about the percentage of these individuals making more than \\$50,000. In the code cell below, you will need to compute the following:\n",
    "- The total number of records, `'n_records'`\n",
    "- The number of individuals making more than \\$50,000 annually, `'n_greater_50k'`.\n",
    "- The number of individuals making at most \\$50,000 annually, `'n_at_most_50k'`.\n",
    "- The percentage of individuals making more than \\$50,000 annually, `'greater_percent'`.\n",
    "\n",
    "**Hint:** You may need to look at the table above to understand how the `'income'` entries are formatted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records: 45222\n",
      "Individuals making more than $50,000: 11208\n",
      "Individuals making at most $50,000: 34014\n",
      "Percentage of individuals making more than $50,000: 24.78%\n"
     ]
    }
   ],
   "source": [
    "# TODO: Total number of records\n",
    "n_records = len(data)\n",
    "\n",
    "# TODO: Number of records where individual's income is more than $50,000\n",
    "n_greater_50k = len(data[(data.income == '>50K')])\n",
    "\n",
    "# TODO: Number of records where individual's income is at most $50,000\n",
    "n_at_most_50k = len(data[(data.income == '<=50K')])\n",
    "\n",
    "# TODO: Percentage of individuals whose income is more than $50,000\n",
    "greater_percent = 100.0 * (n_greater_50k+0.0)/n_records\n",
    "\n",
    "# Print the results\n",
    "print \"Total number of records: {}\".format(n_records)\n",
    "print \"Individuals making more than $50,000: {}\".format(n_greater_50k)\n",
    "print \"Individuals making at most $50,000: {}\".format(n_at_most_50k)\n",
    "print \"Percentage of individuals making more than $50,000: {:.2f}%\".format(greater_percent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Preparing the Data\n",
    "Before data can be used as input for machine learning algorithms, it often must be cleaned, formatted, and restructured — this is typically known as **preprocessing**. Fortunately, for this dataset, there are no invalid or missing entries we must deal with, however, there are some qualities about certain features that must be adjusted. This preprocessing can help tremendously with the outcome and predictive power of nearly all learning algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming Skewed Continuous Features\n",
    "A dataset may sometimes contain at least one feature whose values tend to lie near a single number, but will also have a non-trivial number of vastly larger or smaller values than that single number.  Algorithms can be sensitive to such distributions of values and can underperform if the range is not properly normalized. With the census dataset two features fit this description: '`capital-gain'` and `'capital-loss'`. \n",
    "\n",
    "Run the code cell below to plot a histogram of these two features. Note the range of the values present and how they are distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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HCAAAAAAAGBgEQgAAAAAAwMAgEAIAAAAAAAYGgRAAAAAAADAwCIQAAAAAAICB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\nhkIIAAAAMDcUQgAAAIC58f8BSyVBLIWOQEgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd9d409050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the data into features and target label\n",
    "income_raw = data['income']\n",
    "features_raw = data.drop('income', axis = 1)\n",
    "\n",
    "# Visualize skewed continuous features of original data\n",
    "vs.distribution(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For highly-skewed feature distributions such as `'capital-gain'` and `'capital-loss'`, it is common practice to apply a <a href=\"https://en.wikipedia.org/wiki/Data_transformation_(statistics)\">logarithmic transformation</a> on the data so that the very large and very small values do not negatively affect the performance of a learning algorithm. Using a logarithmic transformation significantly reduces the range of values caused by outliers. Care must be taken when applying this transformation however: The logarithm of `0` is undefined, so we must translate the values by a small amount above `0` to apply the the logarithm successfully.\n",
    "\n",
    "Run the code cell below to perform a transformation on the data and visualize the results. Again, note the range of values and how they are distributed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yuy8gv7hwbslvT5dj/HeyS9ZGbZZbkaxo/FfZ/4fL/n+YWsVsN3mx0/6Qb/l/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\nAEBvaIQAAAAAvaERAgAAAPTG/wcZRvvcxpZZvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd9d45fa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Log-transform the skewed features\n",
    "skewed = ['capital-gain', 'capital-loss']\n",
    "features_raw[skewed] = data[skewed].apply(lambda x: np.log(x + 1))\n",
    "\n",
    "# Visualize the new log distributions\n",
    "vs.distribution(features_raw, transformed = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizing Numerical Features\n",
    "In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as `'capital-gain'` or `'capital-loss'` above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below.\n",
    "\n",
    "Run the code cell below to normalize each numerical feature. We will use [`sklearn.preprocessing.MinMaxScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html) for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.30137</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.02174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age   workclass education_level  education-num  marital-status  \\\n",
       "0  0.30137   State-gov       Bachelors            0.8   Never-married   \n",
       "\n",
       "      occupation    relationship    race    sex  capital-gain  capital-loss  \\\n",
       "0   Adm-clerical   Not-in-family   White   Male       0.02174           0.0   \n",
       "\n",
       "   hours-per-week  native-country  \n",
       "0        0.397959   United-States  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import sklearn.preprocessing.StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Initialize a scaler, then apply it to the features\n",
    "scaler = MinMaxScaler()\n",
    "numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "features_raw[numerical] = scaler.fit_transform(data[numerical])\n",
    "\n",
    "# Show an example of a record with scaling applied\n",
    "display(features_raw.head(n = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Data Preprocessing\n",
    "\n",
    "From the table in **Exploring the Data** above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called *categorical variables*) be converted. One popular way to convert categorical variables is by using the **one-hot encoding** scheme. One-hot encoding creates a _\"dummy\"_ variable for each possible category of each non-numeric feature. For example, assume `someFeature` has three possible entries: `A`, `B`, or `C`. We then encode this feature into `someFeature_A`, `someFeature_B` and `someFeature_C`.\n",
    "\n",
    "|   | someFeature |                    | someFeature_A | someFeature_B | someFeature_C |\n",
    "| :-: | :-: |                            | :-: | :-: | :-: |\n",
    "| 0 |  B  |  | 0 | 1 | 0 |\n",
    "| 1 |  C  | ----> one-hot encode ----> | 0 | 0 | 1 |\n",
    "| 2 |  A  |  | 1 | 0 | 0 |\n",
    "\n",
    "Additionally, as with the non-numeric features, we need to convert the non-numeric target label, `'income'` to numerical values for the learning algorithm to work. Since there are only two possible categories for this label (\"<=50K\" and \">50K\"), we can avoid using one-hot encoding and simply encode these two categories as `0` and `1`, respectively. In code cell below, you will need to implement the following:\n",
    " - Use [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) to perform one-hot encoding on the `'features_raw'` data.\n",
    " - Convert the target label `'income_raw'` to numerical entries.\n",
    "   - Set records with \"<=50K\" to `0` and records with \">50K\" to `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 total features after one-hot encoding.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: income, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: One-hot encode the 'features_raw' data using pandas.get_dummies()\n",
    "features = pd.get_dummies(features_raw)\n",
    "\n",
    "# TODO: Encode the 'income_raw' data to numerical values\n",
    "income = income_raw.apply(lambda x: 0 if x == '<=50K' else 1)\n",
    "\n",
    "# Print the number of features after one-hot encoding\n",
    "encoded = list(features.columns)\n",
    "print \"{} total features after one-hot encoding.\".format(len(encoded))\n",
    "\n",
    "# Uncomment the following line to see the encoded feature names\n",
    "#print encoded\n",
    "display(income.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shuffle and Split Data\n",
    "Now all _categorical variables_ have been converted into numerical features, and all numerical features have been normalized. As always, we will now split the data (both features and their labels) into training and test sets. 80% of the data will be used for training and 20% for testing.\n",
    "\n",
    "Run the code cell below to perform this split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 36177 samples.\n",
      "Testing set has 9045 samples.\n"
     ]
    }
   ],
   "source": [
    "# Import train_test_split\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# Split the 'features' and 'income' data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, income, test_size = 0.2, random_state = 0)\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": [
    "----\n",
    "## Evaluating Model Performance\n",
    "In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Three of these algorithms will be supervised learners of your choice, and the fourth algorithm is known as a *naive predictor*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics and the Naive Predictor\n",
    "*CharityML*, equipped with their research, knows individuals that make more than \\$50,000 are most likely to donate to their charity. Because of this, *CharityML* is particularly interested in predicting who makes more than \\$50,000 accurately. It would seem that using **accuracy** as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that *does not* make more than \\$50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than \\$50,000 is *more important* than the model's ability to **recall** those individuals. We can use **F-beta score** as a metric that considers both precision and recall:\n",
    "\n",
    "$$ F_{\\beta} = (1 + \\beta^2) \\cdot \\frac{precision \\cdot recall}{\\left( \\beta^2 \\cdot precision \\right) + recall} $$\n",
    "\n",
    "In particular, when $\\beta = 0.5$, more emphasis is placed on precision. This is called the **F$_{0.5}$ score** (or F-score for simplicity).\n",
    "\n",
    "Looking at the distribution of classes (those who make at most \\$50,000, and those who make more), it's clear most individuals do not make more than \\$50,000. This can greatly affect **accuracy**, since we could simply say *\"this person does not make more than \\$50,000\"* and generally be right, without ever looking at the data! Making such a statement would be called **naive**, since we have not considered any information to substantiate the claim. It is always important to consider the *naive prediction* for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than \\$50,000, *CharityML* would identify no one as donors. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 - Naive Predictor Performace\n",
    "*If we chose a model that always predicted an individual made more than \\$50,000, what would that model's accuracy and F-score be on this dataset?*  \n",
    "**Note:** You must use the code cell below and assign your results to `'accuracy'` and `'fscore'` to be used later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]\n"
     ]
    }
   ],
   "source": [
    "# TODO: Calculate accuracy\n",
    "accuracy = greater_percent/100.0\n",
    "\n",
    "# TODO: Calculate F-score using the formula above for beta = 0.5\n",
    "beta = 0.5\n",
    "recall = 1.0\n",
    "fscore = (1.0+beta**2)*accuracy*recall/(beta**2*accuracy+recall)\n",
    "\n",
    "# Print the results \n",
    "print \"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy, fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Supervised Learning Models\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 Classifier (SGDC)\n",
    "- Support Vector Machines (SVM)\n",
    "- Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2 - Model Application\n",
    "List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen\n",
    "- *Describe one real-world application in industry where the model can be applied.* (You may need to do 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",
    "Model | Real-World application | Advantages | Disadvantages | Reasons for choosing\n",
    "--- | --- | --- | --- | ---\n",
    "***Random Forest***  | Computer Vision and Remote Sensing | Being an ensemble method it can handle high dimensional data as well as large number of training examples.   It also provides an estimate of what variables are important . | Can overfit the data easily .  | It's one most popular algorithms that genrally performs great in most scenarios .\n",
    "***Support Vector Machines (SVM)*** | Text-categorization and Cancer classification | It can capture complex relationships among data more easily as compared to other classifiers . | It is computationaly expensive to train. It has a lot of parameters to tune ( e.g. kernels, regularization parameters,etc ). As the number of features increase it becomes difficult to train.| It can identify complex patterns among data very easily provided we fine tune the parameters .\n",
    "***Logistic Regression*** | Image Segmentation and Categorization |Easy to train . Can update the model easily . | Difficult to capture complex relationship among datasets without proper parameter tuning . | This is quite a basic algorithm for classification and is easiest to implement ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation - Creating a Training and Predicting Pipeline\n",
    "To properly evaluate the performance of each model you've chosen, it's important that you create a training and predicting pipeline that allows you to quickly and effectively train models using various sizes of training data and perform predictions on the testing data. Your implementation here will be used in the following section.\n",
    "In the code block below, you will need to implement the following:\n",
    " - Import `fbeta_score` and `accuracy_score` from [`sklearn.metrics`](http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics).\n",
    " - Fit the learner to the sampled training data and record the training time.\n",
    " - Perform predictions on the test data `X_test`, and also on the first 300 training points `X_train[:300]`.\n",
    "   - Record the total prediction time.\n",
    " - Calculate the accuracy score for both the training subset and testing set.\n",
    " - Calculate the F-score for both the training subset and testing set.\n",
    "   - Make sure that you set the `beta` parameter!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO: Import two metrics from sklearn - fbeta_score and accuracy_score\n",
    "from sklearn.metrics import accuracy_score,fbeta_score\n",
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "    '''\n",
    "    inputs:\n",
    "       - learner: the learning algorithm to be trained and predicted on\n",
    "       - sample_size: the size of samples (number) to be drawn from training set\n",
    "       - X_train: features training set\n",
    "       - y_train: income training set\n",
    "       - X_test: features testing set\n",
    "       - y_test: income testing set\n",
    "    '''\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    # TODO: Fit the learner to the training data using slicing with 'sample_size'\n",
    "    start = time() # Get start time\n",
    "    learner = learner.fit(X_train[:sample_size],y_train[:sample_size])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # TODO: Calculate the training time\n",
    "    results['train_time'] = end-start\n",
    "        \n",
    "    # TODO: Get the predictions on the test set,\n",
    "    #       then get predictions on the first 300 training samples\n",
    "    start = time() # Get start time\n",
    "    predictions_test = learner.predict(X_test)\n",
    "    predictions_train = learner.predict(X_train[:300])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # TODO: Calculate the total prediction time\n",
    "    results['pred_time'] = end-start\n",
    "            \n",
    "    # TODO: Compute accuracy on the first 300 training samples\n",
    "    results['acc_train'] = accuracy_score(y_train[:300], predictions_train)\n",
    "        \n",
    "    # TODO: Compute accuracy on test set\n",
    "    results['acc_test'] = accuracy_score(y_test, predictions_test)\n",
    "        \n",
    "    # TODO: Compute F-score on the the first 300 training samples\n",
    "    results['f_train'] = fbeta_score(y_train[:300], predictions_train, beta=beta)\n",
    "        \n",
    "    # TODO: Compute F-score on the test set\n",
    "    results['f_test'] = fbeta_score(y_test, predictions_test, beta=beta)\n",
    "       \n",
    "    # Success\n",
    "    print \"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size)\n",
    "        \n",
    "    # Return the results\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Initial Model Evaluation\n",
    "In the 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",
    "- Calculate the number of records equal to 1%, 10%, and 100% of the training data.\n",
    "  - Store those values in `'samples_1'`, `'samples_10'`, and `'samples_100'` respectively.\n",
    "\n",
    "**Note:** Depending on which algorithms you chose, the following implementation may take some time to run!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression trained on 361 samples.\n",
      "LogisticRegression trained on 3617 samples.\n",
      "LogisticRegression trained on 36177 samples.\n",
      "LinearSVC trained on 361 samples.\n",
      "LinearSVC trained on 3617 samples.\n",
      "LinearSVC trained on 36177 samples.\n",
      "RandomForestClassifier trained on 361 samples.\n",
      "RandomForestClassifier trained on 3617 samples.\n",
      "RandomForestClassifier trained on 36177 samples.\n"
     ]
    },
    {
     "data": {
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PJ/Wx5Wm9MeZXl7zswd50AzQKMo9Qt0d6CdjHxnbavN35b3H/8Y5g9Ulq66qW2Hf9Zxhj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\nsiT1HftqSROaiRBJUkdExCSK1b5+PFSWmQlcDew8ynME8Azg/nbEKEn9z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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd980a10d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Import the three supervised learning models from sklearn\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC\n",
    "# TODO: Initialize the three models\n",
    "clf_A = LogisticRegression(random_state=0)\n",
    "clf_B = LinearSVC(random_state=0)\n",
    "clf_C = RandomForestClassifier(n_estimators=10)\n",
    "\n",
    "# TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data\n",
    "n_train = len(y_train)\n",
    "samples_1 = int(n_train/100.0)\n",
    "samples_10 = int(n_train/10.0)\n",
    "samples_100 = n_train\n",
    "\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    results[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_1, samples_10, samples_100]):\n",
    "        results[clf_name][i] = \\\n",
    "        train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
    "\n",
    "# Run metrics visualization for the three supervised learning models chosen\n",
    "vs.evaluate(results, accuracy, fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Improving Results\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-score. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3 - Choosing the Best Model\n",
    "*Based on the evaluation you performed earlier, in one to two paragraphs, explain to *CharityML* which of the three models you believe to be most appropriate for the task of identifying individuals that make more than \\$50,000.*  \n",
    "**Hint:** Your answer should include discussion of the metrics, prediction/training time, and the algorithm's suitability for the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: **\n",
    "***Logistic Regression Clasifier is the classifier that I have chosen ***for the task of identifying individuals that make more than $50,000. Both the training and test scores are high and close to each other making it an ideal candidate for selection . Also, among the three models this one gives the best performance in terms of accuracy and f-score . Also it is less computationaly expensive than training Support Vector Classifier and also takes very less time for predicting as compared to Random Forest Classifier .\n",
    "\n",
    "\n",
    "***Support Vector Classifier*** has both accuracy and f-score less than Logistic Regression for test samples . This classifier is also very complex to train and finding the right set of parameters through grid search would also take quite a lot of time . Also it can be seen that it takes more time for both training and predicting as compared to Logistic Regression Classifier.***Hence this classifier is not used ***.    \n",
    "\n",
    "***Random Forest Classifier*** suffers from high variance . This is visbile from the graph as there is huge difference between the accuracy and test score for training data and test data as compared to other models . Hence, the model basically works perfectly for the training data but as soon as new data is introduced in the form of test samples it cannot predicted correctly and hence the score drops . Also it takes a huge amount of time to predict data as compared to other models .***Hence this classifier is not used ***."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4 - Describing the Model in Layman's Terms\n",
    "*In one to two paragraphs, explain to *CharityML*, 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: ** If all the data points are plotted in a 2-D plane , a Logistic Regression Classifier basically draws boundaries to distinguish different classes from each other. The basic steps involved are as follows :\n",
    "1. First the features of a data point are multiplied to their corresponding weights.\n",
    "2. The resultant values are added and a classification function is applied .\n",
    "3. Based on the value obtained the coressponding label is applied .\n",
    "4. A feedback mechanism which verifies the assigned labels with the true lables is used to update the weights and bias (intercept) according to the error obtained .\n",
    "5. When a new data point is given for prediction , only steps 1-3 are applied to obtain the class label .  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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In+SPLbf7vOOreg5rr91x/wA8ov8Av6f/AImt6mHq0oxnNWUldeav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19QNtp+m3N41n5wt4XlMcUQZ32gnCjuTjgV5u9nY/Hf4apLDBN4eeO+zHK8CyHKDkg/LuUhsZBH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\ng48xgMhc9sn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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Image Source : https://www.mssqltips.com/tipimages2/3471_Example.JPG\n",
    "from IPython.display import Image\n",
    "Image(filename='reg.jpg')"
   ]
  },
  {
   "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",
    "- Initialize the classifier you've chosen and store it in `clf`.\n",
    " - Set a `random_state` if one is available to the same state you set before.\n",
    "- Create a dictionary of parameters you wish to tune for the chosen model.\n",
    " - Example: `parameters = {'parameter' : [list of values]}`.\n",
    " - **Note:** Avoid tuning the `max_features` parameter of your learner if that parameter is available!\n",
    "- Use `make_scorer` to create an `fbeta_score` scoring object (with $\\beta = 0.5$).\n",
    "- Perform grid search on the classifier `clf` using the `'scorer'`, 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_fit`.\n",
    "\n",
    "**Note:** Depending on the algorithm chosen and the parameter list, the following implementation may take some time to run!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.8483\n",
      "F-score on testing data: 0.6993\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.8493\n",
      "Final F-score on the testing data: 0.7007\n"
     ]
    }
   ],
   "source": [
    "# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "import decimal\n",
    "\n",
    "#Ref : http://stackoverflow.com/questions/7267226/range-for-floats\n",
    "def drange(x, y, jump):\n",
    "  while x < y:\n",
    "    yield float(x)\n",
    "    x += float(decimal.Decimal(jump))\n",
    "    \n",
    "# TODO: Initialize the classifier\n",
    "clf = LogisticRegression(random_state=0)\n",
    "\n",
    "# TODO: Create the parameters list you wish to tune\n",
    "parameters = {'solver' : ['newton-cg', 'lbfgs', 'liblinear', 'sag'],\n",
    "             'random_state': [None,0,10,100,200,300],\n",
    "              'fit_intercept':[True,False],\n",
    "              'C':[0.1,0.5,1.0,5.0,10.0,50.0,80,90,100.0]\n",
    "             }\n",
    "\n",
    "# TODO: Make an fbeta_score scoring object\n",
    "scorer = make_scorer(fbeta_score, beta=beta)\n",
    "\n",
    "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method\n",
    "grid_obj = GridSearchCV(clf, parameters, scoring=scorer)\n",
    "\n",
    "# TODO: Fit the grid search object to the training data and find the optimal parameters\n",
    "grid_fit = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Get the estimator\n",
    "best_clf = grid_fit.best_estimator_\n",
    "\n",
    "# Make predictions using the unoptimized and model\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "best_predictions = best_clf.predict(X_test)\n",
    "\n",
    "# Report the before-and-afterscores\n",
    "print \"Unoptimized model\\n------\"\n",
    "print \"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions))\n",
    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5))\n",
    "print \"\\nOptimized Model\\n------\"\n",
    "print \"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions))\n",
    "print \"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5 - Final Model Evaluation\n",
    "_What is your optimized model's accuracy and F-score on the testing data? Are these scores better or worse than the unoptimized model? How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in **Question 1**?_  \n",
    "**Note:** Fill in the table below with your results, and then provide discussion in the **Answer** box."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results:\n",
    "\n",
    "|     Metric     | Benchmark Predictor | Unoptimized Model | Optimized Model |\n",
    "| :------------: | :-----------------: | :---------------: | :-------------: | \n",
    "| Accuracy Score |         0.2478      |      0.8483       |    0.8493       |\n",
    "| F-score        |         0.2917      |      0.6993       |    0.7007       |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer: ** The optimized Logistic Regression Classifier performs way better than the Benchmark Predictor as there is a significant difference between the Accuracy Score and F-Score .   \n",
    "\n",
    "The optimized model performs slightly better than the unoptimized model ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Feature Importance\n",
    "\n",
    "An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than \\$50,000.\n",
    "\n",
    "Choose a scikit-learn classifier (e.g., adaboost, random forests) that has a `feature_importance_` attribute, which is a function that ranks the importance of features according to the chosen classifier.  In the next python cell fit this classifier to training set and use this attribute to determine the top 5 most important features for the census dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6 - Feature Relevance Observation\n",
    "When **Exploring the Data**, it was shown there are thirteen available features for each individual on record in the census data.  \n",
    "_Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** I would choose the following features in the order mentioned below :\n",
    "1. Age\n",
    "2. income\n",
    "3. marital-status\n",
    "4. occupation\n",
    "5. education_level  \n",
    "\n",
    "I believe that wealth(or income in this case), family , status in society ( occupation in this case) and education obtained are the basic factors that determine how successful a person is in his/her life and a successful person would be more willing to donate money to charity ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation - Extracting Feature Importance\n",
    "Choose a `scikit-learn` supervised learning algorithm that has a `feature_importance_` attribute availble for it. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm.\n",
    "\n",
    "In the code cell below, you will need to implement the following:\n",
    " - Import a supervised learning model from sklearn if it is different from the three used earlier.\n",
    " - Train the supervised model on the entire training set.\n",
    " - Extract the feature importances using `'.feature_importances_'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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p3+HfGtXE/0gVYmaHm9llQd32J4L6+k/hgznw1U/KTUTVlSOA+51zj0dl6YXf1pi9+Tnn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\nXTYXASc3P1pAbUEayuNFzTpnUIORo6iB6JlJPsTkGrVulFJ+S/2s3kh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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd9aca7810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Import a supervised learning model that has 'feature_importances_'\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "# TODO: Train the supervised model on the training set \n",
    "model = GradientBoostingClassifier().fit(X_train, y_train)\n",
    "\n",
    "# TODO: Extract the feature importances\n",
    "importances = model.feature_importances_\n",
    "\n",
    "# Plot\n",
    "vs.feature_plot(importances, X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7 - Extracting Feature Importance\n",
    "\n",
    "Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \\$50,000.  \n",
    "_How do these five features compare to the five features you discussed in **Question 6**? If you were close to the same answer, how does this visualization confirm your thoughts? If you were not close, why do you think these features are more relevant?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** From the features I chose in question 6 , education-num is related to education-level . Although capital-loss ,capital-gain and hours-per-week were missing from my observation initially . However age and marital-status were included in my initial observation .  \n",
    "\n",
    "The visualization displays the weights assigned to these features and as it can be seen clearly marital-status and age are amongst the top 5 features .  \n",
    "\n",
    "Capital-gain and capital-loss are more important features according to the visualization . It may be due to the fact that money gained/lost by an individual thorugh investments,etc. also plays a vital role in deciding whether to donate money (since it may be the fact that although the person may have good income , etc. but the capital-loss for that individual outweighs other factors).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection\n",
    "How does a model perform if we only use a subset of all the available features in the data? With less features required to train, the expectation is that training and prediction time is much lower — at the cost of performance metrics. From the visualization above, we see that the top five most important features contribute more than half of the importance of **all** features present in the data. This hints that we can attempt to *reduce the feature space* and simplify the information required for the model to learn. The code cell below will use the same optimized model you found earlier, and train it on the same training set *with only the top five important features*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8493\n",
      "F-score on testing data: 0.7007\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8356\n",
      "F-score on testing data: 0.6690\n"
     ]
    }
   ],
   "source": [
    "# Import functionality for cloning a model\n",
    "from sklearn.base import clone\n",
    "\n",
    "# Reduce the feature space\n",
    "X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "\n",
    "# Train on the \"best\" model found from grid search earlier\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "\n",
    "# Make new predictions\n",
    "reduced_predictions = clf.predict(X_test_reduced)\n",
    "\n",
    "# Report scores from the final model using both versions of data\n",
    "print \"Final Model trained on full data\\n------\"\n",
    "print \"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions))\n",
    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5))\n",
    "print \"\\nFinal Model trained on reduced data\\n------\"\n",
    "print \"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, reduced_predictions))\n",
    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, reduced_predictions, beta = 0.5))"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8 - Effects of Feature Selection\n",
    "*How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?*  \n",
    "*If training time was a factor, would you consider using the reduced data as your training set?*"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** The final model's F-score and accuracy on the reduced data using only five features is slightly less than the accuracy and F-score of optiized model . This may be due to the fact that the top 5 features alone cannot be used to identify all the patterns in the dataset .  \n",
    "\n",
    "If training time was a factor , reduced data can be used as training set as it requires less computational power . "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**References** :\n",
    "1. https://www.quora.com/What-are-applications-of-linear-and-logistic-regression\n",
    "2. http://scikit-learn.org/stable/tutorial/machine_learning_map/\n",
    "3. https://archive.ics.uci.edu/ml/datasets/Census+Income"
   ]
  },
  {
   "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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