iris_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EE393 Term Project\n",
    "#### Data classification and pattern recognition using data set IRIS\n",
    "Ercument Burak Tokman - S013090"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Import Libraries\n",
    "First, let’s import all of the modules, functions and objects we are going to use in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import scipy\n",
    "import numpy\n",
    "import pandas\n",
    "import sklearn\n",
    "from pandas.plotting import scatter_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 - Load The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to use the iris flowers dataset.The dataset contains 150 observations of iris flowers. There are four columns of measurements of the flowers in centimeters. The fifth column is the species of the flower observed. All observed flowers belong to one of three species.\n",
    "\n",
    "Importing dataset from iris.data.csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n",
    "dataset = pandas.read_csv('iris.data.csv', names=names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Examine the Dataset\n",
    "In this step we are going to take a look at the data a few different ways:\n",
    "\n",
    "Dimensions of the dataset.<br>\n",
    "Peek at the data itself.<br>\n",
    "Statistical summary of all attributes.<br>\n",
    "Breakdown of the data by the class variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 - Dimensions of Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 5)\n"
     ]
    }
   ],
   "source": [
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 - Peek at the Data\n",
    "Lets take a look at the first 20 rows of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    sepal-length  sepal-width  petal-length  petal-width        class\n",
      "0            5.1          3.5           1.4          0.2  Iris-setosa\n",
      "1            4.9          3.0           1.4          0.2  Iris-setosa\n",
      "2            4.7          3.2           1.3          0.2  Iris-setosa\n",
      "3            4.6          3.1           1.5          0.2  Iris-setosa\n",
      "4            5.0          3.6           1.4          0.2  Iris-setosa\n",
      "5            5.4          3.9           1.7          0.4  Iris-setosa\n",
      "6            4.6          3.4           1.4          0.3  Iris-setosa\n",
      "7            5.0          3.4           1.5          0.2  Iris-setosa\n",
      "8            4.4          2.9           1.4          0.2  Iris-setosa\n",
      "9            4.9          3.1           1.5          0.1  Iris-setosa\n",
      "10           5.4          3.7           1.5          0.2  Iris-setosa\n",
      "11           4.8          3.4           1.6          0.2  Iris-setosa\n",
      "12           4.8          3.0           1.4          0.1  Iris-setosa\n",
      "13           4.3          3.0           1.1          0.1  Iris-setosa\n",
      "14           5.8          4.0           1.2          0.2  Iris-setosa\n",
      "15           5.7          4.4           1.5          0.4  Iris-setosa\n",
      "16           5.4          3.9           1.3          0.4  Iris-setosa\n",
      "17           5.1          3.5           1.4          0.3  Iris-setosa\n",
      "18           5.7          3.8           1.7          0.3  Iris-setosa\n",
      "19           5.1          3.8           1.5          0.3  Iris-setosa\n"
     ]
    }
   ],
   "source": [
    "print(dataset.head(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 - Statistical Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can take a look at a summary of each attribute.\n",
    "\n",
    "This includes the count, mean, the min and max values as well as some percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       sepal-length  sepal-width  petal-length  petal-width\n",
      "count    150.000000   150.000000    150.000000   150.000000\n",
      "mean       5.843333     3.054000      3.758667     1.198667\n",
      "std        0.828066     0.433594      1.764420     0.763161\n",
      "min        4.300000     2.000000      1.000000     0.100000\n",
      "25%        5.100000     2.800000      1.600000     0.300000\n",
      "50%        5.800000     3.000000      4.350000     1.300000\n",
      "75%        6.400000     3.300000      5.100000     1.800000\n",
      "max        7.900000     4.400000      6.900000     2.500000\n"
     ]
    }
   ],
   "source": [
    "print(dataset.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 - Class Distribution\n",
    "Let’s now take a look at the number of instances (rows) that belong to each class. We can view this as an absolute count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class\n",
      "Iris-setosa        50\n",
      "Iris-versicolor    50\n",
      "Iris-virginica     50\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset.groupby('class').size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Data Visualization\n",
    "We now have a basic idea about the data. We need to extend that with some visualizations.\n",
    "\n",
    "We are going to look at two types of plots:\n",
    "\n",
    "Univariate plots to better understand each attribute.<br>\n",
    "Multivariate plots to better understand the relationships between attributes.\n",
    "## 3.1 Univariate Plots\n",
    "We start with some univariate plots, that is, plots of each individual variable.\n",
    "\n",
    "Given that the input variables are numeric, we can create box and whisker plots of each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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XR8SZkl4K7JVhTWZmNo2KwZ08/fk44DyAiHiG8vMNzcwsB2mGSl4NjADfknSPpCskzZu4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\nCsbBbWZWMP8PH1Vh9f9RexQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1167360f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create a histogram of each input variable to get an idea of the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1168585f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like perhaps two of the input variables have a Gaussian distribution. This is useful to note as we can use algorithms that can exploit this assumption.\n",
    "## 3.2 Multivariate Plots\n",
    "Now we can look at the interactions between the variables.\n",
    "\n",
    "First, let’s look at scatterplots of all pairs of attributes. This can be helpful to spot structured relationships between input variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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awbu3t5Q9VQolg5JhoukGsWwJ3TRRFaUsXXB6LMmrlyP01Ht555YmDvTVs7U1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HV4gqDbzvu2dsw5erm1L0xPvQ+/y0ZeM3hlIIpuSNrrZj9MbKrlxFbl6lLQTaQEm6KQ0Q1Wt/gY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\nMs3j+0YBysHg9c76thrWt1XOw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11676ea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(dataset)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Evaluating ML Algorithms\n",
    "Now it is time to create some models of the data and estimate their accuracy on unseen data.\n",
    "\n",
    "Here is what we are going to cover in this step:\n",
    "\n",
    "Separate out a validation dataset.<br>\n",
    "Set-up the test harness to use 10-fold cross validation.<br>\n",
    "Build 5 different models to predict species from flower measurements<br>\n",
    "Select the best model.\n",
    "## 4.1 - Create a Validation Dataset\n",
    "We need to know that the model we created is any good.\n",
    "\n",
    "Later, we will use statistical methods to estimate the accuracy of the models that we create on unseen data. We also want a more concrete estimate of the accuracy of the best model on unseen data by evaluating it on actual unseen data.\n",
    "\n",
    "That is, we are going to hold back some data that the algorithms will not get to see and we will use this data to get a second and independent idea of how accurate the best model might actually be.\n",
    "\n",
    "We will split the loaded dataset into two, 80% of which we will use to train our models and 20% that we will hold back as a validation dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "array = dataset.values\n",
    "X = array[:,0:4]\n",
    "Y = array[:,4]\n",
    "validation_size = 0.20\n",
    "seed = 7\n",
    "X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have training data in the X_train and Y_train for preparing models and a X_validation and Y_validation sets that we can use later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 - Test Harness\n",
    "We will use 10-fold cross validation to estimate accuracy.\n",
    "\n",
    "This will split our dataset into 10 parts, train on 9 and test on 1 and repeat for all combinations of train-test splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "seed = 7\n",
    "scoring = 'accuracy'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are using the metric of ‘accuracy‘ to evaluate models. This is a ratio of the number of correctly predicted instances in divided by the total number of instances in the dataset multiplied by 100 to give a percentage (e.g. 95% accurate). We will be using the scoring variable when we run build and evaluate each model next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - Build Models\n",
    "We don’t know which algorithms would be good on this problem or what configurations to use. We get an idea from the plots that some of the classes are partially linearly separable in some dimensions, so we are expecting generally good results.\n",
    "\n",
    "Let’s evaluate 6 different algorithms:\n",
    "\n",
    "Logistic Regression <b>(LR)</b><br>\n",
    "Linear Discriminant Analysis <b>(LDA)</b><br>\n",
    "K-Nearest Neighbors <b>(KNN)</b><br>\n",
    "Classification and Regression Trees <b>(CART)</b><br>\n",
    "Gaussian Naive Bayes <b>(NB)</b><br>\n",
    "Support Vector Machines <b>(SVM)</b>\n",
    "\n",
    "This is a good mixture of simple linear (LR and LDA), nonlinear (KNN, CART, NB and SVM) algorithms. We reset the random number seed before each run to ensure that the evaluation of each algorithm is performed using exactly the same data splits. It ensures the results are directly comparable.\n",
    "\n",
    "## 5.1 - Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression : 0.966667 (0.040825)\n"
     ]
    }
   ],
   "source": [
    "kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "cv_result = model_selection.cross_val_score(LogisticRegression(), X_train, Y_train, cv=kfold, scoring=scoring)\n",
    "\n",
    "print('Logistic Regression : %f (%f)' % (cv_result.mean(), cv_result.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 - Show with Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ywy9JjTH8ktQYwy9JjTH8ktQYwy9JjTH8ktQYwy9JjTH8ktQYwy9JjTH8ktQYwy9JjTH8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a30aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('LogisticRegression')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(cv_result)\n",
    "ax.set_xticklabels('LR')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 - Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Discriminant Analysis : 0.975000 (0.038188)\n"
     ]
    }
   ],
   "source": [
    "kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "cv_result = model_selection.cross_val_score(LinearDiscriminantAnalysis(), X_train, Y_train, cv=kfold, scoring=scoring)\n",
    "\n",
    "print('Linear Discriminant Analysis : %f (%f)' % (cv_result.mean(), cv_result.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 - Show with Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozB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\naozBL0mNMfglqTH/D9KcPlmxQmYWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119d7d358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('LinearDiscriminantAnalysis')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(cv_result)\n",
    "ax.set_xticklabels('LDA')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.5 - K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier : 0.983333 (0.033333)\n"
     ]
    }
   ],
   "source": [
    "kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "cv_result = model_selection.cross_val_score(KNeighborsClassifier(), X_train, Y_train, cv=kfold, scoring=scoring)\n",
    "\n",
    "print('KNeighborsClassifier : %f (%f)' % (cv_result.mean(), cv_result.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.5 - Show with Graph\n",
    "We can also create a plot of the model evaluation result. There is a population of accuracy measure for algorithm because algorithm was evaluated 10 times (10 fold cross validation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1KkkaimHd3D0fWJxkI7AWuBF4NMkbgH+uqhtm20GSM5NMJJmYnJwcUluSpKn2H2DOncDh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\nl6TGGPyS1Jh/BYBsUWJad+q0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b22198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('KNeighborsClassifier')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(cv_result)\n",
    "ax.set_xticklabels('KNC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.6 - Classification and Regression Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier : 0.975000 (0.038188)\n"
     ]
    }
   ],
   "source": [
    "kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "cv_result = model_selection.cross_val_score(DecisionTreeClassifier(), X_train, Y_train, cv=kfold, scoring=scoring)\n",
    "\n",
    "print('DecisionTreeClassifier : %f (%f)' % (cv_result.mean(), cv_result.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.6 - Show with Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AHbs+XUnSrhrmjH81sLGqNlXVVuAq4KRp64wBNwBU1Z3AyiSHASRZDvw4cOnIZi1J2mnD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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1167e6278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('DecisionTreeClassifier')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(cv_result)\n",
    "ax.set_xticklabels('CART')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.7 - Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB : 0.975000 (0.053359)\n"
     ]
    }
   ],
   "source": [
    "kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "cv_result = model_selection.cross_val_score(GaussianNB(), X_train, Y_train, cv=kfold, scoring=scoring)\n",
    "\n",
    "print('GaussianNB : %f (%f)' % (cv_result.mean(), cv_result.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.7 - Show with Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119babb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('GaussianNB')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(cv_result)\n",
    "ax.set_xticklabels('GNB')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.8 - Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC : 0.991667 (0.025000)\n"
     ]
    }
   ],
   "source": [
    "kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "cv_result = model_selection.cross_val_score(SVC(), X_train, Y_train, cv=kfold, scoring=scoring)\n",
    "\n",
    "print('SVC : %f (%f)' % (cv_result.mean(), cv_result.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.8 - Show with Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ad6ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('SVC')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(cv_result)\n",
    "ax.set_xticklabels('SVC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 - Comparing the Algorithms\n",
    "We now have 6 models and accuracy estimations for each. We need to compare the models to each other and select the most accurate.\n",
    "We can see that it looks like KNN has the largest estimated accuracy score.\n",
    "\n",
    "We can also create a plot of the model evaluation results and compare the spread and the mean accuracy of each model. There is a population of accuracy measures for each algorithm because each algorithm was evaluated 10 times (10 fold cross validation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "\tkfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "\tcv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n",
    "\tresults.append(cv_results)\n",
    "\tnames.append(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TtJHqHkwfbXdnDoP6ZcFvpQoCqF6EBuu00W7g34E5B6j/FDBL1R1291NunfIF\n4D2Hr3stNRNYUe4N9lWqKb1uvyvpVqo7B1xn+6F2dPBQ9fKcdE8Z/QZwjKQZA9q5PrL9BPAKqtvt\n7AC+LOmdVNOzf1zOgTROFwH8mOooYgbVTUCfGrBO1yQQene57SnAW4DPSxrR7g612Muo/gChevF5\np6T7qD6N/hJJE9vVsYP4JdUh93RJlzZW2v4psIzqtuw9+TuqMDnmsPWwbzZSvYjsR9KLqd75X1+e\nkxnsH9I32X4pMAWYUz7oOVQd9DmxvRv4NvDqgexUf9jea/tfbH8YmAu8xfYDwL1U50DeQhUQjb5M\ndbTUlukiSCA0zfYqoAuY1e6+tIqkt1B9oHB5uaLlWNtjbI+3PR74GwbpUYLtp4A/pJpq6OlI4RNU\nn35/xocvbT9GdSR0oCOMgbYaGK7qRo4ASHoJ1ZHOgu7nw/YpwCmS9rtRme17gYXABwey063U23NS\nTry/CvhhT/WDhaTTG95ETQO6L3hYDlwObLG9tYfVVwIfo7orRFskEH7luZK21n7e30ObvwLePxQu\nfePA43lf92WnwJ8Cv297B9UL/8qGbXyVQRoIsO9F5GzgQ5Le2FD3KNV4hh9g9UVUd5xsu3JTyPOB\nPyiXnW6kCuPX8MznZCVlLrrBYuDVqm4zP1T19Jx0n0O4g+rWOZ8Z8F71zbHAUlWXqd9GdXXUglL3\nFaqjuR6PAGw/bvtvy/fOtEU+qRwREUCOECIiokggREQEkECIiIgigRAREUACISIiigRCREQACYSI\niCgSCBERAcD/B7mecWcqWKKFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1167f8a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the box and whisker plots are squashed at the top of the range, with many samples achieving 100% accuracy.    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7 - Making Predictions\n",
    "The KNN algorithm was the most accurate model that we tested. Now we want to get an idea of the accuracy of the model on our validation set.\n",
    "\n",
    "This will give us an independent final check on the accuracy of the best model. It is valuable to keep a validation set just in case you made a slip during training, such as overfitting to the training set or a data leak. Both will result in an overly optimistic result.\n",
    "\n",
    "We can run the KNN model directly on the validation set and summarize the results as a final accuracy score, a confusion matrix and a classification report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9\n",
      "[[ 7  0  0]\n",
      " [ 0 11  1]\n",
      " [ 0  2  9]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         7\n",
      "Iris-versicolor       0.85      0.92      0.88        12\n",
      " Iris-virginica       0.90      0.82      0.86        11\n",
      "\n",
      "    avg / total       0.90      0.90      0.90        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, Y_train)\n",
    "predictions = knn.predict(X_validation)\n",
    "print(accuracy_score(Y_validation, predictions))\n",
    "print(confusion_matrix(Y_validation, predictions))\n",
    "print(classification_report(Y_validation, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the accuracy is 0.9 or 90%. The confusion matrix provides an indication of the three errors made. Finally, the classification report provides a breakdown of each class by precision, recall, f1-score and support showing excellent results (granted the validation dataset was small)."
   ]
  }
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