1. Iris Dataset.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries\n",
    "Includes\n",
    "* scipy\n",
    "* numpy\n",
    "* matplotlib\n",
    "* pandas\n",
    "* sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "import numpy\n",
    "import matplotlib\n",
    "import pandas\n",
    "\n",
    "import sklearn\n",
    "from sklearn import model_selection # for splitting into train and test\n",
    "from sklearn import linear_model # for logistic model\n",
    "from sklearn import discriminant_analysis # for LinearDiscriminantAnalysis model\n",
    "from sklearn import tree # for decision tree\n",
    "from sklearn import naive_bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pandas.read_csv('Data/iris.data.txt', names=['SepalLength','SepalWidth','PetalLength','PetalWidth','Class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarize the Dataset\n",
    "\n",
    "In this step we are going to take a look at the data a few different ways:\n",
    "\n",
    "1. Dimensions of the dataset.\n",
    "2. Look at the data itself.\n",
    "3. Statistical summary of all attributes.\n",
    "4. Breakdown of the data by the class variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 5)\n"
     ]
    }
   ],
   "source": [
    "# 1. Dimensions of Dataset\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLength  SepalWidth  PetalLength  PetalWidth        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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Look at the data itself.\n",
    "display(dataset.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SepalLength  SepalWidth  PetalLength  PetalWidth\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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Statistical Summary\n",
    "display(dataset.describe()) # it will identify numeric data automatically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "# 4. Class Distribution\n",
    "print(dataset.groupby('Class').size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization\n",
    "\n",
    "We are going to look at two types of plots:\n",
    "\n",
    "1. Univariate plots to better understand each attribute.\n",
    "2. Multivariate plots to better understand the relationships between attributes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Univariate Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box and whisker plots\n",
    "dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograms\n",
    "dataset.hist()\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate Plots\n",
    "\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": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: pandas.scatter_matrix is deprecated, use pandas.plotting.scatter_matrix instead\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot matrix\n",
    "pandas.scatter_matrix(dataset)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Some Algorithms\n",
    "\n",
    "Here is what we are going to cover in this step:\n",
    "\n",
    "1. Separate out a validation dataset.\n",
    "2. Build 5 different models to predict species from flower measurements\n",
    "3. Select the best model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Create a Validation Dataset\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": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split-out validation dataset\n",
    "array = dataset.values\n",
    "X = array[:,0:4]\n",
    "Y = array[:,4]\n",
    "validation_size = 0.20\n",
    "seed = 100\n",
    "X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Build Models\n",
    "\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",
    "1. Logistic Regression (LR)\n",
    "2. Linear Discriminant Analysis (LDA)\n",
    "3. K-Nearest Neighbors (KNN).\n",
    "4. Classification and Regression Trees (CART).\n",
    "5. Gaussian Naive Bayes (NB).\n",
    "6. Support Vector Machines (SVM).\n",
    "\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Algorithms\n",
    "model_LR=sklearn.linear_model.LogisticRegression()\n",
    "model_LDA=sklearn.discriminant_analysis.LinearDiscriminantAnalysis()\n",
    "model_KNN= sklearn.neighbors.KNeighborsClassifier()\n",
    "model_CART=sklearn.tree.DecisionTreeClassifier()\n",
    "model_NB=sklearn.naive_bayes.GaussianNB()\n",
    "model_SVM=sklearn.svm.SVC()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_LR.fit(X_train,Y_train)\n",
    "model_LDA.fit(X_train,Y_train)\n",
    "model_KNN.fit(X_train,Y_train)\n",
    "model_CART.fit(X_train,Y_train)\n",
    "model_NB.fit(X_train,Y_train)\n",
    "model_SVM.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for Train dataset\n",
    "trainResult_LR=model_LR.predict(X_train)\n",
    "trainResult_LDA=model_LDA.predict(X_train)\n",
    "trainResult_KNN=model_KNN.predict(X_train)\n",
    "trainResult_CART=model_CART.predict(X_train)\n",
    "trainResult_NB=model_NB.predict(X_train)\n",
    "trainResult_SVM=model_SVM.predict(X_train)\n",
    "\n",
    "\n",
    "# for test dataset\n",
    "testResult_LR=model_LR.predict(X_test)\n",
    "testResult_LDA=model_LDA.predict(X_test)\n",
    "testResult_KNN=model_KNN.predict(X_test)\n",
    "testResult_CART=model_CART.predict(X_test)\n",
    "testResult_NB=model_NB.predict(X_test)\n",
    "testResult_SVM=model_SVM.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combine result of all models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Actual</th>\n",
       "      <th>LR</th>\n",
       "      <th>LDA</th>\n",
       "      <th>KNN</th>\n",
       "      <th>CART</th>\n",
       "      <th>NB</th>\n",
       "      <th>SVM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Actual               LR              LDA              KNN  \\\n",
       "0   Iris-virginica   Iris-virginica   Iris-virginica   Iris-virginica   \n",
       "1  Iris-versicolor  Iris-versicolor  Iris-versicolor  Iris-versicolor   \n",
       "2      Iris-setosa      Iris-setosa      Iris-setosa      Iris-setosa   \n",
       "3   Iris-virginica   Iris-virginica   Iris-virginica   Iris-virginica   \n",
       "4  Iris-versicolor  Iris-versicolor  Iris-versicolor  Iris-versicolor   \n",
       "5      Iris-setosa      Iris-setosa      Iris-setosa      Iris-setosa   \n",
       "6      Iris-setosa      Iris-setosa      Iris-setosa      Iris-setosa   \n",
       "7      Iris-setosa      Iris-setosa      Iris-setosa      Iris-setosa   \n",
       "8      Iris-setosa      Iris-setosa      Iris-setosa      Iris-setosa   \n",
       "9   Iris-virginica   Iris-virginica   Iris-virginica   Iris-virginica   \n",
       "\n",
       "              CART               NB              SVM  \n",
       "0   Iris-virginica   Iris-virginica   Iris-virginica  \n",
       "1  Iris-versicolor  Iris-versicolor  Iris-versicolor  \n",
       "2      Iris-setosa      Iris-setosa      Iris-setosa  \n",
       "3   Iris-virginica   Iris-virginica   Iris-virginica  \n",
       "4  Iris-versicolor  Iris-versicolor  Iris-versicolor  \n",
       "5      Iris-setosa      Iris-setosa      Iris-setosa  \n",
       "6      Iris-setosa      Iris-setosa      Iris-setosa  \n",
       "7      Iris-setosa      Iris-setosa      Iris-setosa  \n",
       "8      Iris-setosa      Iris-setosa      Iris-setosa  \n",
       "9   Iris-virginica   Iris-virginica   Iris-virginica  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainResult=pandas.DataFrame([Y_train,trainResult_LR,trainResult_LDA,trainResult_KNN,trainResult_CART,trainResult_NB,trainResult_SVM]).T\n",
    "trainResult.columns=['Actual','LR','LDA','KNN','CART','NB','SVM']\n",
    "display(result.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Evaluation\n",
    "1. Accuracy score\n",
    "2. Confusion Matrix\n",
    "3. Classification Report\n",
    "\n",
    "\n",
    "#### Logistic Regression Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.975\n",
      "[[43  0  0]\n",
      " [ 0 35  3]\n",
      " [ 0  0 39]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        43\n",
      "Iris-versicolor       1.00      0.92      0.96        38\n",
      " Iris-virginica       0.93      1.00      0.96        39\n",
      "\n",
      "    avg / total       0.98      0.97      0.97       120\n",
      "\n",
      "--------------------------------------------------------------\n",
      "0.8\n",
      "[[ 7  0  0]\n",
      " [ 0  7  5]\n",
      " [ 0  1 10]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         7\n",
      "Iris-versicolor       0.88      0.58      0.70        12\n",
      " Iris-virginica       0.67      0.91      0.77        11\n",
      "\n",
      "    avg / total       0.83      0.80      0.80        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train\n",
    "\n",
    "print(sklearn.metrics.accuracy_score(Y_train, trainResult_LR))\n",
    "print(sklearn.metrics.confusion_matrix(Y_train, trainResult_LR))\n",
    "print(sklearn.metrics.classification_report(Y_train, trainResult_LR))\n",
    "print('--------------------------------------------------------------')\n",
    "\n",
    "# Test\n",
    "print(sklearn.metrics.accuracy_score(Y_test, testResult_LR))\n",
    "print(sklearn.metrics.confusion_matrix(Y_test, testResult_LR))\n",
    "print(sklearn.metrics.classification_report(Y_test, testResult_LR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Discriminant Analysis (LDA) Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9833333333333333\n",
      "[[43  0  0]\n",
      " [ 0 37  1]\n",
      " [ 0  1 38]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        43\n",
      "Iris-versicolor       0.97      0.97      0.97        38\n",
      " Iris-virginica       0.97      0.97      0.97        39\n",
      "\n",
      "    avg / total       0.98      0.98      0.98       120\n",
      "\n",
      "--------------------------------------------------------------\n",
      "0.9666666666666667\n",
      "[[ 7  0  0]\n",
      " [ 0 11  1]\n",
      " [ 0  0 11]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         7\n",
      "Iris-versicolor       1.00      0.92      0.96        12\n",
      " Iris-virginica       0.92      1.00      0.96        11\n",
      "\n",
      "    avg / total       0.97      0.97      0.97        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train\n",
    "\n",
    "print(sklearn.metrics.accuracy_score(Y_train, trainResult_LDA))\n",
    "print(sklearn.metrics.confusion_matrix(Y_train, trainResult_LDA))\n",
    "print(sklearn.metrics.classification_report(Y_train, trainResult_LDA))\n",
    "print('--------------------------------------------------------------')\n",
    "\n",
    "# Test\n",
    "print(sklearn.metrics.accuracy_score(Y_test, testResult_LDA))\n",
    "print(sklearn.metrics.confusion_matrix(Y_test, testResult_LDA))\n",
    "print(sklearn.metrics.classification_report(Y_test, testResult_LDA))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K-Nearest Neighbors (KNN) Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9833333333333333\n",
      "[[43  0  0]\n",
      " [ 0 36  2]\n",
      " [ 0  0 39]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        43\n",
      "Iris-versicolor       1.00      0.95      0.97        38\n",
      " Iris-virginica       0.95      1.00      0.97        39\n",
      "\n",
      "    avg / total       0.98      0.98      0.98       120\n",
      "\n",
      "--------------------------------------------------------------\n",
      "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": [
    "# Train\n",
    "\n",
    "print(sklearn.metrics.accuracy_score(Y_train, trainResult_KNN))\n",
    "print(sklearn.metrics.confusion_matrix(Y_train, trainResult_KNN))\n",
    "print(sklearn.metrics.classification_report(Y_train, trainResult_KNN))\n",
    "print('--------------------------------------------------------------')\n",
    "\n",
    "# Test\n",
    "print(sklearn.metrics.accuracy_score(Y_test, testResult_KNN))\n",
    "print(sklearn.metrics.confusion_matrix(Y_test, testResult_KNN))\n",
    "print(sklearn.metrics.classification_report(Y_test, testResult_KNN))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classification and Regression Trees (CART) Evaluation\n",
    "\n",
    "Decision tree has overfitted the training dataset. 100% accuracy in train dataset and low accuracy in test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "[[43  0  0]\n",
      " [ 0 38  0]\n",
      " [ 0  0 39]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        43\n",
      "Iris-versicolor       1.00      1.00      1.00        38\n",
      " Iris-virginica       1.00      1.00      1.00        39\n",
      "\n",
      "    avg / total       1.00      1.00      1.00       120\n",
      "\n",
      "--------------------------------------------------------------\n",
      "0.8666666666666667\n",
      "[[ 7  0  0]\n",
      " [ 0 11  1]\n",
      " [ 0  3  8]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         7\n",
      "Iris-versicolor       0.79      0.92      0.85        12\n",
      " Iris-virginica       0.89      0.73      0.80        11\n",
      "\n",
      "    avg / total       0.87      0.87      0.87        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train\n",
    "\n",
    "print(sklearn.metrics.accuracy_score(Y_train, trainResult_CART))\n",
    "print(sklearn.metrics.confusion_matrix(Y_train, trainResult_CART))\n",
    "print(sklearn.metrics.classification_report(Y_train, trainResult_CART))\n",
    "print('--------------------------------------------------------------')\n",
    "\n",
    "# Test\n",
    "print(sklearn.metrics.accuracy_score(Y_test, testResult_CART))\n",
    "print(sklearn.metrics.confusion_matrix(Y_test, testResult_CART))\n",
    "print(sklearn.metrics.classification_report(Y_test, testResult_CART))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gaussian Naive Bayes (NB) Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9833333333333333\n",
      "[[43  0  0]\n",
      " [ 0 38  0]\n",
      " [ 0  2 37]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        43\n",
      "Iris-versicolor       0.95      1.00      0.97        38\n",
      " Iris-virginica       1.00      0.95      0.97        39\n",
      "\n",
      "    avg / total       0.98      0.98      0.98       120\n",
      "\n",
      "--------------------------------------------------------------\n",
      "0.8333333333333334\n",
      "[[7 0 0]\n",
      " [0 9 3]\n",
      " [0 2 9]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         7\n",
      "Iris-versicolor       0.82      0.75      0.78        12\n",
      " Iris-virginica       0.75      0.82      0.78        11\n",
      "\n",
      "    avg / total       0.84      0.83      0.83        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train\n",
    "\n",
    "print(sklearn.metrics.accuracy_score(Y_train, trainResult_NB))\n",
    "print(sklearn.metrics.confusion_matrix(Y_train, trainResult_NB))\n",
    "print(sklearn.metrics.classification_report(Y_train, trainResult_NB))\n",
    "print('--------------------------------------------------------------')\n",
    "\n",
    "# Test\n",
    "print(sklearn.metrics.accuracy_score(Y_test, testResult_NB))\n",
    "print(sklearn.metrics.confusion_matrix(Y_test, testResult_NB))\n",
    "print(sklearn.metrics.classification_report(Y_test, testResult_NB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector Machines (SVM) Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9916666666666667\n",
      "[[43  0  0]\n",
      " [ 0 37  1]\n",
      " [ 0  0 39]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        43\n",
      "Iris-versicolor       1.00      0.97      0.99        38\n",
      " Iris-virginica       0.97      1.00      0.99        39\n",
      "\n",
      "    avg / total       0.99      0.99      0.99       120\n",
      "\n",
      "--------------------------------------------------------------\n",
      "0.9333333333333333\n",
      "[[ 7  0  0]\n",
      " [ 0 10  2]\n",
      " [ 0  0 11]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         7\n",
      "Iris-versicolor       1.00      0.83      0.91        12\n",
      " Iris-virginica       0.85      1.00      0.92        11\n",
      "\n",
      "    avg / total       0.94      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train\n",
    "\n",
    "print(sklearn.metrics.accuracy_score(Y_train, trainResult_SVM))\n",
    "print(sklearn.metrics.confusion_matrix(Y_train, trainResult_SVM))\n",
    "print(sklearn.metrics.classification_report(Y_train, trainResult_SVM))\n",
    "print('--------------------------------------------------------------')\n",
    "\n",
    "# Test\n",
    "print(sklearn.metrics.accuracy_score(Y_test, testResult_SVM))\n",
    "print(sklearn.metrics.confusion_matrix(Y_test, testResult_SVM))\n",
    "print(sklearn.metrics.classification_report(Y_test, testResult_SVM))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result\n",
    "\n",
    "Accuracy for train and test\n",
    "    * LR   - 0.97 and 0.80\n",
    "    * LDA  - 0.98 and 0.96\n",
    "    * KNN  - 0.98 and 0.90\n",
    "    * CART - 1.00 and 0.86\n",
    "    * NB   - 0.98 and 0.83\n",
    "    * SVM  - 0.99 and 0.93\n",
    "     \n",
    "\n",
    "CART seems overfitted to training dataset.\n",
    "Also LR, KNN, NB has high accuracy for Train dataset and low accuracy for test dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation (with 10 folds)\n",
    "\n",
    "\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.\n",
    "\n",
    "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": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: [3 4 5 6 7 8 9], test: [0 1 2]\n",
      "train: [0 1 2 6 7 8 9], test: [3 4 5]\n",
      "train: [0 1 2 3 4 5 8 9], test: [6 7]\n",
      "train: [0 1 2 3 4 5 6 7], test: [8 9]\n"
     ]
    }
   ],
   "source": [
    "# Toy Example to see how cross validation works\n",
    "X_data=[1,2,3,4,5,6,7,8,9,0]\n",
    "Y_data=[10,20,30,40,50,60,70,80,90,00]\n",
    "kfold = sklearn.model_selection.KFold(n_splits=4, random_state=seed)\n",
    "for train, test in kfold.split(X_data,Y_data):\n",
    "    print('train: %s, test: %s' % (train, test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "kfold = sklearn.model_selection.KFold(n_splits=10, random_state=seed)\n",
    "cvTestResults_LR = sklearn.model_selection.cross_val_predict(model_LR, X_train,Y_train, cv=kfold)\n",
    "print(sklearn.metrics.accuracy_score(Y_train, cvTestResults_LR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666666\n"
     ]
    }
   ],
   "source": [
    "cv_results = sklearn.model_selection.cross_val_score(model_LR, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "print(cv_results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR   - 0.966667 0.833333\n",
      "LDA  - 0.975000 0.966667\n",
      "KNN  - 0.983333 0.900000\n",
      "CART - 0.975000 0.800000\n",
      "NB   - 0.975000 0.800000\n",
      "SVM  - 0.991667 0.900000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:442: UserWarning: The priors do not sum to 1. Renormalizing\n",
      "  UserWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:442: UserWarning: The priors do not sum to 1. Renormalizing\n",
      "  UserWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:442: UserWarning: The priors do not sum to 1. Renormalizing\n",
      "  UserWarning)\n"
     ]
    }
   ],
   "source": [
    "# evaluate each model in turn\n",
    "# cross validation\n",
    "models = [model_LR,model_LDA, model_KNN,model_CART,model_NB,model_SVM]\n",
    "names=['LR  ','LDA ','KNN ','CART','NB  ','SVM ']\n",
    "for name,model in zip(names,models):\n",
    "    kfold = sklearn.model_selection.KFold(n_splits=10, random_state=seed)\n",
    "    cv_results_train = sklearn.model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "    cv_results_test = sklearn.model_selection.cross_val_score(model, X_test, Y_test, cv=kfold, scoring='accuracy')\n",
    "    msg = \"%s - %f %f\" % (name,cv_results_train.mean(), cv_results_test.mean())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear discriminant analysis works well on this dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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