performance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pima Indian Diabetes Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(color_codes=True)\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and Reviewing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_frame = pd.read_csv(\"pima data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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   "source": [
    "data_frame.head(5)"
   ]
  },
  {
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   "metadata": {
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     "execution_count": 30,
     "metadata": {},
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   ],
   "source": [
    "data_frame.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definition of features\n",
    "From the metadata on the data source we have the following definition of the features.\n",
    "\n",
    "| Feature  | Description | Comments |\n",
    "|--------------|-------------|--------|\n",
    "| num_preg     | number of pregnancies         |\n",
    "| glucose_conc | Plasma glucose concentration a 2 hours in an oral glucose tolerance test         |\n",
    "| diastolic_bp | Diastolic blood pressure (mm Hg) |\n",
    "| thickness | Triceps skin fold thickness (mm) |\n",
    "|insulin | 2-Hour serum insulin (mu U/ml) |\n",
    "| bmi |  Body mass index (weight in kg/(height in m)^2) |\n",
    "| diab_pred |  Diabetes pedigree function |\n",
    "| Age (years) | Age (years)|\n",
    "| skin | ???? | What is this? |\n",
    "| diabetes | Class variable (1=True, 0=False) |  Why is our data boolean (True/False)? |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "print (data_frame.isnull().values.any())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlated Feature Check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Helper function that displays correlation by color.  Red is most correlated, Blue least."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_corr(data_frame, size=11):\n",
    "    \"\"\"\n",
    "    Function plots a graphical correlation matrix for each pair of columns in the dataframe.\n",
    "\n",
    "    Input:\n",
    "        data_frame: pandas DataFrame\n",
    "        size: vertical and horizontal size of the plot\n",
    "\n",
    "    Displays:\n",
    "        matrix of correlation between columns.  Blue-cyan-yellow-red-darkred => less to more correlated\n",
    "                                                0 ------------------>  1\n",
    "                                                Expect a darkred line running from top left to bottom right\n",
    "    \"\"\"\n",
    "\n",
    "    corr = data_frame.corr()    # data frame correlation function\n",
    "    fig, ax = plt.subplots(figsize=(size, size))\n",
    "    ax.matshow(corr)   # color code the rectangles by correlation value\n",
    "    plt.xticks(range(len(corr.columns)), corr.columns)  # draw x tick marks\n",
    "    plt.yticks(range(len(corr.columns)), corr.columns)  # draw y tick marks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plot_corr' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-0a6463e91d24>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_corr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_frame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'plot_corr' is not defined"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "plot_corr(data_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
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   "outputs": [
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     "execution_count": 34,
     "metadata": {},
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  {
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  {
   "cell_type": "markdown",
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   "source": [
    "The skin and thickness columns are correlated 1 to 1.  Dropping the skin column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
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   "outputs": [],
   "source": [
    "del data_frame['skin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_preg</th>\n",
       "      <th>glucose_conc</th>\n",
       "      <th>diastolic_bp</th>\n",
       "      <th>thickness</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diab_pred</th>\n",
       "      <th>age</th>\n",
       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for additional correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_corr(data_frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlations look good.  There appear to be no coorelated columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mold Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Types\n",
    "\n",
    "Inspect data types to see if there are any issues.  Data should be numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_preg</th>\n",
       "      <th>glucose_conc</th>\n",
       "      <th>diastolic_bp</th>\n",
       "      <th>thickness</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diab_pred</th>\n",
       "      <th>age</th>\n",
       "      <th>diabetes</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>148</td>\n",
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       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>89</td>\n",
       "      <td>66</td>\n",
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       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<div>\n",
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       "      <th>bmi</th>\n",
       "      <th>diab_pred</th>\n",
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       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>6</td>\n",
       "      <td>148</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change diabetes from boolean to integer, True=1, False=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diabetes_map = {True : 1, False : 0}\n",
    "data_frame['diabetes'] = data_frame['diabetes'].map(diabetes_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verify that the diabetes data type has been changed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     \n",
    "     \n",
    "     \n",
    "     \n",
    "     \n",
    "     \n",
    "     \n",
    "     \n",
    "     \n",
    "     \n",
    "     \n",
    "     \n",
    "     \n",
    "     \n",
    "     \n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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   ],
   "source": [
    "data_frame.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No obvious null values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check class distribution \n",
    "\n",
    "Rare events are hard to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of True cases:  268 (34.90%)\nNumber of False cases: 500 (65.10%)\n"
     ]
    }
   ],
   "source": [
    "num_obs = len(data_frame)\n",
    "num_true = len(data_frame.loc[data_frame['diabetes'] == 1])\n",
    "num_false = len(data_frame.loc[data_frame['diabetes'] == 0])\n",
    "print(\"Number of True cases:  {0} ({1:2.2f}%)\".format(num_true, ((1.00 * num_true)/(1.0 * num_obs)) * 100))\n",
    "print(\"Number of False cases: {0} ({1:2.2f}%)\".format(num_false, (( 1.0 * num_false)/(1.0 * num_obs)) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good distribution of true and false cases.  No special work needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spliting the data \n",
    "\n",
    "70% for training, 30% for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RajuAhmed\\PycharmProjects\\MLCourse\\venv\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RajuAhmed\\PycharmProjects\\MLCourse\\venv\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "feature_col_names = ['num_preg', 'glucose_conc', 'diastolic_bp', 'thickness', 'insulin', 'bmi', 'diab_pred', 'age']\n",
    "predicted_class_names = ['diabetes']\n",
    "\n",
    "X = data_frame[feature_col_names].values     # predictor feature columns (8 X m)\n",
    "y = data_frame[predicted_class_names].values # predicted class (1=true, 0=false) column (1 X m)\n",
    "split_test_size = 0.30\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=split_test_size, random_state=42) \n",
    "                            # test_size = 0.3 is 30%, 42 is the answer to everything"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check to ensure we have the the desired 70% train, 30% test split of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69.92% in training set\n30.08% in test set\n"
     ]
    }
   ],
   "source": [
    "trainval = (1.0 * len(X_train)) / (1.0 * len(data_frame.index))\n",
    "testval = (1.0 * len(X_test)) / (1.0 * len(data_frame.index))\n",
    "print(\"{0:0.2f}% in training set\".format(trainval * 100))\n",
    "print(\"{0:0.2f}% in test set\".format(testval * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verifying predicted value was split correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original True  : 268 (34.90%)\nOriginal False : 500 (65.10%)\n\nTraining True  : 188 (35.01%)\nTraining False : 349 (64.99%)\n\nTest True      : 80 (34.63%)\nTest False     : 151 (65.37%)\n"
     ]
    }
   ],
   "source": [
    "print(\"Original True  : {0} ({1:0.2f}%)\".format(len(data_frame.loc[data_frame['diabetes'] == 1]), (len(data_frame.loc[data_frame['diabetes'] == 1])/len(data_frame.index)) * 100.0))\n",
    "print(\"Original False : {0} ({1:0.2f}%)\".format(len(data_frame.loc[data_frame['diabetes'] == 0]), (len(data_frame.loc[data_frame['diabetes'] == 0])/len(data_frame.index)) * 100.0))\n",
    "print(\"\")\n",
    "print(\"Training True  : {0} ({1:0.2f}%)\".format(len(y_train[y_train[:] == 1]), (len(y_train[y_train[:] == 1])/len(y_train) * 100.0)))\n",
    "print(\"Training False : {0} ({1:0.2f}%)\".format(len(y_train[y_train[:] == 0]), (len(y_train[y_train[:] == 0])/len(y_train) * 100.0)))\n",
    "print(\"\")\n",
    "print(\"Test True      : {0} ({1:0.2f}%)\".format(len(y_test[y_test[:] == 1]), (len(y_test[y_test[:] == 1])/len(y_test) * 100.0)))\n",
    "print(\"Test False     : {0} ({1:0.2f}%)\".format(len(y_test[y_test[:] == 0]), (len(y_test[y_test[:] == 0])/len(y_test) * 100.0)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Post-split Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hidden Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are these 0 values possible?\n",
    "\n",
    "How many rows have have unexpected 0 values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# rows in dataframe 768\n# rows missing glucose_conc: 5\n# rows missing diastolic_bp: 35\n# rows missing thickness: 227\n# rows missing insulin: 374\n# rows missing bmi: 11\n# rows missing diab_pred: 0\n# rows missing age: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"# rows in dataframe {0}\".format(len(data_frame)))\n",
    "print(\"# rows missing glucose_conc: {0}\".format(len(data_frame.loc[data_frame['glucose_conc'] == 0])))\n",
    "print(\"# rows missing diastolic_bp: {0}\".format(len(data_frame.loc[data_frame['diastolic_bp'] == 0])))\n",
    "print(\"# rows missing thickness: {0}\".format(len(data_frame.loc[data_frame['thickness'] == 0])))\n",
    "print(\"# rows missing insulin: {0}\".format(len(data_frame.loc[data_frame['insulin'] == 0])))\n",
    "print(\"# rows missing bmi: {0}\".format(len(data_frame.loc[data_frame['bmi'] == 0])))\n",
    "print(\"# rows missing diab_pred: {0}\".format(len(data_frame.loc[data_frame['diab_pred'] == 0])))\n",
    "print(\"# rows missing age: {0}\".format(len(data_frame.loc[data_frame['age'] == 0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Impute with the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "#Impute with mean all 0 readings\n",
    "fill_0 = Imputer(missing_values=0, strategy=\"mean\", axis=0)\n",
    "\n",
    "X_train = fill_0.fit_transform(X_train)\n",
    "X_test = fill_0.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Initial Algorithm - Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# create Gaussian Naive Bayes model object and train it with the data\n",
    "nb_model = GaussianNB()\n",
    "\n",
    "nb_model.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Performance on Training Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this returns array of predicted results\n",
    "prediction_from_trained_data = nb_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of our naive bayes model is : 0.7542\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "accuracy = metrics.accuracy_score(y_train, prediction_from_trained_data)\n",
    "\n",
    "print (\"Accuracy of our naive bayes model is : {0:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance on Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of our naive bayes model is: 0.7359\n"
     ]
    }
   ],
   "source": [
    "# this returns array of predicted results from test_data\n",
    "prediction_from_test_data = nb_model.predict(X_test)\n",
    "\n",
    "accuracy = metrics.accuracy_score(y_test, prediction_from_test_data)\n",
    "\n",
    "print (\"Accuracy of our naive bayes model is: {0:0.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n[[ 52  28]\n [ 33 118]]\n"
     ]
    }
   ],
   "source": [
    "print (\"Confusion Matrix\")\n",
    "\n",
    "# labels for set 1=True to upper left and 0 = False to lower right\n",
    "print (\"{0}\".format(metrics.confusion_matrix(y_test, prediction_from_test_data, labels=[1, 0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n             precision    recall  f1-score   support\n\n          1       0.61      0.65      0.63        80\n          0       0.81      0.78      0.79       151\n\navg / total       0.74      0.74      0.74       231\n\n"
     ]
    }
   ],
   "source": [
    "print (\"Classification Report\")\n",
    "\n",
    "# labels for set 1=True to upper left and 0 = False to lower right\n",
    "print (\"{0}\".format(metrics.classification_report(y_test, prediction_from_test_data, labels=[1, 0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n            max_depth=None, max_features='auto', max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n            oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Create a RandomForestClassifier object\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "rf_model.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9870\n"
     ]
    }
   ],
   "source": [
    "rf_predict_train = rf_model.predict(X_train)\n",
    "\n",
    "#get accuracy\n",
    "rf_accuracy = metrics.accuracy_score(y_train, rf_predict_train)\n",
    "\n",
    "#print accuracy\n",
    "print (\"Accuracy: {0:.4f}\".format(rf_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7100\n"
     ]
    }
   ],
   "source": [
    "rf_predict_test = rf_model.predict(X_test)\n",
    "\n",
    "#get accuracy\n",
    "rf_accuracy_testdata = metrics.accuracy_score(y_test, rf_predict_test)\n",
    "\n",
    "#print accuracy\n",
    "print (\"Accuracy: {0:.4f}\".format(rf_accuracy_testdata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix for Random Forest\n[[ 43  37]\n [ 30 121]]\n\nClassification Report\n\n             precision    recall  f1-score   support\n\n          1       0.59      0.54      0.56        80\n          0       0.77      0.80      0.78       151\n\navg / total       0.70      0.71      0.71       231\n\n"
     ]
    }
   ],
   "source": [
    "import plot as plot\n",
    "\n",
    "print (\"Confusion Matrix for Random Forest\")\n",
    "\n",
    "# labels for set 1=True to upper left and 0 = False to lower right\n",
    "print (\"{0}\".format(metrics.confusion_matrix(y_test, rf_predict_test, labels=[1, 0])))\n",
    "\n",
    "print (\"\")\n",
    "\n",
    "print (\"Classification Report\\n\")\n",
    "\n",
    "# labels for set 1=True to upper left and 0 = False to lower right\n",
    "print (\"{0}\".format(metrics.classification_report(y_test, rf_predict_test, labels=[1, 0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.7446\nConfusion Matrix\n[[ 44  36]\n [ 23 128]]\n\nClassification Report\n             precision    recall  f1-score   support\n\n          1       0.66      0.55      0.60        80\n          0       0.78      0.85      0.81       151\n\navg / total       0.74      0.74      0.74       231\n\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr_model = LogisticRegression(C=0.7, random_state=42)\n",
    "lr_model.fit(X_train, y_train.ravel())\n",
    "lr_predict_test = lr_model.predict(X_test)\n",
    "\n",
    "# training metrics\n",
    "print (\"Accuracy : {0:.4f}\".format(metrics.accuracy_score(y_test, lr_predict_test)))\n",
    "\n",
    "print (\"Confusion Matrix\")\n",
    "\n",
    "print (metrics.confusion_matrix(y_test, lr_predict_test, labels=[1, 0]))\n",
    "\n",
    "print (\"\")\n",
    "\n",
    "print (\"Classification Report\")\n",
    "\n",
    "print (metrics.classification_report(y_test, lr_predict_test, labels=[1, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7056\n[[ 54  26]\n [ 42 109]]\n\nClassification Report\n             precision    recall  f1-score   support\n\n          1       0.56      0.68      0.61        80\n          0       0.81      0.72      0.76       151\n\navg / total       0.72      0.71      0.71       231\n\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "lr_cv_model = LogisticRegressionCV(n_jobs=-1, random_state=42, Cs=3, cv=10, refit=False, class_weight=\"balanced\")  \n",
    "\n",
    "# set number of jobs to -1 which uses all cores to parallelize\n",
    "lr_cv_model.fit(X_train, y_train.ravel())\n",
    "\n",
    "lr_cv_predict_test = lr_cv_model.predict(X_test)\n",
    "\n",
    "# training metrics\n",
    "print( \"Accuracy: {0:.4f}\".format(metrics.accuracy_score(y_test, lr_cv_predict_test)))\n",
    "print (metrics.confusion_matrix(y_test, lr_cv_predict_test, labels=[1, 0])) \n",
    "print (\"\")\n",
    "print (\"Classification Report\")\n",
    "print (metrics.classification_report(y_test, lr_cv_predict_test, labels=[1,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Support vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n  max_iter=-1, probability=False, random_state=42, shrinking=True,\n  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# Create a RandomForestClassifier object\n",
    "svm_model = SVC(kernel='linear', C=1, random_state=42)\n",
    "\n",
    "svm_model.fit(X_train, y_train.ravel())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this returns array of predicted results\n",
    "prediction_from_trained_data = svm_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of our SVM model is : 0.7840\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "accuracy = metrics.accuracy_score(y_train, prediction_from_trained_data)\n",
    "\n",
    "print (\"Accuracy of our SVM model is : {0:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7359\n"
     ]
    }
   ],
   "source": [
    "svm_predict_test = svm_model.predict(X_test)\n",
    "\n",
    "#get accuracy\n",
    "svm_accuracy_testdata = metrics.accuracy_score(y_test, svm_predict_test)\n",
    "\n",
    "#print accuracy\n",
    "print (\"Accuracy: {0:.4f}\".format(svm_accuracy_testdata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix for Support Vector Amchine\n[[ 46  34]\n [ 27 124]]\n\nClassification Report\n\n             precision    recall  f1-score   support\n\n          1       0.63      0.57      0.60        80\n          0       0.78      0.82      0.80       151\n\navg / total       0.73      0.74      0.73       231\n\n"
     ]
    }
   ],
   "source": [
    "import plot as plot\n",
    "\n",
    "print (\"Confusion Matrix for Support Vector Amchine\")\n",
    "\n",
    "# labels for set 1=True to upper left and 0 = False to lower right\n",
    "print (\"{0}\".format(metrics.confusion_matrix(y_test, svm_predict_test, labels=[1, 0])))\n",
    "\n",
    "print (\"\")\n",
    "\n",
    "print (\"Classification Report\\n\")\n",
    "\n",
    "# labels for set 1=True to upper left and 0 = False to lower right\n",
    "print (\"{0}\".format(metrics.classification_report(y_test, svm_predict_test, labels=[1, 0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n       hidden_layer_sizes=(13, 13, 13), learning_rate='constant',\n       learning_rate_init=0.001, max_iter=500, momentum=0.9,\n       nesterovs_momentum=True, power_t=0.5, random_state=42, shuffle=True,\n       solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n       warm_start=False)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "# Create a RandomForestClassifier object\n",
    "ann_model = MLPClassifier(hidden_layer_sizes=(13,13,13),max_iter=500, random_state=42)\n",
    "\n",
    "ann_model.fit(X_train, y_train.ravel())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this returns array of predicted results\n",
    "prediction_from_trained_data = ann_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of our ANN model is : 0.7505\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "accuracy = metrics.accuracy_score(y_train, prediction_from_trained_data)\n",
    "\n",
    "print (\"Accuracy of our ANN model is : {0:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6797\n"
     ]
    }
   ],
   "source": [
    "ann_predict_test = ann_model.predict(X_test)\n",
    "\n",
    "#get accuracy\n",
    "ann_accuracy_testdata = metrics.accuracy_score(y_test, ann_predict_test)\n",
    "\n",
    "#print accuracy\n",
    "print (\"Accuracy: {0:.4f}\".format(ann_accuracy_testdata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix for Artificial Neural Network\n[[ 36  44]\n [ 30 121]]\n\nClassification Report\n\n             precision    recall  f1-score   support\n\n          1       0.55      0.45      0.49        80\n          0       0.73      0.80      0.77       151\n\navg / total       0.67      0.68      0.67       231\n\n"
     ]
    }
   ],
   "source": [
    "import plot as plot\n",
    "\n",
    "print (\"Confusion Matrix for Artificial Neural Network\")\n",
    "\n",
    "# labels for set 1=True to upper left and 0 = False to lower right\n",
    "print (\"{0}\".format(metrics.confusion_matrix(y_test, ann_predict_test, labels=[1, 0])))\n",
    "\n",
    "print (\"\")\n",
    "\n",
    "print (\"Classification Report\\n\")\n",
    "\n",
    "# labels for set 1=True to upper left and 0 = False to lower right\n",
    "print (\"{0}\".format(metrics.classification_report(y_test, ann_predict_test, labels=[ 1,0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n            max_features=None, max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n            splitter='best')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# create Gaussian Naive Bayes model object and train it with the data\n",
    "dt_model = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "dt_model.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this returns array of predicted results\n",
    "prediction_from_trained_data = dt_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of our DT model is : 1.0000\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "accuracy = metrics.accuracy_score(y_train, prediction_from_trained_data)\n",
    "\n",
    "print (\"Accuracy of our DT model is : {0:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7143\n"
     ]
    }
   ],
   "source": [
    "dt_predict_test = dt_model.predict(X_test)\n",
    "\n",
    "#get accuracy\n",
    "dt_accuracy_testdata = metrics.accuracy_score(y_test, dt_predict_test)\n",
    "\n",
    "#print accuracy\n",
    "print (\"Accuracy: {0:.4f}\".format(dt_accuracy_testdata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix for DT\n[[ 52  28]\n [ 38 113]]\n\nClassification Report\n\n             precision    recall  f1-score   support\n\n          1       0.58      0.65      0.61        80\n          0       0.80      0.75      0.77       151\n\navg / total       0.72      0.71      0.72       231\n\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "module 'plot' has no attribute 'show'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-132-a8370420eb42>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# labels for set 1=True to upper left and 0 = False to lower right\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"{0}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt_predict_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'plot' has no attribute 'show'"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "import plot as plot\n",
    "\n",
    "print (\"Confusion Matrix for DT\")\n",
    "\n",
    "# labels for set 1=True to upper left and 0 = False to lower right\n",
    "print (\"{0}\".format(metrics.confusion_matrix(y_test, dt_predict_test, labels=[1, 0])))\n",
    "\n",
    "print (\"\")\n",
    "\n",
    "print (\"Classification Report\\n\")\n",
    "\n",
    "# labels for set 1=True to upper left and 0 = False to lower right\n",
    "print (\"{0}\".format(metrics.classification_report(y_test, dt_predict_test, labels=[ 1,0])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# fpr, tpr\n",
    "naive_bayes = np.array([0.28, 0.52])\n",
    "logistic = np.array([0.54, 0.26])\n",
    "random_forest = np.array([0.43, 0.37])\n",
    "ann = np.array([0.36, 0.44])\n",
    "svm = np.array([0.46, 0.34])\n",
    "dt = np.array([0.52, 0.28])\n",
    "\n",
    "#plotting\n",
    "plt.scatter(naive_bayes[0], naive_bayes[1], label = 'Naive Bayes', facecolors='yellow', edgecolors='yellow', s=100)\n",
    "plt.scatter(logistic[0], logistic[1], label = 'Logistic Regression', facecolors='orange', edgecolors='orange', s=100)\n",
    "plt.scatter(random_forest[0], random_forest[1], label = 'Random Forest', facecolors='blue', edgecolors='black', s=100)\n",
    "plt.scatter(ann[0], ann[1], label = 'Artificial Neural Network', facecolors='gray', edgecolors='black', s=100)\n",
    "plt.scatter(svm[0], svm[1], label = 'Support Vector Machine', facecolors='red', edgecolors='red', s=100)\n",
    "plt.scatter(ann[0], dt[1], label = 'Dicision Tree Classifier', facecolors='green', edgecolors='yellow', s=100)\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic (ROC)')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}