Logistic Regression Multiclass Classification .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression (Multiclass Classification)\n",
    "# By Giriraj Vaishnav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Packages, Function and Classes\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'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, 0.2],\n",
      "       [5. , 3.2, 1.2, 0.2],\n",
      "       [5.5, 3.5, 1.3, 0.2],\n",
      "       [4.9, 3.6, 1.4, 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]]), '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]), 'frame': None, 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\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                \\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\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\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\\n.. topic:: References\\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 ...', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': 'C:\\\\Users\\\\Om Prakash\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\iris.csv'}\n"
     ]
    }
   ],
   "source": [
    "# Read the Cancer Dataset file\n",
    "\n",
    "iris = load_iris()\n",
    "print(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide the data into\"Attributes\" and \"labels\"\n",
    "\n",
    "x_data = iris.data\n",
    "labels = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split 80% of the data to the training set while 20% of the data to test set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_data, labels, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Model and Train it\n",
    "\n",
    "model = LogisticRegression(solver='newton-cg', multi_class='multinomial', random_state=0)\n",
    "H = model.fit(X_train, y_train)\n",
    "\n",
    "# model = LogisticRegression(solver='newton-cg', multi_class='multinomial', random_state=0).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Model Coeff (m) = [[-0.39407173  0.84882841 -2.36093351 -1.01257159]\n",
      " [ 0.41036429 -0.3175274  -0.14620809 -0.78740226]\n",
      " [-0.01629256 -0.53130101  2.50714161  1.79997385]]\n",
      "Logistic Regression Model Coeff (b) = [  9.28284244   2.30752784 -11.59037028]\n"
     ]
    }
   ],
   "source": [
    "# Getting Results\n",
    "\n",
    "print('Logistic Regression Model Coeff (m) =' , model.coef_)\n",
    "print('Logistic Regression Model Coeff (b) =' , model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "# Predicting the data\n",
    "\n",
    "y_predict=model.predict(X_test)\n",
    "print(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prediction Probability : \n",
      " [[1.12598293e-04 5.89208696e-02 9.40966532e-01]\n",
      " [1.22106488e-02 9.64585183e-01 2.32041686e-02]\n",
      " [9.86665271e-01 1.33346961e-02 3.29728603e-08]\n",
      " [1.19530322e-06 2.31815344e-02 9.76817270e-01]\n",
      " [9.72121298e-01 2.78785702e-02 1.32007141e-07]\n",
      " [1.85378678e-06 6.07638023e-03 9.93921766e-01]\n",
      " [9.83515008e-01 1.64849340e-02 5.82145257e-08]\n",
      " [2.89680221e-03 7.41944624e-01 2.55158574e-01]\n",
      " [1.53481471e-03 7.37586069e-01 2.60879116e-01]\n",
      " [2.06193051e-02 9.38045464e-01 4.13352313e-02]\n",
      " [9.54078532e-05 1.93896600e-01 8.06007992e-01]\n",
      " [7.04941631e-03 8.10539860e-01 1.82410724e-01]\n",
      " [3.90131349e-03 8.17846603e-01 1.78252084e-01]\n",
      " [3.07798828e-03 7.63177315e-01 2.33744697e-01]\n",
      " [3.75939139e-03 7.33638843e-01 2.62601766e-01]\n",
      " [9.83846350e-01 1.61536044e-02 4.52851565e-08]\n",
      " [6.53945103e-03 7.73357370e-01 2.20103179e-01]\n",
      " [1.03944692e-02 8.73778825e-01 1.15826706e-01]\n",
      " [9.69079198e-01 3.09206290e-02 1.72546151e-07]\n",
      " [9.85384272e-01 1.46156764e-02 5.17460521e-08]\n",
      " [8.24659937e-04 2.15603241e-01 7.83572099e-01]\n",
      " [9.75685410e-03 7.44302896e-01 2.45940250e-01]\n",
      " [9.44096660e-01 5.59025072e-02 8.33203742e-07]\n",
      " [9.75218722e-01 2.47811490e-02 1.29363582e-07]\n",
      " [1.34605651e-03 4.36836272e-01 5.61817672e-01]\n",
      " [9.94589718e-01 5.41027400e-03 7.72553151e-09]\n",
      " [9.52879342e-01 4.71197064e-02 9.51775325e-07]\n",
      " [1.06292678e-02 9.06333736e-01 8.30369959e-02]\n",
      " [1.29698662e-01 8.65427935e-01 4.87340365e-03]\n",
      " [9.63914293e-01 3.60853333e-02 3.73790102e-07]]\n",
      "\n",
      "Prediction :  [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0]\n",
      "\n",
      "Score :  1.0\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the Model\n",
    "\n",
    "print(\"\\nPrediction Probability : \\n\",model.predict_proba(X_test))\n",
    "\n",
    "print(\"\\nPrediction : \",model.predict(X_test))\n",
    "\n",
    "print(\"\\nScore : \",model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix : \n",
      " [[11  0  0]\n",
      " [ 0 13  0]\n",
      " [ 0  0  6]]\n"
     ]
    }
   ],
   "source": [
    "# Print Confusion Matrix\n",
    "\n",
    "print(\"\\nConfusion Matrix : \\n\",confusion_matrix(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi0AAAGiCAYAAAAr5/biAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAfBUlEQVR4nO3de5yVVb348c/AKCIXETlZGApIrsxLHjXjpHJULDSMEm+lnPPzeOt4DbuYvipFvGTqK8HUMoosjuax1OMN7zfES8eOioW5BBpFwfSYDQxI4jD798ceODPbcQZmnplnP4vPu9d+TbOevZ/57nja853vd6311JRKJSRJkqpdr7wDkCRJWh8mLZIkqRBMWiRJUiGYtEiSpEIwaZEkSYVg0iJJkgqhNs8fvuq2S11vrcwMOGJ63iFIUrsaVy+p6cmf995bf87s9+wmQ0b2aOxtsdIiSZIKIddKiyRJ6kZNa/KOIFMmLZIkparUlHcEmbI9JEmSCsFKiyRJqWpKq9Ji0iJJUqJKtockSZJ6npUWSZJSZXtIkiQVgu0hSZKknmelRZKkVLm5nCRJKgTbQ5IkST3PSoskSaly9ZAkSSoCN5eTJEnKgZUWSZJSZXtIkiQVgu0hSZKknmelRZKkVLm5nCRJKgTbQ5IkST3PSoskSaly9ZAkSSqEKmgPhRDOAcbHGPdpMTYWmArsAiwHZgNnxxjfbu9ctockSVK3CCGcAlxUMbY7cDcwB9gTOBoYDdwcQqhp73xWWiRJSlVO7aEQwlDgWmB/IFYcPgmYF2M8p/n7l0IIp1JOYkYBCz7ovCYtkiQlqlTKbcnzHkADsCtwLuVkZK1rgE0rnl9q/jq4vZOatEiSpA6FEAYBg9o4VB9jrG85EGO8A7ij+XVUHHu+jXOcA7wOPNNeDCYtkiSlKtuJuJOB89oYPx+Y0pkTNs9huQI4GDg0xvhee883aZEkKVXZzmmZBlzXxnh9G2MdCiH0AX4OfAU4McZ4W0evMWmRJClVGVZamltAnUpQKoUQBgO3UV49dHiM8db1eZ1JiyRJ6jEhhH7A/cAIYGyM8Yn1fa1JiyRJqarOGyZ+H/gkMBH4cwjhwy2OvR1jXP1BLzRpkSQpVVWwI24bJgG9KbeHKn0WeOCDXmjSIkmSuk2M8diK79vdi6U9Ji2SJKXKGyZKkqRCqM72UKd5w0RJklQIVlokSUqV7SFJklQIiSUttockSVIhWGmRJClRpVJVbi7XaSYtkiSlyvaQJElSz7PSIklSqhLbp8WkRZKkVNkekiRJ6nlWWiRJSpXtIUmSVAi2hyRJknqelRZJklJle0iSJBWC7SFJkqSeZ6VFkqRUJVZpMWmRJClVic1psT1UhR6Z/wqf+e6v2jx22e1PcfrM+3o4IqXi+OOO5k/z59KwbCFz59zO6E/vkXdIKjivKfUkk5Yq89zLb/CdGx+lROl9x258/AWunzs/h6iUgkmTDueaqy/hhl/fzJFHnUR9/TJm33U9w4cPyzs0FZTXVAE0NWX3qAImLVVideMafvHI85x47Wx696ppdeztFas496Y5XHr7U/TfbNOcIlTRTTn3m8z42fVccOEV3H3PQ3xp4r/x1ltv87UzTsw7NBWU11QBlJqye1QBk5YqMffFV5n58DzOHL8XX957p1bHfvbQPJ6te4NrThhHGDo4pwhVZKNGjWD48GHceef/tRYbGxuZffeDjBu3f46Rqai8ppSHDifihhA2BY4AxgDDgM2AFcBrwCPALTHGxm6McaOw07B/4K6zj2Rg3z78+L5nWh07cvTH+fr4vajt3YsZDz6XU4Qqsh0+NhKAhYtebjVeV7eY7UduR69evWiqkvKvisFrqiAS+zdot9ISQtgeeAG4FtgRWA68CrwD7AzMBP4QQhjevWGmb+st+jGwb582jw3/0CBqe1sUU+cNGNgfgIaGFa3GGxpW0Lt3b/r12zyPsFRgXlMFkVh7qKNKyzVABP4xxthQeTCEMBC4EbgKOCT78CRloaamPE+qVCq1Oe5fxNpQXlPKQ0d/vu8DfLuthAUgxrgcOJty60hSlVq+rPx/4QED+rca79+/H2vWrGHlynfyCEsF5jVVEBvZ6qG/Adt08JzhwMpMopHULRYsrANg5IhtW42PGLEt8aVFeYSkgvOaKojEkpaO2kM/B34ZQjiP8qTb14B3gT7AUGA/4GJgRveFKKmrFiz4M4sXL2HChIO4/4E5ANTW1vL5g8cy++4Hc45OReQ1pTx0lLRMAUrAZUC/No43UJ7Pcm62YUnK2qWXXc2V0y+kvn4ZTzzxNKecfCxDhgxm+pX+zaHO8ZoqgNL7NyotsnaTlhhjCZgSQrgY2I1yq2hzYBXlVUTzYoyruz1KSV32k2t/Sd++m3H6acfztTNOZN68+Xx+/DHU1S3OOzQVlNdUAVRJWycrNZUzv3vSqtsuTSsFVK4GHDE97xAkqV2Nq5fUdPys7Kz69XmZ/Z7t+5XzezT2tniXZ0mSUpVYpcWkRZKkVFXJpnBZcZtVSZJUCFZaJElKle0hSZJUCIktebY9JEmSCsFKiyRJqbI9JEmSCiGxpMX2kCRJKgQrLZIkpSqxfVpMWiRJSlSpydVDkiRJPc5KiyRJqUpsIq5JiyRJqXJOiyRJ0voJIZwDjI8x7tNibDdgGvAp4K/AlTHGyzs6l3NaJElKVVMpu0cnhBBOAS6qGBsCPAAsAPYEvgdMDSGc2NH5rLRIkpSqnOa0hBCGAtcC+wOx4vBJwGrg5BhjI/CnEMIo4GxgRnvntdIiSVKqmpqye2yYPYAGYFfgdxXH9gUea05Y1noEGBlC2Ka9k1ppkSRJHQohDAIGtXGoPsZY33IgxngHcEfz6yqfvw3wQsXY0uavw4AlHxSDlRZJklJVKmX3gMlAXRuPyRsY1ebAuxVja7/frL0XWmmRJClV2c5pmQZc18Z4fRtj7VkF9KkYW/v9ivZeaNIiSZI61NwC2tAEpS2vAkMrxtZ+/1p7L7Q9JElSqnJe8vwB5gD7hBBaFk72B16KMf6lvRdaaZEkKVXVuSPuTOAsYGYI4RLKK42+DpzS0QuttEiSpB4TY3wT+BwwCngGuAA4O8Z4XUevtdIiSVKqsm3rdEqM8dg2xn4PfGZDz2XSIklSokqJ3eXZ9pAkSSoEKy2SJKWqCtpDWTJpkSQpVdW5eqjTbA9JkqRCsNIiSVKqbA9JkqRCcPWQJElSz7PSIklSqmwPSZKkQnD1kCRJUs+z0iJJUqpsD0mSpCLw3kOSJEk5sNIiSVKqbA9JkqRCSCxpsT0kSZIKwUqLJEmpSmyfFpMWSZJSZXtIkiSp51lpkSQpUaXEKi0mLZIkpSqxpMX2kCRJKgQrLZIkpSqxbfxNWiRJSpXtIUmSpJ5npUWSpFQlVmkxaZEkKVGlUlpJi+0hSZJUCFZaJElKle0hSZJUCCYt2RlwxPQ8f7wSs2rpY3mHoMT0Hbpv3iFIasFKiyRJifLeQ5IkqRgSS1pcPSRJkgrBSoskSalK69ZDJi2SJKUqtTkttockSVIhWGmRJClViVVaTFokSUpVYnNabA9JkqRCsNIiSVKiUpuIa9IiSVKqbA9JkiT1PCstkiQlyvaQJEkqhsTaQyYtkiQlqpRT0hJC2BQ4HzgG2BJ4Dvh2jPGJrpzXOS2SJClr5wHHAScCuwMvAveEELbpyklNWiRJSlVTho8N80XgP2KM98YYFwDfAAYAe3fl7dgekiQpUXm1h4A3gUNCCD8CXgVOAN4Fnu3KSU1aJElSh0IIg4BBbRyqjzHWV4ydDtwE1AFrKNdqjmiuunSa7SFJklKVbXtoMuUkpPIxuY2fvDOwHPgSMBqYCcwKIezelbdjpUWSpERl3B6aBlzXxnirKksIYVtgFjAuxvhw8/DvQwg7AVOBQzobgEmLJEnqUHMLqLIN1Ja9gE2ApyvGnwImdCUG20OSJCWq1JTdYwO81vx114rxXYGXuvJ+rLRIkpSonFYP/TcwF5gZQjiZchLzr8CBwL5dObGVFkmSlJkYYxPlNtCDlOfAPAOMBQ7s6o64VlokSUpVqSaXHxtj/BtwavMjMyYtkiQlKsfN5bqF7SFJklQIVlokSUpUqSmf9lB3MWmRJClRtockSZJyYKVFkqRElXJaPdRdTFokSUqU7SFJkqQcWGmRJClRrh6SJEmFUCrlHUG2bA9JkqRCsNIiSVKibA9JkqRCSC1psT0kSZIKwUqLJEmJSm0irkmLJEmJsj0kSZKUAystkiQlynsPSZKkQvDeQ5IkSTmw0iJJUqKabA9JkqQiSG1Oi+0hSZJUCFZaJElKVGr7tJi0SJKUqNR2xLU9JEmSCsFKiyRJibI9JEmSCiG1Jc+2hyRJUiFYaZEkKVHu06Iec/xxR/On+XNpWLaQuXNuZ/Sn98g7JBXUw489xV4HTmw11rBiJRdcfhX7TTiavQ6cyOnfPp/Fry3NKUIVlZ9T1a1Uyu5RDUxaqtSkSYdzzdWXcMOvb+bIo06ivn4Zs++6nuHDh+Udmgrm2T+8wNlTL6VE60+ds6b8gIfmPMnXTzmey6eezdt/q+ffTv82K1auzClSFY2fU+ppNaUc06faTbepktyt+ix86SnuufdhTjv9HABqa2t54Y9zuGv2A5z59XNzjq46rVr6WN4hVJXVq1fzH7+5jR/N+BV9N9uM9xobefqBWwFYVPcKX5z071xx0Xf57H57A7D0L2/wucOO5fvnfosvjDsgz9CrRt+h++YdQlXzc2rDNa5e0qP9mue2m5DZ79ndXrk9916Tc1qq0KhRIxg+fBh33nnfurHGxkZm3/0g48btn2NkKpLHnvo9P5t1E9849QTqly3nlzfesu7YR4d+hBt+egVh1Mh1Y5vUbgLA6tXv9XisKh4/p4rBOS3qdjt8rPyLZOGil1uN19UtZvuR29Grl/9s6tjOO+7APb/5BZOO+CI1Na0/uPr02ZRdd/o4ffpsSmPjGhbVvcL3vn8FWw3ekrFj/imniFUkfk4pD1ZaqtCAgf0BaGhY0Wq8oWEFvXv3pl+/zd93TKq09T8MWa/nTfnBdP5r9v306tWLC86ZzKAtBnZzZEqBn1PFUC0TaLPSYdISQngMWK+3HWMc0+WItO6v4sr5RmvHm5qaejwmpeuoQ8cz4aCxPPjYk3znoh/SuKaJw74wLu+wVOX8nCqG1DaXW59Ky2zgQuBF4L+7NxwBLF/WAMCAAf1588231o3379+PNWvWsHLlO3mFpgTt8okAwF57fJI33nyLGb+60aRFHfJzSnnoMGmJMX4/hLAMuAQYH2N8uduj2sgtWFgHwMgR27KoRb94xIhtiS8tyikqpeTVJa/z9LPPc+j4z7Wa77LjDtsz50n/NlHH/Jwqho1yIm6M8RrgUcoVF3WzBQv+zOLFS5gw4aB1Y7W1tXz+4LE89NDcHCNTKl55dQnnfn8aTz/z/LqxUqnEk08/w8dGDs8vMBWGn1PF0FSqyexRDTZkIu5JgFsd9pBLL7uaK6dfSH39Mp544mlOOflYhgwZzPQrZ+QdmhIwes9/5JM7fZzvXPRDzvjq/2PLLQZyy5338uwfXuDHl1+Qd3gqCD+n1NPWO2mJMb4O3NmNsaiFn1z7S/r23YzTTzuer51xIvPmzefz44+hrm5x3qEpAbW1vbn6svOZ9pNfcMU1M1m2vIFPfHwUM664mL32+GTe4akg/JyqfoktHnJHXKXDHXGVNXfEVdZ6ekfcJz5yWGa/Zz/z+s2594jcp0WSpERtlBNxJUmS8malRZKkRKW2xZ9JiyRJiSqRVnvIpEWSJGUuhPCvwNnASGARMCXG+JuunNM5LZIkJaqplN1jQ4QQJgEzgWuBnYEbgBtDCHt35f1YaZEkKVFNObSHQgg1wAXAj2KM05uHLwoh7AscADze2XObtEiSpCwFYDjl6so6McaD2nz2BjBpkSQpUVlOxA0hDAIGtXGoPsZY3+L7HZq/9gkh3AV8CqgDLowx3tGVGJzTIklSopoyfACTKScflY/JFT92YPPXWcBvgc8B9wK3hRA+25X3Y6VFkiStj2nAdW2M11d8v7r56w9jjL9o/u/PhRD2BL4J3N/ZAExaJElKVJbtoeYWUGWC0pbXmr/+oWL8j8CXuhKD7SFJkhKVcXtofT0LNFCey9LSLsDCzryPtay0SJKkzMQYV4UQLgW+F0JYCjwFfBkYBzinRZIkvV9e9x6KMV4YQlgBTAU+CrwIHBZjfLAr5zVpkSQpUXneeyjGOI3y5N3MOKdFkiQVgpUWSZIS1ZTWTZ5NWiRJSlUe9x7qTraHJElSIVhpkSQpUaW8A8iYSYskSYnKa8lzd7E9JEmSCsFKiyRJiWqqSWsirkmLJEmJSm1Oi+0hSZJUCFZaJElKVGoTcU1aJElKVGo74toekiRJhWClRZKkRKW2jb9JiyRJiXL1kCRJUg6stEiSlKjUJuKatEiSlKjUljzbHpIkSYVgpUWSpESlNhHXpEWSpESlNqfF9pAkSSoEKy2SJCUqtYm4Ji2SJCUqtaTF9pAkSSoEKy2SJCWqlNhEXJMWSZISZXtIkiQpB1ZaJElKVGqVFpMWSZISldqOuLaHJElSIVhpkSQpUalt42/SIklSolKb02J7SJIkFYKVFkmSEpVapcWkRZKkRLl6SJIkKQdWWiRJSpSrhyRJUiE4p0WSJBWCc1okSZJyYKVFyeg7dN+8Q1Bi7t1yn7xDkLqkKbFai0mLJEmJSm1Oi+0hSZJUCFZaJElKVFrNIZMWSZKSZXtIkiRpPYUQdgghrAghnNDVc1lpkSQpUXnviBtC2AS4HuiXxflMWiRJSlQVLHk+H2jI6mQmLZIkKXMhhDHAV4HdgMVZnNOkRZKkRGVZZwkhDAIGtXGoPsZY38ZzZwGnxxhfDSFkEoMTcSVJSlRThg9gMlDXxmNyGz/6x8CTMcYbsnw/VlokSdL6mAZc18Z4ZZXlX4B9gV2yDsCkRZKkRGU5Ebe5BVTf4RPhOGBroLItdFUI4cwY406djcGkRZKkROW0dmgS0LdibAHllURdaheZtEiSpMzEGJdUjjVXXP43xvhKV85t0iJJUqJS28bfpEWSpERVweZyAMQYM9mb1yXPkiSpEKy0SJKUqOqos2THpEWSpESlNqfF9pAkSSoEKy2SJCWqlFiDyKRFkqRE2R6SJEnKgZUWSZISVS37tGTFpEWSpESllbLYHpIkSQVhpUWSpETZHpIkSYXg6iFJkqQcWGmRJClRbi4nSZIKwfaQJElSDqy0SJKUKNtDkiSpEGwPSZIk5cBKiyRJiWoq2R6SJEkFkFbKYntIkiQVhJUWSZIS5b2HJElSIaS25Nn2kCRJKgQrLZIkJSq1fVpMWiRJSlRqc1psD0mSpEKw0iJJUqJSm4hr0iJJUqJSm9Nie0iSJBWClRZJkhJV8t5DkiSpCFw9JEmSlAMrLZIkJSq1ibgmLZIkJcolz5IkqRCc0yJJkpQDKy2SJCXKJc+SJKkQUpuIa3uoih1/3NH8af5cGpYtZO6c2xn96T3yDkkF5zWlrG25787sefeF7PfyLD7z+6sY8a0joFdN3mEpUSYtVWrSpMO55upLuOHXN3PkUSdRX7+M2Xddz/Dhw/IOTQXlNaWsbfGpwG43nMPKl5Ywb9IlvDbzXrY7bQIjzjws79DUrJThf6pBTZ79rtpNt6mO/xWq0MKXnuKeex/mtNPPAaC2tpYX/jiHu2Y/wJlfPzfn6FREXlMb7t4t98k7hKq2x21TaGx4h3mTLl03tv13v8IWu3+MZyZOzTGy6jX2jf/s0TLUgcPGZfZ79oFX7829hNbhnJYQwn7A8cBg4E7gpzHGNS2ObwncFmMc011BbmxGjRrB8OHDuPPO+9aNNTY2MvvuBxk3bv8cI1NReU0pa5tsNYAt9go8f+zlrcYXXfjrnCLSxqDd9lAI4QvAA8BHgE2Bq4BHQwiDWjxtU2DvbotwI7TDx0YCsHDRy63G6+oWs/3I7ejVy66eNozXlLLWf8dtqenVizXvvMuus85iv1dmse/8nzLim4dDTe5/kKtZqVTK7FENOvqk+h7wvRjjgTHGz1JOTkYC94cQ+nV7dBupAQP7A9DQsKLVeEPDCnr37k2/fpvnEZYKzGtKWdtkq4EAfOJHp/LOgiXMO/oSXvvFfQw/cyLbnfqFnKPTWk2UMntUg46Slh2B/1z7TYzxKWAsMAL4bQihdzfGttGqaf4rpTKzXTve1JTaIjZ1N68pZa1Xbfnj/+1H5rFw6vX87fH51F3+W5b88n6GnznRFUTqFh0lLW8B27UciDH+CZgI7A/8HPDKzNjyZQ0ADBjQv9V4//79WLNmDStXvpNHWCowryllrfGdvwPw14eeazX+9pw/UNu/L32HfSiPsFQhr9VDIYQBIYQrQggvhxBWhBB+H0KY0NX301HS8l/AT0IIB7VsB8UY51CenPsvwKyuBqHWFiysA2DkiG1bjY8YsS3xpUV5hKSC85pS1lbVvQFAr01ar+dYW4GpliWyG7umUimzxwa6DjgEOAHYDbgFuDWEcEBX3s/6zGn5I3AXFZNtY4zXU05a/qkrAej9Fiz4M4sXL2HChIPWjdXW1vL5g8fy0ENzc4xMReU1paytjK/x96V/5UMTRrca3+rA3fn762/z98X/m1NkylsI4cOUOzKTY4wPxBgXxhgvBh6hXPDotHaXPMcYVwCHhRC2Bla2cfyGEMKjzcEpQ5dedjVXTr+Q+vplPPHE05xy8rEMGTKY6VfOyDs0FZTXlDJVKrHo4hvZ6apTCT84njfv/B2Dx+zCR44aQzzr51Alq002djn9K6wEDgYerxgvUd4+pdPcXK6KnTn5q5x+2vEMGTKYefPm862zpvLU7/4n77BUYF5TG8bN5Tq29aGfYfjXDqXviA/z7tK/8so1d7B01oN5h1W1enpzub23OSCz37Nv9V+yJTCojUP1Mcb69l4bQhgNPAGcEWO8qrMxmLRI0gcwaVHWCp60nA+c18ah82OMUz7odSGEHYGHgJeBMTHG9zobg3d5liQpURnvrzKN8gTbSh9YZQkhjAFuBV4BxnclYQGTFkmSkpVlN6W5BdRuG6ilEMIxwEzgUeCwGGNDV2Nw725JkpSpEMLRlLdEuYlyhaXLCQtYaZEkKVl5bL8fQvgoMAN4GDgL2CqEsPbw6hjj2509t0mLJEmJymmTv4nA5sABwNKKY48DnZ7hbtIiSZIyE2O8EriyO85t0iJJUqLy3NakO5i0SJKUqDzmtHQnVw9JkqRCsNIiSVKibA9JkqRCsD0kSZKUAystkiQlKqd9WrqNSYskSYlqSmxOi+0hSZJUCFZaJElKlO0hSZJUCLaHJEmScmClRZKkRNkekiRJhWB7SJIkKQdWWiRJSpTtIUmSVAi2hyRJknJgpUWSpETZHpIkSYVQKjXlHUKmbA9JkqRCsNIiSVKimmwPSZKkIii5ekiSJKnnWWmRJClRtockSVIh2B6SJEnKgZUWSZISldo2/iYtkiQlKrUdcW0PSZKkQrDSIklSolKbiGvSIklSolzyLEmSCiG1SotzWiRJUiFYaZEkKVEueZYkSYVge0iSJCkHVlokSUqUq4ckSVIh2B6SJEnKgZUWSZIS5eohSZJUCN4wUZIkKQdWWiRJSpTtIUmSVAiuHpIkScqBlRZJkhKV10TcEEIv4DzgBGBLYC5wSoxxYVfOa6VFkqRElUqlzB4b6FzgZOBEYDTQCNwbQtisK+/HpEWSJGUmhNAH+AYwJcY4O8b4PPBl4MPAEV05t0mLJEmJyqnSshvQH3h47UCMcTnwDDCmK+/HOS2SJCUqyxktIYRBwKA2DtXHGOtbfL9N89fXKp63FBjWlRhyTVoaVy+pyfPnS5KUsix/z4YQplCeXFvpfGBKi+83b/76bsXz3gW6NKfFSoskSVof04Dr2hivr/h+VfPXPsDqFuN9gBVdCcCkRZIkdai5BVSZoLTl1eavQ4HYYnwoML8rMTgRV5IkZWkesBzYb+1ACGEgsDvwaFdOXJPaFr+SJClfIYSLgH8HjgPqgEuAUcDOMcbV7b22PbaHJElS1s4FegM/BfoBjwEHdSVhASstkiSpIJzTIkmSCsGkRZIkFYJJiyRJKgQn4lap7rqttwQQQjgHGB9j3CfvWFRcIYQBwFTgUGAI8CIwNcZ4e66BKVlWWqpXt9zWWwohnAJclHccSsJ1wCGU/7jaDbgFuDWEcECeQSldVlqqUIvben87xji7eezLwOuUb+s9K8fwVFAhhKHAtcD+tN6lUtpgIYQPAxOBQ2KMDzQPXxxCGAscDzyUW3BKlpWW6tRtt/XWRm0PoAHYFfhdzrGo+FYCBwNzKsZLwOCeD0cbAyst1anbbuutjVeM8Q7gDoAQQs7RqOhijA3APS3HQgijgQOAM3IJSsmz0lKduu223pLUHUIIOwK3Uq7iXZtzOEqUSUt1anlb75a6fFtvScpaCGEM5RWOr1NelfZeziEpUSYt1anlbb1bGsr7W0aSlJsQwjHA/cD/AP8cY3w755CUMJOW6tRtt/WWpKyEEI6mvJrxJsoVloacQ1LinIhbhWKM74YQrqK8fPAv/N9tvZcAN+canCQBIYSPAjMor3I8C9iqxQTv1VZc1B1MWqpXt9zWW5IyMpHyooEDKK9sbOlxwN2WlbmaUqmUdwySJEkdck6LJEkqBJMWSZJUCCYtkiSpEExaJElSIZi0SJKkQjBpkSRJhWDSIkmSCsGkRZIkFYJJiyRJKoT/D0oZnJrZ2jUCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Confusion Matrix \n",
    "\n",
    "cm = confusion_matrix(y_test, model.predict(X_test))\n",
    "\n",
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "\n",
    "df_cm = pd.DataFrame(cm, range(3), range(3))\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.set(font_scale=1.4) # for label size\n",
    "sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 16}) # font size\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        11\n",
      "           1       1.00      1.00      1.00        13\n",
      "           2       1.00      1.00      1.00         6\n",
      "\n",
      "    accuracy                           1.00        30\n",
      "   macro avg       1.00      1.00      1.00        30\n",
      "weighted avg       1.00      1.00      1.00        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print Classification Report\n",
    "\n",
    "print(classification_report(y_test, model.predict(X_test)))"
   ]
  }
 ],
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