Iris species  Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iris Species Classification Problem :\n",
    "\n",
    "### In this problem we have to classify the species of the iris flower . This dataset contains 150 datapoints and 4 features on the the basis of which we have to classify the species. The first 4 colunms of the dataset is sepal length,sepal width,petal length and petal width . \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rahul\\Anaconda3\\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",
      "C:\\Users\\Rahul\\Anaconda3\\lib\\site-packages\\sklearn\\grid_search.py:42: 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. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# importing Necsesarry libraries\n",
    "\n",
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the dataset \n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
       "        [4.9, 3. , 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.3, 0.2],\n",
       "        [4.6, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.6, 1.4, 0.2],\n",
       "        [5.4, 3.9, 1.7, 0.4],\n",
       "        [4.6, 3.4, 1.4, 0.3],\n",
       "        [5. , 3.4, 1.5, 0.2],\n",
       "        [4.4, 2.9, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.1],\n",
       "        [5.4, 3.7, 1.5, 0.2],\n",
       "        [4.8, 3.4, 1.6, 0.2],\n",
       "        [4.8, 3. , 1.4, 0.1],\n",
       "        [4.3, 3. , 1.1, 0.1],\n",
       "        [5.8, 4. , 1.2, 0.2],\n",
       "        [5.7, 4.4, 1.5, 0.4],\n",
       "        [5.4, 3.9, 1.3, 0.4],\n",
       "        [5.1, 3.5, 1.4, 0.3],\n",
       "        [5.7, 3.8, 1.7, 0.3],\n",
       "        [5.1, 3.8, 1.5, 0.3],\n",
       "        [5.4, 3.4, 1.7, 0.2],\n",
       "        [5.1, 3.7, 1.5, 0.4],\n",
       "        [4.6, 3.6, 1. , 0.2],\n",
       "        [5.1, 3.3, 1.7, 0.5],\n",
       "        [4.8, 3.4, 1.9, 0.2],\n",
       "        [5. , 3. , 1.6, 0.2],\n",
       "        [5. , 3.4, 1.6, 0.4],\n",
       "        [5.2, 3.5, 1.5, 0.2],\n",
       "        [5.2, 3.4, 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.6, 0.2],\n",
       "        [4.8, 3.1, 1.6, 0.2],\n",
       "        [5.4, 3.4, 1.5, 0.4],\n",
       "        [5.2, 4.1, 1.5, 0.1],\n",
       "        [5.5, 4.2, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.1],\n",
       "        [5. , 3.2, 1.2, 0.2],\n",
       "        [5.5, 3.5, 1.3, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.1],\n",
       "        [4.4, 3. , 1.3, 0.2],\n",
       "        [5.1, 3.4, 1.5, 0.2],\n",
       "        [5. , 3.5, 1.3, 0.3],\n",
       "        [4.5, 2.3, 1.3, 0.3],\n",
       "        [4.4, 3.2, 1.3, 0.2],\n",
       "        [5. , 3.5, 1.6, 0.6],\n",
       "        [5.1, 3.8, 1.9, 0.4],\n",
       "        [4.8, 3. , 1.4, 0.3],\n",
       "        [5.1, 3.8, 1.6, 0.2],\n",
       "        [4.6, 3.2, 1.4, 0.2],\n",
       "        [5.3, 3.7, 1.5, 0.2],\n",
       "        [5. , 3.3, 1.4, 0.2],\n",
       "        [7. , 3.2, 4.7, 1.4],\n",
       "        [6.4, 3.2, 4.5, 1.5],\n",
       "        [6.9, 3.1, 4.9, 1.5],\n",
       "        [5.5, 2.3, 4. , 1.3],\n",
       "        [6.5, 2.8, 4.6, 1.5],\n",
       "        [5.7, 2.8, 4.5, 1.3],\n",
       "        [6.3, 3.3, 4.7, 1.6],\n",
       "        [4.9, 2.4, 3.3, 1. ],\n",
       "        [6.6, 2.9, 4.6, 1.3],\n",
       "        [5.2, 2.7, 3.9, 1.4],\n",
       "        [5. , 2. , 3.5, 1. ],\n",
       "        [5.9, 3. , 4.2, 1.5],\n",
       "        [6. , 2.2, 4. , 1. ],\n",
       "        [6.1, 2.9, 4.7, 1.4],\n",
       "        [5.6, 2.9, 3.6, 1.3],\n",
       "        [6.7, 3.1, 4.4, 1.4],\n",
       "        [5.6, 3. , 4.5, 1.5],\n",
       "        [5.8, 2.7, 4.1, 1. ],\n",
       "        [6.2, 2.2, 4.5, 1.5],\n",
       "        [5.6, 2.5, 3.9, 1.1],\n",
       "        [5.9, 3.2, 4.8, 1.8],\n",
       "        [6.1, 2.8, 4. , 1.3],\n",
       "        [6.3, 2.5, 4.9, 1.5],\n",
       "        [6.1, 2.8, 4.7, 1.2],\n",
       "        [6.4, 2.9, 4.3, 1.3],\n",
       "        [6.6, 3. , 4.4, 1.4],\n",
       "        [6.8, 2.8, 4.8, 1.4],\n",
       "        [6.7, 3. , 5. , 1.7],\n",
       "        [6. , 2.9, 4.5, 1.5],\n",
       "        [5.7, 2.6, 3.5, 1. ],\n",
       "        [5.5, 2.4, 3.8, 1.1],\n",
       "        [5.5, 2.4, 3.7, 1. ],\n",
       "        [5.8, 2.7, 3.9, 1.2],\n",
       "        [6. , 2.7, 5.1, 1.6],\n",
       "        [5.4, 3. , 4.5, 1.5],\n",
       "        [6. , 3.4, 4.5, 1.6],\n",
       "        [6.7, 3.1, 4.7, 1.5],\n",
       "        [6.3, 2.3, 4.4, 1.3],\n",
       "        [5.6, 3. , 4.1, 1.3],\n",
       "        [5.5, 2.5, 4. , 1.3],\n",
       "        [5.5, 2.6, 4.4, 1.2],\n",
       "        [6.1, 3. , 4.6, 1.4],\n",
       "        [5.8, 2.6, 4. , 1.2],\n",
       "        [5. , 2.3, 3.3, 1. ],\n",
       "        [5.6, 2.7, 4.2, 1.3],\n",
       "        [5.7, 3. , 4.2, 1.2],\n",
       "        [5.7, 2.9, 4.2, 1.3],\n",
       "        [6.2, 2.9, 4.3, 1.3],\n",
       "        [5.1, 2.5, 3. , 1.1],\n",
       "        [5.7, 2.8, 4.1, 1.3],\n",
       "        [6.3, 3.3, 6. , 2.5],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [7.1, 3. , 5.9, 2.1],\n",
       "        [6.3, 2.9, 5.6, 1.8],\n",
       "        [6.5, 3. , 5.8, 2.2],\n",
       "        [7.6, 3. , 6.6, 2.1],\n",
       "        [4.9, 2.5, 4.5, 1.7],\n",
       "        [7.3, 2.9, 6.3, 1.8],\n",
       "        [6.7, 2.5, 5.8, 1.8],\n",
       "        [7.2, 3.6, 6.1, 2.5],\n",
       "        [6.5, 3.2, 5.1, 2. ],\n",
       "        [6.4, 2.7, 5.3, 1.9],\n",
       "        [6.8, 3. , 5.5, 2.1],\n",
       "        [5.7, 2.5, 5. , 2. ],\n",
       "        [5.8, 2.8, 5.1, 2.4],\n",
       "        [6.4, 3.2, 5.3, 2.3],\n",
       "        [6.5, 3. , 5.5, 1.8],\n",
       "        [7.7, 3.8, 6.7, 2.2],\n",
       "        [7.7, 2.6, 6.9, 2.3],\n",
       "        [6. , 2.2, 5. , 1.5],\n",
       "        [6.9, 3.2, 5.7, 2.3],\n",
       "        [5.6, 2.8, 4.9, 2. ],\n",
       "        [7.7, 2.8, 6.7, 2. ],\n",
       "        [6.3, 2.7, 4.9, 1.8],\n",
       "        [6.7, 3.3, 5.7, 2.1],\n",
       "        [7.2, 3.2, 6. , 1.8],\n",
       "        [6.2, 2.8, 4.8, 1.8],\n",
       "        [6.1, 3. , 4.9, 1.8],\n",
       "        [6.4, 2.8, 5.6, 2.1],\n",
       "        [7.2, 3. , 5.8, 1.6],\n",
       "        [7.4, 2.8, 6.1, 1.9],\n",
       "        [7.9, 3.8, 6.4, 2. ],\n",
       "        [6.4, 2.8, 5.6, 2.2],\n",
       "        [6.3, 2.8, 5.1, 1.5],\n",
       "        [6.1, 2.6, 5.6, 1.4],\n",
       "        [7.7, 3. , 6.1, 2.3],\n",
       "        [6.3, 3.4, 5.6, 2.4],\n",
       "        [6.4, 3.1, 5.5, 1.8],\n",
       "        [6. , 3. , 4.8, 1.8],\n",
       "        [6.9, 3.1, 5.4, 2.1],\n",
       "        [6.7, 3.1, 5.6, 2.4],\n",
       "        [6.9, 3.1, 5.1, 2.3],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [6.8, 3.2, 5.9, 2.3],\n",
       "        [6.7, 3.3, 5.7, 2.5],\n",
       "        [6.7, 3. , 5.2, 2.3],\n",
       "        [6.3, 2.5, 5. , 1.9],\n",
       "        [6.5, 3. , 5.2, 2. ],\n",
       "        [6.2, 3.4, 5.4, 2.3],\n",
       "        [5.9, 3. , 5.1, 1.8]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " 'DESCR': 'Iris Plants Database\\n====================\\n\\nNotes\\n-----\\nData Set Characteristics:\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML iris datasets.\\nhttp://archive.ics.uci.edu/ml/datasets/Iris\\n\\nThe famous Iris database, first used by Sir R.A Fisher\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\nReferences\\n----------\\n   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...\\n',\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)']}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data in testing and training set\n",
    "\n",
    "x_iris , y_iris = iris.data , iris.target\n",
    "x , y = x_iris[: , :2] , y_iris\n",
    "x_train , x_test , y_train , y_test = train_test_split(x,y,test_size = 0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Sepal width')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the dataset\n",
    "\n",
    "colors = ['red', 'greenyellow', 'blue']\n",
    "for i in range(len(colors)):\n",
    "        xs = x_train[:, 0][y_train == i]\n",
    "        ys = x_train[:, 1][y_train == i]\n",
    "        plt.scatter(xs, ys, c=colors[i])\n",
    "        \n",
    "plt.legend(iris.target_names)\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is a very important step as our data have varying values so we have to scale it before feeding it into our machine learning model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform( x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fiting the data into loigstic regression classifer . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='newton-cg', tol=0.1,\n",
       "          verbose=0, warm_start=True)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model  = LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
    "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
    "          penalty='l2', random_state=None, solver='newton-cg', tol=0.1,\n",
    "          verbose=0, warm_start=True)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PREDICTING THE REULTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_on_training_exaples = model.predict(x_train)\n",
    "prediction_on_testing_exaples = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 0, 0, 2, 1, 2, 0, 0, 0, 0, 2, 1, 2, 0, 0, 1, 2, 1, 1, 0, 2,\n",
       "       0, 0, 0, 2, 2, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1,\n",
       "       2, 1, 1, 2, 1, 2, 0, 1, 2, 1, 1, 0, 2, 0, 0, 2, 2, 2, 2, 2, 1, 0,\n",
       "       1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 0, 0, 0, 1, 2, 0, 0, 0, 1, 0, 0, 2,\n",
       "       1, 1, 1, 1, 1, 1, 2, 1, 0, 0, 0, 0, 1, 2, 2, 1, 2])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_on_training_exaples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 0, 0, 1, 0, 0, 2, 1, 1, 0, 2, 1, 2, 2, 2, 0, 0, 2, 2,\n",
       "       0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 2, 0, 1, 2, 2, 0, 2, 2, 1, 1, 0, 1,\n",
       "       2])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_on_testing_exaples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EVALUATING THE MODELS PERFORMENCE :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1) CONFUSION MATRIX :\n",
    "\n",
    "THIS IS A MATRIX WHICH EVALUATES THE NUMBER OF CORRECT PREDICTIONS MADE BY THE MODEL .\n",
    "\n",
    "                           PREDICTION:+VE               PREDICTION:-VE\n",
    "          TEST CLASS:+VE     TRUE +VE                       FALSE +VE\n",
    "          TEST CLASS:-VE     FALSE -VE                      TRUE -VE\n",
    " ONLY THE DIAGONAL ELEMENTS THAT IS TRUE+VE AND TRUE-VE ARE THE CORRECTLY CLADDIFIED DATAPOINTS AND ALL OTHERS ARE MISSCLASSIFIED .\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_for_train_set = confusion_matrix(y_train,prediction_on_training_exaples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### IN THIS CASE THE DIAGONAL ELEMENTS THAT IS 32+22+29 THAT IS EQUAL TO 83 DATAPOINTS ARE CORRECTLY CLASSIFIED AND THE REST ARE MISS CLASSIFIED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[33,  0,  0],\n",
       "       [ 1, 28,  9],\n",
       "       [ 0, 10, 24]], dtype=int64)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_for_train_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1d2ba533630>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization of confusion matrix\n",
    "plt.matshow(cm_for_train_set)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_for_test_set = confusion_matrix(y_test,prediction_on_testing_exaples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IN THIS CASE THE DIAGONAL ELEMENTS THAT IS 17+10+11 THAT IS EQUAL TO 38 DATAPOINTS ARE CORRECTLY CLASSIFIED AND THE REST ARE MISS CLASSIFIED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  1,  0],\n",
       "       [ 0,  8,  4],\n",
       "       [ 0,  7,  9]], dtype=int64)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_for_test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1d2ba5339b0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD3CAYAAAAwh5neAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADqlJREFUeJzt3X+s3fVdx/Hni7ZYO6wbdpitrbYYRpzLlKVjU6IycK7iXP1DE2ZYUJc0GkFmZrYui+FfosvmEvmnGcjMCAwZKjHEjv1yWTI6SkW20rFVnHChG3TMjajQ9t6Xf5xTc6X3nvO5537OOd/zua9H8g333J7zOZ9v6Kuf74/P9/OWbSKiTedMuwMRMT4JeETDEvCIhiXgEQ1LwCMaloBHNCwBj+ggSbdKekbS117y++slPSbpiKQ/H9ZOAh7RTbcBuxf/QtJbgD3A623/DPChYY0k4BEdZPuLwHMv+fUfAjfZfrH/nmeGtZOAR8yO1wC/KOmgpH+W9MZhH1g/gU5FNO9tb3mZv/vcfNF7H3rkxSPAC4t+td/2/oKPrgdeAbwZeCNwl6QLPWC+eQIeUcGJ5+Y5eGBb0Xs3vOrfXrC9a4SvmQPu6Qf6K5IWgC3As8t9IIfoEVWYeS8Ubavw98AVAJJeA5wLnBj0gYzgERUYWKDek5mS7gAuB7ZImgNuBG4Fbu3fOjsJXDvo8BwS8IgqjDnlsnPwovbsdy7zR9espJ0EPKKSmiN4LU2cg0va3Z/dc0zSvmn3Z5yWm+HUIknbJX1e0tH+zK0bpt2n5RiYx0XbJM18wCWtA24Gfg14LfBOSa+dbq/G6jZeMsOpYaeB99r+aXq3hv6oy/9vF3DRNkkzH3DgUuCY7cdtnwTupDedr0nLzHBqku3jtg/3f34eOApsnW6vlmZg3i7aJqmFgG8Fnlz0eo6O/iWI0UnaAVwCHJxuT5a3ULhNUgsX2bTE77p3tSNGJuk84FPAe2z/YNr9WYqncH5dooWAzwHbF73eBjw9pb5EZZI20Av37bbvmXZ/lmPDqe7lu4mAPwhcJGkn8BRwNfA70+1S1CBJwC3AUdsfnnZ/BhPzSx5MTtfMn4PbPg1cBxygdxHmLttHptur8enPcPoycLGkOUnvnnafxugy4F3AFZIe7m9XTbtTSzGw4LJtkloYwbF9H3DftPsxCQNmODXH9pdY+hpLJ3VxBG8i4BHT1pvokoBHNGvBCXhEkzKCRzTMiFNeN+1unGXmr6KfIWnvtPswSWtpf2dhX8+M4CXbJDUTcKDzfwkqW0v7OwP7KuZ9TtE2STlEj6igt6JL98bLsQR8y/nrvGP7hnE0vayf2LqeXT+7cSqTBb/56OaJf+fGc87jRzdcMPH99enTk/5KNrKJzTp/4vv6Av/FSb9YfEy9Zi6y7di+ga8c2D78jY246pJfnXYXJmb+O0PX2m/GQX+2+L22Jn74XaJ7PYqYUQuoaCsxaOUeSX8qyZK2DGsnAY+owIiTXl+0FbqNJVbukbQdeCvwREkjCXhEBWcuspVsRe0tv3LPR4D3UbjmQa6iR1QyP+apqpLeATxl+197T9IOl4BHVGDEfPkB8RZJhxa9HlqbTNIm4IPAiq7oJuARlSyUX0U/MUJtsp8CdgJnRu9twGFJl9r+9nIfSsAjKuhNVR3fJS3bXwUuOPNa0reAXbYH1ibLRbaICs48bFKylai1ck9G8IgKbKpOdBm2co/tHSXtJOARVZRPYpmkBDyigl5lk+6d8SbgEZWM8yLbqBLwiAqMsiZbRMsygkc0qqtrsiXgERX0KptkBI9o1ppZ0SVirbGVETyiZbkPHtGo3oIPOUSPaFQ3F11MwCMqMOQ2WUSrMpMtonFdrGxS1CNJuyU9JumYpH3j7lTErOk9D66ibZKGjuCS1gE301uLeQ54UNK9th8dd+ciZsmsHqJfChyz/TiApDuBPUACHtHXOwfv3iF6ScC3Ak8uej0HvGk83YmYXV2cqlryT85SvT6rqoKkvZIOSTr07HfnV9+ziBlixOmFdUVbiaVqk0n6C0lfl/SIpL+T9PJh7ZQEfA5YXCp0G/D0S99ke7/tXbZ3vfLHunc/MGLcahYfZOnaZPcDr7P9euAbwAeGNVIS8AeBiyTtlHQucDVwb2kvI9aC2lfRl6pNZvvTts8UaH+A3mA70NBzcNunJV0HHADWAbfaPlLUy4g1ZAUX2VZcumgJvw98ctibiia62L4PuG+FHYhYM1Y4k22U0kX/R9IHgdPA7cPem5lsEZVM4mkySdcCbweutD20hHACHlFBb8mmsZcP3g28H/hl2/9d8pkEPKIGq/gWWIl+bbLL6Z2vzwE30rtq/kPA/f0Kow/Y/oNB7STgERXUXvBhmdpkt6y0nQQ8opJZnYseEUNM4hx8FAl4RCUJeESjsqJLRMsMp2f0cdGIGCLn4BGNS8AjGpVz8IjGOQGPaFdKF0U0ys45eETDxPxCbpNFNCvn4BGNyn3wiJa5dx7eNQl4RCW5ih7RKJNz8IiGZSZbRNMWFroX8O7duIuYQXbvEL1kK7FMbbLzJd0v6Zv9/75iWDtjGcG/8cgm3vbqnxtH05107CMXTrsLE/PqL+6cdhcmZuFzD6zs/XUP0W8D/gr4m0W/2wd81vZNkvb1X79/UCMZwSMqscu2srbOrk0G7AE+3v/548BvDmsn5+ARlazgKvqotcl+3Pbx3nf5uKQLhn0gAY+owJSfX7PK2mQrkUP0iEpcuK3CdyS9CqD/32eGfSABj6jB4AUVbatwL3Bt/+drgX8Y9oEcokdUUnMm2zK1yW4C7pL0buAJ4LeHtZOAR1RS82GTZWqTAVy5knYS8IgKMhc9omUGEvCIduV58IiWJeARrVr1LbCxSMAjanAuskW0LYfoES3LCB7RrozgEQ1LwCMa1X/YpGsS8IhaMoJHNCy3ySLapYzgEY2qsFzLOCTgEVUoh+gRTcsIHtGwhWl34GwJeEQNHV3wYeiqqkvVSIqIs8llW1Fb0p9IOiLpa5LukLRxlD6VLJt8G7B7lMYj1pRKC6NL2gr8MbDL9uuAdcDVo3RpaMCXqZEUEeO1HvhhSeuBTcDTozRSrfCBpL2SDkk6dIoXazUbMTNWcIi+5UxW+tvexe3Yfgr4EL21z48D37f96VH6VO0iW7942n6AzTq/gzcMIsasUm2yft3vPcBO4D+Bv5V0je1PrLRLKV0UUYPp3SYr2Yb7FeDfbT9r+xRwD/ALo3Qrt8kiKqk4F/0J4M2SNgH/Q6+ayaHBH1layW2yO4AvAxdLmuvXRYqIl6p0Fd32QeBu4DDwVXo5LakffpahI/iAGkkRsVjd2mQ30is4uCo5RI+oYCWTWCYpAY+opYNTVRPwiFoygke0S3maLKJROQePaFwCHtGwBDyiXV08RM9c9IiGZQSPqKWDI3gCHlGDc5ssom0ZwSPaJLp5kS0Bj6glAY9oVGayRTQuAY9oV66iR7QsI3hEozpaHzxTVSMqqVyb7OWS7pb0dUlHJf38KH3KCB5RS90R/KPAP9n+LUnn0itftGIJeEQltW6TSdoM/BLwuwC2TwInR2krh+gRtZSviz6wNhlwIfAs8NeS/kXSxyS9bJQuZQSv4Nzvr51/J+d+Y6SBZCadOlw+JK9w2eSBtcno5fINwPW2D0r6KLAP+LPib+hbO38zI8atUmUTYA6Y61c4gV6VkzeM0qUEPKKSWlfRbX8beFLSxf1fXQk8OkqfcogeUUvdq+jXA7f3r6A/DvzeKI0k4BG11K1N9jAw6Dy9SAIeUUOeJotoXAIe0a48TRbRsByiR7Sqo0+TJeARtSTgEW3KqqoRrUvAI9oldy/hCXhEDSldFNG47g3gCXhELbnIFtGyBDyiUXnYJKJxCXhEmzLRJaJxWuhewhPwiBrysElE27o40SWrqkbUUm/ZZAAkresXPvjHUbuUETyikjFcZLsBOApsHrWBjOARNRiwy7YCkrYBvw58bDXdGhpwSdslfb5fwvSIpBtW84URrdJC2cbw2mQAfwm8D1jVmX3JIfpp4L22D0v6EeAhSffbHqnSQkSLVngffGBtMklvB56x/ZCky1fTr6EjuO3jtg/3f36e3jnB1tV8aURzSg/Pyw7RLwPeIelbwJ3AFZI+MUq3VnQOLmkHcAlwcPA7I9aeirXJPmB7m+0dwNXA52xfM0qfiq+iSzoP+BTwHts/WOLP9wJ7ATayaZS+RMy2WZ3oImkDvXDfbvuepd5jez+wH2Czzu/grkaM1zjmotv+AvCFUT8/NOCSBNwCHLX94VG/KKJpBjo4F73kHPwy4F30TvQf7m9XjblfETNnBbfJJmboCG77S/TuAkTEIFlVNaJdeR48olV5XDSiXb2ZbN1LeAIeUUsHnwdPwCMqyQge0Sq7k/fBE/CISnIVPaJlOUSPaFSqi0Y0LiN4RMO6l+8EPKKW3CaLaJWB+QQ8oknCGcEjmtbBgKfwQUQtlVZVrVmLICN4RA2m5sMm1WoRJOARldQ6B7d9HDje//l5SWdqESTgEVMzhnPw1dYiSMAjarBhofgYfYukQ4te7+8vO/7/DKtFUCIBj6il/Bx8YG0yKKtFUCIBj6ik1jl4zVoEuU0WUUvd4oNVahFkBI+ooWJlk5q1CMYS8Of53onP+O7/GEfbA2wBTkz4O3tuvHsa3zq9/Z28ae3rT5a/tXh0nqixBNz2K8fR7iCSDg27cNGStbS/M7OvayXgEWuOgfnuLemSgEdUYXACPk5nTRRo3Fra39nY1xyij89SM4Fatpb2dyb2taP1wZsJeMTUZQSPaFgCHtEoG+bnp92LsyTgEbVkBI9oWAIe0apUF41ol8GZ6BLRsIzgEQ3LOXhEo3KbLKJtLl90cWIS8Igq1tCCDxFrTkcfNsmiixG1eKFsKyBpt6THJB2TtG/ULmUEj6jAgCuN4JLWATcDbwXmgAcl3TtKbbKM4BE12DVH8EuBY7Yft30SuBPYM0q3MoJHVOJ6t8m2Ak8uej0HvGmUhhLwiAqe53sHPuO7txS+feOQ2mRLrYk+0vF/Ah5Rge3dFZubA7Yver0NeHqUhnIOHtE9DwIXSdop6VzgauDeURrKCB7RMbZPS7oOOACsA261fWSUtuQOzr6JiDpyiB7RsAQ8omEJeETDEvCIhiXgEQ1LwCMaloBHNCwBj2jY/wIVefsotpVvCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of confusion matrix\n",
    "plt.matshow(cm_for_test_set)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) MODEL'S SCORE :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set is =  80.95238095238095 %\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on training set is = ',model.score(x_train,y_train)*100 , '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set is =  73.33333333333333 %\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on test set is = ',model.score(x_test,y_test)*100 , '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### LET' S CHECK WHAT OUR MODEL PREDICT ON A NEW DATAPOINT :\n",
    "            SEPAL LENGTH : 7.9\n",
    "            SEPAL WIDTH : 2.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_datapoint = [[7.9,2.6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_prediction = model.predict(new_datapoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### THIS MEANS THAT FOR SEPAL LENGTH : 7.9 & SEPAL WIDTH : 2.6 OUR MODEL PREDICT THE SPECIE IS VIRGINICA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VISUALIZING THE RESULTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ON TRAINING SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "x_set,y_set = x_train,y_train\n",
    "x1,x2 = np.meshgrid(np.arange(start=x_set[:,0].min()-1,stop=x_set[:,0].max()+1,step=0.01),np.arange(start=x_set[:,1].min()-1,stop=x_set[:,1].max()+1,step=0.01))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XucFOWV8PHfmQuDXAQUmIEoTFBhUTRjJOKFOKP4EkFR0I3RkGzU8JJs4rqbxFwUZY2RmCVszBpiXCREkxBZXyNxUVlRdAZR1KBOvARBl4AgDHgDBWSu5/2jqqFn6OrpS3VVdff5fj7zcaa6q+qUQJ+p5zl1HlFVjDHGmJKwAzDGGBMNlhCMMcYAlhCMMca4LCEYY4wBLCEYY4xxWUIwxhgDWEIwARORTSJyrg/HuVNEbsxgv2EiskdESrONIepE5LMisj7sOEz+EHsOwQRJRDYBM1T18UI8nzH5zO4QjMmAiJRF+XjGZMISggmNiFSIyM9FZJv79XMRqYh7/Xsist19bYaIqIgc6752t4jc4n4/UEQeEpFdIvK+iDwlIiUi8jtgGLDMHSb6nohUu8cpc/c9QkR+457jAxH5k0esV4jI0yJym4i8D9zkbr9KRNa5+z4qIsPj9pkoIutFZLeI3CEiDSIyI5PjieM2EdnpHu9lERnjvjZZRP4qIh+JyNsicq27vU5EtsbFM1pE6t3/T6+JyIVxr90tIr8UkYfd4zwnIsdk+2ds8oslBBOmWcBpQA3wKeBU4AYAETkP+DZwLnAsUJvkON8BtgKDgErgekBV9cvAW8AUVe2jqnMT7Ps7oBdwAjAYuC3JecYBG933zRGRqe65LnbP/RRwrxv/QOB+4DrgSGA9cEamxwMmAmcBI4H+wBeA99zXfg18TVX7AmOAJ7oGLiLlwDJghXu+fwIWi8iouLddDvwQGAC8CcxJ8v/CFCBLCCZM04GbVXWnqr6D82H0Zfe1S4HfqOprqrrPfc1LKzAEGK6qrar6lKYwOSYiQ4BJwNdV9QN334Yku2xT1V+oapuqfgx8DbhVVdepahvwY6DG/a1+MvCaqj7gvnY70JTF8VqBvsDf4cz9rVPV7XHXf7yIHO5ex4sJYj8N6AP8RFVbVPUJ4CGcJBDzgKo+7557MU6iNkXEEoIJ01Bgc9zPm91tsde2xL0W/31XP8X5jXaFiGwUkR+keP6jgfdV9YMU3981huHAf7hDMLuA9wEBPkGX+N0EtbXL/ikfz/0Anw/8EtghIgtE5HB3v0twEtBmd1jq9ASxDwW2qGpH3LbNbqwx8QlrH04CMUXEEoIJ0zacD8GYYe42gO3AUXGvHe11EFX9SFW/o6ojgCnAt0VkQuzlJOffAhwhIv1TjLfrsbbgDNX0j/s6TFWf6Rq/iEiX60n3eKjq7ap6Cs7w1kjgu+72P6vqRThDQX8C7ksQ+zbgaBGJ/zc/DHg7xWs3RcASggnTvcANIjLIHXOfDfzefe0+4Ep3IrSX+1pCInKBiBzrfuh+CLS7XwA7gBGJ9nOHXJYDd4jIABEpF5Gz0oj/TuA6ETnBjaOfiHzefe1h4EQRmepOYH8TqMr0eCLyGREZ584F7AX2A+0i0kNEpotIP1Vtjbv+rp5z9/uee511OMlzSRrXawqcJQQTpluAtcDLwCvAi+42VHU5zrj7kzjDQWvcfZoTHOc44HFgj/u+O1S13n3tVpyksytWfdPFl3HG4F8HdgL/kmrwqroU+DdgiYh8CLyKMyeBqr4LfB6YizP5e7x7rYni7/Z4wOHAXcAHOEM97wHz4q5hk7vP14EvJTh2C3Che7x3gTuAf1DV11O9XlP47ME0kxdEZDTOB2SFO+mZV9yhmq3AdFV9Mux4jEnE7hBMZInINHdIZADOb87L8ikZiMjnRKS/OM9WXI8zQfxsyGEZ48kSgomyrwHvAP+LMy7+j+GGk7bTcWJ/F2e8fqpbXmpMJNmQkTHGGMDuEIwxxrjyqqHWwPJyre7ZM+wwjDFJvLBnJCD0scfaImPPnhfeVdVB3b0vrxJCdc+erB07NuwwjDEepGEFCNTVlocdiolTXy+bu3+XDRkZY3xSvtF5dtCSQf6yhGCMyVr5xtm0bRlvySDPWUIwxmStbct4+vezZJDv8moOwRgTPdKwAoCaAm6W3adPK5ddtpUhQ/ZTEtFfozs6YPv2nixZchR79mSWnC0hGGMyFksGhT5UdNllWxkzpi8VFdU4PRSjR1U58sj3uOyyrSxc+MmMjhHRXGeMibpiSQYAQ4bsp6LiyMgmAwARoaLiSIYM2Z/xMSwhGGPSt3o1UBzJAKCkhEgngxgRyWpIyxKCMSY9jY1I+z6bRC5AlhCMMWmR3TuBwp5EjqqnnvofzjtvFBMnHsuCBT/x/fiWEIwxKSumeYOoaW9v5+abv8lddy3noYf+ysMP38ubb/7V13NYQjDGpMSSQeqWLYNzzoHRo53/LluW/TFffvl5hg07lqOPHkGPHj2YPPkyVq58MPsDx7GEYIzp1oBx9YAlg1QsWwY33gjbtoGq898bb8w+KezY8TZDhhx94OeqqqPYsePtLKPtLLSEICI9ReR5EfmLiLwmIj8MKxZjjLfyjbPZNbfFkkGKbrsN9nep/Ny/39menUPXrvG78inMB9OagXNUdY+IlAOrRWS5qtoSg8ZESNuW8ZSVWjJI1fbt6W1PVWXlUWzfvuXAz01NWxk8eGh2B+0itDsEdexxfyx3v2z5NmMiRBpWICKMHx92JPljyJD0tqfqxBM/w+bNb7B1699oaWnhkUeWcM45F2Z30C5CnUMQkVIRaQR2Ao+p6nMJ3jNTRNaKyNp3WluDD9KYIhWbRK49yzrcpONb34Ku63j17Olsz0ZZWRk33jifr371c5x//mgmTbqU4447IbuDdj2Hr0dLk6q2AzUi0h9YKiJjVPXVLu9ZACwAGNu3r91BGBOAkjUPADaJnIkpU5z/3nabM0w0ZIiTDGLbs1FbO5na2snZH8hDJFK/qu4SkXrgPODVbt5ujMmlxka0pY8lgyxMmeJPAghamFVGg9w7A0TkMOBc4PWw4jHG4LSl2L0zL/r2GP+FeYcwBLhHREpxEtN9qvpQiPEYU/RibSls3qA4hfanrqovAyeHdX5jTGf2JLKxJ5WNMZYMDGAJwZiiZ20pTIwlBGOK2IBx9eya22JrG+SJ66+/ijPOGMyUKWNycnxLCMYUsV1zWygrLbe1DfLEtGlXcNdd/5Oz41tCMKZIxeYNrC2F//ouW8yIc6oZObqEEedU03fZYl+O+5nPnEW/fkf4cqxErLbMmCJkk8i503fZYqpunEnJ/n0AlG/bTNWNMwH4aMr0MEPrlt0hmLyweMcEqtfcS0n9SqrX3MviHRPCDilvWTLIrUG3zTqQDGJK9u9j0G2zQooodXaHYCJv8Y4JzFx/Lfs6nI5hm5urmLn+WgCmV64MM7S8Y8kg98q2v5XW9iixOwQTebM2zjiQDGL2dfRk1sYZIUWU3ywZ5FbbkGFpbY8SSwgm8t5qHpzWdpNY7O7A5NY735pDR89enbZ19OzFO9+ak/Wxv/3ty7n88tP529/WU1t7FPff/+usjxnPhoxM5A2r2Mnm5qqE201qbKgoOLGJ40G3zaJs+1u0DRnGO9+a48uE8s9+dm/Wx0jGEoKJvDkjFnaaQwDoVbKfOSMWhhhV/rC1DYL30ZTpka8oSsSGjEzkTa9cyYJR8xhe0YTQwfCKJhaMmmcTyikYMK7e1jYwKbM7BJMzi3dMYNbGGbzVPJhhFTuZM2Jhxh/i0ytXWgJI1+rV7GpwnkQ2JhWWEExOWKlo+KR9H2Wl5fYkskmZDRmZnLBS0XBZWwqTCUsIJiesVDQ8VlFkMmUJweSEV0molYrmliWDwrV9+xb+4R/OZvLk0VxwwQn89rf/4fs5LCGYnJgzYiG9SvZ32malorllC90UttLSMr7//X/nkUfWsWTJsyxe/EvefPOvvp7DJpVNTsQmjv2qMjLdaGxkV0OLJYOI2NG6nE2t82nWHVRIJdXlV1NZPimrYw4ePITBg4cA0KdPX445ZjQ7drzNscce70fIgCUEk0NRLRX1sxw2Ehobkd07QcIOxICTDN5ouYUOnDvkZm3ijZZbALJOCjFbt25i3bqX+NSnxvlyvBgbMjJFJVYOu7m5CqXkQDlsPrfTlt3OvEzdWXZ3EAWbWucfSAYxHexnU+t8X46/d+8errnmEq677uf06XO4L8eMsYRgikqhlcOWb5wN2LxBlDTrjrS2p6O1tZVrrrmEKVOmM3HixVkfr6vQEoKIHC0iT4rIOhF5TUT+OaxYTPEopHLYAePqadsy3pJBxFRIZVrbU6Wq3HDDVznmmNFceeW3szqWlzDvENqA76jqaOA04Jsi4t/siDEJFEw5bGMju+a2IGITB1FTXX41JXS+Cy2hJ9XlV2d13BdffJoHH/wdzz77BFOn1jB1ag0NDY9kdcyuQptUVtXtwHb3+49EZB3wCcDfOipj4hRK51TZvdPaUkRUbOLY7yqjU04Zz+uvqx8heopElZGIVAMnA88leG0mMBNgWEVFoHGZwlMI5bDWliL6Kssn+VZRFKTQE4KI9AH+CPyLqn7Y9XVVXQAsABjbt29u06MJ3Tc2XMOCbVNop5RS2pk5dBl3jLzd13NEtRw2FfYkssmlUBOCiJTjJIPFqvpAmLGY8H1jwzX8attUYgX17ZS5P+N7UshHlgzC09HhTOpGfc5GVenoyHz/MKuMBPg1sE5VfxZWHCY6FmybwqFPV4m7vbidXd0AWDIIy/btPWlufg/V6A5SqCrNze+xfXvP7t/sIcw7hDOBLwOviEiju+16VfV32tzkjXZK09peNBobqW9otmQQoiVLjuKyy7YyZMg7lET06a2ODidxLVlyVMbHCLPKaDX2sL2JU0o77Qn+SpbSHkI00RF7EtmEZ8+echYu/GTYYeRcRHOdKUYzhy4Dut6Sq7u9ONm8gQlS6FVGxsTEJo5zXWWULywZmKBZQjBZO7dxLit3jT3w84T+a3m85nsZHeuOkbf7lgDyuavpgHH10GDJIFPDT15OzeT59Bqwg30fVNL4yNVsfin5cwGZ7FNobMjIZOVgMpADXyt3jeXcxrmhxpXvXU2tLUXmhp+8nHGX3kLvI5oQUXof0cS4S29h+MnLfd2nEFlCMFk5mAziSac7hjDkc1dTaVgBArVn2Q18Jmomz6esR+f202U99lMz2bv9dCb7FCJLCKYg5WtX0wPzBra2QcZ6DUjcZtpre6b7FCJLCKYg5WNX05I1zsP6Nm+QnX0fJG4z7bU9030KkSUEk5UJ/deSqFTU2R6eOSMW0quk8xBAlLuaSsMKtKWPJQMfND5yNW0tnYcL21p60viId/vpTPYpRJYQTFYer/leXFJwvrKpMvLL9MqVLBg1j+EVTQgdDK9oYsGoedGsMmp0HtS3ZOCPzS9N4rn7bmDv+1WoCnvfr+K5+25IWjGUyT6FyGatTNauHPIob3589IHyziuHPNrtPkGUhOZFV9PGxgNrGxj/bH5pUtof5pnsU2gsIZisxMo7YxU9sfJOwPPDOJN9ClWsLYWtbWCiwIaMTFYyKe/M55JQP9mTyCZqLCGYrGRS3pmvJaF+smRgosgSgslKJuWd+VgS6qcB4+oBSwYmeiwhmKxkUt6ZbyWhfhowrp5dc1ssGZhIskllk5VMFq0vhIXuM7VrbotVFJnIkigvCdfV2L59de3YcHvkFKt87hwaFcU2b2DdQ6Ojvl5eUNVuPzztDsF0y8pEs1dsbSli3UNjDeNi3UMBSwoRZnMIpltWJpqd8o2zi64thXUPzU+WEEy3rEw0O21bxhdVMgDrHpqvLCGYbhV7mWg2pGFFUS50Y91D85MlBNOtYi4TzUZsErkYF7qx7qH5KdS/qSKyCLgA2KmqY8KMxXgr5jLRTBVbRVFXsYljqzLKL6GWnYrIWcAe4LepJAQrO40mr5JUP0tV86nstdiTgYmevCg7VdVVIlIdZgwmO14lqU/vPoF7mib5UqqaT2WvZ1c3QIMlA5OfbA7BZMWrJHXBtim+larmS9nrgHH11N/TTP9+lgxMfop8QhCRmSKyVkTWvtPaGnY4pguv0tN2StN6fybniFrZa6wtRU1N2JEYk5nIJwRVXaCqY1V17KBy+80rarxKT0tpT+v9mZwjSmWvsXkDW+jG5LNuE4KIVIjIF0XkehGZHfsKIjgTfV4lqTOHLvOtVDXqZa82iWwKRSp3CA8CFwFtwN64r6yJyL3AGmCUiGwVka/6cVwTHK/F7O8Yebtvi9x7nSMKE8q2toEpJKlUGR2lqufl4uSqenkujltsMinJ/MaGa1iwbQrtlFJKOzOHLuOOkbf7eo5MFrn3iiuTY/lqxw7YuBGam6GiAkaMgMrKpGsbeHX7tC6gJqpSSQjPiMiJqvpKzqMxacukJPMbG67hV9umAk5LhXbK3J9JmBSSnQPwrSQ03bgCs2MHrF8PHR3Oz83NsH49X1z3g1ioh/Dq9jmwupFjTn3IuoCaSPJ8ME1EXgEUJ2kcB2wEmnH+CaiqnhRUkDH2YNqhqtfcy+bmqkO2D69oYtPpiW/Ayuofoz3B7wKltNFW93/SOgeQ9vm9pBtXYNascZJAF5sYxpV1mxPuctGs8+l9RNMh2zvaSygp7Thk+973q3hwzsPZx2pMAn48mHaBj/GYHMmkJNOrJDTdUtFk58ikJDTduAKTIBkADGOL5y5eXT2l5NBkkOz9xgTJc1JZVTer6mbgltj38duCC9Ekk0lJpldJaLqlosMqdvpaEppuXIGpqEi4eWfFMM9dvLp6akfif3LWBdREQSpVRifE/yAipcApuQnHpCuTksyZQ5fhjAbGU3d7eufwsyQ03bgCM2IElHT+p7K/pBcLR8zx3MWr2+cbay62LqAmsjyHjETkOuB64DAR+TC2GWgBFgQQm0lBJp1IYxO0qVYZpXIOPxrPpRtXYCor4fXX2cQwhrGFnRXDWDhiDisrp3vukqzb57ubaqzKyESSZ0JQ1VuBW0XkVlW9LsCYTJoyKcm8Y+Tt4X/QJhDVuIRmpETSWttg80uTQv2gP2XarRx3+gNISQfaUcIbay7mhaX2T9l4S3aH8Gn32/8X9/0BqvpizqIykRJU2WlU+bnQTVCLz58y7VZGnnk/scXapLSDkWfeD2BJwXhKVnb6pPttT2As8BecIaOTgOdUNfCuLVZ2Go6gyk6jyO+2FF7lqH6XnV429zMJy1s72ktY8r0/+3Yekx9SLTtNVmV0tqqeDWwGPu02mDsFOBl4079QTdQlKzvNl06kmchFj6KgFp/3Km/12m4MpFZl9HfxTymr6quANfgtIkGVnUZJrnoUBbX4vFd5q9d2YyC1hLBORBaKSJ2I1IrIXcC6XAdmoiOostMoSdajKBtBLT7/xpqL6ToarOpsN8ZLKrNkVwL/CPyz+/Mq4Fc5i8hETlBlp1ERGyrKhaAWn49NHFuVkUmH56RyFNmkcnrS7Whq4Iv1V/Jjbkz5eQO/hdkJ1e9zW1fX6Mi6l5GI3Keql8Y1ueskjOZ2JnWR7RwaYTP61HMXz9ObfQBUNW/m2vUzAQJJCkGVpAZx7jCvxWQu2RxCbIjoAmBKgi8TYQu2TeHQ3szibjddnV3dwA0PDTuQDGJ6duxjxsZZgcRQM3n+gQ/QmLIe+6mZPD/vzh3mtZjMJXtSebv77QTgKVV9I5iQjB8i2zk0ourvaWYYiZvYDW5+K5AYgipJDeLcYV6LyVwqVUbVwH+KyP+KyH0i8k8iYmWnERfZzqFRsno1Z1c3cHZ1AwBvcXTCtyXrauqnoEpSgzh3mNdiMtdtQlDV2ap6DjAGWA18F3gh14GZ7ES2c2gExJLA2V9qh7paqKul7jcTuZ4fsZdend7bXVdTPwVVkhrEucO8FpO5bstOReQG4EygD/AScC3wVI7jMlmKbOfQEMXuBAAnEXSx/e7f8H+v+FVoVUZBlaQGce4wr8VkrtuyUxF5EWgDHgYagGdVdX/SnXLEyk79s3jHhIJ6dsBTYyNnT93tfF9aCp/tvgVXY1Mju653nrSuO8v/h9OCMnPIpegX17G3qpzeTa3IH0azYPt9nu+fsGMxMzbOYnDzW6GU3JrcSbXsNKXnEESkLzDe/boU2GHN7fJX1+6l4DxdvGDUvMJJCqtXO0NCkHIi6Kr+KreXUR4mhZlDLuXjb26g/bCDRQSlH7dz2C9HJkwKE3Ys5tr1M+nZcbDKan9JL+aNWmBJoQBk3dwuRkTGAF8CvgJ8AdgKPJF1hCY0szbO6JQMAPZ19GTWxhkhReSj2ETxl9qhXz9naCiDZABQt2giAPWrWv2MMBD6xXWdkgFA+2Gl6BcTd52ZsXFWp2QAwZbcmmhIpXXFv+EMFd0O/FlVffvXISLnAf8BlAILVfUnfh3beCvEDqUH5geqSTg/kKm6RRNp+PpS6lf1pv/h5dTkSX3d3qrEdzVe271Ka4MquTXRkEqV0fmqOldVn/E5GZQCvwQmAccDl4vI8X4d33grmA6ljY0Hy0ZLSw9UDPmt9s5plFU/y64PW/PmbqF3U+I4vbZ7ldYGVXJroiHMXrinAm+q6kZVbQGWABeFGE/RyPcOpQfKRqfuPpgEMhwWStX42bPzaghJ/jCa0o87P3NS+nE78ofRCd+/cMQc9peEV3JroiHMhPAJYEvcz1vdbZ2IyEwRWSsia99pjf4/xHwwvXIlC0bNY3hFE0IHwyua8mJCOf4hslzdDXQnX5LCgu33cdgvR9J7Wwt0KL23tXhOKIPTq2neqAU0VQynA6GpYrhNKBeh7BeJzVzXRjuQuIneAmABOFVGuQ4qKGGXfT69+wS2Ng9EEbY2D+Tp3ScwvXKlZ1xhxtvd8wMTntnBjD9uZPB7zew8soKFl4xg5RmZPRHrdazh/VqoqWqmV7ly0dOnc++Pf0D9wxfR//ByZl18A1UXP8THlSUctqODpgcu4OGNt2R6uQmdMu3WhK2sk5WKPlp1JTU959NLdrCz5zAaq66E7d5dSFdWTs+7BOB1LdZpNTPJ1lRO9KjrAap6YVYnFjkduElVP+f+fJ173Fu99imUstOwyz67dkJ1KBP6r2XNhyceEtdXqpZzT9OkwOPtLhGA8wF+7d3r6dlycGnI/T1KmHfFqLSTgtex5v/oeHqf04OyuPvptg747S++xLid73DSDctpP+zgi6Ufd/D+ovN9SwqnTLuVkWfej8T9canCx4+dxNdm3ZewVPTN847o1G0UnCeF//f5Czjm1IcO2f7cfTfk3Qdm146qUHjX6Jesn0MQkaT346rakOz1bk8sUgZswGme9zbwZ+CLqvqa1z6FkhCSLVofxML0ZfWP0Z7w5lBJdONWSlvC9+ck3jQfJLv3O2uoeq/5kO1NR1Zw+b+fntapvY71+0fG8HFlj0O2720RBn7QnPC1w7a38+t5jWmd38tlcz9DSemhayFLmzJj3EuHbG+qGM6Sh06g9xFNh7zW0V6S8Fh736/iwTkP+xJvUC6adX7BX6Nfsl4PIdsP/O6oapuIXA08ilN2uihZMigkYZd9ptvx1Ov9fsabadno4AQf4Mm2Z3KsjwclLtXsVa6er31c6d/0nJQc+uEGoB5/jIOb36LXgIFpHSsfu5B6xVxI1xi0VHoZHQfcilMaemDMQFVHZHtyVX0EeCTb4+SbYRU7E94hBFX2WUq7xx1Ceu/3I95UhoWS2XlkRcLf6ncembiVdSbHOuyd1oR3AftahYEftCS+Q9iR+EMpE9pRgiS6Q/BoXLuzYhj7PqhM+Nuz17HysQtpMVxj0FL5NeY3OGsotwFnA78FfpfLoApd2GWfXp1QJ/RfmzCumUOX+Ruvj88PLLxkBPt7dP5rvL9HCQsvSf/3Fa9jNb3izBnEa+uAxqYKml5x5gzilX7cwQvzz0/7/F7eWHMxXUd2VWHvE5/yLBX16jb6xpqLC6YLaTFcY9BS+TXxMFVdKSKiqpuBm0TkKeBfcxxbwUpl0fpcStYJ1aua6Mx+r2Ufb6y/UDUZ9xfqKjZx7EeVkeexKgcyfOvBKqN9rUJjUwWbd/dgc+VAzn/6XapOdIaWDnunlaZX4OcrbgJafemD9MLS6wAOrTJ69Do+GnVu4iojd2ohUaXNu5tqCqICJ1lH1UK5xqCl0u30aeCzwP04PYzeBn6iqqNyH15nhTKpnEzY5ai50GlYqF8/ONnf/g9epaJBlKMme23IFVem3Uo73Y6j+dih1EpCg+dbt1MR+QywDugP/AjoB8xV1Wf9CDQdhZ4Qwi5H9Vu28wOp8CoVXX5mFZOebsppOeq8K5zfiVI+fzfdQ9PtOJqPHUq9SkWLuSQ0CL62vwYQkcMBVdWPsg0uU4WeEMIuR/VFBusPZMOrVLStBMoSzOv6WY7a5E5cp3X+iuFcfvqmxOdZU01V8+aU90n3/VHgVSpazCWhQci67DRGRMbiTCz3dX/eDVylqraMps/CLkfNSvz8QIAtJbxKRRMUmSR9fybnSHYsz/M3v0VjIwm7pqbbcTQfO5R6lX5aSWg0pFJltAj4hqpWq2o18E2cBGF8lo9dSBOtTxwkr/LSdo+/2ZmWo3ptT/f8b/EJdn3YyurVCY6XZsfRfOxQ6lX6aSWh0ZBKQvhIVQ+soayqq4HQho0KWdjlqOmIQqM58C4VXVY7NOflqAsvGZH2+X8/sy91iybS1tFKw6q2zudJs+NoPnYo9SoVtZLQaEil7PR5EflP4F6c4vUvAPUi8mkAVX0xh/EVlbDLUVMRxERxOpKVnb52XL/clqPGHSvd89ctmkj9VSuoX3WwNDU2EZxq1VC674+CZKWiJnypJITYaGfX5w7OwEkQ5/gaUZGbXrkyUgkACHyiOF1vThrAkivHHHhG4M2mCtjt/f74zqXxzxQks/KMyrSTSXdxJUoKu77+Fg+ePggpORLtKGHXmrdgqXd5aSYdSsMuVd380qSECcDPclQrbc1MylVGUVDoVUZRE7W7gUSG92th3FH7D+lEuveJFq6+8a8pdy59bmvPbpNCIul2SE10nvqrVgBw2XfncvbnH0irq2kmySCKpap+lqNaaevn1vURAAASAUlEQVShUq0y6nYOQUQqReTXIrLc/fl4EfmqH0GaaApiWUq/1FQ1d/rQBSgrgaoT6fQhDc7PVSeS8P01VelXH4EzVJTteeoWTaRu0URqL/5Tp2QAIAK9z/lLpw9wgJ4d+5ixcVb68W6c5dux/FQzeX6nD3CAsh77qZk8P9RjFZtUhozuxqkqiv2N2QD8F/DrHMVkwhArG4WcPE2cK73KE9/henYhTdK5NBOZdEj1kqhlMyTvapquqJaq+lmOaqWtmUslIQxU1fviFrBpE/Hqs2jyTaZtp6NiX6vQu8ehH7KHvZN4ictknUszkUmHVC+JV6NI3tU0XTsrhiV8mC3sUlWvzqWZlKP6eaxik0rZ6V4RORK3PaaInEbSKTuTD6JSNpqtxqaKhJ1Im14h7c6lmcikQ6qXN94rS7uradrxRrRU1c9yVCttzVwqdwjfBv4bOMZtdDcI+PucRmVyJh8mitMRm6A9pGqo8nD2XzEqrc6lmcikQ6qXF7b3AvZx3JFtCNDRUULDH6ey5Kff8+5qmm68ES1V9bMc1UpbM5dSlZG73OUonDva9aqa+H48x6zKKEMhlY362W3Uz/Ncc88GpjRso7TDeaJ4We1Qbv/KSF/PkalEJbH3XFIP4EsrbVOc/Kwy+jzOmgivAVOB/4o9lGYibvVqZ2ho6m5noriuNtBkcO3d66l6r5kSnAZw1969ngnP+Duxl+55rrlnA1Of3EZZh/PbTVkHTH1yG9fcsyH0a4mV0PbuoU51UQ9l3FH7+cof6wCoXxXK72GmiKQyh3Cjqn4kIuOBzwH34KygZiIqYX+hgKuGvMoxZ/xxY6jnmdKw7ZCJW3G3+3WOTHmV0NZUNVO3aCLSY68lBZNTqSSEWI3D+cCvVPVBILMBV5M7Pi5L6YdMOoQGcR6vLqRe2zM5R6a8SlJj22vvnEZZ9bPUr2q1xGByIpWE8Lbby+hS4BERqUhxPxOEEIeFkknWITTM83h1IfXansk5MuVVkhq/ffzs2dQtmgjYEJLxXyof7JcCjwLnqeou4AjguzmNynSr07BQLBFE6GGyZB1CwzzPstqhdP09XN3tfp0jU14ltIlKVW0IyeSC9TLKM/lUNmpVRulLt/He6ptvpm3TafQ/vDzhojvGQA6W0PSTW7l0EzAaOFVV16ayXzEnhHxKBLmQyQey1z5+f7hn0j3VT41Njey6fieCUHtWKo8WFT7rdtpZ1BPCaKAD+E/gWksIHvK0v5Dfki1y7/VB7rXP8jOrmPR0U1rHSsar22qm3VOzEeuaWuzPK1i300P59hxCLqjqOlVdH8a580EUykajJJOyT699pjRs87WENFmpaNDqFk08UIXU2Bj46SPDup1mLvL3lyIyE5gJMKzC36qOqCn2YSEvmZR9plt2mmkJaXelokEbP3v2gSGk1avLGR9+wVngrNtp5nJ2hyAij4vIqwm+LkrnOKq6QFXHqurYQeWFeStcKI3mciWTss90y04zLSFNpVQ0aDVVNQfWbS7GKiSvrqbW7bR7OUsIqnquqo5J8PVgrs6ZbywRpCaTsk+vfZbVDvW1hDSdUtGgxT+vUExDSNbtNHORHzIqODZRnLZUFrlPZ5/XjuvnW5WRZ7fVgCeUvdQtmsjqm29m16bTimYIybqdZi6sKqNpwC9wWmnvAhpV9XPd7ZfPVUb5Mj8Qdgmll6CeAwhCWM803Hvzv/LnRydZaWoRSrXKKJS/Gaq6FFgaxrkDFVLb6Ux1LaGMddsEQk0KXUtIY91GgbxLCkFdS6I/yytuvgmA+kfPK/rSVJOY9STKhYj2F+pOlEoo4wXVbTQIYXdOvXz2DwHrg2QSs3tHH3VanzgP5weiVkIZE1S30SBEoXNq3aKJ1F+1gvpVrfZ0s+nE/ib4IF/mB7rjtWB9mCWU4L2Qvd/dRoMQ1LV092d5oALJTQw2hGTAhoyyUmhlo1EtoQyq22gQotY51Vppm3h2h5Cu+IniPBwWSiaqJZSZlJ1GVVDXks6fZfwQkt0pFDdLCCnqND+Q53cCyRy7/AMui/uw2nPJCDZH4IP3hDd2M/CDZgQY+EEzJ7yxOy8TAjhJIYjYN+/ukXIyj08KZSXF8byCOZQlhG4UyvxAKqJa3nnNPRuY+uTBtZDLOmDqk84ayN2tY2BSV7do4oE+SPWrrGtqMbI5BA+FNj+QiqiWd05pOJgMYsTdbvwV64MENq9QjCwhdFGMiSAmquWdXh1Kvbab7FlSKE6WEMCZKI4lgtiDZEWUCGKCWkw+XV4dSr22G3/Er9tcTM3xillR/5M6sBDN1N22EA3RLe9cVjuUrhX16m43uVV75zTKqp9l14fF2Uq72BTfpHKe9RcKUlTLO2MTx1MatlHa4dwZLKsdahPKARk/ezZgD7EVg+JJCLG209UU5XBQqrxKIsPuNvracf04/eX3GPxeM+8OqOC14/oFdm6/hf3/MlN1iybS8PWl1K/qbUmhQBV8QiiW5wdyKexy1LDP76d8v5baO6ex+uabqV91mvVBKkAFO4dQzNVCfgu7HDXs8/upEK5l/OzZ9P/xYBS1eYUCU3AJwRKB/8IuRw37/H4qlGux5xUKU2EkhPiy0dJSSwQ+C7scNezz+6mQrgWsNLXQ5HVCSFg2alVDvgu7HDXs8/upkK4lpvbOafT/8WB2fdjK6tVhR2OyEcqaypka27evri0rO1g2CnYnEJCwK2PCPr+fCulauqq/agVgfZCiJtU1lfMqIfQdKDp2ihRc22kTfV0XrE+lLXghf/AnY0khelJNCPk1ZNS3T9E/TWyCF1uwvncPRcRZsH7cUfsZ3q/Fc59YeWnVe82UcLC8dMIzO4ILPCR1iyZSVv0s9atsCCnf5FdCMCYEXgvW11R5VwYVQnlpNmKlqW0drTSsags7HJOiUBKCiPxURF4XkZdFZKmI9A8jDmNSkWzBei+FUl6ajVhpqj2vkD/CukN4DBijqicBG4DrQorDmG7FFqZPdTsUXnlpNux5hfwRSkJQ1RWqGruPfBY4Kow4jElFqgvWxyvE8tJsxD+vYPMK0RWFOYSrgOVeL4rITBFZKyJrWz+y3zBM8Dbv7sFzW3uyt0VQhb0twnNbeyatMlp5RiXzrhhF05EVdABNR1Yw74pRRVFl5KX2zmnULZpo8woRlrOyUxF5HKhK8NIsVX3Qfc8sYCxwsaYQSN9P9tWxN3VbOWWMiTgrTQ1W6GWnqnquqo5J8BVLBl8BLgCmp5IMjDGFw+YVoimsKqPzgO8DF6rqvjBiMMaEKz4p2BBSNIQ1hzAf6As8JiKNInJnSHEYY0JUt2iilaZGSFhVRseq6tGqWuN+fT2MOIwx0WBDSNEQhSojY4zplBSslXY4LCEYYyIj9rzCrg9b7W4hBLYgqklJsXbuNMGrvXMa4JSm1q9qtdLUANkdgulWMXfuNOGxeYXgWUIw3Sr2zp0mPJYUgmUJwXTLOneaMMX3QbLnFXLLEoLplnXuNGGL9UGy5xVyyxKC6ZZ17jRRYUNIuWUJwXTLOneaKIkfQrLnFfyVs26nuWDdTo0xMatvvpm2TadRVlLO+PFhRxNtoXc7NcaYXIpft9mGkPxhCcEYk7di6zaDzSv4wRKCMSbv1S2aSFn1szavkCVLCMaYghAbQtr1oa3bnClLCMaYghEbQrJ5hcxYQjDGFBxrpZ0ZSwjGmIJUt2iiDSGlyRKCMaZgxQ8hWR+k7llCMMYUPOuDlBpLCMaYohA/r2BDSIlZQjDGFI3YvIINISVmCcEYU1Ri8wo2hHQoSwjGmKJkLS8OFUpCEJEficjLItIoIitEZGgYcRhjips9r9BZWHcIP1XVk1S1BngImB1SHMaYIhfrg7TrQ3u6OZSEoKofxv3YG8ifRRmMMQVn/OzZNoREiHMIIjJHRLYA00lyhyAiM0VkrYisbf2oeP+gjDG5V+xJIWcJQUQeF5FXE3xdBKCqs1T1aGAxcLXXcVR1gaqOVdWx5X3LcxWuMcYAxZ0UcpYQVPVcVR2T4OvBLm/9A3BJruIwxph0xa/bXEyJIawqo+PifrwQeD2MOIwxxkvtndOK7m4hrDmEn7jDRy8DE4F/DikOY4xJqpiSQlhVRpe4w0cnqeoUVX07jDiMMSYV8UNIhcyeVDbGmBTU3jntwLrNhdoczxKCMcakKLZuc6Eu0WkJwRhj0hBrjleIQ0iWEIwxJgPxQ0iF0gfJEoIxxmQoNoRUKH2QRDV/2giJyDvA5rDjyNJA4N2wg/BZoV1ToV0P2DXli1xd03BVHdTdm/IqIRQCEVmrqmPDjsNPhXZNhXY9YNeUL8K+JhsyMsYYA1hCMMYY47KEELwFYQeQA4V2TYV2PWDXlC9CvSabQzDGGAPYHYIxxhiXJQRjjDGAJYTAichPReR1EXlZRJaKSP+wY8qWiHxeRF4TkQ4RyesyQBE5T0TWi8ibIvKDsOPJlogsEpGdIvJq2LH4RUSOFpEnRWSd+/cu79vni0hPEXleRP7iXtMPw4jDEkLwHgPGqOpJwAbgupDj8cOrwMXAqrADyYaIlAK/BCYBxwOXi8jx4UaVtbuB88IOwmdtwHdUdTRwGvDNAvhzagbOUdVPATXAeSJyWtBBWEIImKquUNU298dngaPCjMcPqrpOVdeHHYcPTgXeVNWNqtoCLAEuCjmmrKjqKuD9sOPwk6puV9UX3e8/AtYBnwg3quyoY4/7Y7n7FXjFjyWEcF0FLA87CHPAJ4AtcT9vJc8/aAqdiFQDJwPPhRtJ9kSkVEQagZ3AY6oa+DWVBX3CYiAijwNVCV6apaoPuu+ZhXPruzjI2DKVyjUVAEmwzeqyI0pE+gB/BP5FVT8MO55sqWo7UOPOKy4VkTGqGujcjyWEHFDVc5O9LiJfAS4AJmiePAjS3TUViK3A0XE/HwVsCykWk4SIlOMkg8Wq+kDY8fhJVXeJSD3O3E+gCcGGjAImIucB3wcuVNV9YcdjOvkzcJyIfFJEegCXAf8dckymCxER4NfAOlX9Wdjx+EFEBsUqDkXkMOBc4PWg47CEELz5QF/gMRFpFJE7ww4oWyIyTUS2AqcDD4vIo2HHlAl3sv9q4FGcicr7VPW1cKPKjojcC6wBRonIVhH5atgx+eBM4MvAOe6/oUYRmRx2UFkaAjwpIi/j/GLymKo+FHQQ1rrCGGMMYHcIxhhjXJYQjDHGAJYQjDHGuCwhGGOMASwhGGOMcVlCMCZFIlInIoeUAnpt9+F8U+ObtolIfb53kzXRZgnBmOiaitN11ZhAWEIwBUNEeovIw25P+VdF5Avu9lNEpEFEXhCRR0VkiLu9XkR+LiLPuO8/1d1+qrvtJfe/o9KMYZGI/Nnd/yJ3+xUi8oCI/I+IvCEic+P2+aqIbHDjuUtE5ovIGcCFwE/dB6+Ocd/+ebdv/gYR+axP/+uMAayXkSks5wHbVPV8ABHp5/a8+QVwkaq+4yaJOTidZgF6q+oZInIWsAgYg9My4CxVbRORc4EfA5ekGMMs4AlVvcptRfC82xgQnD73J+P0vl8vIr8A2oEbgU8DHwFPAH9R1WdE5L+Bh1T1fvd6AMpU9VT3ydx/xWlxYIwvLCGYQvIKME9E/g3ng/QpERmD8yH/mPuBWgpsj9vnXnDWDRCRw90P8b7APSJyHE630/I0YpgIXCgi17o/9wSGud+vVNXdACLyV2A4MBBoUNX33e3/DxiZ5PixRm4vANVpxGVMtywhmIKhqhtE5BRgMnCriKwAlgKvqerpXrsl+PlHwJOqOs3tt1+fRhgCXNJ1wSARGYdzZxDTjvPvL1HL7WRix4jtb4xvbA7BFAwRGQrsU9XfA/NwhmHWA4NE5HT3PeUickLcbrF5hvHAbvc3+H7A2+7rV6QZxqPAP7kdORGRk7t5//NArYgMEJEyOg9NfYRzt2JMIOw3DFNITsSZhO0AWoF/VNUWEfl74HYR6Yfzd/7nQKyL6Qci8gxwOAfnFebiDBl9G2dMPx0/co//spsUNuGsfZGQqr4tIj/GWfFrG/BXYLf78hLgLhG5Bvj7NOMwJm3W7dQULXcRkmtVdW3IcfRR1T3uHcJSYJGqLg0zJlOcbMjImPDdJM5auq8CfwP+FHI8pkjZHYIxxhjA7hCMMca4LCEYY4wBLCEYY4xxWUIwxhgDWEIwxhjj+v/6tlVI30fOtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RED REGION & BLUE DOT : SETOSA \n",
      "GREEN REGION & RED DOT : VERSICOLOR\n",
      "BLUE REGION & GREEN DOT : VIRGINICA\n"
     ]
    }
   ],
   "source": [
    "plt.contourf(x1,x2,model.predict(np.array([x1.ravel(),x2.ravel()]).T).reshape(x1.shape),alpha=0.75,cmap=ListedColormap(('red','green','blue')))\n",
    "\n",
    "plt.xlim(x1.min(),x1.max())\n",
    "plt.ylim(x2.min(),x2.max())\n",
    "for i,j in enumerate(np.unique(y_set)):\n",
    "             plt.scatter(x_set[y_set == j ,0],x_set[y_set == j,1],c=ListedColormap(('blue','red','yellowgreen'))(i),label=j)\n",
    "plt.title(\"logistic regression\")\n",
    "plt.xlabel(\"sepal length\")\n",
    "plt.ylabel(\"sepal width\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"RED REGION & BLUE DOT : SETOSA \\nGREEN REGION & RED DOT : VERSICOLOR\\nBLUE REGION & GREEN DOT : VIRGINICA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ON TEST SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RED REGION & BLUE DOT : SETOSA \n",
      "GREEN REGION & RED DOT : VERSICOLOR\n",
      "BLUE REGION & GREEN DOT : VIRGINICA\n"
     ]
    }
   ],
   "source": [
    "x_set,y_set = x_test,y_test\n",
    "x1,x2 = np.meshgrid(np.arange(start=x_set[:,0].min()-1,stop=x_set[:,0].max()+1,step=0.01),np.arange(start=x_set[:,1].min()-1,stop=x_set[:,1].max()+1,step=0.01))\n",
    "plt.contourf(x1,x2,model.predict(np.array([x1.ravel(),x2.ravel()]).T).reshape(x1.shape),alpha=0.75,cmap=ListedColormap(('red','green','blue')))\n",
    "\n",
    "plt.xlim(x1.min(),x1.max())\n",
    "plt.ylim(x2.min(),x2.max())\n",
    "for i,j in enumerate(np.unique(y_set)):\n",
    "             plt.scatter(x_set[y_set == j ,0],x_set[y_set == j,1],c=ListedColormap(('blue','red','yellowgreen'))(i),label=j)\n",
    "plt.title(\"logistic regression\")\n",
    "plt.xlabel(\"sepal length\")\n",
    "plt.ylabel(\"sepal width\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"RED REGION & BLUE DOT : SETOSA \\nGREEN REGION & RED DOT : VERSICOLOR\\nBLUE REGION & GREEN DOT : VIRGINICA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ON K-NEAREST NEIGHBOUR CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors=10,p=2, metric='minkowski',weights='distance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FITTING TRAINING DATA TO THE K-NN CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "           weights='distance')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PREDICTING THE RESULTS ON TRAINING AND TESTING SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_knn_on_training_exaples = classifier.predict(x_train)\n",
    "prediction_knn_on_testing_exaples = classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 1, 1, 2, 0, 0, 1, 2, 1, 1, 0, 2,\n",
       "       0, 1, 0, 1, 2, 1, 0, 0, 1, 1, 0, 1, 0, 1, 2, 2, 1, 1, 1, 1, 2, 1,\n",
       "       2, 1, 1, 2, 1, 1, 0, 2, 2, 1, 2, 0, 1, 0, 0, 2, 2, 1, 1, 2, 1, 0,\n",
       "       1, 1, 1, 2, 1, 2, 1, 2, 2, 2, 0, 0, 0, 1, 2, 0, 0, 0, 1, 0, 0, 2,\n",
       "       1, 1, 2, 1, 2, 1, 2, 1, 0, 0, 0, 0, 2, 2, 2, 1, 2])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_knn_on_training_exaples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 0, 2, 0, 0, 2, 1, 1, 0, 2, 1, 2, 1, 2, 0, 0, 1, 2,\n",
       "       0, 1, 0, 1, 0, 1, 0, 1, 2, 0, 2, 0, 1, 2, 2, 0, 2, 1, 1, 1, 0, 1,\n",
       "       2])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_knn_on_testing_exaples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EVALUATING THE MODELS PERFORMENCE :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1) CONFUSION MATRIX :\n",
    "\n",
    "THIS IS A MATRIX WHICH EVALUATES THE NUMBER OF CORRECT PREDICTIONS MADE BY THE MODEL .\n",
    "\n",
    "                           PREDICTION:+VE               PREDICTION:-VE\n",
    "          TEST CLASS:+VE     TRUE +VE                       FALSE +VE\n",
    "          TEST CLASS:-VE     FALSE -VE                      TRUE -VE\n",
    " ONLY THE DIAGONAL ELEMENTS THAT IS TRUE+VE AND TRUE-VE ARE THE CORRECTLY CLADDIFIED DATAPOINTS AND ALL OTHERS ARE MISSCLASSIFIED .\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_for_train_set_KNN = confusion_matrix(y_train,prediction_knn_on_training_exaples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_for_test_set_KNN = confusion_matrix(y_test,prediction_knn_on_testing_exaples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### IN THIS CASE THE DIAGONAL ELEMENTS THAT IS 33+36+32 THAT IS EQUAL TO 101 DATAPOINTS ARE CORRECTLY CLASSIFIED AND THE REST ARE MISS CLASSIFIED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[33,  0,  0],\n",
       "       [ 0, 38,  0],\n",
       "       [ 0,  2, 32]], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_for_train_set_KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1d2b907d9b0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(cm_for_train_set_KNN)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### IN THIS CASE THE DIAGONAL ELEMENTS THAT IS 17+8+10 THAT IS EQUAL TO 35 DATAPOINTS ARE CORRECTLY CLASSIFIED AND THE REST ARE MISS CLASSIFIED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  1,  0],\n",
       "       [ 0,  7,  5],\n",
       "       [ 0,  8,  8]], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_for_test_set_KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1d2ba47afd0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(cm_for_test_set_KNN)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) MODEL'S SCORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of KNN classifier on training set =  98.09523809523809  %\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of KNN classifier on training set = \",classifier.score(x_train,y_train)*100,\" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of KNN classifier on testing set =  68.88888888888889  %\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of KNN classifier on testing set = \",classifier.score(x_test,y_test)*100,\" %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### LET' S CHECK WHAT OUR KNN CLASSIFIER PREDICT ON A NEW DATAPOINT :\n",
    "            SEPAL LENGTH : 7.9\n",
    "            SEPAL WIDTH : 2.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = [[7.9,2.6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data_prediction = classifier.predict(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### THIS MEANS THAT FOR SEPAL LENGTH : 7.9 & SEPAL WIDTH : 2.6 OUR MODEL PREDICT THE SPECIE IS VIRGINICA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VISUALIZING THE RESULTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ON TRAINING SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xt8FfWZ+PHPkwvhKlEuCVQhxQqlYhuVKlYKQVzqDQXpRZfWWupS27ruat1uFaUuFWwtrV2r1qWU1rYsrmulFi8riiY0KlisqTcE/VFAhAQUgnLL9fn9MXPCSXLm5FzmnDmX5/165SWZc2bmGTDnyXy/zzxfUVWMMcaYgqADMMYYkxksIRhjjAEsIRhjjHFZQjDGGANYQjDGGOOyhGCMMQawhGDSTES2isi5PhznPhG5JYH9RojIAREpTDaGTCcinxWRTUHHYbKH2HMIJp1EZCtwlao+nYvnMyab2R2CMQkQkaJMPp4xibCEYAIjIiUi8jMR2el+/UxESsJe/66I7HJfu0pEVEQ+5r72GxG5zf3zYBF5VEQaRWSviPxZRApE5HfACGCVO0z0XRGpcI9T5O57nIj82j3HPhH5o0esV4rIcyJyp4jsBW51t88RkY3uvk+KyMiwfaaJyCYR2S8i94pIjYhclcjxxHGniOx2j/eKiIxzX7tARN4QkQ9F5F0RucHdXiUiO8LiGSsi1e7f0+sicnHYa78RkXtE5DH3OOtF5MRk/41NdrGEYII0D5gAVAKfAs4AbgYQkfOA64FzgY8Bk6Mc5zvADmAIUAbcBKiqfgXYDkxX1f6qekeEfX8H9AVOBoYCd0Y5z5nAFvd9C0VkhnuuS91z/xlY4cY/GHgIuBEYBGwCPpPo8YBpwCRgNFAKfAl4333tV8A3VHUAMA54pmvgIlIMrAJWu+f7Z2C5iIwJe9vlwH8AxwJvAwuj/F2YHGQJwQRpNrBAVXer6h6cD6OvuK99Efi1qr6uqofc17y0AMOAkaraoqp/1hgmx0RkGHA+cLWq7nP3rYmyy05V/bmqtqrqYeAbwO2qulFVW4FFQKX7W/0FwOuq+rD72l1AfRLHawEGAB/HmfvbqKq7wq7/EyJyjHsdf40Q+wSgP/BDVW1W1WeAR3GSQMjDqvqie+7lOIna5BFLCCZIw4FtYd9vc7eFXnsn7LXwP3f1Y5zfaFeLyBYR+V6M5z8B2Kuq+2J8f9cYRgL/6Q7BNAJ7AQE+Qpf43QS1o8v+MR/P/QC/G7gHaBCRJSJyjLvfLJwEtM0dljorQuzDgXdUtT1s2zY31pDwhHUIJ4GYPGIJwQRpJ86HYMgIdxvALuD4sNdO8DqIqn6oqt9R1VHAdOB6EZkaejnK+d8BjhOR0hjj7Xqsd3CGakrDvvqo6vNd4xcR6XI98R4PVb1LVU/HGd4aDfybu/0vqnoJzlDQH4EHI8S+EzhBRMJ/5kcA78Z47SYPWEIwQVoB3CwiQ9wx9/nA793XHgS+5k6E9nVfi0hELhKRj7kfuh8Abe4XQAMwKtJ+7pDLE8C9InKsiBSLyKQ44r8PuFFETnbjGCgiX3Bfeww4RURmuBPY3wbKEz2eiHxaRM505wIOAkeANhHpJSKzRWSgqraEXX9X6939vuteZxVO8nwgjus1Oc4SggnSbcAG4BXgVeCv7jZU9QmccfdncYaDXnD3aYpwnJOAp4ED7vvuVdVq97XbcZJOY6j6pouv4IzBvwnsBv411uBVdSXwI+ABEfkAeA1nTgJVfQ/4AnAHzuTvJ9xrjRR/j8cDjgF+CezDGep5H1gcdg1b3X2uBr4c4djNwMXu8d4D7gWuUNU3Y71ek/vswTSTFURkLM4HZIk76ZlV3KGaHcBsVX026HiMicTuEEzGEpGZ7pDIsTi/Oa/KpmQgIp8TkVJxnq24CWeCeF3AYRnjyRKCyWTfAPYA/w9nXPybwYYTt7NwYn8PZ7x+hlteakxGsiEjY4wxgN0hGGOMcWVVQ63BxcVa0bt30GEYYzLYSwdGA9C/vwQcSeY4cOCl91R1SE/vy6qEUNG7NxvGjw86DGNMBpKa1e4foGpycbDBZJjqatnW87uyLCEYY0xXUypqqL7febzDEkFyLCEYY7KW1KyGGksEfrGEYIzJOh3DQ1gy8JMlBGNM1ghqeKh//xYuu2wHw4YdoSBDazPb22HXrt488MDxHDiQ2N+NJQRjTFYIcnjosst2MG7cAEpKKnB6KGYeVWXQoPe57LIdLF360YSOYQnBGJPZ6uqQ/buB4IaHhg07ktHJAEBEKCkZxLBhexI+hiUEY0zGCs0VFBUWM3FicHEUFJDRySBERJIa0rKEYIzJOAUvPIw2Owu22aRx+mTo9IgxJi/V1SE1q9Hm/lRNLrZk0MWf//x/nHfeGKZN+xhLlvzQ9+NbQjDGZASpWY3s301RoSWCSNra2liw4Nv88pdP8Oijb/DYYyt4++03fD2HJQRjTKCkZnXHXEHV5GDnCvyyahWccw6MHev8d9Wq5I/5yisvMmLExzjhhFH06tWLCy64jDVrHkn+wGFsDsEYE4wMqB5KhVWr4JZb4MgR5/udO53vAaZPT/y4DQ3vMmzYCR3fl5cfz9/+tj6JSLsL7A5BRHqLyIsi8jcReV1E/iOoWIwx6ZXLw0N33nk0GYQcOeJsT073tWv8rnwK8g6hCThHVQ+ISDFQKyJPqKotMWhMjsqHlhO7dsW3PVZlZceza9c7Hd/X1+9g6NDhyR20i8ASgjpLtR1wvy12v2z5NmNyUW0t0nYIyN1EEDJsmDNMFGl7Mk455dNs2/YWO3b8naFDP8Ljjz/A4sX/ndxBuwh0UllECkWkDtgNPKWq3QbERGSuiGwQkQ17WlrSH6QxJilSsxppO0TpwNwbHorkuuug6zpevXs725NRVFTELbfczde//jkuvHAs55//RU466eTkDtr1HL4eLU6q2gZUikgpsFJExqnqa13eswRYAjB+wAC7gzAmS+TD8FAkoYnjO+90homGDXOSQTITyiGTJ1/A5MkXJH8gDxlRZaSqjSJSDZwHvNbD240xmSyPhoe8TJ/uTwJItyCrjIa4dwaISB/gXODNoOIxxiQv34aHck2QdwjDgPtFpBAnMT2oqo8GGI8xJkH5OjyUa4KsMnoFODWo8xtjkhO+WA0CVZMsEWS7jJhDMMZkl9BiNUG3pTb+soRgjIlNWKsJsKGhXGQJwRgT1bFnVtN4RzNgdwRBu+mmOVRXP8qgQUNZtcr/gkxLCMaYiDomigNax9h0N3PmlcyefQ3f+94VKTm+tb82xnTStR21JYP4DVi1nFHnVDB6bAGjzqlgwKrlvhz305+exMCBx/lyrEjsDsEYA1jpqF8GrFpO+S1zKTjiPJxXvHMb5bfMBeDD6bODDK1HlhBMVljeMJV5W65ie9NQRpTsZuGopcwuWxN0WNnPJop9N+TOeR3JIKTgyCGG3DnPEoIxyVreMJW5m27gULvTMWxbUzlzN90AYEkhSaE1CWyi2D9Fu7bHtT2T2ByCyXjztlzVkQxCDrX3Zt6WqwKKKDcUb5kPYMnAZ63DRsS1PZNYQjAZb3vT0Li2m9i0vjOR0oE2ROS3PdctpL13307b2nv3Zc91C5M+9vXXX87ll5/F3/++icmTj+ehh36V9DHD2ZCRyXgjSnazrak84naTmNAEcmVlwIHkoNA8wZA751G0azutw0aw57qFvswf/PSnK5I+RjSWEEzGWzhqaac5BIC+BUdYOGppgFFlP5tATp0Pp8/O+AnkSCwhmIwXmji2KiN/hJeXGhPOEoJJGT9LRWeXrbEE4IOCFx4G7O7ARGYJwaSElYpmJm3ub8nAeLIqI5MSViqaeWyoyPTEEoJJCSsVzSzHnlkN2FCRic4SgkkJr5JQKxUNRuMdzZYMstyuXe9wxRVTuOCCsVx00cn89rf/6fs5LCGYlFg4ail9C4502malosGwoaLcUFhYxL//+094/PGNPPDAOpYvv4e3337D13NYQjApMbtsDUvGLGZkST1COyNL6lkyZrFNKKeZDRUFo6HlCdYfupC1B8ez/tCFNLQ8kfQxhw4dxsknnwZA//4DOPHEsTQ0vJv0ccNZlZFJmUwtFc2nzqmNdzRTVGjJIJ0aWp7grebbaMe5Q27Set5qvg2AsuLzfTnHjh1b2bjxZT71qTN9OV6I3SGYvBIqh93WVI5S0FEOu7xhatCh+S40VGTN69Jra8vdHckgpJ0jbG2525fjHzx4gGuvncWNN/6M/v2P8eWYIZYQTF7Jl3LY8BXPTHo1aUNc2+PR0tLCtdfOYvr02UybdmnSx+sqsIQgIieIyLMislFEXheRfwkqFpM/8qEc1pJBsEqkLK7tsVJVbr7565x44li+9rXrkzqWlyDvEFqB76jqWGAC8G0R+USA8Zg8kOvlsJYMgldRfA0FdL4LLaA3FcXXJHXcv/71OR555HesW/cMM2ZUMmNGJTU1jyd1zK4Cm1RW1V3ALvfPH4rIRuAjgL91VMaEydXOqbYecuYITRxvbbmbJm2gRMqoKL4m6Qnl00+fyJtvqh8hesqIKiMRqQBOBdZHeG0uMBdgRElJWuMyuScXO6faXUHmKSs+37eKonQKPCGISH/gD8C/quoHXV9X1SXAEoDxAwakNj2awH1r87Us2TmdNgoppI25w1dx7+i7fD1HppbDxqWuDtl/dJjLkoHxQ6AJQUSKcZLBclV9OMhYTPC+tflafrFzBiAAtFHkfo/vSSFbdX3q2BJBerS3O5O6IhJ0KFGpKu3tie8fWEIQ52/2V8BGVf1pUHGYzLFk53RCyeAoYcnO6XmfEGyOIFi7dvVm0KD3KSkZlLFJQVVpanqfXbt69/xmD0HeIZwNfAV4VUTq3G03qaq/0+Yma7RRGNf2fGCJIDM88MDxXHbZDoYN20NBhj691d7uJK4HHjg+4WMEWWVUS/dfB00eK6SNtgj/SxbSFkA0AaqtRdoOOX8WqJpkiSBoBw4Us3TpR4MOI+UCn1Q2JmTu8FWd5hAcytzhq4IKKa3C7wZKBxZTWRlgMCYvWUIwGSM0T5DqKqNMZKWjJhOIavZUco4fMEA3jB8fdBimi3Pr7mBN49F/l6mlG3i68rsBRuTIlq6mlgz8N/LUJ6i84G76HtvAoX1l1D1+Ddtejv5cQCL7ZIvqanlJVXv88MzQ6RGTLY4mA+n4WtM4nnPr7gg0rmzramrJwD8jT32CM794G/2Oq0dE6XdcPWd+8TZGnuq9JkEi++QiSwgmKUeTQTjpdMcQhGzpalq8ZX7QIeScygvupqhX5/bTRb2OUHmBd/vpRPbJRZYQTE7Klq6mre9MtLsDn/U9NnKbaa/tie6TiywhmJyUDV1Nba3j1Di0L3Kbaa/tie6TiywhmKRMLd0AdC1MUHd7cBaOWkrfgs5DABnV1bS2FrC5g1Soe/waWps7Dxe2Nvem7nHv9tOJ7JOLLCGYpDxd+d2wpOB8ZUKV0eyyNSwZs5iRJfUI7YwsqWfJmMUZUWUkNauRtkOUDrRkkArbXj6f9Q/ezMG95agKB/eWs/7Bm6NWDCWyTy6yslOTtETKO7OlJNRvVmJqghBr2ak9mGaSEirvDFX0hMo7Ac8P+ET2yXahRCAiTJ5kP3YmM9mQkUlKIuWd2VIS6pfwuwJLBiaT2f+dJimJlHdmS0losqxTqck2lhBMUkaU7GZbU3nE7X7uk21srsBkIxsyMklJpLwz40tCk3DsmdWWDEzWsjsEk5REFq3PxYXuQxrvaLZEYLKWlZ2amORrmWg8jj2z2hJCmFzuHpptrOzU+CYfy0QT0XhHM0WFlgzgaPfQUMO4UPdQwJJCBrM5BNOjfCsTjVf4vMHEiQEHkyGse2h2sjsE06N8KRONV0dZaY1NIHdl3UOzkyUE06N8KBONl1USRXdoXxn9jquPuN1kLhsyMj3K5TLReBVvmW/JIAbWPTQ7BXqHICLLgIuA3ao6LshYjLdcLhONhyWC2IUmjq3KKLsEWnYqIpOAA8BvY0kIVnaambxKUv0sVQ207LW2Fmk7BFgyMNkpK8pOVXWtiFQEGYNJjldJ6nP7T+b++vN9KVUNsuz12DOraaxxykmtgsjkusAfTHMTwqN2h5CdKl5YEXHCuZBW2iL8vjGypJ6tZ13uyzkSOVY8bIjI5IpY7xAyflJZROaKyAYR2bCnpSXocEwXXqWnbRTG9f5EzpHKsldLBiYfZXxCUNUlqjpeVccPKbYfzkzjVXpaSFtc70/kHKkqe51SUQNYMjD5p8c5BBEpAWYBFeHvV9UFqQvLZIuFo5Z2Gt8HpyT1q+VPdJpDCG1PpFTV6xwpKXutq6O6pgkEqtd2vyMtKrC5BJO7YplUfgTYD7wENPl5chFZAVQBg0VkB/B9Vf2Vn+cwqRWtJPXsga/7UhmUzrLXKTP2U/RMLRMXdP99p3bBAtp2nkL12n6UHlNMZaXvpzcmULEkhONV9bxUnFxVUzcjmEcSKcn81uZrWbJzOm0UUkgbc4ev4t7Rd/l6jtlla+L+0PaKK5FjxSM0TERhYcRkMPX5Blbs/weGHmliR6nwvcbf8MDaf4y6JKZXt0/rAmoyVSwJ4XkROUVVX015NCZuiZRkfmvztfxi5wxAAGijyP2eiEkh2jkA30pC443LN7W1zoBo1eSIL099voEbfrOJ3s3tAIxoVJb1+ho0wy6uiLiPV7fPwRV1nHjGo9YF1GQkz7JTEXkVUJykcRKwBWfISABV1U+mK8gQKzvtLpGSzKLqpyKWhBbSSmvVP8R1DsC3ktB44/LLlIoaGDgQTo08BrTiOy9Q/n730dKtHM+ogr9HvEu4ZN6FEXv5tLcVUFDY3m37wb3lPLLwsQSiN6ZnfjyYdpGP8ZgUSaQk06skNN5S0WjnSKQkNN64fOWRDACGRkgGACN413Mfr66eUtA9GUR7vzHp5Fl2qqrbVHUbcFvoz+Hb0heiiSaRkkyvktB4S0VHlOz2tSQ03rh8UVvb41t2DyqJuH07J3jOIXh19dT2yD9y1gXUZIJYnkM4OfwbESkETk9NOCZeiXQinTt8Fc5oYDh1t8d3Dj87ocYbVzKmVNQ4X19uc4aLolg6axRHenX+UTlIX34/dpHnPl7dPt964VLrAmoylueQkYjcCNwE9BGRD0KbgWZgSRpiMzFIpCQzNEEba5VRLOfwoyQ03riS5jGJHK56zmqqgTfaV7CIWxjBO7zDCH43diFrymZ77het2+d7WyutyshkJM+EoKq3A7eLyO2qemMaYzJxSqQk897Rd6W2cidBqYyro7Q0TlWTitnFFXzNo6LIy7aXzw/0g/70mbdz0lkPIwXtaHsBb71wKS+ttB9l4y3aHcJp7h//N+zPHVT1rymLymSUdJWdpkqnRBDDXUEqpWvx+dNn3s7osx9CnApepLCd0Wc/BGBJwXiKVmX0E/e/vYHxwN9whow+CawH7AH+PDFvy1Wd2kYAHGrvzbwtV3X8OdJrmZAQOpJBwIkgJNri834mhJPOergjGYSIONstIRgv0aqMpqjqFGAbcJrbYO504FTg7XQFaIIXrew0iE6ksfIrGUTqaZSodC0+71Xe6rXdGIityujj4U8pq+prgHVxySPpKjv1k1/JoGrZNMC/pOBVXup32alXeavXdmMgtoSwUUSWikiViEwWkV8CG1MdmMkc6So79Yvfw0R+JoV0LT7/1guX0rUJgaqz3RgvsSSErwGvA/8C/CvwhrvN5InZZWtYMmYxI0vqEdoZWVLPkjGLO6qbvF4LlM9zBn4lhW0vn8/6B2/m4N5yVIWDe8tZ/+DNvlcjvbTyRjY/93na2wpQdVpmbH7u8zZ/YKIKfAnNeFgvo/jE29E02/lRTTT1+Qau+sMWhr7fxO5BJSydNYo1nzk6nFM9Z3VK10QIshOq3+e2rq6ZI+leRiLyoKp+MazJXSdBNLczsQusc2iadUoChYXw2cQ/qbt2NS1/v4kbfrMJoCMpVC2bRvWc1YD/q6mlqyQ1HecO8lpM4qINGf2L+9+LgOkRvkwGW7JzOqFkcJS423NAbW3nuYKqyUklA4Cr/rClIxmE9G5u56o/bIl0et9FK0lNNb/PHeS1mMRFe1J5l/vHqcCfVfWt9IRk/BBo59BUqa11eg+Bs35BlJbVifDqatp1e1HFOtj+Wd/OG5KuktR0nDvIazGJi2WBnArgyyIyEmcZzT/jJIi6VAZmklNIm8faAinsHJoiHXcCFaT0AbPdg0oirnvQtdtp69YJFKWgevPQvrKIayikoxOq3+cO8lpM4nr831pV56vqOcA4oBb4N5zEYDJYOjuHpkxdXfdhoRSK1NX0SK8Cls4a1Wlb6aKhtLa3UL2281ey0lWSmo5zB3ktJnE93iGIyM3A2UB/4GXgBpy7BJPB0t451E+hoaEKfB8WiiY0cRytygigsrwS3DLUkJqrV1K9th+lxxRTmWC40TqkxiNachIk4hoOfp07Vccz6dFj2amI/BVoBR4DaoB1qnok6k4pYmWn/lneMNWXltUpUVvLlG/3T1si8EvN1SvR5n5UTfK/AikWdXXQ+EEL0usgk++bydxXtqPn9OFgWS/6NTQjzxxm9r0voc39Iu5/efsKfijzOV63s0NG8NuPR2/xbbKHH0toAqCqp4nIAJxmdv8A/FJEGlTVmttlqWjdSzMmKWShyffNpHrOaurqSPguIRmNH7RQumgoleWVzH1lO4cvHUBbH6eI4OCwEgovLWI5p7PkkyO67Tv1+QauW7KVfnoYgBG6jes2zuWNjW2sKHDWxg4q0Zn06XEOQUTGAV8Gvgp8CdgBPJPiuEwK9dS9NEgdq5hlqaKKdTR+4F8zvHhVljuZSM/p05EMQtr6FKLn9Im431V/2EI/Dnfa1o9D/HTQ3I6ntOusjCTnxVIr8SNgAHAXMNbtgjrfj5OLyHkisklE3haR7/lxTNOzTO1Q2mkCOcuGi0Imznd+NOJ5TqFmbatv53cemoODZb0ivu61vaeSW+l10IfoTKaLZcjowlSc2F2b+R6cYagdwF9E5E+q+kYqzmeOGlGym21N5RG3m+QVVayjdesEYn2aWdsLqV7b4uuQTL+GZg4OK+m+vb6lI2mElC4a2mPJrTb3c1ZGMTktyF64ZwBvq+oWVW0GHgAuCTCevJEPHUqzSoFzh5BM6Wpo39DwjjxzmMLDnYfeCg+3cfC+sVRNKu74Kj2mmMabdnP9+0s4SN9O7z/Sq4Dr319C9ZzVCBLIvIhJryATwkeAd8K+3+Fu60RE5orIBhHZsKcluLHZXJIvHUqD4twdEPMzCuF3BokOH1VNKqaooJjqOaupnrOaf/zZmxy57WP029kM7Uq/nc30uWc0/33wwU77VVa6a0ZXXcFc7mMrI2hHqB9UwuIrx7Ci4HKqJhVHLFU1uSfIf+WujXYgchO9JcAScMpOUx1UugRd9vnc/pPZ0TQYRdjRNJjn9p/M7LI1nnGlNN66OueZgwT11KHUj2ONHNhMZXkTfYuVQy1CXX0J2/Y74/EXNrxH+SlweEgxffa0cOHtp/FY2eCOY9YuWED1WidJeHVK9Xo+IOT0mbc7y2IWtKPtBbz1wqW8tPJGpjYs5ysb53EC29nOCfx+7KKOUtEXx36dyt5301ca2N17BHXlX4Nd3l1IHyz6Cv/dfplzwn1AcDeMMfO6Fuu0mhjP5xBEJNKjrh1U9eKkTixyFnCrqn7O/f5G97i3e+2TK88hdC37BGfIJl2/pXfthOpQppZu4IUPTukW11fLn+D++vNTFu+Uipqk2lWHdygFZ6hj8ZVj4k4KXse6+wefoM+UInoVHr2hbm2H9Tt6M27zBxx3dhFtfY6+Vni4nb3PtXZKCiGh8ftQAqithdb2o3cQkRLG6TNvZ/TZD3VaI1kVDj/1Sa668UH6cahj+0H6cufYJbx93nGduo2C86Tw/3vxIk4849Fu28PXZOiIqb2Q0tKCjB0q6tpRFWK/xnwT63MI0RJC1J9QVa2J9nqPJxYpAjbjNM97F/gL8I+q+rrXPrmSECpeWBFxUndkST1bz7o85ecvqn4qYp8jJ/93v3ErpDXi+/2KN5mEsOI7L0ScDK0fVMLlPznLl2P9/vFxHI5QnXOwWRi8rynia30amvnV7u4JIaR2wQJat07oeIgMoK6+jsabdnebXL7sjk9TUBhhLeRW5Z/OfLnb5q2M4I9PjYvYS6i9rSDisQ7uLeeRhY91PNxWumgojTc5RQaZ+vzBJfMuTOga81HSD6Yl+4HfE1VtFZFrgCeBQmBZtGSQS4Iu+4y346nX+5OKt66OKTP2J76/K9YOpckc6/CQyB+IfYvV87XDQ4ohStFWqDw1XGV5JdXQreJICiIkA8Drn3EE79D32CERX/M6VqgLaWUlVK892p6jes5q3yug/OLVObWnazTeYnkw7SQReUhE3hCRLaEvP06uqo+r6mhVPVFVF/pxzGwQ9ML08XY89Xp/wvHW1jrJYODApJvWde1E2tP2RI7VZ0/kSeFDLeL5mtf2RGh75B9T8fhn3F0ywrOrqNexQu/v+vBZ1bJpFFWs87WJn18SvUbjLZYqo18Dv8DpZzQF+C3wu1QGleuCLvv06oQ6tXRDxLjmDl/lX7yhxnU+PXwWa4fSZI5V/6ozZxCutR3q6kuof9WZMwhXeLid+lejnytUDeQl/IP5rRcupevIripsf/iT3UpFD9KXpaMWenYbfeuFSz27kNbWOsNFRRXrOr0+cf58qpZNo2rZNKTXwYxJDIlco4kuloTQR1XX4Mw3bFPVW4FzUhtWbgu67PPe0XfxzeF/pJBWQCmklW8O/yNPV343Ylz3jr7Ln3hDyWDgQN+uZc1nylh85RjqB5XQDh3lkolUGXkd67Gywazf0ZuDzYKqM3ewfkdvtu3vxWNlg9n7XCt9Gpzyzj4NzZ4TypFUX9n57zD0odv4wdHfyH9y3Q3UPPR52tsKUHXGyJ/930v5wY9/yT/xi07x/hO/4JZNX2T5z8/ntz+4mYN7y1EVDu4tZ/2DN/PSyhtZ/+DN7KsvR9uF93eV85sF3+P+/zyX1nZn7iDScFbI5PtmUroo2CfaQ7a9fD7rH/S+xq7b83VCOR6hThhGAAAXkUlEQVSxdDt9Dvgs8BBOD6N3gR+q6pjUh9dZrkwqRxN0OWqqpPLBM69S0XSUoyZ7/toFCzp9AEfa5z+e/i/g6PMNVZOKO35Dv7x9BbcXfZcTWhs63v+TUbs4sORPHe+PVNJas7YVRTvuBqIlga5CE+JAQu2+rSQ0/ZKuMup4g8ingY1AKfADYCBwh6qui7pjCuR6Qgi6HDVlwoeJfOZVKvrE2eWc/1x9SstRF1/p/E7k1/ljKaENH2b6wVWfiqnktuvQlCAUFhTR2t7S8WRzIsITQ0/PUYR4lYrab/Cp5VtC6HijyDGAquqHyQaXqFxPCEGXo6ZCqltSeJWKthZAUYRiEz/LUevdSWi/zh9vCW0iJbd19c7kREdJaRIJISTU2iKWhOBVKprPJaHp4Nt6CCIyHmdieYD7/X5gjqraMpo+C7oc1W/p6E/kVSoaqWw/2vsTOUe0YyVy/njPk0hcofbYXVd8S1asrS28Sj+tJDQzxDKpvAz4lqpWqGoF8G2cBGF8FnQ5qp/S1azOq1S0zeP/bD/LUXcPKvH1/PGW0PpZcpsuXqWfVhKaGWJJCB+qascayqpaCwQ2bJTLgi5H9Us6O5d6lYqumjw85eWoS2eN8vX8S2eNilhG+p39P+8Y6ok1rnSJVjobiVepqJWEZoZY7vNeFJH/AlbgFK9/CagWkdMAVPWvKYwvr4QmjrO5yijdbaxDk6eRqnleP2mgL1VG0c4Rkuz5a65eSQ3wBn/ipyXzGdq0nd0lI7i5YCGPl8ym8aYrqabzmH8scaVSKEnF8xRzaOLYqowyUywJIVRU9v0u2z+DkyDsmQQfzS5bk1UJIFxQaxq8ff6xPPC1cR2dSN+uL4EoXTGidS71suYzZXF/0HrFFepZFE4QBh5TRK8b3uWRi4cgBYPQ9gJKX9hO5UqY2vAgX954EyOu3N7pgz+RuPwsx5WITYuj2/by+RETgJ/lqFbamphYVkybko5ATHYLKhmMHNjM+CFQ0supluvXSznz+COM2/wB14SVZJa/3+SUaJ5YSL+Te1HkjrSE3g/0mBQi6VoqGjrPh8P3csK5J3aKa/wQ2LjkehqfPC9i/X7XrqZS2M7osx+i785NXPej/+3oaho6B5B0CW0yx2q8aXcC6SCyruWo/Y6r58wv3gYQ9we5n8fKN7H0MioTkV+JyBPu958Qka+nPjSTDaZU1AS62tnHi0sp6dN53qWoAMpPoVN9Pjjfl59CRzIIf39lefzVR+AM10Q6z7jTy7vFVdLnCJdffx9VkyI/zHXSWQ93anENIAInXPpKpxbXoXNc9Yf4W4p5xRvtWF3nL2oXLOiYO/Br4ZzKC+7u9GwCQFGvI1RecHegx8o3sfxr/ganqmie+/1m4H+AX6UoJpMFOpIABLrS2XFlkcsVo3YhjaBvcWJrL3mVeB4pj/y7VrTyyni7mqajhDb0wV+Ns1Z06Ilmr4V+EuVnOaqVtiYuliqjwar6INAOTttqiLNdpskdtbWd7wgCXvbykMeKk/F2IT3Uktjgh1eJZ+/6yB/u0corvbp0ev20+V1CG1Jz9cpOdwWh5Tlbt07oSBDhi/r4wc9yVCttTVwsCeGgiAzCbY8pIhOIOmVncllHC4oMWf+4rr4kYifS+leJu3NpIiKVfh6UEl6+56K4yyu9upoeeuZT3cpRU1FC29GBtbk/jTft7lRSOnFi58TgNz/LUa20NXGxDBldD/wJONFtdDcE+HxKozIZp9MQUQYJTQR3qxoqO4YjV47pXk1TNpiRO+KvMvISqfTz3wq+ywNPL2D22PgqXV5aeSNA97WTn7yRD8ee61QZ8Q67B/Vi6axRzP/t8wwc9dGjTx+7Qh/kkdpSeJWq3rL0b84+PZSQOsNE/i+W42c5qpW2Ji6mXkbucpdjcNZX3KSqgTRDz/VeRpkskWUu/Sxv9PM8196/mek1Oylsd54oXjV5OHd9dbRv56hdsAC2fzahMfaeyiW91iEIH9+vnrPa+U3ebWfdNWFEUj1ndUauimb84Wcvoy8A/6eqr4vIzcBpInKbPZCWPxK5O/CzvNHP81x7/2ZmPLuzo1yyqB1mPLsTwDMpxHuO1q0TKD0m/muJpVyyalIxNWtbu1X3VK91xvdDdwUTJ0JdXbEz9BP2Pul1kIG3dr+rMAZim0O4RVU/FJGJwOeA+3FWUDN5INGS0kTKGxMR73mm1+zsVjsv7vZkztF1BbR41wiA2MslI5V6hsb3O6qC1rbQ+IFzN1FUUEzVJOeL5v7sv2lPR7w9rdxm8ksscwihGocLgV+o6iMicmvqQjKZoCMRFBbCZ+Mf+0ikE2ci4j2PVxdSr+2xnkN6HQSc4aJEnt6F5MslQ+P7oWGloop19J97sXOXsPbo+xJZ1Mbkh1gSwrtuL6NzgR+JSAmx3VmYbBRazAaSqiTaPagkYq9+vztxxnueNo91Cry6k8Z6jsn3zQScO4XSYxJ7WOvQvrKIawXEWy4ZmguoXjvBeZq418GO+GoXLKBx64ROCcLmDkxILB/sXwSeBM5T1UbgOODfUhqVSbvQE8d+lZWmqxNnvOdZNXk4Xcso1N3u1zlCQzXx8rtcsmpSMaXHFKPN/TqGhlq3TqBq2bSOL3CGl+q6N1M1eSjmFdMygVUZ+S+VTxzna5VRR9lnAr95p7opm1eVEtidQi7zfQlNP7mVS7cCY4EzVHVDLPtZQvBHt6qhDHnILJpEkovXPv4nqrf43JBTOG7oHvY2lLHynm/ylyfP863Pj4mfdTvtzLey0xR5DbgU+K+Azp9/6uqYMiPsAfMsSAIhiZSweu1z8lv7Of+5el/KYevq6xhd81u+Mu+HlPRx2lkPGlbPlfNvZ+zHC/P6AyhI1u00cYEkBFXdCCBdWzsaX3W6E6ggq5JAuGhln14f4l77TK/Z2W1SuadjRRJa02DWqhndu626paL24ROMaOW79m8SXcbf04rIXGAuwIiSzF0rNtME2ZLab4mUsMZbdppoOWypR7dV66wZHOt2mriUlY+KyNMi8lqEr0viOY6qLlHV8ao6fkixTXr1qGs30hyQyGLy0cpO4z1WJKEnfffWR76r2FtfRvXalqiTuCY1rNtp4lKWEFT1XFUdF+HrkVSdM5/5XTaaSRIpYfXaZ9Xk4b6Vw1Ytm8bKe66m6XDnUtGmI8W82dJI6aKhANSsbaVmrUefbuM763aauIwfMjJRZPFEcTwSWUw+2j6vnzTQtyqjvl/6KRv2QGW5HO2euruQbft7OXcRy6ZRPWc10usg1Wv7RT2WIFaZ5APrdpq4oMpOZwI/x2ml3QjUqernetrPyk5TXzJ65JmLueQfH+O4QfvY+/6xvHn4cMKtof2Urmca0sHrWkLPL/jVWsJKL01IRpedqupKYGUQ585K4XcCCfYW6kntggWcNqbRLaF0KjQGDdnH+MO92bb/LeAk388Zq3R1Tk2HaNfCsmkd1UvVa5N7UMxKL00i7P40Q6W7ZLR16wRm/bx7CWVJnyN8bvCnePK9Qx57pl4iZaeZqqdrCR9mqlnbSmHB0R/ReNZXsNJLkwhLCBkmyMXrS4dGLss7rqwB3huQ1ljCpatzajrEei1Vy6Y5C+24WrdOoGZt7HMMVnppEmEJIUMEmQjAaZW8t6GMQcO6d9tsbGpOezzh0tU5NR3iuZbQCmhw9EG4WPnVOdXkF2tjHbBQuSiFhYGWi06cP583WxojLkD/xp7g7g4gfZ1T0yHRawk99xBr+aqVXppE2B1CEMLXHBg4EE7NjNVKPBesD7jKKJGy00wVz7XULlhA69YJnbZpt+bdkVnppUmEJYQ06hgWqiBjnxn42BP7uCzsw+rArFFsy4AP3pPf2s/gfU0IMHhfEye/tT8rEwI4SSHe2IsKiuOaVAYnKVgCMPGwhJAGQc8PxCpTyzuvvX8zM549uhZyUTvMeNZZA7mndQyyWWgOITR/UFdnS1+a1LI5hBTqmB+ArGgnEe+C9ekyvWZnt1WKxd2eDyrLKymqWJfwSmzGxMoSQgpkykRxvDK1vNOrQ6nX9lwUuluwnkgmlWzIyCedhoUyaKI4Hpla3tlWQLc1DELb80lV2ANrXZ9HCHVVtWUwTTLy7EfKXx0dRrsOC2VhMoDMLe9cNXl4t9oadbfnm6pl01CUurrIr1vLbZMMu0OIR9fuoinqKxSUTC3vDE0cT6/ZSWG7c2ewavLwnJ5QjqaoYh2NWyd0m2SuWjYNcMpVq9c65aqJVCeZ/GUJIZquCaCCnEsCXXmVRAbdbfT1kwZy1ivvM/T9Jt47toTXTxqYtnP7Ldm/y4nz51Nz9UoaP+gHdB8i6lqdVL3W2W7JwfQkkPbXiUpL++vwh8Yga+cD/NS1HBWcoaTFV45JS1II+vx+8vNaaq5eiTb3o2pSMdVrWzruECLp+pCbrb2QX2Jtf20JoetdAGRNVVC6rPjOCxEnm+sHlXD5T87K+fP7ye9rCa2hAERNCOEi9UWyBJHbMno9hKClu7V0tgu6HDXo8/vJ72sJVR7FI9RiO+To0JIzGW1DS/krPxJC12EgsCQQh6DLUYM+v59ScS2li4ay/9a/J7x/1wRRPWd1x7yDlbHml5xNCHYX4J+ls0ZFHPdOVzlq0Of3UyqupbK8Eu6b6Ud4wNGhJycxtFhSyCO5kxC63gXkeDVQOgVdjhr0+f2UTdeSyHCUyW5ZPancbcF5qwgyKVC7YAGnndzAzG/8muPKGti7ewhvNn/QY1vwoEt1/VA9Z7XdIeSAnJxU3lRyICdaRJjsUT1nNWd87v+44nt30qu3s0bxoPLdjD/cm43Lvk3fWfdE3C9TO8caE01WJQTA5gJM2oSGSy6//r6OZBBS0ucIM7/xa558L/K+0TrHWkIwmSqQXkYi8mMReVNEXhGRlSJSGtOOA/qnODJjOquaVOy5MP1xZd4L1udSqazJH0E1t3sKGKeqnwQ2AzcGFIcxnooq1lG9toXGhsi/0e/12A7eZaTZWCpr8kcgCUFVV6tqqLH7OuD4IOIwJpqJ8+dTumgof7j7apoOd16wvulwb95safTcN1M7xxoTTSbMIcwB/sfrRRGZC8wFKLHfrkyaVZZXwpd+yoY9UFku9C1WDrUIdXuIWmWUTeWlxoSkLCGIyNNAeYSX5qnqI+575gGtwHKv46jqEmAJwICPDsieGlmTU7bt79VjmWlXXp1jjclUKUsIqnputNdF5KvARcBUzaaHIYwxJkcFMmQkIucB/w5MVtVDQcRgjDGms6CqjO4GBgBPiUidiNwXUBzGGGNcgdwhqOrHgjivMcYYb0HdIRhjjMkwlhCMMcYAmfEcgskCudC50xgTnSUE0yPr3GlMfrAhI9OjaJ07jTG5wxKC6ZF17jQmP1hCMD2yzp35rXptS9AhmDSxhGB6ZJ0781fVsmmAJYV8YQnB9GjNZ8pYfOUY6geV0A7UDyph8ZVjbEI5T1hSyB+STX3lBnx0gI6/tcd1oo0xKVA9ZzWlxxRTacuYZ53qanlJVXv88LQ7BGNMTEoXDaXxA7tLyGWWEIwxMaksd24NbOgod1lCMMbEzOYTcpslBGNMXEJJoWZtaw/vNNnGEoIxJm6li4aiqCWFHGMJwRgTt8ryyo6kYHKHJQRjTEIqyyuRXgdtPiGHWEIwxiRs8n0zAZtkzhWWEIwxSQlNMpvsZwnBGGMMYAnBGGOMyxKCMcYYwBKCMcYYVyAJQUR+ICKviEidiKwWkeFBxGGMMeaooO4Qfqyqn1TVSuBRYH5AcRhjjHEFkhBU9YOwb/uBPe5ojDFBKwrqxCKyELgC2A9MifK+ucBcgBJbw9cYY1ImZXcIIvK0iLwW4esSAFWdp6onAMuBa7yOo6pLVHW8qo4vHlCcqnCNMUmqqws6ApOslN0hqOq5Mb71v4HHgO+nKhZjTGoVVayjcesEwH5py2ZBVRmdFPbtxcCbQcRhjPHHxPlOXUhtbcCBmKQEVWX0Q3f46BVgGvAvAcVhjPFJ1bJptLa32NBRFgtkUllVZwVxXmNMatnQUXazJ5WNMb6xoaPsZgnBGOOr0kVDaW1vsaSQhSwhGGN8VVleSVHFOlrbbdGcbGMJwRjju9DQUc3a1oAjMfGwhGCMSYnSRUNR60qTVUQ1e/7BRGQPsC3oOJI0GHgv6CB8lmvXlGvXA3ZN2SJV1zRSVYf09KasSgi5QEQ2qOr4oOPwU65dU65dD9g1ZYugr8mGjIwxxgCWEIwxxrgsIaTfkqADSIFcu6Zcux6wa8oWgV6TzSEYY4wB7A7BGGOMyxKCMcYYwBJC2onIj0XkTRF5RURWikhp0DElS0S+ICKvi0i7iGR1GaCInCcim0TkbRH5XtDxJEtElonIbhF5LehY/CIiJ4jIsyKy0f3/Luvb54tIbxF5UUT+5l7TfwQRhyWE9HsKGKeqnwQ2AzcGHI8fXgMuBdYGHUgyRKQQuAc4H/gEcLmIfCLYqJL2G+C8oIPwWSvwHVUdC0wAvp0D/05NwDmq+imgEjhPRCakOwhLCGmmqqtVNdTgZR1wfJDx+EFVN6rqpqDj8MEZwNuqukVVm4EHgEsCjikpqroW2Bt0HH5S1V2q+lf3zx8CG4GPBBtVctRxwP222P1Ke8WPJYRgzQGeCDoI0+EjwDth3+8gyz9ocp2IVACnAuuDjSR5IlIoInXAbuApVU37NQWyYlquE5GngfIIL81T1Ufc98zDufVdns7YEhXLNeUAibDN6rIzlIj0B/4A/KuqfhB0PMlS1Tag0p1XXCki41Q1rXM/lhBSQFXPjfa6iHwVuAiYqlnyIEhP15QjdgAnhH1/PLAzoFhMFCJSjJMMlqvqw0HH4ydVbRSRapy5n7QmBBsySjMROQ/4d+BiVT0UdDymk78AJ4nIR0WkF3AZ8KeAYzJdiIgAvwI2qupPg47HDyIyJFRxKCJ9gHOBN9MdhyWE9LsbGAA8JSJ1InJf0AElS0RmisgO4CzgMRF5MuiYEuFO9l8DPIkzUfmgqr4ebFTJEZEVwAvAGBHZISJfDzomH5wNfAU4x/0ZqhORC4IOKknDgGdF5BWcX0yeUtVH0x2Eta4wxhgD2B2CMcYYlyUEY4wxgCUEY4wxLksIxhhjAEsIxhhjXJYQjImRiFSJSLdSQK/tPpxvRnjTNhGpzvZusiazWUIwJnPNwOm6akxaWEIwOUNE+onIY25P+ddE5Evu9tNFpEZEXhKRJ0VkmLu9WkR+JiLPu+8/w91+hrvtZfe/Y+KMYZmI/MXd/xJ3+5Ui8rCI/J+IvCUid4Tt83UR2ezG80sRuVtEPgNcDPzYffDqRPftX3D75m8Wkc/69FdnDGC9jExuOQ/YqaoXAojIQLfnzc+BS1R1j5skFuJ0mgXop6qfEZFJwDJgHE7LgEmq2ioi5wKLgFkxxjAPeEZV57itCF50GwOC0+f+VJze95tE5OdAG3ALcBrwIfAM8DdVfV5E/gQ8qqoPudcDUKSqZ7hP5n4fp8WBMb6whGByyavAYhH5Ec4H6Z9FZBzOh/xT7gdqIbArbJ8V4KwbICLHuB/iA4D7ReQknG6nxXHEMA24WERucL/vDYxw/7xGVfcDiMgbwEhgMFCjqnvd7f8LjI5y/FAjt5eAijjiMqZHlhBMzlDVzSJyOnABcLuIrAZWAq+r6lleu0X4/gfAs6o60+23Xx1HGALM6rpgkIiciXNnENKG8/MXqeV2NKFjhPY3xjc2h2ByhogMBw6p6u+BxTjDMJuAISJylvueYhE5OWy30DzDRGC/+xv8QOBd9/Ur4wzjSeCf3Y6ciMipPbz/RWCyiBwrIkV0Hpr6EOduxZi0sN8wTC45BWcSth1oAb6pqs0i8nngLhEZiPP//M+AUBfTfSLyPHAMR+cV7sAZMroeZ0w/Hj9wj/+KmxS24qx9EZGqvisii3BW/NoJvAHsd19+APiliFwLfD7OOIyJm3U7NXnLXYTkBlXdEHAc/VX1gHuHsBJYpqorg4zJ5CcbMjImeLeKs5bua8DfgT8GHI/JU3aHYIwxBrA7BGOMMS5LCMYYYwBLCMYYY1yWEIwxxgCWEIwxxrj+P3PZO5ADf+tqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RED REGION & BLUE DOT : SETOSA \n",
      "GREEN REGION & RED DOT : VERSICOLOR\n",
      "BLUE REGION & GREEN DOT : VIRGINICA\n"
     ]
    }
   ],
   "source": [
    "x_set,y_set = x_train,y_train\n",
    "x1,x2 = np.meshgrid(np.arange(start=x_set[:,0].min()-1,stop=x_set[:,0].max()+1,step=0.01),np.arange(start=x_set[:,1].min()-1,stop=x_set[:,1].max()+1,step=0.01))\n",
    "plt.contourf(x1,x2,classifier.predict(np.array([x1.ravel(),x2.ravel()]).T).reshape(x1.shape),alpha=0.75,cmap=ListedColormap(('red','green','blue')))\n",
    "\n",
    "plt.xlim(x1.min(),x1.max())\n",
    "plt.ylim(x2.min(),x2.max())\n",
    "for i,j in enumerate(np.unique(y_set)):\n",
    "             plt.scatter(x_set[y_set == j ,0],x_set[y_set == j,1],c=ListedColormap(('blue','red','yellowgreen'))(i),label=j)\n",
    "plt.title(\"logistic regression\")\n",
    "plt.xlabel(\"sepal length\")\n",
    "plt.ylabel(\"sepal width\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"RED REGION & BLUE DOT : SETOSA \\nGREEN REGION & RED DOT : VERSICOLOR\\nBLUE REGION & GREEN DOT : VIRGINICA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ON TEST SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RED REGION & BLUE DOT : SETOSA \n",
      "GREEN REGION & RED DOT : VERSICOLOR\n",
      "BLUE REGION & GREEN DOT : VIRGINICA\n"
     ]
    }
   ],
   "source": [
    "x_set,y_set = x_test,y_test\n",
    "x1,x2 = np.meshgrid(np.arange(start=x_set[:,0].min()-1,stop=x_set[:,0].max()+1,step=0.01),np.arange(start=x_set[:,1].min()-1,stop=x_set[:,1].max()+1,step=0.01))\n",
    "plt.contourf(x1,x2,classifier.predict(np.array([x1.ravel(),x2.ravel()]).T).reshape(x1.shape),alpha=0.75,cmap=ListedColormap(('red','green','blue')))\n",
    "\n",
    "plt.xlim(x1.min(),x1.max())\n",
    "plt.ylim(x2.min(),x2.max())\n",
    "for i,j in enumerate(np.unique(y_set)):\n",
    "             plt.scatter(x_set[y_set == j ,0],x_set[y_set == j,1],c=ListedColormap(('blue','red','yellowgreen'))(i),label=j)\n",
    "plt.title(\"logistic regression\")\n",
    "plt.xlabel(\"sepal length\")\n",
    "plt.ylabel(\"sepal width\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"RED REGION & BLUE DOT : SETOSA \\nGREEN REGION & RED DOT : VERSICOLOR\\nBLUE REGION & GREEN DOT : VIRGINICA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    'solver':('newton-cg','lbfgs'),\n",
    "    'C':(1,10,100),\n",
    "    'max_iter':(100,500,1000),\n",
    "    'warm_start':(True,False),\n",
    "    'verbose':(0,1,2),\n",
    "    'tol':(0.1,0.01,0.001,0.0001)\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(model,parameters,cv=10,n_jobs=-1,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='newton-cg', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=-1,\n",
       "       param_grid={'solver': ('newton-cg', 'lbfgs'), 'C': (1, 10, 100), 'max_iter': (100, 500, 1000), 'warm_start': (True, False), 'verbose': (0, 1, 2), 'tol': (0.1, 0.01, 0.001, 0.0001)},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7555555555555555"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 100,\n",
       " 'max_iter': 100,\n",
       " 'solver': 'newton-cg',\n",
       " 'tol': 0.001,\n",
       " 'verbose': 0,\n",
       " 'warm_start': True}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=100, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='newton-cg', tol=0.001,\n",
       "          verbose=0, warm_start=True)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}