Breast Cancer Classification on Wisconsin Dataset using Machine Learning .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Breast Cancer Classification on Wisconsin Dataset using Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VINEET SHARMA\n",
    "\n",
    "### Email : vineetsharma14@gmail.com\n",
    "### Website : www.vineetvsharma.com\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective :\n",
    "\n",
    "***Practise the concepts of Machine Learning on the Toy Datasets***\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dataset :** https://scikit-learn.org/stable/datasets/index.html \n",
    "\n",
    "*Additional Links :* \n",
    "\n",
    "**Kaggle :** https://www.kaggle.com/uciml/breast-cancer-wisconsin-data/home\n",
    "\n",
    "**UCI Machine Learning :** https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data from Scikit Learn Toy Datasets\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
       "         1.189e-01],\n",
       "        [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
       "         8.902e-02],\n",
       "        [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
       "         8.758e-02],\n",
       "        ...,\n",
       "        [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
       "         7.820e-02],\n",
       "        [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
       "         1.240e-01],\n",
       "        [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
       "         7.039e-02]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
       "        1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "        1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
       "        0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "        1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "        0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "        1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "        1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
       "        0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "        0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "        1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "        1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
       "        1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "        1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "        1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
       "        1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 0, 1, 0, 0, 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, 0, 0, 0, 0, 0, 0, 1]),\n",
       " 'target_names': array(['malignant', 'benign'], dtype='<U9'),\n",
       " 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry \\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 3 is Mean Radius, field\\n        13 is Radius SE, field 23 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.',\n",
       " 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "        'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "        'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "        'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "        'smoothness error', 'compactness error', 'concavity error',\n",
       "        'concave points error', 'symmetry error',\n",
       "        'fractal dimension error', 'worst radius', 'worst texture',\n",
       "        'worst perimeter', 'worst area', 'worst smoothness',\n",
       "        'worst compactness', 'worst concavity', 'worst concave points',\n",
       "        'worst symmetry', 'worst fractal dimension'], dtype='<U23'),\n",
       " 'filename': 'c:\\\\users\\\\vineet\\\\envs\\\\breastcancerclassification\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\breast_cancer.csv'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the \"raw_data\" variable to understand the dataset structure "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]\n",
      " [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]\n",
      " [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]\n",
      " ...\n",
      " [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]\n",
      " [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]\n",
      " [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]\n"
     ]
    }
   ],
   "source": [
    "print(raw_data['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data['data'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*In the above cell, the shape of the data is 569 x 30, which means - 569 rows and 30 columns*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0\n",
      " 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1\n",
      " 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1\n",
      " 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0\n",
      " 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1\n",
      " 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1\n",
      " 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0\n",
      " 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
      " 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1\n",
      " 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1\n",
      " 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0\n",
      " 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 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 0 0 0 0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "print(raw_data['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['malignant' 'benign']\n"
     ]
    }
   ],
   "source": [
    "print(raw_data['target_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry \n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 3 is Mean Radius, field\n",
      "        13 is Radius SE, field 23 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "print(raw_data['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error',\n",
       "       'fractal dimension error', 'worst radius', 'worst texture',\n",
       "       'worst perimeter', 'worst area', 'worst smoothness',\n",
       "       'worst compactness', 'worst concavity', 'worst concave points',\n",
       "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data['feature_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c:\\\\users\\\\vineet\\\\envs\\\\breastcancerclassification\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\breast_cancer.csv'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data['filename']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Pandas Dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data = raw_data['data'], columns=raw_data['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension           ...             worst radius  \\\n",
       "0                 0.07871           ...                    25.38   \n",
       "1                 0.05667           ...                    24.99   \n",
       "2                 0.05999           ...                    23.57   \n",
       "3                 0.09744           ...                    14.91   \n",
       "4                 0.05883           ...                    22.54   \n",
       "\n",
       "   worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       "0          17.33           184.60      2019.0            0.1622   \n",
       "1          23.41           158.80      1956.0            0.1238   \n",
       "2          25.53           152.50      1709.0            0.1444   \n",
       "3          26.50            98.87       567.7            0.2098   \n",
       "4          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   worst fractal dimension  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add the Target Column to have the complete data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target']= raw_data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension   ...    worst texture  worst perimeter  worst area  \\\n",
       "0                 0.07871   ...            17.33           184.60      2019.0   \n",
       "1                 0.05667   ...            23.41           158.80      1956.0   \n",
       "2                 0.05999   ...            25.53           152.50      1709.0   \n",
       "3                 0.09744   ...            26.50            98.87       567.7   \n",
       "4                 0.05883   ...            16.67           152.20      1575.0   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  target  \n",
       "0          0.4601                  0.11890       0  \n",
       "1          0.2750                  0.08902       0  \n",
       "2          0.3613                  0.08758       0  \n",
       "3          0.6638                  0.17300       0  \n",
       "4          0.2364                  0.07678       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*In the above cell, the shape of the dataframe is 569 by 31; i.e. 569 rows and 31 (30 features + 1 target) columns*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>11.51</td>\n",
       "      <td>23.93</td>\n",
       "      <td>74.52</td>\n",
       "      <td>403.5</td>\n",
       "      <td>0.09261</td>\n",
       "      <td>0.10210</td>\n",
       "      <td>0.11120</td>\n",
       "      <td>0.04105</td>\n",
       "      <td>0.1388</td>\n",
       "      <td>0.06570</td>\n",
       "      <td>...</td>\n",
       "      <td>37.16</td>\n",
       "      <td>82.28</td>\n",
       "      <td>474.2</td>\n",
       "      <td>0.12980</td>\n",
       "      <td>0.25170</td>\n",
       "      <td>0.3630</td>\n",
       "      <td>0.09653</td>\n",
       "      <td>0.2112</td>\n",
       "      <td>0.08732</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>14.05</td>\n",
       "      <td>27.15</td>\n",
       "      <td>91.38</td>\n",
       "      <td>600.4</td>\n",
       "      <td>0.09929</td>\n",
       "      <td>0.11260</td>\n",
       "      <td>0.04462</td>\n",
       "      <td>0.04304</td>\n",
       "      <td>0.1537</td>\n",
       "      <td>0.06171</td>\n",
       "      <td>...</td>\n",
       "      <td>33.17</td>\n",
       "      <td>100.20</td>\n",
       "      <td>706.7</td>\n",
       "      <td>0.12410</td>\n",
       "      <td>0.22640</td>\n",
       "      <td>0.1326</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.2250</td>\n",
       "      <td>0.08321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>11.20</td>\n",
       "      <td>29.37</td>\n",
       "      <td>70.67</td>\n",
       "      <td>386.0</td>\n",
       "      <td>0.07449</td>\n",
       "      <td>0.03558</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1060</td>\n",
       "      <td>0.05502</td>\n",
       "      <td>...</td>\n",
       "      <td>38.30</td>\n",
       "      <td>75.19</td>\n",
       "      <td>439.6</td>\n",
       "      <td>0.09267</td>\n",
       "      <td>0.05494</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1566</td>\n",
       "      <td>0.05905</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>15.22</td>\n",
       "      <td>30.62</td>\n",
       "      <td>103.40</td>\n",
       "      <td>716.9</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.20870</td>\n",
       "      <td>0.25500</td>\n",
       "      <td>0.09429</td>\n",
       "      <td>0.2128</td>\n",
       "      <td>0.07152</td>\n",
       "      <td>...</td>\n",
       "      <td>42.79</td>\n",
       "      <td>128.70</td>\n",
       "      <td>915.0</td>\n",
       "      <td>0.14170</td>\n",
       "      <td>0.79170</td>\n",
       "      <td>1.1700</td>\n",
       "      <td>0.23560</td>\n",
       "      <td>0.4089</td>\n",
       "      <td>0.14090</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>20.92</td>\n",
       "      <td>25.09</td>\n",
       "      <td>143.00</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>0.10990</td>\n",
       "      <td>0.22360</td>\n",
       "      <td>0.31740</td>\n",
       "      <td>0.14740</td>\n",
       "      <td>0.2149</td>\n",
       "      <td>0.06879</td>\n",
       "      <td>...</td>\n",
       "      <td>29.41</td>\n",
       "      <td>179.10</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>0.14070</td>\n",
       "      <td>0.41860</td>\n",
       "      <td>0.6599</td>\n",
       "      <td>0.25420</td>\n",
       "      <td>0.2929</td>\n",
       "      <td>0.09873</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.22160</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.16280</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.14180</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.26500</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "559        11.51         23.93           74.52      403.5          0.09261   \n",
       "560        14.05         27.15           91.38      600.4          0.09929   \n",
       "561        11.20         29.37           70.67      386.0          0.07449   \n",
       "562        15.22         30.62          103.40      716.9          0.10480   \n",
       "563        20.92         25.09          143.00     1347.0          0.10990   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "559           0.10210         0.11120              0.04105         0.1388   \n",
       "560           0.11260         0.04462              0.04304         0.1537   \n",
       "561           0.03558         0.00000              0.00000         0.1060   \n",
       "562           0.20870         0.25500              0.09429         0.2128   \n",
       "563           0.22360         0.31740              0.14740         0.2149   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension   ...    worst texture  worst perimeter  \\\n",
       "559                 0.06570   ...            37.16            82.28   \n",
       "560                 0.06171   ...            33.17           100.20   \n",
       "561                 0.05502   ...            38.30            75.19   \n",
       "562                 0.07152   ...            42.79           128.70   \n",
       "563                 0.06879   ...            29.41           179.10   \n",
       "564                 0.05623   ...            26.40           166.10   \n",
       "565                 0.05533   ...            38.25           155.00   \n",
       "566                 0.05648   ...            34.12           126.70   \n",
       "567                 0.07016   ...            39.42           184.60   \n",
       "568                 0.05884   ...            30.37            59.16   \n",
       "\n",
       "     worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "559       474.2           0.12980            0.25170           0.3630   \n",
       "560       706.7           0.12410            0.22640           0.1326   \n",
       "561       439.6           0.09267            0.05494           0.0000   \n",
       "562       915.0           0.14170            0.79170           1.1700   \n",
       "563      1819.0           0.14070            0.41860           0.6599   \n",
       "564      2027.0           0.14100            0.21130           0.4107   \n",
       "565      1731.0           0.11660            0.19220           0.3215   \n",
       "566      1124.0           0.11390            0.30940           0.3403   \n",
       "567      1821.0           0.16500            0.86810           0.9387   \n",
       "568       268.6           0.08996            0.06444           0.0000   \n",
       "\n",
       "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
       "559               0.09653          0.2112                  0.08732       1  \n",
       "560               0.10480          0.2250                  0.08321       1  \n",
       "561               0.00000          0.1566                  0.05905       1  \n",
       "562               0.23560          0.4089                  0.14090       0  \n",
       "563               0.25420          0.2929                  0.09873       0  \n",
       "564               0.22160          0.2060                  0.07115       0  \n",
       "565               0.16280          0.2572                  0.06637       0  \n",
       "566               0.14180          0.2218                  0.07820       0  \n",
       "567               0.26500          0.4087                  0.12400       0  \n",
       "568               0.00000          0.2871                  0.07039       1  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    357\n",
       "0    212\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['target']).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*from the above cell of - print (raw_data['DESCR']); we know that -*\n",
    "\n",
    "**Class Distribution: 212 - Malignant, 357 - Benign.**\n",
    "\n",
    "\n",
    "***So, Class 0 means - Malignant and Class 1 means - Benign***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the data for better comprehension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Seaborn Heat Map Plot to get the co-relation among features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the co-relation between variables\n",
    "co_rel_data=df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26bff7c8438>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Seaborn Heat Map for quick observation of the co-relation\n",
    "plt.figure(figsize=[20,15])\n",
    "sns.heatmap(data=co_rel_data,annot=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*From the above heat map we can figure out that there is a strong co-relation between - mean radius, mean perimeter and mean area as the values are 1 and 0.99 for them. So, we visualize the other plots considering these feature_names which have a strong co-relations*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Seaborn Pair Plot to get a co-relation among features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x26bfff38668>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P540xrcaYU40xs6LP+bu3R1cscaTQVQX2lFzdCVCNc5Zl2NjUxc3Pvsvu9j4uvHslO/f1DIxrrNnZztUPrub6x98G4OcvbqKu3Mddly7UcYw8l27iuAioBp4CngZqosfGt+40E0dRlV1a3Wj9BDV+tQaC/NefP+DGTx+F0yHcdNZcnlm7m9s+f9wBg+J3XbaQYo+Daz9xBDefcwy1fq+OY+S5dIsc7gO+nqFYxo6uPeD0QIFv5PfGU1QN4X579XmxzrpR41PEsrj2E0ewo7WHQrcTt9PBxSfX89uVO7jprLkcObkEp0PwuIQ7X9rMyTOrmVzmxe/T1ka+S3c/jmrg28DR2GXVATDGfDLNuPJbZ4P9wz/VkiFF1fuvo4lDjUOWZRCgNxjhpmXrBwbAbz1/HuceP5UbnljH7646meauPu54aTNfP202tdGkoa2N/JduV9XDwPvY02//FbvG1JtpXjP/dezavx4jFbHEoWs51DhjWYbmrn46+/oJWeaQgoU3PLGOyWVebj1/HgUOobLIw7+deyxHTS6lokh38hsr0k0clcaYXwOhaL2qv8OuPzW+dTRo4lDqIJZlaGzvoTjUSlHvXqrooLq44ID3NLT14hDhsMpCLAx15YXUlOiYxliTbuIIRZ8bReSzIrIAqEvzmvktHLQHx4vS6GLylIDLC+07MxeXUjnW1ddPTe9mfA+cQcHP5+G9/1P85jNFLJi2v6ZbXbmPnft6uOCulewLhIa5mspn6SaOf4vu+nc98I/Ar4Bvph1VPutqBEx6iUPEPr9jR8bCUirXfME2Ch6/BNqjn+v2HfiXXcEPT5sM2EnjjksXctsfP6ChrZerH1ytlW7HqJQHx0XECcwyxjwLdACfyFhU+awj2kpIJ3HEzteuKjUOWJahJdBPhRXcnzRi2ncws6KAR686mZ5ghHAkMlCwUCvdjl0ptziMMRHg7AzGMjbs22I/l9Smd53iGmjbnn48SuWQZRk27u3i169sJuJwg/+gasD+et5r6uP6x9/GW+CgL2QNvKSVbseudLuqlovIL0TkoyJyfOyRkcjy1b4t4HCl3+IoroHefRAMZCYupXKgNRDk6bd2cv6iaaxvcxP6/MP7k4e/nuDnH8ZbNombzprLT/53IwVO+0eOVrod29KtVbUk+vzDQccMMH7XcezbAsWT7WKF6SiaZD+374SaI9OPS6kcMFaEby2twIT38WEwxE2rwlzy6d9zZLWH95v7+f7Te1iz0+7erSv3McXv47UbP6GVbse4dFeOT4xxjcFaN0Npmt1UYLc4wO4T1sShxqBwOExlz2acj14M7Ts41l/PDZ+7n6/87x6qS7x8/dTZNHfvTxr3XL6IyaU69XY8SLeramKxItD6IZROTf9axbEWh45zqLHHikSQ7kacoQCc8WOoWwTtO6j8nyv43inV/HFDE+VFBTz8lZN46YZTeOzqxcyZVKJJY5zQxJGMfVvsGlPlw+5TlRifH5zuQ2ehKJXvImGkYwfOzl0QaIa1j8Anvz+QPOpKndSV+zAG3C7Bsoy2NMaZdMc4Jpa96+3n8unpX0scUFRzQOJYuaWVe17ZwtaWAEfWlnDpyYex+PBKJIGaWF19IR59cycvbWymuz/Mgno/X/vkLMp18FFlkmVhmjYgj0bXa/jr4YIH4J3fw9JvwAv/TKGvkDsuXUjEGIrdLiaV+DRpjDNpJw4RWQJMH3wtY8wD6V43L+1ZD+IE/7TMXK+4eiBxPLRyO99ftp7yQjczqopYvqmV59/Zw6fmTuLf/uYYakq9cS8RsQy/fX07P/3jB3T0hjisopBir4sHVmzn2bcb+f21S5hWUZiZeNWEZwJN+5MG2M+PXQ6XL4P+blo/dz/9Tj9T/U5KPW5cLu3UGI/SrY77IDATWAvEVvIYYHwmjl2rofwwu4spE4onQcObrN6+j39Z9i7H1fn52qmz8BY4CYYt/nd9I0++tYvT/uNlbjjzSL5wwrSB6YzGGF7+oJmf/O9GNjR2cvSUUm44Yw4zq4sB2NoS4EfPb+DKB1bx9N8vxVug8+VVeizLIKG+uIv8sMJEvH7++U97+MKJAY6eUqpJYxxLt8WxCJhrzATYkciyYNcqOOwjmbtmcQ30tPLdR1+nqsTNdZ88YuAHvNvl4Oz5UzlhegW/enUrNz29nv/68wcsmVmFyyGs2t7Gjn091JR4+PqpszhpRsUBXVozqoq49pQjuPWFjdy/fBtXf3xm5uJWE45lGdoCfVQ4nHb31ODk4a8HcdAWCHL5ksP56Qsb+cXFC3IXrBp16SaO9cBkoDEDseS3lg+gvwuqMzh1NrqWI9y2g8tOP5VC96H/HLV+H9/77FGs3dnOyx808/rWViKWXVX0s8fWsmRmJS5n/N/sjq8v57hpZdz+0mYuPqmeEm9B3PcpNRzLMmxv7aYq2IAUAOfcDsuu3T/Gce7dEGjG6Z3GT5dtpLm7nwJtbYxr6SaOKmCDiLwB9McOGmPGXymSHcvt5wyuuQgXVuMCTioPcHy9f8j3iQgL6stZUF+e9D0+v3Aa33t6PY+tauDLH8nAbDA14bT39lPDPoq8bnjwb+yW8hk/Bl85hHqgdCqBvh7+7vGtNHeH+NkFx+HSwfBxLd3E8YNUThKRe4GzgCZjzDHRYz8ArgSao2/7Z2PM82nGlznbl0NhJZRMydgl/9pWySeA02r7E5o5lYqZ1cXMrC7isTd38ndLp4/afdT4FA6FKO9vRHr32omifYf9ePTSgfeErlvLPncd3/l0NZNKvXzz0bV2V1VRDgNXoyqt9mR086ZDHgmceh9wZpzjPzPGzI8+8idpGAPbXoOaualvFxvHrz4sJIiLWZ59GbtmPB+fXcPGvV2s39U5qvdR40s4FMTZvhXp3mNvXOYsgEset9drxPjr2dweYXdHP9c//jYb93bR3N2vxQvHubQSh4icLCJviki3iARFJCIiI/50Msa8AozuT8tMat8OXbth0jEZu+TOLovXGg2driq8gV0Zu248S2ZW4nIIz7w9uvdR44cV7sfZ04w43XZttj9+H/57ATx3PZz6Azt5+OvpOvcBAs5yeoIRbjlvHk+u3qnFCyeAdEewfgFcBHwI+ICvRI+l6joRWSci94pI8h36o2V7dHxj0tEZu+T/bLJ3P3MUVeEZ5cRR5HFx9JRS/rRhLxNhApxKUziItG1HOnaACUMkBGf8Gyy41O6mWnYtnPcbgpc/x+XPdlNR4uXoKaUcUV3Ej86dp6VFJoC0pz4YYzYBTmNMxBjzG+CUFC91B/aakPnYs7Rui/cmEblKRFaJyKrm5uZ4b8m87cvBXXzoXgNpWLYpxNwKkKIqPIHdGbvuUI4/rJxtrT1sbtYy7qMtJ5/RTLEsTM8+JNwLkTA8eC788gR44Bw44cqBsiL0tfNOYw/N3SEK3U5qSr1MKvNRXeLRpDEBpJs4ekTEDawVkZ+IyDdJcUjMGLM3mnws4B7gxCHed7cxZpExZlF1dZp7YiSq4U2onmOXCcmATW0RNu6z+NgUCHkrKehtRiKju4XmwuiMrD+/t3dU76Ny9BnNBMuCrkbECkH33v1TbiG6Qvwy+MT3wF+P5fVz9+pO7rp0IdXFntzGrbIu3Z+El0WvcR0QAKYB56VyIREZXKv8XOw1IrnX1wHNGzO6fuOFbWEAFk+2E4dgcPeM7g/0ymIP9RWF/PWDMfYbsMoOy4puZSxghaGgMP4K8bKpcPYviIiLL5w4nVq/Fi+ciNKdVbUdEKDWGPOvxphvRbuuhiUijwArgDki0iAiXwZ+IiLviMg67P3Lv5lObBmzew1goGpOxi75x60hZvuhygchbxUAnp7R7646ekopq7a30RfSfZ7VQXr3gavAblU7nHYBzjgzqHB64fW7eH9vgMllXvw+HQSfiNKtVfU54KeAG5ghIvOBH460ANAYc1Gcw79OJ5ZRs3ut/Vx5REYutzdg8XazxeXRBkzIWwmAu3sXTMrILYZ0zJQy/rB+D29tb2PJEVWjezM1tjh9dmWE3jZ4/Ir9q8LPuR1e/AF0N9krxLt2YU65kaml0/D7dDxjosrEAsATgZcAjDFrRWR6mtfML41v28UIvaUZudz/7bC7qU6KJolY4vD0jH7VliNrS3AILN/cqolD2SwLgr2Y/jZEHPuTBuyfQXXFs9Cx014A63SDp5iKovjVmtXEkO4YR9gY05GRSPJV41qoODxjl3tpR5hqHxxWYn9vnG7CBSW4s5A4Ct0uDq8uZsXm1lG/lxoDLAvTtQfatiC/+bSdHOKNa5gI+KeDiWBFQoivIifhqvyRbuJYLyIXA04RmSUiPweWZyCu/BAMwL6tGUscwYjhrw1hFtUcuAA95K3EHdiTkXuM5MjJJazb1a7jHArT146YCET3DKe37dAp5/56DIJxeejzVNr/FxxawHCiS/cT8A/A0dgFDh8BOoFvpBtU3mh6HzCZ2fEPWLUnQk8YFtYceDzsKc/K4DjAnEklhCKGdQ3ju6GoRhAJ2wPhVmR/K+O1/4Szf7E/efjrMRc+zEMbImzv89FBCQ6nlhJR6c+q6jHGfNcYc0J03vp3jTF9mQou5wa2is1MVdlXGsI4BY47aHgh5K3AHchOZfrZk+0+sje3jZ2KL2oUhPqRrr2AtT9RNKyC//shfPY2uO5NuPwZGkwVR9VV8OPn38OhLQ0VlW6tqkUi8nsReStaKmRddDrt+NC0AVxeu4x0Bvy1IcxRFVB40JSEkLeSgmAHjnBvRu4znFJvAVPLfZo4JjAr3IcJdoHbZ7c4Ll8Gcz5rv9jdZE8GKSjEFBTSTSF3v7KZb54+R+tPqQHpzqp6GLgBeAew0g8nzzS9N7C7Wbpaey3ebbG4LM5ykJDHHmx09+yhr3T098yYXVPC6u37sCyj0yknGCvch7TtQAavDPfXwwUPwMdvsBcBFlZi3MX0GA+lPsOPzp1HZZFbPytqQLo/EZuNMc8YY7YaY7bHHhmJLB80vw9lmalP9eouezD6+DiNl/BA4shOOZBZk4rp7AuzpaU7K/dTeSIcRLqbkfZtccqJXG4njRf+2S5qGOzB5/UytbxQ60+pQ6Tb4vgXEfkV8CIH7gD4+zSvm3u9bXa9njmfycjlXmsIU1IAM8sOfS3k3d/iyIbZk+xxjtXb2ziipiQr91Q5Fg5C2zbE4Ri6nEhRNVz4EAahx1NBkSYLNYR0E8eXgCOBAvZ3VRlg7CeO5o32cwYq4hpjT8OdVwXOOP8Xw57sJo7aMi/FHhdvbW/nwhMyV/FX5SnLgkCL/YtQbBzNX39g8vDXQ0ktON1EnG58bi1cqIaWbuI4zhhzbEYiyTfN79vPZdPSvtS2TovGgOFvh1gOYrm8RFyFWUscDhGOqClm9Y62rNxP5VigCawQrLwDTr7GrkN1zu0HjXE8CMYiYgx4yrRrSg0r3cSxUkTmGmM2ZCSafNL8ATg9GZlR9WqDPb4xf5gK2yFvJZ4sJQ6AWTXFPL66gY7eEGW+gqzdV2VZJIyxLLucyOn/Cm1bYcXP7U2ZrngWjAUOF7g8GKDbUUqZK90fC2q8S3dw/CPYe3FsjE7FfWfcTMdt2QhldRmZUfVqQ5gaH9QWDv2esMeftRYH7B/nWKOtjvHLsjDBABLshs4Gu1yBfzp89HpY8xDcfxaE+yESxESCNAYLKfFqF5UaWbq/WpyZkSjyUfPGjJQaiViG5bvDLJ58YJmRg4U8lRS1vZv2/RI1s7oYh8BbO9o5ZU5m1qmoPBPsRiL94HLbazOMge49sPJOe+pt8/sQ6sF4auj3VDHZrbOnVGLSShzjaurtYP3ddsG3GR9P+1LvtFh0BWH+CMVoQ55yCnpbECuEcYx+15HP7WRaRSFvbdcWx7gUDkKo1x4Qf/SSA8ukf/R6ePlW+PiNGF85ve5KCr1a7VYlTmsIxNP6of3sT39g/LUGu4z6wWVGDhb2ViAYCnqzt0PfrJoS1u5sJ2KZrN1TZUE4BJ2NEO6DUA+c8eP9e4UvuxZ6W2H+RVAxk96CcgJh/TGgkqOfmHiaP7CfM7D479VdYWaUgn+EruNQlhcBAsyeVEx3f5gP9nZl7Z5qlFkW9HdA3z57DOPeM+xFfZ/8/v7kUVAIRdUYl5c9vQ5o7EI2AAAgAElEQVQtJaKSpokjnub37e0zS2tHfu8wekKGN/dEWDDMbKqY/YsAs1PsEOwS6wCrtG7V+NHXbndRPXb5gSvDn7kOln7D7q4K9WCKJ7ErXIK/SMc1VPI0ccTTvBFKp9rTFNPwemOYsAXHJ5A4YosAPVmqkgtQVeyhosjNm9t0nGNcCPfbZUPC/UOvDD/ndox/Og9tiNAVRPcMVynRxBFP83sZWfj314YIbgfMTWDDtEhBMZbDndUWh4gwe1KxVsodDywLOnfbA+FWOO6GTJROwVTNore4jjOPm8acSSXa2lAp0cRxsFAftG3LSKmRV3aGOboSPInsfSNC0FeV1RYHwJxJpTR29NHQ1pPV+6oM6++0u1f/5g7o77JXgg/akIkLHgSXl4hl8Hp8WrhQpSUnS0RF5F7gLKDJGHNM9FgF8CgwHdgGXGCMyX4fSuuH9mraNBNHQ5fFpnaLr8xN/JywpwJ3lnYCjDmq1h7neH3LPuoWDrNCUeWvcD907YGO6MB3X5s9sePSp+zXnQXgcmOMBUXVmjBU2nLV4riPQxcPfgd40RgzC7va7neyHRRg78EBaXdVvbTTnoZ78Daxwwl5K7Pe4phWUUiJ18WKLa1Zva/KEMuCnn0QaIbnrof7Pms/B5qhpxm6GiHUh7EsjK8Kl5YTURmQk8RhjHkFOLhj/Rzg/ujX9wN/k9WgYpo22IPiZXVpXeblnWEm+WBaceLnhLyVFPQ2I5FgWvdOhkOEoyaXsnxTC8boeo4xxbIg2GUXMDx4f41l19oTPCpmgrcUKarGoUlDZUg+jXFMMsY0AkSfc1MHo+k9O2mkMaOqP2J4rSHMwprhy4wcLOStQjBZrVkFMHdKKbs7+ti5b/S3rlUZYlkQ7LbHM6xI/FlUVgR6WqCw3C47olSG5FPiSIiIXCUiq0RkVXPzKKyy3vtu2gv/VuyK0BOGEycld17QZ+dKT/fOtO6frGOm2LtL/XVT9latj2ej/hkFCPZCT6s9/dYK798zPMZfD043VM8Bl5YTUZmVT4ljr4jUAkSfm+K9yRhztzFmkTFmUXV1AgskktHbbteoqkhv3+8/bw/hdY5cZuRgIZ/95/F2N6R1/2RN8XupLvHw0kZNHJkwqp9RiO7mtxkeONteGf7Q39pFC2PJw18PFz5sJwyXVrtVmZdPieMZ4Iro11cAy7Iewd5oddry1BOHMYY/bQtzfDW4E5mGO0jIW4kRZ9ZbHCLCcXV+Xt3UQn84ktV7qxQEmvYXLoT9e4af/q9w3Sr47G32Yj9vnH2KlcqAnCQOEXkEWAHMEZEGEfky8O/A6SLyIXB69Pvs2vOO/ZxGOfW3my329hhOmpzCyeIg6K3C271j5Pdm2IJpfnqDEd7cqqvI85plQSQUf0wDY2/U5PKB02U/lBoFOflkGWMuGuKlU7MayMH2vANeP/jKU77E85tDuAROTiVxACFfDd7O7FerP3pqKW6Xgxfe3cNHZiXZx6ayIxKGzl32Qr94e4Y7veApAV8F+CpzF6ca9/Kpqyr3dr8FlTOTmwo1iDGG57aEWFANxSluqdFfVIuvc4u96U4WeVxOFkzz8/w7jYQjVlbvrRIQCcO+LfbOlOKy99UYvDL8nNvthX79XVBYCQ79r61Gj366YoI9dlXcylkpX+LtZotd3YalU9IIo2gKznCAgt64cwNG1eKZlbQGgry+VWtX5RXLsnfu695rL+57/DJwF9ljGV98bv+YxorboXiy3eJQahRp4ojZs84uNVJ5RMqXeOqDIG4HLEmxmwqgv9Au5e7r2Jz6RVK0YFo5vgInv39rV9bvrYbR02yXFYkt8mtYBX+4wX6tdCpUH2l/ffhHoXSKtjbUqNNPWEzDm/Zz9ZyUTg9GDM9sDnHyZChKY+fX/iK7uVLYsSn1i6TI7XKw9Igqnl23m7ZA9lavqxGEg/ZajcFjGg2r4OHP26vGG9+G335eWxsqazRxxOx8HUqmpDww/pcdYdr64BPpVSoh7KkgXFBK0b716V0oRafPnUR/2OKJ1dldS6KGYFl2SzjQHL9UutMDk+fBFc9CzVxtbais0E8Z2APRO1am3NoA+O17Qaq8sDDd9V4i9JZOz1niqK8oZG5tCff8dQt9IV3TkTORsL0qvD06w650Kpx794ED4hc+DJ5SKPDaRTl1+q3KEk0cYO/4F2iGycemdPqOTotXdkY4ox6cGfgb7Sudga99E45wX/oXS8F5x9fR1NXPQyuzPy1YYSeNpnfhN5+G/55v7x3e12EPgF/xLHxtjV0yvWSyXYeqeJK2NFRW6acNYOvL9vPkeSmd/sC7QUTgU+nv/QRAT9lsHCZMcfPqzFwwSXOnlHHs1DJ+/n+baOrKTfKa0Lr3wqOXHrQy/DL76/+aB/+9AB46F9Bqxio3NHEAbHoRSmrt3+CS1NFv+O2GIB+bAlW+zITTU34URpz4G1/LzAVT8MUl0+kJhrnp6fVabj3bIsH4K8NjrQp/PXzhESgchTpYSiVAE0cwAFtfgrpFKZ3+8IYgPWH425mZC8lyeenxz6a84c9ZXwgYM8Xv4/MLp/HCu3u5+5UtOYlhwoqtDB/MX2+X+v/62/DlP+tAuMop/eRt+rM9R37aSUmf2hU03P12PwtrYGaG68l1TDqZwo5NFO17N7MXTsJZ82o5aUYF//6H93nszewWXpyQLMvupnK4468MLyiE8ulQomMaKrf007fuMXsK7qTkB8Z/vS5Iez9clvpkrCF1Tl6M5XAz+f37Mn/xBIkI155yBMfWlfHtJ9fxy79s0m6r0WJZ9u6TvzoNHrsEvKUHrgwvqdU1GipvTOzE0d0EH7wA0z9mdw8kYXe3xV1v97O0Fmb5Mx9apKCYfXWnUr11Gb72DzJ/gwS5XQ6uP30OS2ZWcusLG7nqwdXs08WBmdfTDL+7aP/K8Oe+ZR8vq4Pa4+yKzdrKUHliYn8SV99nr7yd8+mkT/3Rij4iFnx5bubDimmZcQ4Rl4/DV37X3gY0R9wuB9d94gguO/kw/vJ+E6f9x8ssW7tLWx+ZFA7GXxkuTp1uq/LOxP009nfDyjvsQfGy5JZ7P7c5xHNbwlw4CyYVjlJ8QMRdyp7Zl1LavJop7/1q9G6UABHhM8fW8qNzj6W8sICv/24t59+5gtXbdf+OjHC54w+I617hKg9N3MTx2n9C7z6YN9TWIPE1dFl896+9zPLD+anXQ0xYR+1H6ag5kWlrb6Oodd3o33AE9RWF/PDsY/jKR2ewuamb8+5YzqW/ep2XP2jGsrQFkrLCanuK7eABcZ1yq/KUjOXuhkWLFplVq1Ylf2LTe3DXx6B+MXzshoRP6w4aLnwmwLZOi599BKYWJ3/rVDhD3Ry+8p+wnF7e+ewzhD2pbzSVSX2hCH/csJc/rG+kvSfEVL+Pz86r5WOzqjluWhkl3jSqPaYvtU1VMiypz6hlRSvhBu2WRmG1dlGNX3nx+UzVxEsc/d32zJWu3XD2L8GX2Mh2IGT48h96eHNPhJtOgBMmpRBwGnwdm5i+6od0V87nvVPvwyoYxT6yJIUiFm9u28crHzazflcnkWjLo6bEw6RSL/7CAorcLkq8LiqK3Ewt9zF7Ugnz6soodI9afaW8+I+Z8i83arzLi89nqiZWVbRgAB69xN5F7bR/TThpbO+w+Oqfeni/1eJbC7KfNAB6y45g19HXUvfOLzj6hQvYsvj/EahMrbZWphU4HSyZWcWSmVX0BiNs3NvFtpYAezr7aOsJ0tjRR18oQm8wQmdfiFDETiwuh7BoejmfmjuZzxxby+Qyb47/JEqpREycFkfzRvj9lfa+4ku/DjNH3t68vc9w3/ogd73dj0Pg28fDopo0g05TcfMapr57J65QF4HyI2mb+gnapn6S7uoFIPnfrWEZQ3tPiO2tAd7f08WanW3s3NeLACdMr+DTx07mtKMmMa0i7RZVXvxGpy0ONYS8+HymavwljkgIgt0Q6oOeFmh6HzY+DxuW2dttfuSbUHcCAH1hQyBkCEagN2zo6DfsDRg2t1u8uSfMa7sihCxYUgtXH525WlTpcoR68O9+mdLmVRS2b0SMRdBXw766U+mcdBK9ZTMJeyqwXD4iLh/G6cl1yMPa3d7Lii2tvL6llZ1tvQBMK/dx/GHlzJ5UQn1FIdUlnoEuL4/LgcvpwClCsdeF0xH3/2Be/MfUxKGGkBefz1SNv8Tx7tPw+BUHHvOW2Ws15l8KhftX3z6wtpPv/yX+/tr1ZU5OnFLAp2d6Obw8f3v0HMFufHveoKjhVXx7V+MI9x7wevMJ19O68Bs5ii55DW09rNrWxrqGDj5s6qa5q3/Y9z/7Dx/hmKlx673kxX9MTRxqCHnx+UzVmE4cItIMZHPTiCqgJYv3y6SJFnuLMebM0QgmGRn8jObLv5/GcaBU48iLz2eqxnTiyDYRWWWMSa2Mbo5p7GNbvvwdaBz5GUe25f9oqlJKqbyiiUMppVRSNHEk5+5cB5AGjX1sy5e/A43jQBmPQ0T8InJtpq8b5z6niMiSlM7VMQ6llMofIjIdeNYYc0yC7xfsn+VWkvf5AdBtjPlp0jFq4lBKqfwhIr8DzgE2An8B5gHlQAHwPWPMsmhy+UP09cXA3wCnATcCu4EPgX5jzHUiUg3cCcTKL38D2AWsBCJAM/APxpi/JhyjJg6llMofg1scIuICCo0xnSJShf3DfhZwGLAFWGKMWSkiU4DlwPFAF/B/wNvRxPFb4HZjzKsiUg+8YIw5Kp0WR/6ubFNKKSXAj0XkY4AFTAVi1fK2G2NWRr8+EXjZGLMPQEQeB2ZHXzsNmGv3aAFQKiIl6QSliUMppfLXJUA1sNAYExKRbUCsGmhg0PuGW4nuABYbYw4oKzEokSRNZ1UppVR+6QJiLYIyoCmaND6B3UUVzxvAx0WkPNq9dd6g1/4IXBf7RkTmx7lPUjRxKKVUHjHGtAKvich6YD6wSERWYbc+3h/inF3Aj4HXgT8DG4CO6Mtfi15jnYhsAK6JHv8f4FwRWSsiH00mRh0cV0qpcUBEio0x3dEWx1PAvcaYp0bjXtriUEqp8eEHIrIWWA9sBZ4erRtpi0MppVRStMWhlFIqKZo4lFJKJUUTh1JKqaRo4lBKKZUUTRxKKTWBiciZIrJRRDaJyHcSOkdnVSml1MQkIk7gA+B0oAF4E7jIGLNhuPPGdIvjzDPPNIA+9BHvkRf0M6qPIR5J6w9HFu9q612+vTWwdVdb7/L+cGRxKtc5yInAJmPMFmNMEIiVdB/WmC5y2NLSkusQlBqWfkZVJvSHI4s/2Nv9zFcfWl3V0NZLXblv+h2XLnxm9qTisz0u54o0Lj0V2Dno+wbgpJFOGtMtDqWUmghauoK3xZIGQENbL199aHVVS1fwtjQvHa9E7ogtojHd4lBjl2UZWgNBguEIbpeTyiI3DkfqZZ6VGs/CllUbSxoxDW29hC2rNs1LNwDTBn1fh72D4LA0caisGZwsIpbh357bwB83NFFX7uOeyxcxZ1KJJg+l4nA5HI115b7pg5NHXbkPl8PRmOal3wRmicgM7O1kvwBcPNJJ2lWlssKyDBv3dnHu7a+x9Ja/cPGvXueKJTNYMM1PQ1svVz6witZAMNdhjm/tO+DVn0FHQ64jUUmqKnFff8elC1vqyn2AnTTuuHRhS1WJ+/p0rmuMCWPv1fEC8B7wmDHm3ZHO0xaHyorWQJArH1jF4D7aG59cx01nzeXqB1fT0NZLMBzJcZTjmGXBQ+dBywfw9u/g6r+Cy53rqFSCPC7nitmTis9+9KrFt4Utq9blcDRWlbivT3NgHABjzPPA88mco4lDZUUwHCFeH63fVwDYv0G5Xc5chDYxbH7RThr1i2HHCnj393DcF3IdlUqCx+VcMbXctyTXcYB2VakscbucxJrZMXXlPtp7QwNjHJVF+hvwqNnyEjjd8LEboGwavPnrXEekxjBNHCoryn0F3HXZQgb30d512ULm15Xx1LVLdWB8tO1YCZWz7ORx+CnQ8AZ07Mp1VGqM0q4qNeosy/Bhczf/9ecPuOmsuVQWuakp8TClzIfLpb+7jLpICBrfhrln298fthTWPAgbn4cTr8xtbGpM0sShRt3ggfE/bmgC7BbHU9cupbrEk+PoJoC2bWCFwH+Y/X3pVCiphU0vauJQKdFf99SoG2pgXGdRZUnLh/Zz6VT7WQSmLICtL9utEaWSpIlDjbqhBsZ1FlWWtB6UOAAmz4NQj92FpSYsEblXRJpEZH0y541a4ogXkIg8KiJro49tIrI2eny6iPQOeu3O0YpLZV9lkZt7Ll90wMC4zqLKotZN4PWDp3j/sUlH28/bl+cmJpUv7gPOTPak0RzjuA/4BfBA7IAx5sLY1yJyG9Ax6P2bjTHzRzEeNcqGqj/lcAhzJpXw1LVLtTZVLrTvhOKaA4/5yqF0Cux8PTcxqeSF+xfT3XQbVrgWh6uR4prrcXnSWgBojHlFRKYne96oJY7hAhIRAS4APjla91fZFSspEhsEP7j+lMMhOhCeKx1xEgfY03N3vZX9eFTywv2LaXrvGR67rIr2HeCvn84FDz5DzVFnp5s8UpGrMY6PAnuNMR8OOjZDRNaIyMsi8tEcxaVSFK+kiNafygPGQOcuKKw+9LXKWdC1G7r2Zj8ulZzuptsGkgbYdcceu6yK7qZ0y6qnJFeJ4yLgkUHfNwL1xpgFwLeA34pIabwTReQqEVklIquam5uzEKoaiWUZekNhnTkVlVef0d42CPVCUdWhr1UdYT/rAHn+s8K1A0kjpn2HfTwHsp44RMQF/C3waOyYMabfGNMa/Xo1sBmYHe98Y8zdxphFxphF1dVxfotSWRXrotrcFNCZU1F59RntjK4OL4oTR/l0+7lp2O2lVT5wuBrx1x94zF9vH89FODm452nA+8aYgdrOIlId3TQdETkcmAVsyUFsKkmxLqr/fvFDbjlvns6cyjed0T154rU43MVQWAVN72U3JpW84prrueDBloHk4a+HCx5sobgmrbLqIvIIsAKYIyINIvLlRM4btcHxaECnAFUi0gD8izHm19gbhTxy0Ns/BvxQRMJABLjGGLNvtGJTmRNb3NfQ1stPX9jITWfNxe8roK7cR22ZT2dO5Vp3dPzCVxH/dX+9tjjGApdnBTVHnc0Xn8/0rKqLUgonnZsOZ6iAjDFfjHPsSeDJ0YpFZc7BU259bntxX0NbL2t2tnP1g6sHyolo0sgDA4nDH/91/zT44AV7vw6HrgfOay7PCvzTtKy6GlsG7+J33W/XsH5XB529YR658mQ+Ndee7qldVHmmu8nuknIO8e9RMhXCfdCVk65yNUZpkUOVsNh4RnWxh388Yw43PrluYM3GXZct5OZzjsHhcOjivnzSvdde7DeU0in2874tUDZ16PcpNYi2OFTCYuMZ15wycyBpgD3t9uoHV+NwOKgu8WjSyCfdTSMkjuhszn2bsxOPGhc0caiExYoV+n0FumZjrOjaM3ziKKwCR4Hd4lAqQZo4VMJixQp7ghFdszFWBJrBWzb06w4nFFdzyOIypYahiUMlLFas8LhpZdx16UJds5HvQr0Q7B56RlVMUbVdCFGpBOnguBrSUNVuK4o8+H1urXab7wIt9rNnmBYHQFGNlh1RSdHEoeLSarfjQCBaJ8s7QoujuAYCTRDuB5f+m6qRDdtVJSJOEbk1W8Go/KHVbseBnlb72TdSiyNax6qjYfj3KRU1bIvDGBMRkYUiIsYYk62gVO4N3id8wTQ/15wyE7+vgGA4gmUZ7ZYaC2ItjhG7qqKJo3MXVM4c3ZjUuJBIV9UaYJmIPA4EYgeNMb8ftahUzrldTj41t4YvLZ1BscfFVx9+K26XlcpjscQxUoujMFrHqmvP6Majxo1EZlVVAK3Yu/V9Lvo4azSDUrlhWYbmrn52tfVQ4ISvnTqbvpA1kDRAu6zGlECLXWrE5Rv+fYWV9nOskq5SIxixxWGM+VI2AlG5dfBg+G++eAKPvLGdf/r0Udz2+eNo7w1x50ubWbOzXRf7jRWBFnsNh4zQMiwohAKftjhUwkZscYjIbBF5UUTWR7+fJyLfS+C8e0WkKXZe9NgPRGSXiKyNPj4z6LV/EpFNIrJRRM5I9Q+kUnPwYHhtmYcrlszgsnvf4MK7V3Lzsxv4xzPmsGCaXxf7jRU9reCJu5HmoQortdChSlgiXVX3AP8EhACMMeuw99QYyX3AmXGO/8wYMz/6eB5AROZGr3l09JzbYxs7qewYPBgOUOgpOKQe1Y1PruNrp87SxX5jRaAFvAkmDl+FJg6VsEQSR6Ex5o2DjoVHOskY8wqQ6GZM5wC/i24huxXYBJyY4LkqA2J1qGIilhW3HtXM6iIdGB8rekYoNzJYYSV0auJQiUlkVlWLiMwEDICInA+k8wm7TkQuB1YB1xtj2oCpwMpB72mIHjuEiFwFXAVQX18f7y0qCbHV4ZZl8burTqa1O8iezj6AgQ2aYurKfbicDk0aI8ibz2hPK9TOT+y9vnJ7EaAxI4+JqAkvkRbH3wN3AUeKyC7gG8A1Kd7vDmAmMB87+dwWPR7vkxp33Ygx5m5jzCJjzKLq6uoUw1Cwf0D8u0+tY1NzgC/cvZJzfvkaNz+7gYhluPOgelR3XrqQmmJdWTySvPiMhvogGEi8xeH12xs69XeNblxqXEikxWGMMaeJSBHgMMZ0iciMVG5mjNkb+1pE7gGejX7bAEwb9NY6QOcGjrLWQJCf/WkjN5xxJB29IW46a+7AzKkv/uZNll23hMeuXkw4YuFyOqgp9uByaV3MMSG2ajzRxBErvd7dlPi4iJqwEkkcTwLHG2MCg449ASxM9mYiUmuMiXVznQvEZlw9A/xWRP4DmALMAg4eV1EpGqpYoWVZXLFkBl+6782BxX23nDePn76wkTU72+kLWkwtL8x1+CoVPbECh4kOjscSx16oOmJ0YlLjxpCJQ0SOxJ7lVCYifzvopVLAO9KFReQR4BSgSkQagH8BThGR+djdUNuAqwGMMe+KyGPABuyB9783xuhCgQw4eH3Gp+bWcNNZR2MweJwOnA45YJ3GjU+u46az5nLzsxt0yu1YFquMm3CLI1oIMdA0OvGocWW4Fscc7BXifuzV4jFdwJUjXdgYc1Gcw78e5v0/An400nVVcgavz1gwzc+XP3I4F92zkupiD98+cw43PLHukNZGbMMmnXI7hqXTVaXUCIZMHMaYZdg1qhYbY1ZkMSaVQYPXZ3z7zDn0BCPc9vnjqChyc+sL7x+yTuPmc45hit/H5FKvzp4ay5JtcXhKQJx2V5VSI0hkjKNVRF4EJhljjhGRecDZxph/G+XYVAbE1mdUF3so9ri44Ym3DmhhNHcFWbOzHbCTx/SqQrwFOuV2zOtpsROBuyix94vDTjKxwohKDWM0V46rPBDrdvraqbMOKVZ445PruOaU/WW068p9OEToDerw0pgXaLZnR0kSs+B8fujWxKFGNmorx1V+iO0TfkRNUdyV4LFxjLpyH7eeP4+mzn4dFB8PAq0j78NxMI+2OFRicrFyXOWAQyTuSvDaMi9PXLMYf6GbQH+IEm+BDoqPBz1J1KmK8fmhdfPoxKPGlVRXjn91VKNSGWNZhl3tPYjALefNO2Al+C3nzcMyhvPvXEGBU6gt8zG9skjHN8aDQBJ1qmK8ZXZ9K6VGkMh+HFuAA1aOj35YKl2xRX9hK0Kh20l3f4T7l2/lprPm4vcV0N4b4v7lW7nhjCOpK/fx/p4ujplSqkljvAg0w6SjkzvH64dQL/R3g6d4dOJS48KIiUNE/MDlwHTAJdECaMaYr41qZCollmVo7w3S2N7H1Q+t5pcXL8Bf6OZHz23giiUzBkqlx2pPPbFqB3dccjw//78POb5+Xq7DV5kQitac8pYnd97gRYCaONQwEhnjeB67cu07gDW64ah0xFaJ7+no46Zl62lo66Wm1EsoYnHewmkUup385osn0BeKsLujD3+hiy+cdBgtXUG+efocHdsYL2Krv2OJIFGxrq1AC1QcntmY1LiSSOLwGmO+NeqRqLTFVon//KIFVBd7+OXFCwj0h+kJRrj52Q0HrN94cvVO3M7DOLy6iJk1Rfh9bu2mGi9iU2q9SSaO2OpxnVmlRpBI4nhQRK7ErmTbHztojEl0kyaVJcFwhCWHVzKlzMu/n3cMhe4CNjV1D7Q+YP/6jYe/chKhiIW/0EWZT0uljytptzg0cajhJZI4gsCtwHfZv0eGAbQtm2eKPE6u/eRMHA7B53YRsQyFbmfc9RsiUF5UQIlHu6fGnVi9qWRbHLHEoYsA1QgSmY77LeAIY8x0Y8yM6EOTRh4Khg1FbidNXf1cfM/rbNzbRU8wcsCWsGBPxXWKUObR7qlxKdUWh9NtlyjRFocaQSKJ412gJ9kLi8i9ItIkIusHHbtVRN4XkXUi8lR0xhYiMl1EekVkbfRxZ7L3m+gsy2CMoT9suPrB1TS09XLnS5spLyrg1vPnxd3JTzdlGqe69tr7cDhTaE16/Zo41IgS6aqKAGtF5C8cOMYx0nTc+4BfAA8MOvYn4J+MMWERuQW7BtaN0dc2G2MS3CBZDd6cqcDloD8UocDpIGKZga6pNTvb+ddnNvDtM+fwyJUnYxmD2+mgWpPG+NbVCL6K1M71lumeHGpEiSSOp6OPpBhjXhGR6Qcd++Ogb1cC5yd7XXXo5kx15T7u+9IJdPaFaersP6C0yJqd7dzwhF0ufc7kYmr9vhGursa8zkYoTHINR4zPr3tyqBElsnL8/lG6998Bjw76foaIrAE6ge8ZY/4a7yQRuQq4CqC+vn6UQstvgzdnAqgu9gB2VdsCp3DHJccPVMKNFS8sKyzAU6CtjGzI+We0qxFq5qZ2rrcc9m7IbDxq3Blu69jHjDEXiMg77J9NNcAYk/IyYxH5LnaF3YejhxqBemNMq4gsBJ4WkaONMZ1x7ns3cDfAouelMn4AACAASURBVEWLDolrIhi8OdMFC+u45pSZ7AsEaQ0EeXL1Tr78kcP5+UXzKfEW4C1w0t0Xwu1yUq7TbrMip59Ry7I3Y5r+kdTO9/mhrx3C/eDSz4uKb7gWx9ejz2dl8oYickX0mqcaYwyAMaaf6PiJMWa1iGwGZgOrMnnv8cIXXQFeVexGRLj83jcOWNz361e3cN7CafzDI2t57KqTKfW5dUe/iaKnBUxk/2K+ZA1eBFhWl7m41LgyZN+FMaZRRJzAr40x2w9+pHIzETkTezD8bGNMz6Dj1dF7ISKHA7OALancY7yzLMPezn5uWraejt4Q1zy0+pDFfectnEZlkZvbLzmeUHS2lSaNCaJjp/1cVJ3a+QN7j+sWsmpow3Z6G2MiQI+IJFmfGUTkEWAFMEdEGkTky9izrEqAPx007fZjwDoReRt4ArhGV6bHFxvfqC72UOv3Dbk506RSLw+t2E57T0g3ZppI2qOJo7gmtfNjiwZ1EaAaRiKzqvqAd0TkT0AgdnCk6bjGmIviHP71EO99EngygVgmNMsy9IXCNLT1ctNZc9nR2hN3c6bqEg8/f/FDlm9p5bpPHqHFCyeSjjQTx0CLY09m4lHjUiKJ47noQ+XQ/g2Z7J386so8OPta+fOXD+fD1hDf//MemrtD3HXpQoLhCMu3tHLXZQuZUubTbqqJpH0HFBSBO8Wy6LHE0aVdVWpoCU3HFREf9qynjVmIScXR2RekozfMf7/4Afd9cSH14e24n70E2ndwrL+exz7/MOuCU6kodhOxDE9du5TKIi0pMuG070y9tQHgLLC7q7p0d2g1tBEn9ovI54C1wP9Gv58vIs+MdmBqP8syA0njvIXTqPf04H7cThoAtO+g4PFLOLKsHwEml3ipLvFo0piI2ramlzgACis0cahhJbIi7AfAiUA7gDFmLTBjFGNSB2kJ9ON0wHc/O5fKIjdWqH9/0ohp34GbMOXeAi0nMlFZEdi3FUqnpncdnyYONbxEfsKEjTEdBx2bkAvvssGyDM1d/exq62FfoJ+mzj76ghEiFvzouQ2cf+cKPtwXAv9BK5L99eB04/EkMmylxqX2HRDpT3/9RWGFXbZEqSEk8lNmvYhcDDhFZBbwNWD56IY1MQ2uQbXk8EouW3zYAaVDbjlvHs1dQf5/e+cdZld1HfrfumV6rxp1IYSQBJIAUQIEg+lgJMA2xRSB7WAnxo4Tx3l+sZPgOI84LnEJMTG2sWjGRpgikA2y6UVIDEhIoqmXkWY0VdNnbjnr/bHPZa5GM6N7Z24baf++73zn3D3n7L3m3H3Ounuvvdb6lz838Osl91Hy5FLzsiiZSvCah+jxlxD3umnLkUPLVrMf64gjr9w4AIaDxuZhsQwiFsXxZUwSp37gYeBZ4DvJFOpoJdpH42/OO5Ybf7WGyoJsfvCpE5lXHMCnXdx/7XRu/t12bv1DNw/c9Aw5EiYsfpq1iJocGyLiqKbJXbsy5hFHOaBmumrwyNZiIbZVVT3AN90w6KqqnckX6+gkEApTWZDN9z41HxETvPB7n5zHDF8rvp5G8ytw/cM88Il/5GsvBWihhMb2fiaV5lJTbEOKHPU0bDQv/ZwxjjvzXeN6e51VHJYhOaziEJFTgXsxHt+ISDvwWVV9K8myHXXkZnn5x0tmc+uyN3nkr07ngWunky8tSOs2eOk/TbjrxXdR8Pr3+PEVP6Ah7FBeYONQWVwaNkBpAtatFLjhStrrDvlTe2+QX726g6c37GNPaw/52T5OmFjMx4+v4hPza6gqyhl7+5aMJ5apql8BfxMJcy4iZwO/BkYdHdcyNKGw8vVHN1BZ4KcqtBdfX4uJOVQyHa7+BbzyQ1hxO1x8J34NkZ/lp6LALru1AME+aN4M864ae115ruIYtHJva2MXt/zaBNScP7mYi+dNoCcQZvP+Tv7t6ff495XvcdaxFVxywgROn1HO9PI8fF4PobBDW0+Qzr4guVleJhTlIGL77HgmFsXRGZ0bQ1VfFRE7XZVgQiGHYNhh2a2nMiW7F19XHYRD8MBVHxnAueZ+aPoA8isJiVUalijq3wEnBOWzxl6XP8dMd0WNOBra+7juntUEw8q3F8/juOrCgy7Ze6CXV7c0s3p7M69saf6oPNvnoT/kHHRudVE2t50zk1vPnG777zglFsWxVkR+jjGMK3At8KKInAygqm8nUb4jHsdR2nr7aTjQzxcefIuzjynhPy6sMA/u8qUHOfnxyM1w+Q8J5VfRk1VKjn3oLBHq3jT7qjmJqS+/6qO+F3aUv37wLbr7Q3x78QlMKcs75PRJJblce+oUrlk0mX0H+tja1EljZz+BkEO2z0NRjp+8bB89gRBv7mzlO0+/x7rdbfz0upOs8hiHxKI4InnA/3VQ+ZkYRfLx4S4UkXsxuTcaVfUEt6wMk/lvOrATuEZV28SMXX8CXAb0ALcc6Uopsvy2ob2Pf35yE2cfU8KdZ3qQZZfDlXcP6eSnZTNplCom2KRMlmjq1kJB9ejzcAymoNp4oQO/WbOLdXsO8KXzjh1SaUQjIkwqzWVS6fApii+cU80T6/fxSO0eZlcX8uXzEzBKsqSUwzoAqup5I2zDKg2XZcAlg8q+ATynqrOA59zPAJdi8nDMwqTdvDuef2Q8Ell+W1Xg5yefmMidF1TgWX6TURi9bUM6+YV8+Uwoybe/0iwDqMKu10efLnYoCifAgT20d/Xx/Wc/ZN7EIs6aWZ6QqkWEKxdO5C9mlvPT57ewp7Xn8BdZMoqkxqZQ1ZeBwXk1lgCRPOb3AVdGld+vhjeAEhGpSaZ86cYsv/VzXFYTp2TtxqMhuPhOmLwIXvsxLL5rQHmUTEWv+w3+wiqrNCwH07rdLNWunpe4OgtrwAny+xfX0NEX4qYzpiXUoC0i3Hj6NDwi/GCVjZ063khHfIpqVa2Hj7IMRiKyTQL2RJ1X55YdebEPHAd6mqj2ODx607F4uxth5dcGjOCL74Ln/81sl/8QLZ+F48vFW1AFHhuHyjKI3avNPtEjDmB1bS2nTPsLppXnJ65ul7L8LC6aW81T7+zj/1xyPBNLhp/esmQWmfQWGurnzCExsUTkNhGpFZHapqZxmKXMcdDG9+C1u/D1t+MN9UKwd2CkcWC3WXJ71lehqxEnv4pWXxVSUG2Vxjgh5X10zxrILkxsjvCiiQBUBveyeMHExNU7iAvnVqMKv31zz+FPtmQMMb2JRORMEfmMiNwc2cbQ5v7IFJS7b3TL64ApUedNBvYNvlhV71HVRaq6qLJylHmV04h2NyEv3Aln/DWE+uD+JXDvxfDsP8HH/2VAeVTPQ29ZyffX+QjjtdNT44iU99Hda6BiNkjiflhobhl9ZLEgt4lZVaNMChUDlYU5LJhSwsNrdxMKO4e/wJIRxJKP4wHgB8DZwKnutmgMba4AlrrHS4Eno8pvFsMZQHtkSutIwgn1wxlfNFFMH7np4OW2K26H875lpqvUIRwKsfjkqVQU2BVUlmHo64DmD6FydkKrfacZdjgTOCm/OenOeh87rpKmzn7e3NmW1HYsiSMWG8ciYK6qxh1KXUQeBs4FKkSkDrOk97vAIyLyOWA38Gn39D9gluJuxSzHvTXe9jIdx1HCHj/e0hnGmDnEcltKpsENj0J3E56iKcwuK7SjDcvw1L9j9olw/Ivi8c0BTtcazg3vpSWhNR/KwiklZPs8PLOpnr9I0MotS3KJKaw6MIFRGKlV9fph/nT+EOcq8KV42xgvRHKGZ3uLqXQaEX+eGVlEK4+SqeDxgjcLVfAUTgCrNCwjUb/e7MuPTViVIUd5aluIk/JryO2uRcIB1JuVsPoHk+P3Mn9yMX/c1MC/XjHP/lAaB8QyKVoBvCciz4rIisiWbMGONCI5w4WwMXJ7/XDjYzD7cnNCZDUVgIYIF1SD1yZlshyG+g0mnlluScKqfG1vmNY+paS8BtEw2V3JN1yfNqOcxs5+NuwdnDPOkonE8ma6I9lCHA0EQw6V/n4qeuqR3904sPT20/fBx75u4gKt+TlccAealYcnu/CwdVosNL5npjcTyB+3B8nzwcSaGtgLuR3b6SuemdA2BjN/cjECvLy5iYVTEqcELckhFs/xl4baUiHckYITDlPeX0e1r3tAaYDZL19qlMaz/wQf+zrqzULzq/F4vekV2pL5hEMmIm5p4hRH2FFW7QyxqArCBcb/Nrdje8LqH46iHD/HVObz0uZxuMT+KCSWVVVniMibItIlIgERCYtIRyqEG+84jtLd14d0N+LJLjCpOIcyiFefALesRIsmI0WT8Phsuk5LDLRug3AgoSOO2gYzTXVmDTj+fIJZxeS2J19xAMyfXML63Qdo7w2mpD3L6InFxnEXcD2wBcgFPu+WWUbAcZTmzh7yeuqRvnbo2g8Hdg4ZfwqPF4K9SE4JWKVhiZWmD8w+gVn6Vu0M4ffAKW48h0DeBHI6dyWs/pGYP7mYsCqrtzUf/mRLWonJY0hVtwJeVQ2r6q8xS2wtI9DSHaBcOhHxGsesR24yWfwGxZ/i2gfBmwV5ZVZpWOKjeYvZF01KWJXP7w4yvwLyXOtnIK+anM4dCat/JI6tLCDb52H1tmQvALaMlViM4z0ikgWsF5HvYZblJj5wzRGE4yheHDweD3S3mZAiB3ab7fl/M+FFckuheAr4ss0UViTPs8USKy1bIa8C/ImJ8bSjPcyOduXiEwbKAnkTKN33Mp5gD45/5JDqY8Xn9TB7QiGvW8WR8cQy4rjJPe92oBsTFuSTyRRqPOM4yq6WLkq9fYgTNlNUuaVww3ITTqSuFn53Izzx19C+xyiNggk2DpUlfpo2Q3ECRxu7QgCcWj1QFsgzwQ5TNV01t6aILY1dNHf1p6Q9y+g47IhDVXeJSC5Qo6rfToFM45rOvn6mZXUiff3Q33Fw1NslP4Pn7oCuRljyMzSvEimoslNUlvhRhdatMO2shFX54p4QUwpgQtTAIpDrKo6uXfSUJSi74AjMm1gEwBvbW/jE/OQFV7SMjVhWVV0BrAeecT8vtA6AQ+M4Sl6oAw9hcMJmZBG99PbJv4Gr7jErqMqPRcqmm6kqiyVeelqhrx0KE/Ny7Q0qa+rDHxnFIwRyTUF2Z2qi186oKCDH72HN9sFpfCyZRCzzI3cApwEHAFR1PSbtqyUKx1FauvrweTC/Bnuah156C6jHj+SVWqVhGT2t7hLZosQojjX1YQJhOHlQMF/Hn0fIX0BO1+6hL0wwXo9wXHUha3ZYO0cmE4viCKmqjQMwAo6jNBzopiIrgAS6IRQwQQyHWnrr9SNZ+eDLSY+wliODjxRHYpJkvlwXIssDJwwRYzCQW0VOZ2oUB8CcCUVs3t9FW3cgZW1a4iMWxbFJRD4DeEVkloj8N/B6kuUaNziO0tHTR01OAGnbCfcvhqb3Yf3DQyy9fQhySiHJq1MsRwGt2wExCysSwMt7QpxQDtlDBCwI5lalzDgOcHyNCbezdqedrspUYlEcXwbmAf3Aw0AH8NXRNigis0VkfdTWISJfFZE7RGRvVPllo20jVTiO8uH+Top8YTPS8OfDZ5ZD4/tw+hdM7KmL74TPPgs3r0CLp5rpKRu80DJWWrdDQZUJljlG9nU5bD3gHDJNFSGQW0lWzz5jt0sBMysLyPJaO0cmE8uqqh7gm+42ZlT1Q2AhgIh4gb3A45j8Gz9S1R8kop1U0NIdoCxbkb5WMz3lhGDdQ3DiJ2HLKlh4PVTOAV8W6s1CsnLBl7zw1JajiNbtH+UFHyuv1plluCcNoziCuVV4nBBZvY0E8hMzNTYSfq+HY6sKrJ0jg4llVdUiEXlMRN4WkQ2RLUHtnw9sU9XUjYMThOMohf4wVX07kGWXw12nwG8+DSdcDRt/D/OuNoELPcZzXPy51hhuSRyt26EwcfaNshyYNkxA5kBOBQDZ3XUJaS8W5tQU8n59Bx19Nm5VJhLLVNVDwDKM098VUVsiuA4z/RXhdlcx3SsipQlqI+E4jtLe3UN2XzMyOP3r8pvhpBuMQ9+1D0JWPvhyzTSWxZII+tqhtzUhiiPsKK/WhVhYAcNliA3mmqFIdtfeMbcXK3NqinAUaq2dIyOJRXE0qeoKVd2hqrsi21gbdsOYLAaWu0V3AzMx01j1wA+Hue42EakVkdqmpvSEYG7pDlDiDRjP8KGW3Hp8ZlMHetshu8jaNY4ikt5HIyuqEqA43m1xONB/6DLcaIKREUcKEjpFmFVViM8j1s6RocTyNvtXEfkl8BzGQA6Aqj42xrYvBd5W1f1uffsjfxCRXwBPD3WRqt4D3AOwaNGiuPOgjxnHodTXD52NkJ1nQon486C3DV77sfEK9/rBm40WVCP5VVZpHGUkvY8m0IfjFde+sXAExaHeLILZJeR0pW6qKstn7Byrt1s7RyYSyxvtVuB4wA84bpkCY1Uc1xM1TSUiNaoayWt+FSbXeWbhONC6DV9OMWQXwoFdh4YUKagCjw9VRfMqEKs0LInmoxHH2I3jr9SFOKYISg9jfgvmVKbUxgEm/Mjj6/bS0RekKMeG5ckkYpmqWqCqi1R1qare6m6fHUujIpIHXMjByud7IrLRNbyfB/zdWNpICt2NZiK4Yx+EeuHx2w4NKeLPQxE0rwSPXUFlSQYt201U3DE6kXYHldqG8IijjQiB3EqyUzjiAJg7sRhHYa2drso4YlEcb4jI3EQ2qqo9qloe7ZGuqjep6omqOl9VF0eNPjKDcMgst/VmmdwaTmho+4YTQvLL8FjPcEuySNCKqtV7Q4QcOCUGxRHMqSCruz5lvhwAs6qMP4cNs555xKI4zsbk4vjQXfG0MYHLcccH4ZAJge5xp52uvNskZxoqpIgv24YTsSSXli0JCTXycl2IHC/MKzv8ucHcSjwaIqu3Ycztxorfzc/xyhabhzzTiGUC/pKkS5HJOI4JWAjQ32WOe9vgw2fhmvvhkZsHbBzXPmgTMlmSS28b9LRA0eQxV/XinhAnloN/iDAjg/koSm7XXgL5icsBcjjmTy7moTW7qW/vpaY4MQmrLGMnpnwcqRAkI3EcaHzXTEupwvKlA0pi8V2w8TG44ffGyc+XY5SGNYZbkknzVrMfYwKnne0OuzuUS2NMVx5RHDldu+msPm1MbcfD/MklPLRmN69sbuaaU6ekrF3LyNi0cyPR3Qgv/Afklg0oDTD7FbfD1NPM0tucIiiotgmZLMmnJZJnfGwjjud3G4/sU2McIAdzylE85KQoL0eEKaW5lOVn8ZKdrsoorOIYjnDI5Ao/44ugwzj65Vea0YZ18LOkiubNxtZWWH34c0fghd0m219NrAENPD6CuRVkpygvRwQRYf6kYl7e3EQw7Bz+AktKsIpjKMKhgbDVOcXQsnVoQ3hBtfHbsDGoLKmi8QMomjSwUGMUdAeVN/aFWRSnOS6QW5nS8OoRTpleSmdfyHqRZxBWcQwmFDRJmAJd5nNWAZTOgJseh9mXm7JIbo28MruCypJamt4/9EdMnLxSFyLowKlxDloCudVpURzzJ5WQ7fOw6r3UreiyjIydX4kmHIKOugFl0L7HOPVFDOKfvh8+9nXILTfBC7OGCSdqsSSDYC+07YKpZ46pmlU7QxT64YQYluFGE8irwd//PL7+A4SyS8YkQzxk+TzMn1zMs+82cMcV8/B4honGaEkZdsQRTVcD9HeCEzTLbiNKAwYi33Y3GR+O3FITAddiSRWN7wMKpdNGXUXIUZ7bGeS0avDG2X37802Ik5yOHaNuf7ScPqOc/R39NitghmDffNF4/JBdYLxj/XlDG8TLZkJBhVUaltTTsNHsy2aOuoq19WHaA3DGKMJcBfKM02E6FMei6aXk+j08sS51od0tw2PffhHCIZPnoMVdJx/sGdognpVv7RqW9NCw0eR1KRi9k+nK7UFyvCOHUR+OQG4VjvjIa98y6vZHS7bPy6LpZazcWE9fMHVhTyxDYxUHuN7hLdC130S7fezzJojckp8NKI+SqXDdb6xnuCV91L8DpdPNVOkoCDnKH7abaaqc0Vg3PT768yeS1/bBqNofKx87rpLOvhBPvbMvLe1bBrCKw3Gg8T0I9w/YNOpq4Y9fN74Zt6yEr6yDW5+Bqnl2isqSHsJBaHgHKmaNuorX94Zp64O/HEMaj/7CqWlTHHNriphcmsuy13eimvpUPJYB0vYWFJGdbsDE9SJS65aVicifRGSLu09++tjuJvjt9cauEW3TqKuFey8x5Y5jopFapWFJF/s3QagfKmePuoontgTJ8xG3/0Y0fQVTye7dj68v9UZqEeGiudW8u6+DtTuskTydpPtNeJ6qLlTVRe7nbwDPqeosTMbBbyRdglCvm+7VO7RNw+uHsmOs0rCklz1vmn3F6BRHZ8BMU50zEbJiCGo4HL1FxjBf0Lx+9JWMgXOOq6Qk189Pn0u9ncUyQKa9DZcA97nH9wFXJq0lx4HuZjNf/JV1Jj/4zSsOtmlc86AbViTTbpPlqGPnywORCkbBH7cH6QvDhWPzHaS3+BhUPBQ2vT22ikZJts/LFQsm8tq2FlbbPB1pI50OgAqsEhEFfu7maa6OJHBS1XoRSY4l2nFMSJHO+oMd/K55AD73HAS7TUgHG07Ekgk4Dux8FSaePOoqHnwvwNQCmD1Gvz31ZtNbOIOi/WvGVtEYOH9OFX/cVM+3n3qXp798Nr54HVIsYyadd/wsVT0ZuBT4koicE8tFInKbiNSKSG1T0ygjZvY0Qdv2Qx38HrnJTF05YRMPyCoNyyhISB+NZt86k4ejZsGoLl/fGGZDk8Pl003m47HSXX4ihc3r8PUfGHtloyDb5+XGM6bxQUMn976Wep8SSxoVh6ruc/eNwOPAacB+EakBcPeNQ1x3j5sDfVFl5SgWowOEAsM7+KljbRqWMZGQPhrN5mfMlOqkRYc/dwiWbQyQ54OPJyidRWfFSYg6lNQ9n5gKR8Fp08tYNK2U7z/7Ie/Xd6RNjqOVtLwdRSRfRAojx8BFwCZgBbDUPW0p8GRCG3Yc46sBwzv4ef1WaVgyB1V49zGommvyvsTJ7g6Hp7YFuXgq5CVoYrq3eCaB3Eoqtz+WmApHgYjwV395DPnZPr7wwFu0dQfSJsvRSLrekNXAqyLyDrAWWKmqzwDfBS4UkS3Ahe7nxBDx1/jlBfDoLcbQONjB79oHoWAUsRgslmSx920TzeCYc0d1+d3r+/EIXDX6KCWHIh4OTPwYJQ2vk9u+NYEVx0dRrp+/v+A46tt7+fx9tXT3h9Imy9FGWhSHqm5X1QXuNk9V/59b3qKq56vqLHefuMXaPa6/RsTBb+Xfm7hUt6yEr6wfcPCzCZksmcTan4M/F6b/ZdyXbjsQZvkHQS6aCuUJjpLTOvkCHE8WEzf9b2IrjpNZ1YXcft4s1u1p4+ZfraXVjjxSwtEzJxMKHOrgd8+5gEDZDJPD2SoNSybRsg02/R6OvdDESIuT767px++F649LvGjhrCJap1xI5fYn0uZJHuG0GWV85fxZbNh7gMV3vcq63W1pledo4OhRHL6soW0avqz0yGOxHI4/3wHihRM+Ffelq3YG+dPOENccC6VJWhzYPH0xYX8e02r/3dhi0sjpM8r558vn0hcM88m7X+efn9jE/o6+tMp0JHP0KI68Srju4UFBCx825RZLpvHBSnh/Bcy/1mSajIOWXodvvdLHjCK4OpG2jUGEswppOuaTlDS8TvnOp5PXUIzMqi7ku1fP54I51Ty0Zhdn/+fzfOmht/nDxnrae4LpFu+I4uiZm/F4zMqUz//ZTFv5sozSsCuoLJlGxz5YcbtZFj7vqrguDTnKV57rpa1P+dbZ4Ety926dciHF9a8y48076JhwBsHc9P4Qy8/2cetZM7jsxBqeebeBV7Y0sXJjPQIcV13IginFzK0p4rjqQmZU5lNdmGMzCo4CGc9RJhctWqS1tbXpFsOSmWTE2yDuPhrsg2WfgP0b4RM/guLYnS8cVf7hhT4e2xLkqwvGHl4kVrK76jhmzbfoqljAexfcj3ozx3E27Chb9neyaV8HWxs72dHcTUffwOorn0eoKsqmqjCH8vwsKguzmVCcw/TyfObUFHFsVQHe5CiWjOifo+XoGXFYLJlOOAi//xzsfRPO/ae4lEZXQPnaC708uzPETbNTpzQA+gsms3fubUzZdBdznv8sW876EcG8zMhb4/UIx9cUcXyN8YFRVQ70Bqlr66WhvZfmrgCt3QHae4Nsa+ri7d1tHOgJEvk5XZTj4+xZFVw0dwLnz6miMMefvn8mg7CKw2LJBHpa4fEvwJZVcNoXYNqZMV0WdpQ/7ghx5xt9NHQpfzUPlsxIsqxD0FFzJnUaZuL7v+KkJ89j/6zr2X/cDfQVpUGYERARSvOyKM3L4sRJxUOeEww7NLT3saO5m/frO1i9rYU/bGwgy+vhvOMruezEGs6dXUVx7tGrROxUleVIJSOmAobto6oQ6DbBNresgjV3Q+8BozRmX3rI6Y4qfSHoCCiNPcr2Aw5v7w+xameIhm5laiF8eT7Mjc+OnnCyehqo3PYoxfvXIBqmo/IUWqZdSmfVIvoKpxP2FyYmYFYKcVTZ2tjF6m0trNnRQltPEK8I8ycXc8q0Uo6vKWJqWR5VhdmU5PnJy/Lh9woy8v85vm7CIKzisBypZMSDOWQf/Z8zoHkzaFTu7EmnwOlfPCTD38/f6uL7qzsIOYfWneuDBVU+Lprh58xJvmTNxY8Kb18rRbufo3DPi2R37vqoXBHUm8WWq/9EoHh6+gQcJY4qHzZ08ubOVjbta2dbUzeBIb4cEWM/uezEGn5y3UlDVZU5X9YoGNeKQ0SagF2HPTFxVADNKWwvkRxtsjer6iXJECYeEthHM+X7s3IczGjlyIj+OVrGteJINSJSG5WtcFxhZR/fZMo9sHJkphypxjoxWCwWiyUurOKwWCwWS1xYxREf96RbgDFgZR/fZMo9sHIcTKbIkVKsxRPJmAAAB6BJREFUjcNisVgscWFHHBaLxWKJC6s4hkFE7hWRRhHZFFVWJiJ/EpEt7r40nTIOh4hMEZEXROR9EXlXRP7WLc9o+UUkR0TWisg7rtzfdstniMgaV+7ficgRHQtfRHaKyEYRWS8itW5ZSr67ePq9GH4qIltFZIOInJxkOe4Qkb3ufVkvIpdF/e3/unJ8KCIXJ1COuJ6lZN6TTMIqjuFZBgxeZ/0N4DlVnQU8537ORELA11R1DnAG8CURmUvmy98PfFxVFwALgUtE5AzgP4EfuXK3AZ9Lo4yp4jxVXRi11DNV390yYu/3lwKz3O024O4kywGmHyx0tz8AuH37OmCee83PRMSbIDnifZaSeU8yBqs4hkFVXwYGp65dAtznHt8HXJlSoWJEVetV9W33uBN4H5hEhsuvhi73o9/dFPg48KhbnnFyp4iUfHdx9vslwP3u9/YGUCIiNUmUYziWAL9V1X5V3QFsBU5LkBzxPktJuyeZhFUc8VGtqvVgOhSQGSFAR0BEpgMnAWsYB/KLiFdE1gONwJ+AbcABVY3Ewq7DPLhHMgqsEpG3ROQ2tyyd391wbU8C9kSdl4rv5nZ3CujeqOm6lMgR47OUjnuScqziOIIRkQLg98BXVbUj3fLEgqqGVXUhMBnzq3HOUKelVqqUc5aqnoyZ9viSiJyTboGGYah4S8n8bu4GZmKmMeuBH6ZKjjiepVTfk7RgFUd87I8MO919Y5rlGRYR8WM6+kOq+phbPG7kV9UDwIuYeeUSEYmkAJgM7EuXXKlAVfe5+0bgcYwCTed3N1zbdUB00pCkfjequt/9YeEAv2BgOiqpcsT5LKX0nqQLqzjiYwWw1D1eCjyZRlmGRUw8518B76vqf0X9KaPlF5FKESlxj3OBCzBzyi8An3JPyzi5E4mI5ItIYeQYuAjYRHq/u+HaXgHc7K4kOgNoj0zfJINBtoKrMPclIsd1IpItIjMwhum1CWoz3mcppfckbaiq3YbYgIcxw+Eg5lfE54ByzAqKLe6+LN1yDiP72Zjh8QZgvbtdlunyA/OBda7cm4B/ccuPwbwItgLLgex0y5rEe3AM8I67vQt80y1PyXcXT7/HTMv8D8YOtRFYlGQ5HnDb2YB5QddEnf9NV44PgUsTKEdcz1Iy70kmbdZz3GKxWCxxYaeqLBaLxRIXVnFYLBaLJS6s4rBYLBZLXFjFYbFYLJa4sIrDYrFYLHFhFYclEo21wj1+Pd3yWI5ORGSiiDx6+DNjqutKNxihJQlYxXGEEuVpHReqemaiZbFYDoeI+FR1n6p+6vBnx8SVQFyKY7TPzNGIVRwJRESmi8gHIvJLEdkkIg+JyAUi8pobt/8097x8N0jbmyKyTkSWRF3/ioi87W5nuuXnisiLIvKoW/9Drkfr4PZfFJE7ReQl4G9F5AoxeSzWicifRaTaPa9cRFa55T8nKr6OiHRFtfl0VPldInKLe/xdEXnPDTb3g6TdUEtcZEj/+7GIvO62f7j2bhGR5SLyFCao43Rx82+4f3tCRJ4SkR0icruI/L17/RsiUuaeN1NEnhETEPIVETnelXsx8H0xeTtmDnWee/0yEfkvEXkBE77fEgvp9kA8kjZgOiZ+/4kYpfwWcC/mxbwEeMI9707gRve4BNgM5AN5QI5bPguodY/PBdoxcW88wGrg7CHafxH4WdTnUgbSA38e+KF7/FMGvLIvx3jGVrifu6LafDqqrruAW4AyjHdupN6SdN93u2VU//uFe3wOsOkw7d2C8Qovi5I/cs0tmEgBhUCl2/4X3b/9CBNsEIzX9iz3+HTgefd4GfCpKNlGOu9pwJvu7288bXZolnh2qOpGABF5F5PsRUVkI+bBABN/aLGI/IP7OQeYigmGdpeILATCwHFR9a5V1Tq33vVuXa8O0f7voo4nA78TE+MnC9jhlp8DXA2gqitFpC2O/68D6AN+KSIrMQ+dJXNId/97GEw+DREpEhN7bLj2AP6kqsPl3XhBTQ6MThFpB55yyzcC88VErD0TWB41AMoeXEkM5y1X1fAwMliGwCqOxNMfdexEfXYYuN8CfFJVP4y+UETuAPYDCzC/7PqGqTfM8N9dd9TxfwP/paorRORc4I6ovx0u1kyIg6cycwBUNeROQZyPybp2OybRkiUzSHf/G9yvdIT2Tufg/hrv/+LB5GpZOEIdxHDeSDJYhsDaONLDs8CXI/PEInKSW14M1KsJG30TMNb0l8XAXvd4aVT5y8ANbtuXYqa0BrMLmCsm4mgxRlFEfr0Vq0nb+VVMbgTL+CKZ/e9at86zMZFh20dob0yoyYuxQ0Q+7dYrIrLA/XMnZprrcOdZRoFVHOnhO5i0qBtcY+B33PKfAUtF5A3MNMFYfwndgRmevwI0R5V/GzhHRN7GTCPsHnyhqu4BHsFEBX0IE7UWzMP4tIhsAF4C/m6MMlpSTzL7X5uYJd3/y0Bu+OHaSwQ3AJ8TkUg04SVu+W+Br7vG9JkjnGcZBTY6rsViSQgi8iLwD6pam25ZLMnFjjgsFovFEhd2xGGxWCyWuLAjDovFYrHEhVUcFovFYokLqzgsFovFEhdWcVgsFoslLqzisFgsFktcWMVhsVgslrj4/y7qyUY30ZV0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 402.375x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df, hue='target', vars=['mean radius', 'mean perimeter'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*List of all the feature name. We can plot the pairplots as needed for further investigation*  \n",
    "\n",
    "       'mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
    "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
    "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
    "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
    "       'smoothness error', 'compactness error', 'concavity error',\n",
    "       'concave points error', 'symmetry error',\n",
    "       'fractal dimension error', 'worst radius', 'worst texture',\n",
    "       'worst perimeter', 'worst area', 'worst smoothness',\n",
    "       'worst compactness', 'worst concavity', 'worst concave points',\n",
    "       'worst symmetry', 'worst fractal dimension'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Seaborn Scatter Plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b89a82f28>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='mean radius', y = 'mean area', hue = 'target', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*List of all the feature name. We can plot the pairplots as needed for further investigation*  \n",
    "\n",
    "       'mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
    "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
    "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
    "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
    "       'smoothness error', 'compactness error', 'concavity error',\n",
    "       'concave points error', 'symmetry error',\n",
    "       'fractal dimension error', 'worst radius', 'worst texture',\n",
    "       'worst perimeter', 'worst area', 'worst smoothness',\n",
    "       'worst compactness', 'worst concavity', 'worst concave points',\n",
    "       'worst symmetry', 'worst fractal dimension'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Seaborn Count Plot to get a reference of data distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b89abe7f0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['target'], label='Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Preparing/Preprocessing the Data (for the Classifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The dataframe df has the target columns; so we drop it to prepare the train and the test data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X is the Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(labels='target',axis=1) # axis = 1 means along the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension           ...             worst radius  \\\n",
       "0                 0.07871           ...                    25.38   \n",
       "1                 0.05667           ...                    24.99   \n",
       "2                 0.05999           ...                    23.57   \n",
       "3                 0.09744           ...                    14.91   \n",
       "4                 0.05883           ...                    22.54   \n",
       "\n",
       "   worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       "0          17.33           184.60      2019.0            0.1622   \n",
       "1          23.41           158.80      1956.0            0.1238   \n",
       "2          25.53           152.50      1709.0            0.1444   \n",
       "3          26.50            98.87       567.7            0.2098   \n",
       "4          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   worst fractal dimension  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y is the Output "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     0\n",
       "5     0\n",
       "6     0\n",
       "7     0\n",
       "8     0\n",
       "9     0\n",
       "10    0\n",
       "11    0\n",
       "12    0\n",
       "13    0\n",
       "14    0\n",
       "15    0\n",
       "16    0\n",
       "17    0\n",
       "18    0\n",
       "19    1\n",
       "20    1\n",
       "21    1\n",
       "22    0\n",
       "23    0\n",
       "24    0\n",
       "Name: target, dtype: int32"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data into training set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398, 30)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171, 30)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>13.74</td>\n",
       "      <td>17.91</td>\n",
       "      <td>88.12</td>\n",
       "      <td>585.0</td>\n",
       "      <td>0.07944</td>\n",
       "      <td>0.06376</td>\n",
       "      <td>0.02881</td>\n",
       "      <td>0.01329</td>\n",
       "      <td>0.1473</td>\n",
       "      <td>0.05580</td>\n",
       "      <td>...</td>\n",
       "      <td>15.34</td>\n",
       "      <td>22.46</td>\n",
       "      <td>97.19</td>\n",
       "      <td>725.9</td>\n",
       "      <td>0.09711</td>\n",
       "      <td>0.1824</td>\n",
       "      <td>0.1564</td>\n",
       "      <td>0.06019</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>0.07014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>13.37</td>\n",
       "      <td>16.39</td>\n",
       "      <td>86.10</td>\n",
       "      <td>553.5</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>0.07325</td>\n",
       "      <td>0.08092</td>\n",
       "      <td>0.02800</td>\n",
       "      <td>0.1422</td>\n",
       "      <td>0.05823</td>\n",
       "      <td>...</td>\n",
       "      <td>14.26</td>\n",
       "      <td>22.75</td>\n",
       "      <td>91.99</td>\n",
       "      <td>632.1</td>\n",
       "      <td>0.10250</td>\n",
       "      <td>0.2531</td>\n",
       "      <td>0.3308</td>\n",
       "      <td>0.08978</td>\n",
       "      <td>0.2048</td>\n",
       "      <td>0.07628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>14.69</td>\n",
       "      <td>13.98</td>\n",
       "      <td>98.22</td>\n",
       "      <td>656.1</td>\n",
       "      <td>0.10310</td>\n",
       "      <td>0.18360</td>\n",
       "      <td>0.14500</td>\n",
       "      <td>0.06300</td>\n",
       "      <td>0.2086</td>\n",
       "      <td>0.07406</td>\n",
       "      <td>...</td>\n",
       "      <td>16.46</td>\n",
       "      <td>18.34</td>\n",
       "      <td>114.10</td>\n",
       "      <td>809.2</td>\n",
       "      <td>0.13120</td>\n",
       "      <td>0.3635</td>\n",
       "      <td>0.3219</td>\n",
       "      <td>0.11080</td>\n",
       "      <td>0.2827</td>\n",
       "      <td>0.09208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>12.91</td>\n",
       "      <td>16.33</td>\n",
       "      <td>82.53</td>\n",
       "      <td>516.4</td>\n",
       "      <td>0.07941</td>\n",
       "      <td>0.05366</td>\n",
       "      <td>0.03873</td>\n",
       "      <td>0.02377</td>\n",
       "      <td>0.1829</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>13.88</td>\n",
       "      <td>22.00</td>\n",
       "      <td>90.81</td>\n",
       "      <td>600.6</td>\n",
       "      <td>0.10970</td>\n",
       "      <td>0.1506</td>\n",
       "      <td>0.1764</td>\n",
       "      <td>0.08235</td>\n",
       "      <td>0.3024</td>\n",
       "      <td>0.06949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>13.62</td>\n",
       "      <td>23.23</td>\n",
       "      <td>87.19</td>\n",
       "      <td>573.2</td>\n",
       "      <td>0.09246</td>\n",
       "      <td>0.06747</td>\n",
       "      <td>0.02974</td>\n",
       "      <td>0.02443</td>\n",
       "      <td>0.1664</td>\n",
       "      <td>0.05801</td>\n",
       "      <td>...</td>\n",
       "      <td>15.35</td>\n",
       "      <td>29.09</td>\n",
       "      <td>97.58</td>\n",
       "      <td>729.8</td>\n",
       "      <td>0.12160</td>\n",
       "      <td>0.1517</td>\n",
       "      <td>0.1049</td>\n",
       "      <td>0.07174</td>\n",
       "      <td>0.2642</td>\n",
       "      <td>0.06953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>17.35</td>\n",
       "      <td>23.06</td>\n",
       "      <td>111.00</td>\n",
       "      <td>933.1</td>\n",
       "      <td>0.08662</td>\n",
       "      <td>0.06290</td>\n",
       "      <td>0.02891</td>\n",
       "      <td>0.02837</td>\n",
       "      <td>0.1564</td>\n",
       "      <td>0.05307</td>\n",
       "      <td>...</td>\n",
       "      <td>19.85</td>\n",
       "      <td>31.47</td>\n",
       "      <td>128.20</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>0.1486</td>\n",
       "      <td>0.1211</td>\n",
       "      <td>0.08235</td>\n",
       "      <td>0.2452</td>\n",
       "      <td>0.06515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>15.00</td>\n",
       "      <td>15.51</td>\n",
       "      <td>97.45</td>\n",
       "      <td>684.5</td>\n",
       "      <td>0.08371</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.06505</td>\n",
       "      <td>0.03780</td>\n",
       "      <td>0.1881</td>\n",
       "      <td>0.05907</td>\n",
       "      <td>...</td>\n",
       "      <td>16.41</td>\n",
       "      <td>19.31</td>\n",
       "      <td>114.20</td>\n",
       "      <td>808.2</td>\n",
       "      <td>0.11360</td>\n",
       "      <td>0.3627</td>\n",
       "      <td>0.3402</td>\n",
       "      <td>0.13790</td>\n",
       "      <td>0.2954</td>\n",
       "      <td>0.08362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>11.80</td>\n",
       "      <td>17.26</td>\n",
       "      <td>75.26</td>\n",
       "      <td>431.9</td>\n",
       "      <td>0.09087</td>\n",
       "      <td>0.06232</td>\n",
       "      <td>0.02853</td>\n",
       "      <td>0.01638</td>\n",
       "      <td>0.1847</td>\n",
       "      <td>0.06019</td>\n",
       "      <td>...</td>\n",
       "      <td>13.45</td>\n",
       "      <td>24.49</td>\n",
       "      <td>86.00</td>\n",
       "      <td>562.0</td>\n",
       "      <td>0.12440</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.1449</td>\n",
       "      <td>0.05356</td>\n",
       "      <td>0.2779</td>\n",
       "      <td>0.08121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>14.53</td>\n",
       "      <td>13.98</td>\n",
       "      <td>93.86</td>\n",
       "      <td>644.2</td>\n",
       "      <td>0.10990</td>\n",
       "      <td>0.09242</td>\n",
       "      <td>0.06895</td>\n",
       "      <td>0.06495</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>0.06121</td>\n",
       "      <td>...</td>\n",
       "      <td>15.80</td>\n",
       "      <td>16.93</td>\n",
       "      <td>103.10</td>\n",
       "      <td>749.9</td>\n",
       "      <td>0.13470</td>\n",
       "      <td>0.1478</td>\n",
       "      <td>0.1373</td>\n",
       "      <td>0.10690</td>\n",
       "      <td>0.2606</td>\n",
       "      <td>0.07810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>13.71</td>\n",
       "      <td>18.68</td>\n",
       "      <td>88.73</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.09916</td>\n",
       "      <td>0.10700</td>\n",
       "      <td>0.05385</td>\n",
       "      <td>0.03783</td>\n",
       "      <td>0.1714</td>\n",
       "      <td>0.06843</td>\n",
       "      <td>...</td>\n",
       "      <td>15.11</td>\n",
       "      <td>25.63</td>\n",
       "      <td>99.43</td>\n",
       "      <td>701.9</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.2566</td>\n",
       "      <td>0.1935</td>\n",
       "      <td>0.12840</td>\n",
       "      <td>0.2849</td>\n",
       "      <td>0.09031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "149        13.74         17.91           88.12      585.0          0.07944   \n",
       "124        13.37         16.39           86.10      553.5          0.07115   \n",
       "421        14.69         13.98           98.22      656.1          0.10310   \n",
       "195        12.91         16.33           82.53      516.4          0.07941   \n",
       "545        13.62         23.23           87.19      573.2          0.09246   \n",
       "261        17.35         23.06          111.00      933.1          0.08662   \n",
       "227        15.00         15.51           97.45      684.5          0.08371   \n",
       "399        11.80         17.26           75.26      431.9          0.09087   \n",
       "453        14.53         13.98           93.86      644.2          0.10990   \n",
       "523        13.71         18.68           88.73      571.0          0.09916   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "149           0.06376         0.02881              0.01329         0.1473   \n",
       "124           0.07325         0.08092              0.02800         0.1422   \n",
       "421           0.18360         0.14500              0.06300         0.2086   \n",
       "195           0.05366         0.03873              0.02377         0.1829   \n",
       "545           0.06747         0.02974              0.02443         0.1664   \n",
       "261           0.06290         0.02891              0.02837         0.1564   \n",
       "227           0.10960         0.06505              0.03780         0.1881   \n",
       "399           0.06232         0.02853              0.01638         0.1847   \n",
       "453           0.09242         0.06895              0.06495         0.1650   \n",
       "523           0.10700         0.05385              0.03783         0.1714   \n",
       "\n",
       "     mean fractal dimension           ...             worst radius  \\\n",
       "149                 0.05580           ...                    15.34   \n",
       "124                 0.05823           ...                    14.26   \n",
       "421                 0.07406           ...                    16.46   \n",
       "195                 0.05667           ...                    13.88   \n",
       "545                 0.05801           ...                    15.35   \n",
       "261                 0.05307           ...                    19.85   \n",
       "227                 0.05907           ...                    16.41   \n",
       "399                 0.06019           ...                    13.45   \n",
       "453                 0.06121           ...                    15.80   \n",
       "523                 0.06843           ...                    15.11   \n",
       "\n",
       "     worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       "149          22.46            97.19       725.9           0.09711   \n",
       "124          22.75            91.99       632.1           0.10250   \n",
       "421          18.34           114.10       809.2           0.13120   \n",
       "195          22.00            90.81       600.6           0.10970   \n",
       "545          29.09            97.58       729.8           0.12160   \n",
       "261          31.47           128.20      1218.0           0.12400   \n",
       "227          19.31           114.20       808.2           0.11360   \n",
       "399          24.49            86.00       562.0           0.12440   \n",
       "453          16.93           103.10       749.9           0.13470   \n",
       "523          25.63            99.43       701.9           0.14250   \n",
       "\n",
       "     worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       "149             0.1824           0.1564               0.06019          0.2350   \n",
       "124             0.2531           0.3308               0.08978          0.2048   \n",
       "421             0.3635           0.3219               0.11080          0.2827   \n",
       "195             0.1506           0.1764               0.08235          0.3024   \n",
       "545             0.1517           0.1049               0.07174          0.2642   \n",
       "261             0.1486           0.1211               0.08235          0.2452   \n",
       "227             0.3627           0.3402               0.13790          0.2954   \n",
       "399             0.1726           0.1449               0.05356          0.2779   \n",
       "453             0.1478           0.1373               0.10690          0.2606   \n",
       "523             0.2566           0.1935               0.12840          0.2849   \n",
       "\n",
       "     worst fractal dimension  \n",
       "149                  0.07014  \n",
       "124                  0.07628  \n",
       "421                  0.09208  \n",
       "195                  0.06949  \n",
       "545                  0.06953  \n",
       "261                  0.06515  \n",
       "227                  0.08362  \n",
       "399                  0.08121  \n",
       "453                  0.07810  \n",
       "523                  0.09031  \n",
       "\n",
       "[10 rows x 30 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149    1\n",
       "124    1\n",
       "421    1\n",
       "195    1\n",
       "545    1\n",
       "261    0\n",
       "227    1\n",
       "399    1\n",
       "453    1\n",
       "523    1\n",
       "Name: target, dtype: int32"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create and Train the Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reference Links :**\n",
    "\n",
    "Sklearn SVM : https://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm\n",
    "\n",
    "Sklearn SVC : https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_classifier = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\vineet\\envs\\breastcancerclassification\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the Model/Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reference Links :**\n",
    "\n",
    "Sklearn Metrics : https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics\n",
    "\n",
    "Sklearn Confusion Matrix : https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html\n",
    "\n",
    "Sklearn Classification Report : https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = svc_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([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, 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, 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, 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, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "conf_mat = confusion_matrix(y_true=y_test,y_pred=y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,  63],\n",
       "       [  0, 108]], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b89e09208>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the heat map of the Confusion Matrix for better understanding\n",
    "\n",
    "sns.heatmap(data=conf_mat,annot=True)"
   ]
  },
  {
   "attachments": {
    "Confusion%20Matrix.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Confusion%20Matrix.png](attachment:Confusion%20Matrix.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        63\n",
      "           1       0.63      1.00      0.77       108\n",
      "\n",
      "   micro avg       0.63      0.63      0.63       171\n",
      "   macro avg       0.32      0.50      0.39       171\n",
      "weighted avg       0.40      0.63      0.49       171\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\vineet\\envs\\breastcancerclassification\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# Classification Report \n",
    "\n",
    "print(classification_report(y_true=y_test, y_pred=y_predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Above Classification Report is bad. It indicates that the Model is not classifying the Class 0***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improve the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling the data by Unity Normalization i.e. Bring all the values between 0 and 1. It is a common practice to improve the model's efficacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reference Links :** \n",
    "\n",
    "Sklearn Processing : https://scikit-learn.org/stable/modules/preprocessing.html\n",
    "\n",
    "Sklearn MinMaxScaler : https://scikit-learn.org/stable/modules/preprocessing.html#scaling-features-to-a-range\n",
    "\n",
    "Sklearn MinManScaler : https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html\n",
    "\n",
    "\n",
    "\n",
    "Rescaling the Data for Machine Learning : https://machinelearningmastery.com/rescaling-data-for-machine-learning-in-python-with-scikit-learn/\n",
    "\n",
    "Prepare Data for Machine Learning : https://machinelearningmastery.com/prepare-data-machine-learning-python-scikit-learn/\n",
    "\n",
    "Standardize or Normalize : https://medium.com/@rrfd/standardize-or-normalize-examples-in-python-e3f174b65dfc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.29624369 0.27730808 0.28381849 ... 0.21839623 0.15474078 0.12800949]\n",
      " [0.27812332 0.22590463 0.26940639 ... 0.32576197 0.09520993 0.18006104]\n",
      " [0.34276899 0.14440311 0.355879   ... 0.40203193 0.24876799 0.31400475]\n",
      " ...\n",
      " [0.32317939 0.2404464  0.29937215 ... 0.12093614 0.17602996 0.05222109]\n",
      " [0.30799745 0.33513696 0.3052226  ... 0.66291727 0.31815494 0.42777213]\n",
      " [0.21984426 0.36557322 0.20605023 ... 0.2696299  0.22255076 0.11648016]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_scaled = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.30094852 0.39633558 0.29206797 ... 0.34879725 0.37377388 0.19381928]\n",
      " [0.65568288 0.52700096 0.62493149 ... 0.61477663 0.25425916 0.04863957]\n",
      " [0.46488294 0.43876567 0.45344922 ... 0.52027491 0.32808467 0.14470944]\n",
      " ...\n",
      " [0.16026098 0.37029894 0.1629473  ... 0.34054983 0.27052142 0.38663084]\n",
      " [0.37496573 0.68032787 0.38007987 ... 0.52268041 0.53820341 0.400739  ]\n",
      " [0.3239761  0.17984571 0.32229269 ... 0.53642612 0.2769747  0.39737991]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Classifier with the scaled values of X and Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_classifier2 = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\vineet\\envs\\breastcancerclassification\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_classifier2.fit(X=X_train_scaled,y = y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted_scaled = svc_classifier2.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "conf_mat2 = confusion_matrix(y_true=y_test,y_pred=y_predicted_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 62   1]\n",
      " [  2 106]]\n"
     ]
    }
   ],
   "source": [
    "print(conf_mat2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b89ec82e8>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Heat Map for Visualization of the Confusion Matrix\n",
    "\n",
    "sns.heatmap(data=conf_mat2, annot=True)"
   ]
  },
  {
   "attachments": {
    "Confusion%20Matrix.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Confusion%20Matrix.png](attachment:Confusion%20Matrix.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.98      0.98        63\n",
      "           1       0.99      0.98      0.99       108\n",
      "\n",
      "   micro avg       0.98      0.98      0.98       171\n",
      "   macro avg       0.98      0.98      0.98       171\n",
      "weighted avg       0.98      0.98      0.98       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report\n",
    "\n",
    "print (classification_report(y_true=y_test, y_pred=y_predicted_scaled))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***The above Classification Report looks Great ! We have about 98 % accuracy of prediction. So, we draw the inference that feature scaling improved the Model's Efficacy in this case.***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Hyper Tuning Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although we are getting 98% accuracy with the default tuning parameters as below -\n",
    "\n",
    "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
    "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
    "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
    "  shrinking=True, tol=0.001, verbose=False)\n",
    "  \n",
    "We can still use GridSearch to find values of C and Gamma; which **MAY BE** even better; thereby improving the efficacy of the model further"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reference :**\n",
    "\n",
    "Parameter Estimation Using Grid Search with Cross Validation : https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_digits.html\n",
    "\n",
    "Tuning Hyper Parameters of an Estimator : https://scikit-learn.org/stable/modules/grid_search.html\n",
    "\n",
    "Sklearn GridSearchCV : https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1, 0.1, 0.01, 0.001,0.0001],\n",
    "                     'C': [0.1, 1, 10, 100, 1000]},\n",
    "                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_tuned_svc_classifier = GridSearchCV(estimator=SVC(),param_grid=tuned_parameters,refit=True,cv=5,verbose=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 29 candidates, totalling 145 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ......... C=0.1, gamma=1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ........... C=0.1, gamma=1, kernel=rbf, score=0.95, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ........... C=0.1, gamma=1, kernel=rbf, score=0.95, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ........... C=0.1, gamma=1, kernel=rbf, score=0.95, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV]  C=0.1, gamma=1, kernel=rbf, score=0.9230769230769231, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ....... C=0.1, gamma=0.1, kernel=rbf, score=0.8875, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ....... C=0.1, gamma=0.1, kernel=rbf, score=0.9125, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ....... C=0.1, gamma=0.1, kernel=rbf, score=0.9125, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ....... C=0.1, gamma=0.1, kernel=rbf, score=0.9125, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV]  C=0.1, gamma=0.1, kernel=rbf, score=0.8846153846153846, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.1, gamma=0.01, kernel=rbf, score=0.6282051282051282, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV]  C=0.1, gamma=0.001, kernel=rbf, score=0.6282051282051282, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV]  C=0.1, gamma=0.0001, kernel=rbf, score=0.6282051282051282, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ........... C=1, gamma=1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............. C=1, gamma=1, kernel=rbf, score=0.95, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ........... C=1, gamma=1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV]  C=1, gamma=1, kernel=rbf, score=0.9615384615384616, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ......... C=1, gamma=0.1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ......... C=1, gamma=0.1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ......... C=1, gamma=0.1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ......... C=1, gamma=0.1, kernel=rbf, score=0.9375, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV]  C=1, gamma=0.1, kernel=rbf, score=0.9487179487179487, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ........ C=1, gamma=0.01, kernel=rbf, score=0.8875, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.925, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.925, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ........ C=1, gamma=0.01, kernel=rbf, score=0.9125, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV]  C=1, gamma=0.01, kernel=rbf, score=0.8846153846153846, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV]  C=1, gamma=0.001, kernel=rbf, score=0.6282051282051282, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV]  C=1, gamma=0.0001, kernel=rbf, score=0.6282051282051282, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ............ C=10, gamma=1, kernel=rbf, score=0.95, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] .......... C=10, gamma=1, kernel=rbf, score=0.9875, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] .......... C=10, gamma=1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV]  C=10, gamma=1, kernel=rbf, score=0.9743589743589743, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ........ C=10, gamma=0.1, kernel=rbf, score=0.9875, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ........ C=10, gamma=0.1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV]  C=10, gamma=0.1, kernel=rbf, score=0.9743589743589743, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ....... C=10, gamma=0.01, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ....... C=10, gamma=0.01, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ......... C=10, gamma=0.01, kernel=rbf, score=0.95, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ....... C=10, gamma=0.01, kernel=rbf, score=0.9375, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV]  C=10, gamma=0.01, kernel=rbf, score=0.9487179487179487, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ...... C=10, gamma=0.001, kernel=rbf, score=0.8875, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.925, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.925, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ...... C=10, gamma=0.001, kernel=rbf, score=0.9125, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV]  C=10, gamma=0.001, kernel=rbf, score=0.8846153846153846, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.625, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV]  C=10, gamma=0.0001, kernel=rbf, score=0.6282051282051282, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ........... C=100, gamma=1, kernel=rbf, score=0.95, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ......... C=100, gamma=1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ......... C=100, gamma=1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV]  C=100, gamma=1, kernel=rbf, score=0.9743589743589743, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ....... C=100, gamma=0.1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ....... C=100, gamma=0.1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV]  C=100, gamma=0.1, kernel=rbf, score=0.9487179487179487, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ...... C=100, gamma=0.01, kernel=rbf, score=0.9875, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ...... C=100, gamma=0.01, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV]  C=100, gamma=0.01, kernel=rbf, score=0.9743589743589743, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ..... C=100, gamma=0.001, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ..... C=100, gamma=0.001, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ....... C=100, gamma=0.001, kernel=rbf, score=0.95, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ..... C=100, gamma=0.001, kernel=rbf, score=0.9375, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV]  C=100, gamma=0.001, kernel=rbf, score=0.9487179487179487, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] .... C=100, gamma=0.0001, kernel=rbf, score=0.8875, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.925, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.925, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] .... C=100, gamma=0.0001, kernel=rbf, score=0.9125, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV]  C=100, gamma=0.0001, kernel=rbf, score=0.8846153846153846, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] .......... C=1000, gamma=1, kernel=rbf, score=0.95, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ........ C=1000, gamma=1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV]  C=1000, gamma=1, kernel=rbf, score=0.9743589743589743, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ...... C=1000, gamma=0.1, kernel=rbf, score=0.9375, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ......... C=1000, gamma=0.1, kernel=rbf, score=1.0, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ...... C=1000, gamma=0.1, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV]  C=1000, gamma=0.1, kernel=rbf, score=0.9615384615384616, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ..... C=1000, gamma=0.01, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ........ C=1000, gamma=0.01, kernel=rbf, score=1.0, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ..... C=1000, gamma=0.01, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV]  C=1000, gamma=0.01, kernel=rbf, score=0.9487179487179487, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] .... C=1000, gamma=0.001, kernel=rbf, score=0.9875, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] .... C=1000, gamma=0.001, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV]  C=1000, gamma=0.001, kernel=rbf, score=0.9743589743589743, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] ... C=1000, gamma=0.0001, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] ... C=1000, gamma=0.0001, kernel=rbf, score=0.9625, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] ..... C=1000, gamma=0.0001, kernel=rbf, score=0.95, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] ... C=1000, gamma=0.0001, kernel=rbf, score=0.9375, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV]  C=1000, gamma=0.0001, kernel=rbf, score=0.9487179487179487, total=   0.0s\n",
      "[CV] C=1, kernel=linear ..............................................\n",
      "[CV] ................. C=1, kernel=linear, score=0.9625, total=   0.0s\n",
      "[CV] C=1, kernel=linear ..............................................\n",
      "[CV] .................. C=1, kernel=linear, score=0.975, total=   0.0s\n",
      "[CV] C=1, kernel=linear ..............................................\n",
      "[CV] ................. C=1, kernel=linear, score=0.9875, total=   0.0s\n",
      "[CV] C=1, kernel=linear ..............................................\n",
      "[CV] ................... C=1, kernel=linear, score=0.95, total=   0.0s\n",
      "[CV] C=1, kernel=linear ..............................................\n",
      "[CV] ..... C=1, kernel=linear, score=0.9743589743589743, total=   0.0s\n",
      "[CV] C=10, kernel=linear .............................................\n",
      "[CV] ................. C=10, kernel=linear, score=0.975, total=   0.0s\n",
      "[CV] C=10, kernel=linear .............................................\n",
      "[CV] ................. C=10, kernel=linear, score=0.975, total=   0.0s\n",
      "[CV] C=10, kernel=linear .............................................\n",
      "[CV] ................ C=10, kernel=linear, score=0.9875, total=   0.0s\n",
      "[CV] C=10, kernel=linear .............................................\n",
      "[CV] ................. C=10, kernel=linear, score=0.975, total=   0.0s\n",
      "[CV] C=10, kernel=linear .............................................\n",
      "[CV] .... C=10, kernel=linear, score=0.9615384615384616, total=   0.0s\n",
      "[CV] C=100, kernel=linear ............................................\n",
      "[CV] ................ C=100, kernel=linear, score=0.975, total=   0.0s\n",
      "[CV] C=100, kernel=linear ............................................\n",
      "[CV] ............... C=100, kernel=linear, score=0.9625, total=   0.0s\n",
      "[CV] C=100, kernel=linear ............................................\n",
      "[CV] .................. C=100, kernel=linear, score=1.0, total=   0.0s\n",
      "[CV] C=100, kernel=linear ............................................\n",
      "[CV] ................ C=100, kernel=linear, score=0.975, total=   0.0s\n",
      "[CV] C=100, kernel=linear ............................................\n",
      "[CV] ... C=100, kernel=linear, score=0.9358974358974359, total=   0.0s\n",
      "[CV] C=1000, kernel=linear ...........................................\n",
      "[CV] .............. C=1000, kernel=linear, score=0.9625, total=   0.0s\n",
      "[CV] C=1000, kernel=linear ...........................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............... C=1000, kernel=linear, score=0.925, total=   0.0s\n",
      "[CV] C=1000, kernel=linear ...........................................\n",
      "[CV] ................. C=1000, kernel=linear, score=1.0, total=   0.0s\n",
      "[CV] C=1000, kernel=linear ...........................................\n",
      "[CV] .............. C=1000, kernel=linear, score=0.9625, total=   0.0s\n",
      "[CV] C=1000, kernel=linear ...........................................\n",
      "[CV] .. C=1000, kernel=linear, score=0.9358974358974359, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 145 out of 145 | elapsed:    1.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'kernel': ['rbf'], 'gamma': [1, 0.1, 0.01, 0.001, 0.0001], 'C': [0.1, 1, 10, 100, 1000]}, {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=4)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_tuned_svc_classifier.fit(X=X_train_scaled,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_tuned_svc_classifier.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_tuned_svc_classifier.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_y_predictions = grid_tuned_svc_classifier.predict(X=X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "grid_conf_mat = confusion_matrix(y_true=y_test, y_pred=grid_y_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[63  0]\n",
      " [17 91]]\n"
     ]
    }
   ],
   "source": [
    "print(grid_conf_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b89f5d9e8>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVoAAAD8CAYAAAA2Y2wxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAEBdJREFUeJzt3XuUXWV5x/HvMzOJiSCCWCAkQBKCUkQtLgxWlnjBVaJys4KVspDatNOWIuKtINWydFXFSyW41NKpXFJAJAVtUqSgReO9kSC0ImkFIySTCyGSMYiEMHOe/jEHHckkZyaZd/aZne8na6/Mucw+z4LJL0+e/e69IzORJJXTUXUBklR3Bq0kFWbQSlJhBq0kFWbQSlJhBq0kFWbQSlJhBq0kFWbQSlJhXaU/4NHzTvTUM21jn8vvqroEtaH+rWtiV/fx5MaVI86cSc+dvcufNxJ2tJJUWPGOVpLGVWOg6gq2YdBKqpeB/qor2IZBK6lWMhtVl7ANg1ZSvTQMWkkqy45WkgrzYJgkFWZHK0llpasOJKkwD4ZJUmGODiSpMA+GSVJhdrSSVJgHwySpMA+GSVJZmc5oJaksZ7SSVJijA0kqzI5WkgobeLLqCrZh0EqqF0cHklRYG44OvAuupHppNEa+tRAR74yIH0fEPRFxfURMiYhZEbEsIu6LiBsiYnKr/Ri0kupljII2IqYD5wFHZ+aRQCfwFuBjwKWZeRiwCZjfqiSDVlKt5MCTI95GoAuYGhFdwDOBdcBrgBubry8ETm21E4NWUr1kY+TbjnaTuQb4JLCKwYD9BXAn0JeZT11QoReY3qokg1ZSvYxidBAR3RGxfMjW/dRuImIf4BRgFnAgsAfwumE+MVuV5KoDSfUyilUHmdkD9Gzn5dcCP8vMhwEi4kvAy4G9I6Kr2dXOANa2+hw7Wkn1MnarDlYBL4uIZ0ZEAMcD9wLfAE5rvudsYHGrHRm0kupl7Ga0yxg86PVD4EcM5mUPcAHwroi4H9gXuKJVSY4OJNVL/9hd+DszLwYuftrTK4G5o9mPQSupXtrwzDCDVlK9eK0DSSrMjlaSCrOjlaTC7GglqbAxXHUwVgxaSfWSLc+IHXcGraR6cUYrSYUZtJJUmAfDJKmwgYGqK9iGQSupXhwdSFJhBq0kFeaMVpLKyobraCWpLEcHklSYqw4kqTA72t3M1D2YcsZ5dEw7GBK2fOEyuo44mq4XHgOZ5C/72HLtAnLzI1VXqoqc8Aev4lOf+hCdHR1cedX1fPwTn626pInPoN29TPnDbgZW3MmWKz8KnV0w+RlsXf8gW2+5FoBJx53E5Hln8MQi/3Dtjjo6Ovj0ZR9m3uvPoLd3Hf/1/Vv495u/yooV91Vd2sQ2ES8qExGHA6cA04Fk8B7mSzJzReHaJrYpU+mc8wK2XHfp4OOBfnj8ty/fFs+YwuB/Uu2O5r70KH760wf42c9WAbBo0WJOPukEg3ZXTbSONiIuAM4Avgj8oPn0DOD6iPhiZl5SuL4Jq2PfA8hfbmbKmefTMX0WA6vv54mbemDrE0x+w1lMmvsa8vFf8fhn3ld1qarIgdMPYHXv2l8/7l2zjrkvParCimqiDZd3dbR4fT7w0sy8JDOvbW6XMHir3fnly5vAOjrpmHEoW79zC7/6+DvgiSeY/NrTAdj6lWt47OK30X/nUia94sSKC1VVImKb57IN/9k74QwMjHwbJ62CtgEcOMzz05qvDSsiuiNieUQsv+qeVbtS34SVfRvJvo00HvwJAP13f5fOgw79rfc8uXwpXS8+tory1AbW9K7joBm/+eM1Y/o01q17qMKK6iEbjRFv46XVjPZ84PaIuA9Y3XzuYGAOcO72vikze4AegEfPO3G3/Cs6H+2j0beR2G86uWENnc9/MY31q4jfOZB8ePCfi10vPIbGht6KK1VV7lh+N3PmzGLmzINYs2Y9b37zKZz11r+uuqyJrw1HBzsM2sy8NSKex+CoYDoQQC9wR2a236rgNvPEjZcz9a3vgc4uGj9fz5brFgwu99pvBmSD3PQwW25wxcHuamBggHec/35u+coX6Ozo4OqFN3DvvT+puqyJrw2vdRClZ0K7a0erHdvn8ruqLkFtqH/rmm0H16P02IfOHHHm7PF31+3y542E62gl1Ut/+/1j26CVVC9tODowaCXVy0Q7GCZJE814LtsaKYNWUr3Y0UpSYQatJBXmhb8lqSzvGSZJpRm0klSYqw4kqTA7WkkqrA2DttX1aCVpQsmBxoi3ViJi74i4MSL+NyJWRMTvR8RzIuJrEXFf8/d9Wu3HoJVUL40c+dbaZcCtmXk48GJgBXAhcHtmHgbc3ny8QwatpFrJRo5425GI2As4DrgCIDO3ZmYfgzerXdh820Lg1FY1GbSS6mUUHe3Q2241t+4he5oNPAxcFRF3RcTnI2IPYP/MXAfQ/H2/ViV5MExSvYxiddfQ224Nowt4CfD2zFwWEZcxgjHBcOxoJdVK9jdGvLXQC/Rm5rLm4xsZDN6HImIaQPP3Da12ZNBKqpfGKLYdyMz1wOqIeH7zqeOBe4ElwNnN584GFrcqydGBpFoZ42sdvB24LiImAyuBtzHYoC6KiPnAKuD0VjsxaCXVyxiegZuZdwNHD/PS8aPZj0ErqVa8epckldZ+15QxaCXVS/ZXXcG2DFpJtdKGdxs3aCXVjEErSWXZ0UpSYQatJBWWA1F1CdswaCXVih2tJBWWDTtaSSrKjlaSCsu0o5WkouxoJamwhqsOJKksD4ZJUmEGrSQVlu13OVqDVlK92NFKUmEu75KkwgZcdSBJZdnRSlJhzmglqTBXHUhSYXa0klTYQKOj6hK2YdBKqhVHB5JUWMNVB5JUlsu7JKmw3XJ0cNqXB0p/hCagx9d+u+oSVFOODiSpMFcdSFJhbTg5MGgl1YujA0kqzFUHklRYG94E16CVVC+JHa0kFdXv6ECSyrKjlaTC2nFG234reyVpFyQx4m0kIqIzIu6KiJubj2dFxLKIuC8iboiIya32YdBKqpXGKLYRegewYsjjjwGXZuZhwCZgfqsdGLSSamWAGPHWSkTMAN4AfL75OIDXADc237IQOLXVfgxaSbXSiJFvEdEdEcuHbN1P290C4G/4TQO8L9CXmf3Nx73A9FY1eTBMUq00RrHqIDN7gJ7hXouIE4ENmXlnRLzqqaeH202rzzFoJdXKGF5U5ljg5Ih4PTAF2IvBDnfviOhqdrUzgLWtduToQFKtjNXBsMx8X2bOyMyZwFuAr2fmmcA3gNOabzsbWNyqJoNWUq00Ika87aQLgHdFxP0MzmyvaPUNjg4k1UqJe7pk5lJgafPrlcDc0Xy/QSupVhrtdwauQSupXkaz6mC8GLSSasVb2UhSYY4OJKmwdrx6l0ErqVYG7GglqSw7WkkqzKCVpMLa8JZhBq2kerGjlaTCSpyCu6sMWkm14jpaSSrM0YEkFWbQSlJhXutAkgpzRitJhbnqQJIKa7Th8MCglVQrHgyTpMLar581aCXVjB2tJBXWH+3X0xq0kmql/WLWoJVUM44OJKkwl3dJUmHtF7MGraSacXQgSYUNtGFPa9BKqhU7WkkqLO1oJaksO9rdyLs++U6OOX4ufT/v4y9e+1cAXPS5C5kxewYAe+y1J49t/iXnzDu3yjI1zq5Z9G/ctORWMpPTTp7HWX/0Rm77+rf53BXXsvLB1Vz/zws48nefV3WZE5rLu3YjX/3Xr7Hk6iW8d8F7fv3cR8655Ndfd3/gz3hs86+qKE0VuW/lA9y05Fau//wCJnVN4i/f/X6Oe/lc5sw+hAUf+QAf/MSnqy6xFtovZqGj6gLq6p5l9/Bo36Pbff24E4/jG4uXjl9BqtzKB1bzohccztQpU+jq6uTo33sht3/rexw682BmHTKj6vJqo58c8TZedjpoI+JtY1nI7uTIY45k08ZNrH1gbdWlaBzNmX0Id/73PfT9YjOPb9nCt79/B+sferjqsmonR/FrvOzK6OCDwFXDvRAR3UA3wBF7v4AZex60Cx9TP68+5VUsXfzNqsvQODt05sH86Zmn8+fnX8Qzp07leXNm09nZWXVZtTPhDoZFxP9s7yVg/+19X2b2AD0AJxz0unYcmVSmo7ODY+e9nHNff17VpagCbzrpBN500gkALLj8ag7Y77kVV1Q/E3F51/7ACcCmpz0fwPeKVFRzL3nFUaz+aS8b12+suhRV4Oeb+th3n71Zt34Dt3/zu1z7T5+quqTamXAdLXAzsGdm3v30FyJiaZGKauLCz1zAi172Ip79nL249gfXcM0/XMNtN3yVV578SpZ6EGy39c6L/p6+zZvp6urib999Ds/e61n85ze/y0cv/Uce6fsF57z3Yg4/bDY9l3646lInrIEcm442Ig4C/gU4gMH87snMyyLiOcANwEzgAeDNmfn0ZvS395VjVNT2ODrQcG6+67NVl6A2NOm5s2NX9/HHh7xxxJnzhQe/vN3Pi4hpwLTM/GFEPAu4EzgV+BPgkcy8JCIuBPbJzAt29Dku75JUK2O16iAz12XmD5tfPwqsAKYDpwALm29byGD47pAnLEiqlRIz2oiYCRwFLAP2z8x1MBjGEbFfq++3o5VUKw1yxFtEdEfE8iFb99P3FxF7AjcB52fm5p2pyY5WUq2MZnnX0KWow4mISQyG7HWZ+aXm0w9FxLRmNzsN2NDqc+xoJdXKQOaItx2JiACuAFZk5tB1eEuAs5tfnw0sblWTHa2kWhnDq3cdC5wF/CginlriehFwCbAoIuYDq4DTW+3IoJVUK2N1MCwzv8PgyVnDOX40+zJoJdXKRDwFV5ImFC/8LUmFlT7bdWcYtJJqxduNS1Jhjg4kqTBHB5JUmB2tJBXm8i5JKmysLvw9lgxaSbXi6ECSCjNoJakwVx1IUmF2tJJUmKsOJKmwgSxx17BdY9BKqhVntJJUmDNaSSrMGa0kFdZwdCBJZdnRSlJhrjqQpMIcHUhSYY4OJKkwO1pJKsyOVpIKG8iBqkvYhkErqVY8BVeSCvMUXEkqzI5Wkgpz1YEkFeaqA0kqzFNwJakwZ7SSVJgzWkkqzI5WkgpzHa0kFWZHK0mFuepAkgprx4NhHVUXIEljKTNHvLUSEfMi4v8i4v6IuHBnazJoJdVKjuLXjkREJ/BZ4HXAEcAZEXHEztRk0EqqlTHsaOcC92fmyszcCnwROGVnanJGK6lWxnBGOx1YPeRxL3DMzuyoeNDetvo/ovRnTBQR0Z2ZPVXXofbiz8XY6t+6ZsSZExHdQPeQp3qG/L8Ybj87leKODsZXd+u3aDfkz0VFMrMnM48esg39C68XOGjI4xnA2p35HINWkoZ3B3BYRMyKiMnAW4AlO7MjZ7SSNIzM7I+Ic4HbgE7gysz88c7sy6AdX87hNBx/LtpUZt4C3LKr+4l2PC9YkurEGa0kFWbQjpOxOpVP9RERV0bEhoi4p+paVJZBOw7G8lQ+1crVwLyqi1B5Bu34GLNT+VQfmfkt4JGq61B5Bu34GO5UvukV1SJpnBm042PMTuWTNPEYtONjzE7lkzTxGLTjY8xO5ZM08Ri04yAz+4GnTuVbASza2VP5VB8RcT3wfeD5EdEbEfOrrklleGaYJBVmRytJhRm0klSYQStJhRm0klSYQStJhRm0klSYQStJhRm0klTY/wMIlVBcNt4O1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Heat Map for Visualization of the Confusion Matrix\n",
    "\n",
    "sns.heatmap(grid_conf_mat, annot=True)"
   ]
  },
  {
   "attachments": {
    "Confusion%20Matrix.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Confusion%20Matrix.png](attachment:Confusion%20Matrix.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      1.00      0.88        63\n",
      "           1       1.00      0.84      0.91       108\n",
      "\n",
      "   micro avg       0.90      0.90      0.90       171\n",
      "   macro avg       0.89      0.92      0.90       171\n",
      "weighted avg       0.92      0.90      0.90       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report \n",
    "\n",
    "print(classification_report(y_test,grid_y_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GridSearch's Accuracy is worse as compared to the default model's accuracy. Additional, as seen from the heat map; the GridSeach Tuned Classifier give False Negative which is a risky prediction in the Cancer Detection Use Case. \n",
    "\n",
    "#### In this case, a False Negative means that the Patient has Cancer but the Classifier predicted that the Patient does not have Cancer.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scope of Improvements/Enhancements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Can try ensemble methods to experiment if the Accuracy can be improved further"
   ]
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}