Code-User Knowledge Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project:User Knowledge Modeling\n",
    "\n",
    "- ***Estimation of students' knowledge status about the subject of Electrical DC Machines.***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Attribute Information:**\n",
    "\n",
    "- STG (The degree of study time for goal object materails), (input value) \n",
    "- SCG (The degree of repetition number of user for goal object materails) (input value) \n",
    "- STR (The degree of study time of user for related objects with goal object) (input value) \n",
    "- LPR (The exam performance of user for related objects with goal object) (input value) \n",
    "- PEG (The exam performance of user for goal objects) (input value) \n",
    "- UNS (The knowledge level of user) (target value) \n",
    "  - Very Low: 50 \n",
    "  - Low:129 \n",
    "  - Middle: 122 \n",
    "  - High 130"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Abstract:**\n",
    "\n",
    "We develop a statistical machine learning framework to study the effect of eight input variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) on two output variables, namely heating load (HL) and cooling load (CL), of residential buildings. We systematically investigate the association strength of each input variable with each of the output variables using a variety of classical and non-parametric statistical analysis tools, in order to identify the most strongly related input variables. Then, we compare a classical linear regression approach against a powerful state of the art nonlinear non-parametric method, random forests, to estimate HL and CL. Extensive simulations on 768 diverse residential buildings show that we can predict HL and CL with low mean absolute error deviations from the ground truth which is established using Ecotect (0.51 and 1.42, respectively). The results of this study support the feasibility of using machine learning tools to estimate building parameters as a convenient and accurate approach, as long as the requested query bears resemblance to the data actually used to train the mathematical model in the first place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STG</th>\n",
       "      <th>SCG</th>\n",
       "      <th>STR</th>\n",
       "      <th>LPR</th>\n",
       "      <th>PEG</th>\n",
       "      <th>UNS</th>\n",
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       "      <td>0.10</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.90</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.08</td>\n",
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       "      <td>0.98</td>\n",
       "      <td>0.24</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    STG   SCG   STR   LPR   PEG  UNS\n",
       "0  0.00  0.00  0.00  0.00  0.00    0\n",
       "1  0.08  0.08  0.10  0.24  0.90    0\n",
       "2  0.06  0.06  0.05  0.25  0.33    0\n",
       "3  0.10  0.10  0.15  0.65  0.30    0\n",
       "4  0.08  0.08  0.08  0.98  0.24    0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=pd.read_excel('Book1.xlsx')\n",
    "dataset.columns=['STG','SCG','STR','LPR','PEG','UNS']\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 258 entries, 0 to 257\n",
      "Data columns (total 6 columns):\n",
      "STG    258 non-null float64\n",
      "SCG    258 non-null float64\n",
      "STR    258 non-null float64\n",
      "LPR    258 non-null float64\n",
      "PEG    258 non-null float64\n",
      "UNS    258 non-null int64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 12.2 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">**Data Preprocessing**\n",
    " - Categorical Data\n",
    " - Label Encoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>STG</th>\n",
       "      <th>SCG</th>\n",
       "      <th>STR</th>\n",
       "      <th>LPR</th>\n",
       "      <th>PEG</th>\n",
       "      <th>UNS</th>\n",
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       "      <td>0.98</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "    STG   SCG   STR   LPR   PEG  UNS\n",
       "0  0.00  0.00  0.00  0.00  0.00    0\n",
       "1  0.08  0.08  0.10  0.24  0.90    0\n",
       "2  0.06  0.06  0.05  0.25  0.33    0\n",
       "3  0.10  0.10  0.15  0.65  0.30    0\n",
       "4  0.08  0.08  0.08  0.98  0.24    0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['UNS']=le.fit_transform(dataset['UNS'])\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr=dataset.corr()\n",
    "plt.figure(figsize=(10,4))\n",
    "sns.heatmap(corr,annot=True,cmap='summer')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((258, 5), (258,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=dataset.iloc[:,0:5].values # Independant variables\n",
    "y=dataset.iloc[:,-1].values #dependant variables\n",
    "x.shape,y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.   0.   0.   0.   0.  ]\n",
      " [0.08 0.08 0.1  0.24 0.9 ]\n",
      " [0.06 0.06 0.05 0.25 0.33]\n",
      " ...\n",
      " [0.54 0.82 0.71 0.29 0.77]\n",
      " [0.5  0.75 0.81 0.61 0.26]\n",
      " [0.66 0.9  0.76 0.87 0.74]]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">**Feature Scaling**\n",
    "- Standard Scaler\n",
    "- Min Max Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import  StandardScaler,MinMaxScaler\n",
    "sc=StandardScaler() #z-score\n",
    "mms=MinMaxScaler() #(0-1)->normalisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_sc =sc.fit_transform(x)\n",
    "x_norm=mms.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lrIaK5FzCsltJfo/kUZIPk3x9wvJHSb6pUkVLfdC8LSJSLzSe+RQyl0qdofqYmW0H8LMAPkPyeWuWXwvg0yT1uVJZNTIyErsEEZGK0HjmU8hcKnrJz8weA/AcgJ41y58AsACgq5L7k9qWyWRilyAiUhEaz3wKmUtFGyqSPwngBwDG1yy/EsATZjZWyf1JbWtvb49dgohIRWg88ylkLpWaKf1dJN8CYBbAz5mZkTy1/HoAPwbg1Uk/ODY2hr1796KxsRErKyvYs2cP9u3bh5GREbS2tqKhoQEzMzPo7e3F5OQkzAy9vb0YHR1FW1sbAGBubg79/f0YHx8HSWSzWYyPj6Ojo2P1L00n3awmIiJn0ngpterEiRNYXl7GwMAARkZGsLCwgIGBgQ33EfPz86vPuR6alf7nLkjOmVnbmmW3Apgzs99dbznJPQA+DuAFZrZUvN3Bgwet2p/Cy2azmJycrOo+pHwzMzPo6Oio+n7ybw6+/6zL3ZnLcF3usdhliACgxsvzEGo8k/UlvddXI5fDhw8f2r1799VrlweZNsHMvgjgGwDeGmJ/Uhv0MWMRqRcaz3xyO20CgE0kjxc93l3Gz74PwLuLPgEoKbe0tHTujUREaoDGM59C5lLWPVRmVnIzZGa3rvn+EIAXlbM/qW+at0VE6oXGM59qcR4qkbJp3hYRqRcaz3yq2XmoRMrR3NwcuwQRkYrQeOZTyFxS0VA99dRTsUuQBC0tLbFLEBGpCI1n8SV9OjVkLqloqKampmKXIAmUi4jUC41nPoXMJRUNVXd3d+wSJIFyEZF6ofHMp5C5pKKhmp2djV2CJFAuIlIvNJ75FDKXSv3pGddyuVzsEiRBqFz6+rZgbIxB9nX+BgF4r1HSoK9vS+wSapLeZ3wKmUsqGirND+JTqFyGho4G2c9GefpjH88++ywuuOCC2GXIGsrFL73P+KR5qCpM84P4pFz8UjY+KRe/lI1PmoeqwvRxVp+Ui1/Kxifl4pey8UnTJlRYJpOJXYIkUC5+KRuflItfysankLmkoqGanp6OXYIkUC5+KRuflItfysankLmkoqHq6emJXYIkUC5+KRuflItfysankLmkoqHSbw4+KRe/lI1PysUvZeOTzlBV2PLycuwSJIFy8UvZ+KRc/FI2PoXMJRUNleYH8Um5+KVsfFIufikbnzQPVYVpfhCflItfysYn5eKXsvFJ81BVWGtra+wSJIFy8UvZ+KRc/FI2PoXMJRUNVUNDQ+wSJIFy8UvZ+KRc/FI2PoXMJRUN1czMTOwSJIFy8UvZ+KRc/FI2PoXMJRUNVW9vb+wSJIFy8UvZ+KRc/FI2PoXMJRUN1eTkZOwSJIFy8UvZ+KRc/FI2PoXMJRUNlZnFLkESKBe/lI1PysUvZeNTyFxS0VDpVKxPysUvZeOTcvFL2fikS34VNjo6GrsESaBc/FI2PikXv5SNTyFzSUVD1dbWFrsESaBc/FI2PikXv5SNTyFzSUVDJSIiIlJNqWio5ubmYpcgCZSLX8rGJ+Xil7LxKWQujcH2FFF/f3/sEiRBLecyOLgdY2NPxy5D1rg7M4jrckOxy5A1+vq2YGjoaOwyqqqWx7N6FjKXVJyhGh8fj12CJKjlXPLNlOnh7gEHNeix9pGGXz5qeTyrZyFzSUVDRTJ2CZJAuYhIvdB45lPIXFLRUGWz2dglSALlIiL1QuOZTyFzSUVDpVOxPikXEakXGs980iW/CtuxY0fsEiRBR0dH7BJERCpC41n5Qpw9CplLKhoq8WllZSV2CSIiFaHxzKeQuZTVUJG8meQjJB8ieZTkA4X/PklyuvD1UZI7Se4n+TjJb5L8J5Lbq/UipDbNz8/HLkFEpCI0nvkUMpeS56Ei+VIArwNwpZk9S7IHQMbMTpB8OYD3mNnrirYHgDeb2TdIvh3ARwH864pWLzVtYGAgdgkiIhWh8cynkLmUc4bqQgATZvYsAJjZhJmdKPFnDwL4kXKLk/o2MjISuwQRkYrQeOZTyFzKaaj+FsDFJI+R/CTJl5Xxs68GcF95pUm9a2pqil2CiEhFaDzzKWQuJV/yM7M5klcB2AXgFQDuIXmTmd15lh/7HMlWAA0ArkzaYGxsDHv37kVjYyNWVlawZ88e7Nu3DyMjI2htbUVDQwNmZmbQ29uLyclJmBl6e3sxOjq6+lek5+bm0N/fj/HxcZBENpvF+Pg4Ojo6Vm9I0xwhIiLxaAyWJM888wza29tx8uRJdHV1YXFxEUtLSxgYGMDIyAiam5vR0tKCqakpdHd3Y3Z2FrlcbnV9S0sLMpkMpqen0dPTg+npaSwvL6+ub2xsxNTU1Ib7iPn5+dXnXA/NbN2VZ0PyjQDeamb/Zp17qPYDeA+AbwL4MIAfNbM9a5/n4MGDNjg4eF41lCqbzWJycrKq+5DyDQ8PY+vWrbHLOC/5N4fzO3akeu7OXIbrco/FLkPOwLofg2t5PIslxHtzNXI5fPjwod27d1+9dnnJl/xIvojkpUWLtgMYPtfPmdkygFsAXEPyslL3J/Wvs7MzdgkiIhWh8cynkLmUcw9VG4C7SD5K8iEAlwO4tZQfNLNFALchf8ZKBACQy+VilyAiUhEaz3wKmUs591AdArBznXX7Aexfs+zla76/rezqpK4tLi7GLkFEpCI0nvkUMhfNlC7RaN4WEakXGs988joPVc06cuRI7BIkgeZtEZF6ofGsfCE+qOB1HqqalclkYpcgCZSLiNQLjWc+hcwlFQ1Ve3t77BIkgXIRkXqh8cynkLmkoqE6efJk7BIkgXIRkXqh8cynkLmU/Cm/WtbV1RW7BElQy7n09W3B2BhjlyFnGASgXLzp69sSu4Sqq+XxrJ6FzCUVDdXi4iI6OjpilyFr1HIuQ0NHY5dQVaOjo+jv749dxnmp5/m4azmXelfL41k9C5lLKi75LS0txS5BEigXv5SNT8rFL2XjU8hcUtFQaX4Qn5SLX8rGJ+Xil7LxSfNQVZjmB/FJufilbHxSLn4pG580D1WFNTc3xy5BEigXv5SNT8rFL2XjU8hcUtFQtbS0xC5BEigXv5SNT8rFL2XjU8hcUtFQTU1NxS5BEigXv5SNT8rFL2XjU8hcUtFQdXd3xy5BEigXv5SNT8rFL2XjU8hcUtFQzc7Oxi5BEigXv5SNT8rFL2XjU8hcUtFQ5XK52CVIAuXil7LxSbn4pWx8CplLKhoqzQ/ik3LxS9n4pFz8UjY+aR6qCtP8ID4pF7+UjU/KxS9l45PmoaowfZzVJ+Xil7LxSbn4pWx80rQJFZbJZGKXIAmUi1/Kxifl4pey8SlkLqloqKanp2OXIAmUi1/Kxifl4pey8SlkLqloqHp6emKXIAmUi1/Kxifl4pey8SlkLqloqPSbg0/KxS9l45Ny8UvZ+KQzVBW2vLwcuwRJoFz8UjY+KRe/lI1PIXNJRUOl+UF8Ui5+KRuflItfysYnzUNVYZofxCfl4pey8Um5+KVsfNI8VBXW2toauwRJoFz8UjY+KRe/lI1PIXNpDLaniBoaGmKXIAnWy2VwcDvGxp4OXE1l3J0ZxHW5odhlSEFf3xYMDR2NXUbFaCzzS9n4FDKXVJyhmpmZiV2CJFgvl3wzZTX6gIMa9Dj1qNXGfD0ay/xSNj6FzCUVDVVvb2/sEiSBchEpj44Zv5SNTyFzSUVDNTk5GbsESaBcRMqjY8YvZeNTyFxS0VCZ2bk3kuCUi0h5dMz4pWx8CplLKhoqnYr1SbmIlEfHjF/Kxidd8quwiy66KHYJkmB0dDR2CSI1RceMX/WUTTabjV1CxYTMpaSGiuTNJB8h+RDJoyR/kmQTyQ+TfILkwyT/keRrCtu3kfwUyX8meYTkIZLXV/elSK1pa2uLXYJITdEx45ey8SlkLuech4rkSwG8DsCVZvYsyR4AGQDvB3AhgCsKy/sBvKzwY38M4NsALjWzH5DsBfBLVXkFIiIiIpGVcobqQgATZvYsAJjZBIDvA7gewDuKlo+a2edJvgDATwC4xcx+UFg3bmYfqcorkJo1NzcXuwSRmqJjxi9l41PIXEppqP4WwMUkj5H8JMmXAXghgKfNLGnGrBcD+OapZkpkPf39/bFLEKkpOmb8UjY+hczlnJf8zGyO5FUAdgF4BYB7AHyw1B2QvBnAzwLoM7Mz7g4fGxvD3r170djYiJWVFezZswf79u3DyMgIWltb0dDQgJmZGfT29mJychJmht7eXoyOjq5eG52bm0N/fz/Gx8dBEtlsFuPj4+jo6MDKygqA+rrJTkRKp2NfpHwLCwvn/Z47Pz+PgYEBjIyMoKmpCZ2dnZiYmEBnZydyuRwWFxdX12cyGbS3t+PkyZPo6urC4uIilpaWVtc3NzejpaUFU1NT6O7uxuzsLHK53Or6lpYWZDIZTE9Po6enB9PT01heXl5dv7i4iP7+/g33EcWvaT0sd44Gkm8E8KsArgRwiZnNrln/QuTPar2w+CwVyTkzO+PusIMHD9rg4GBZNZQrm81q0jWHjh8/js2bN5+xPP8GWJtzutyduQzX5R6LXYasYl0d++sdMxJfPWVTT++Z1cjl8OHDh3bv3n312uXnvORH8kUkLy1atB3A4wDuAHA7yUxhuwtJvsXMngTwDQAfINlQWNcMgBV4HVJHdOZApDw6ZvxSNj6FzKWUe6jaANxF8lGSDwG4HMCtAG4BMA7gUZIPA7iv8D0A/DKAbgBPkjwE4MsA3lvh2qXGjY+Pn3sjEVmlY8YvZeNTyFxKuYfqEICd66z+jcJj7c/MIH9ZUGRdHR0dsUsQqSk6ZvxSNj6FzCUVM6WLT6c+MCAipdEx45ey8SlkLmqoJJr5+fnYJYjUFB0zfikbn0LmkoqG6plnnoldgiQYGBiIXYJITdEx41c9ZVMvn/ADwuaSiobqbPNGSDzKRaQ8Omb8UjY+hcwlFQ1VU1NT7BIkgXIRKY+OGb+UjU8hc0lFQ9XZ2Rm7BEmgXETKo2PGL2XjU8hcUtFQTUxMxC5BEqyXS1/fFuTnga3FBxzUoMepR/7/pfqhscwvZeNTyFzOOQ9VPdBvDj6tl8vQ0NHAlVRWPdzO+f3vfx/Pf/7zY5cha2gs80vZ+KQzVBWWy+VilyAJlItfysYn5eKXsvEpZC6paKgWFxdjlyAJlItfysYn5eKXsvEpZC6paKjqaX6QeqJc/FI2PikXv5SNT5qHqsI0P4hPysUvZeOTcvFL2fikeagqLJPJxC5BEigXv5SNT8rFL2XjU8hcUtFQtbe3xy5BEigXv5SNT8rFL2XjU8hcUtFQnTx5MnYJkkC5+KVsfFIufikbn0LmkoqGqqurK3YJkkC5+KVsfFIufikbn0LmkoqGSh9n9Um5+KVsfFIufikbnzRtQoUtLS3FLkESKBe/lI1PysUvZeNTyFxS0VBpfhCflItfysYn5eKXsvFJ81BVmOYH8Um5+KVsfFIufikbnzQPVYU1NzfHLkESKBe/lI1PysUvZeNTyFxS0VC1tLTELkESKBe/lI1PysUvZeNTyFxS0VBNTU3FLkESKBe/lI1PysUvZeNTyFxS0VB1d3fHLkESKBe/lI1PysUvZeNTyFxS0VDNzs7GLkESKBe/lI1PysUvZeNTyFxS0VDlcrnYJUgC5eKXsvFJufilbHwKmUsqGirND+KTcvFL2fikXPxSNj5pHqoK0/wgPikXv5SNT8rFL2XjU8hcGoPtKSJ9nNWnauQyOLgdY2NPV/x5q+XuzCCuyw3FLqMu9fVtwdDQ0dhlVJTGMr+UjU8hc0lFQ5XJZGKXIAmqkUu+mbKKP2/1XIbaqrd2jI0xdgkVp7HML2XjU8hcUnHJb3p6OnYJkkC5iJRHx4xfysankLmkoqHq6emJXYIkUC4i5dEx45ey8SlkLqloqPSbg0/KRaQ8Omb8UjY+6QxVhV1xxRWxS5AEy8vLsUsQqSk6ZvwKkU02m636PupNyGMmFQ2V+KR5W0TKo2PGL2Xjk8t5qEjeTPIRkg+RPErygcJ/nyQ5Xfj6KMmdJBtJfpDkE0XLb67mC5Hao3lbRMqjY8YvZeNTyFxKaqhIvhTA6wBcaWYvAfBKAG82s+0AfhnAATPbXnh8HcAHAFwE4McL2+wC0FSVVyA1q7W1NXYJIjVFx4zKw7fmAAAJ0ElEQVRfnrO59957sXPnTvT09GDnzp249957N7TMm7PVGDKXUuehuhDAhJk9CwBmNrHehiQ3AbgewCVmtlTYfhbArRsrVepNQ0ND7BJEaoqOGb+8ZnPvvffiAx/4AG6//XZcc801ePDBB7F3714AwB133FH2shtuuAEA8IY3vCHOC1oj6fUV1xgyl1Iv+f0tgItJHiP5SZIvO8u2LwTwdKGJElnXzMxM7BJEaoqOGb+8ZnPbbbfh9ttvx65du9DU1IRdu3Zh06ZNaGlpOW1ZS0sLNm3adM7tbr/9dtx2222xX9aqpNdXXGPIXEo6Q2VmcySvQv7S3SsA3EPyJjO781w/S/LtAN4JoBvATjP7bvH6sbEx7N27F42NjVhZWcGePXuwb98+jIyMoLW1FQ0NDZiZmUFvby8mJydhZujt7cXo6Cja2toAAHNzc+jv78f4+DhIIpvNYnx8HB0dHVhZWQGgT0eIpJGOe6k3c3NzOHnyJLq6urC4uIilpSUMDAxgZGQEzc3NaGlpwdTUFLq7uzE7O4tjx45hx44dGB4eRktLCzKZDI4fPw4zw4kTJ7C8vIyBgQEcP34cJDE1NbX6nntqu6WlpdX33EsvvRTHjh3D0tLSuu+58/PzqzU1NTWhs7MTExMT6OzsRC6Xw+Li4ur6TCaD9vb2sl5TLpdbXX/s2DEMDg5ieHgYPT09mJ6exoUXXohjx45heHgYjY2Np72m8+0jil/Tekr+0zNmtgJgP4D9JL8F4K0A7kzY9EkAW0i2m9msmX0WwGdJPgzgjHNvfX19+NrXvnbGk2zdunX1666uLgDApk2bEtd3d3cDAC6++OLE9QAwOTl59hcowR0/fhybN2+u6HPqDVSK1dtxX41jRiojRDbZbBZtbW2rTUBHR8fquuL3vFPL29rasG3bNhw5cgS7du1aXb9582aYGS666KLTlpFEV1fX6nvuqe2am5tXn//AgQPYtm0bmpub133PPTWZZvGypHuZiteX85qK12/btg1DQ0Orr6+1tXW1xq1bt+L48eOnvabz7SOKX9PJkyfPeC1A6Telv4jkpUWLtgMYTtrWzBYA3AHgEySbCz/fAEB/6EhOY6a/YSdSDh0zfnnN5sYbb8QNN9yAAwcOYHl5GQcOHMDCwgIWFxdPW7a4uIiFhYVzbnfDDTfgxhtvjP2yViW9vuIaQ+ZS6hmqNgAfJ/l8AM8hfxbqV86y/c0A3g/gYZKzABYB3AXgxAZqlTrT29sbuwSRmqJjxi+v2Zy6efy9730vjh07hm3btuFDH/rQeS+75ZZb3NyQDiS/vuIaQ+bC2F31wYMHbXBwsKr7yGazdXfqvx4MDw+fcWl2o/KX/Hz+ppjk7sxluC73WOwy6hTr7rivxjEjlREiG72Xla8auRw+fPjQ7t27r167XDOlSzTF18JF5Nx0zPilbHwKmUsqGqonnngidgkiIiIborNTvqWioZqbm4tdgiRQLiLl0THjl7LxKWQuqWio+vv7Y5cgCZSLSHl0zPilbHwKmUsqGqrx8fHYJUgC5SJSHh0zfikbn0LmUvLEnrWMZOwSJEE1cunr24KxsVrKexBALdVbO/r6tsQuoeI0lvmlbHwKmUsqGirNnu1TNXIZGjpa8eesNo+3mS4sLJw2o7D4oLHML2XjU8hcdMlPolEufikbn5SLX8rGp5C5pKKhKv7bQOKHcvFL2fikXPxSNj6FzCUVDdXKykrsEiSBcvFL2fikXPxSNj6FzCUVDdX8/HzsEiSBcvFL2fikXPxSNj6FzCUVDdXAwEDsEiSBcvFL2fikXPxSNj6FzCUVDdXIyEjsEiSBcvFL2fikXPxSNj6FzCUVDdV9990XuwRJoFz8UjY+KRe/lI1PIXNJRUP1xS9+MXYJkkC5+KVsfFIufikbn0LmkoqG6rnnnotdgiRQLn4pG5+Ui1/KxqeQudDMgu0syf333z8OYLia+5icnOzJZrMT1dyHlE+5+KVsfFIufikbn6qUy9bdu3f3rl0YvaESERERqXWpuOQnIiIiUk1qqEREREQ2qK4aKpKvJvk4ySdJ3pSw/gKS9xTW/wPJS8JXmT4l5PJuko+SfIjk/SS3xqgzjc6VTdF2byRpJK8OWV9alZILyX9fOG4eIXl36BrTqoTxbAvJB0geKYxpr41RZ9qQ/AzJMZIPr7OeJG8v5PYQySsrXUPdNFQkGwD8PoDXALgcwJtIXr5ms70ApszshQA+BuAjYatMnxJzOQLgajN7CYC/APA7YatMpxKzAcl2ADcA+IewFaZTKbmQvBTAbwL4KTN7MYBfD15oCpV4zNwC4PNmtgPAzwP4ZNgqU+tOAK8+y/rXALi08PgVAJ+qdAF101AB+AkAT5rZt80sB+DPAVy7ZptrAdxV+PovAOwmyYA1ptE5czGzB8xsofDtgwA2B64xrUo5ZgDg/cg3uUshi0uxUnK5HsDvm9kUAJjZWOAa06qUbAxAR+HrTgAnAtaXWmb2VQCTZ9nkWgD/0/IeBPB8khdWsoZ6aqh+BMB3i74/XliWuI2ZPQdgGkB3kOrSq5Rciu0F8H+rWpGccs5sSO4AcLGZ/e+QhaVcKcfMNgDbSH6N5IMkz/abuVROKdncCuAtJI8D+CsA7whTmpxDue9FZWus5JNFlnSmae2cEKVsI5VV8r85ybcAuBrAy6pakZxy1mxIPg/5S+NvC1WQACjtmGlE/tLFy5E/o3uA5BVm9v0q15Z2pWTzJgB3mtltJF8K4E8K2fyg+uXJWVT9/b+ezlAdB3Bx0febceap1tVtSDYifzr2bKcIZeNKyQUkXwngZgCvN7NnA9WWdufKph3AFQD2k3wKwDUAvqQb06uu1LHsL81s2cy+A+Bx5Bssqa5SstkL4PMAYGYHATQD6AlSnZxNSe9FG1FPDdU/AbiU5I+SzCB/M+CX1mzzJQBvLXz9RgBfMc1sWm3nzKVwWenTyDdTuhcknLNmY2bTZtZjZpeY2SXI39/2ejP7RpxyU6OUsew+AK8AAJI9yF8C/HbQKtOplGyeBrAbAEhehnxDNR60SknyJQC/WPi03zUAps3smUruoG4u+ZnZcyR/DcDfAGgA8Bkze4Tk+wB8w8y+BOAO5E+/Pon8mamfj1dxOpSYy0cBtAH4QuEzAk+b2eujFZ0SJWYjgZWYy98AeBXJRwGsAPjPZnYyXtXpUGI2NwL4I5LvQv6S0tv0i3v1kfwz5C+B9xTuX/stAE0AYGZ/gPz9bK8F8CSABQBvr3gNyllERERkY+rpkp+IiIhIFGqoRERERDZIDZWIiIjIBqmhEhEREdkgNVQiIiIiG6SGSkRERGSD1FCJiIiIbJAaKhEREZEN+v/Xr2pBY3uwZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SKfS5Eo93Jpm45Ld27VrvEiRFmYhINdMYFo93JploqI4cOeJdgqQoExGpZhrD4vHOJBMNlXfXKrMpExGpZhrD4vHOJBMN1cTEhHcJkqJMRKSaaQyLxzuTTDRU3d3d3iVIytjYmHcJIiJLpjEsHu/P+kU1VCRH5lh2A8mfkjxA8gGSr59j+UMk37xSRUv1854vRERkOTSGSdpKnaH6tJltBfBrAL5E8nmp5W8A8AWS+p6pAEjmCxERqVYawyRtRS/5mdnDAJ4B0JZa/iiAkwDWreT+pHo1NDR4lyAismQawyRtRRsqkr8A4FkA+dTyCwA8amaDK7k/qV5r1qzxLkFEZMk0hknaSs2U/h6SVwI4AeA3zMxITi+/GsC/A3D5XE8cHBzEzp07UV9fj6mpKWzfvh27du1Cf38/mpubUVdXh+HhYbS3t2NoaAhmhvb2dgwMDKClpQUAMDIygs7OTuTzeZBELpdDPp9Ha2vrzG+fzuVyK/RWRURqm8ZLqVaHDx/G5OQkurq6VrSPGB0dnXnN+dCs9F93QXLEzFpSy24AMGJmfzTfcpLbAXwGwM+Z2Xjxdnv27LFy35mfy+UwNDRU1n3I4gwPD6O1tbUi+0o+HGL/WpfbG87FjomHvcsQAUCNlyWo5BgmpanUZ/2+ffv29vT0XJReXpFpE8zs6wB+AOBtldifxKevHItINdMYJmmLbahWkzxU9HjvIp77IQDvLfoGoGTY+Pj4whuJiASlMUzSFnUPlZmV3AyZ2Q2pn/cCOGcx+5PapTlcRKSaaQyTNJ0tEheaw0VEqpnGMElTQyUuGhsbvUsQEVkyjWGSlomG6vHHH/cuQVKampq8SxARWTKNYfF4f9ZnoqE6duyYdwmSokxEpJppDIvHO5NMNFTr16/3LkFSlImIVDONYfF4Z5KJhurEiRPeJUiKMhGRaqYxLB7vTFbqV8+ENjEx4V2CpFQyk46OTRgcZMX2tzTdAKLXKFnQ0bHJu4SqoM+VeLwzyURDpflC4qlkJr29Byq2r+WI8Ms+nn76aZx22mneZUgRZRKTPlfi8c4kE5f8NF9IPMokJuUSjzKJSbnE451JJhoqfb01HmUSk3KJR5nEpFzi8c4kEw1VQ0ODdwmSokxiUi7xKJOYlEs83plkoqE6fvy4dwmSokxiUi7xKJOYlEs83plkoqFqa2vzLkFSlElMyiUeZRKTconHO5NMNFTeXavMpkxiUi7xKJOYlEs83plkoqGanJz0LkFSlElMyiUeZRKTconHO5NMNFTec1PIbMokJuUSjzKJSbnE451JJhoq77kpZDZlEpNyiUeZxKRc4vHOJBMNVXNzs3cJkqJMYlIu8SiTmJRLPN6ZZKKhqqur8y5BUpRJTMolHmUSk3KJxzuTTDRUw8PD3iVIijKJSbnEo0xiUi7xeGeSiYaqvb3duwRJUSYxKZd4lElMyiUe70wy0VANDQ15lyApyiQm5RKPMolJucTjnUkmGioz8y5BUpRJTMolHmUSk3KJxzuTTDRU3qcBZTZlEpNyiUeZxKRc4vHOJBMN1cDAgHcJkqJMYlIu8SiTmJRLPN6ZZKKhamlp8S5BUpRJTMolHmUSk3KJxzuTTDRUIiIiIuWUiYZqZGTEuwRJUSYxKZd4lElMyiUe70zqXfdeIZ2dnd4lSEqtZtLdvRWDg094lyEluL2hGzsmer3LkAV0dGxCb+8B7zJmqdUxrJp5Z5KJM1T5fN67BEmp1UySZsr0qIoHAtSgx0KPqP9AqdUxrJp5Z5KJhoqkdwmSokxEpJppDIvHO5NMNFS5XM67BElRJiJSzTSGxeOdSSYaKu/TgDKbMhGRaqYxLB7vTDLRUJ1//vneJUhKa2urdwkiIkumMSwe78/6TDRUEs/U1JR3CSIiS6YxTNIWNW0CyesA7AAwBeBZAMcArAPQAqAdwL8VNv09AB8BsAHAOIAJAFebWbzvvoqL0dFRtLW1eZchIrIkGsMkreSGiuTLAbwOwAVm9jTJNgANZnaY5KsAvM/MXle0PQC8xcx+QPLtAD4J4DUrWr1Ura6uLu8SRESWTGOYpC3mkt8GAEfM7GkAMLMjZna4xOfuAfCCxRYntau/v9+7BBGRJdMYJmmLaai+DeAMkgdJfo7kpYt47uUA7lpcaVLLVq1a5V2CiMiSaQyTtJIv+ZnZCMkLAWwDcBmAO0h+0MxuO8XT/oJkM4A6ABfMtcHg4CB27tyJ+vp6TE1NYfv27di1axf6+/vR3NyMuro6DA8Po729HUNDQzAztLe3Y2BgYOY3S4+MjKCzsxP5fB4kkcvlkM/n0draOnPjoPf8FCIisjQav6VUhw8fxuTkJLq6ula0jxgdHZ15zfnQzOZdeSok3wTgbWb2K/PcQ3UvgPcB+CGAjwF4oZltT7/Onj17rLu7e0k1lCqXy2FoaKis+5DF6evrw+bNm73LWHHJwL+0Y0oq6/aGc7Fj4mHvMmRBDDl+1+oYVs0q9Vm/b9++vT09PRell5d8yY/kOSTPLlq0FUDfQs8zs0kA1wO4mOS5pe5PatvatWu9SxARWTKNYZK2mHuoWgB8meRDJO8H8CIAN5TyRDMbA3ATkjNWIpiYmPAuQURkyTSGSdpi7qHaC+CSedbdC+De1LJXpX6+adHVSc0aGxvzLkFEZMk0hkmaZkoXF5rDRUSqmcYwSctEQ7V//37vEiRFc7iISDXTGBaP92d9JhqqhoYG7xIkRZmISDXTGBaPdyaZaKjWrFnjXYKkKBMRqWYaw+LxziQTDdXRo0e9S5AUZSIi1UxjWDzemZT8Lb9qtm7dOu8SJKVWM+no2ITBQXqXISXpBqCsouvo2ORdwpxqdQyrZt6ZZKKhGhsbQ2trq3cZUqRWM+ntPeBdwrIMDAygs7PTu4yKiTf/9mxZy6Ra1OoYVs28M8nEJb/x8XHvEiRFmcSkXOJRJjEpl3i8M8lEQ6X5QuJRJjEpl3iUSUzKJR7vTDLRUGm+kHiUSUzKJR5lEpNyicc7k0w0VI2Njd4lSIoyiUm5xKNMYlIu8XhnkomGqqmpybsESVEmMSmXeJRJTMolHu9MMtFQHTt2zLsESVEmMSmXeJRJTMolHu9MMtFQrV+/3rsESVEmMSmXeJRJTMolHu9MMtFQnThxwrsESVEmMSmXeJRJTMolHu9MMtFQTUxMeJcgKcokJuUSjzKJSbnE451JJhoq77kpZDZlEpNyiUeZxKRc4vHOJBMNlffcFDKbMolJucSjTGJSLvF4Z5KJhsr7q5QymzKJSbnEo0xiUi7xeGeSiYaqoaHBuwRJUSYxKZd4lElMyiUe70wy0VAdP37cuwRJUSYxKZd4lElMyiUe70wy0VC1tbV5lyApyiQm5RKPMolJucTjnUkmGirvrlVmUyYxKZd4lElMyiUe70wy0VBNTk56lyApyiQm5RKPMolJucTjnUkmGirvuSlkNmUSk3KJR5nEpFzi8c4kEw2V99wUMpsyiUm5xKNMYlIu8XhnkomGqrm52bsESVEmMSmXeJRJTMolHu9M6l33XiF1dXXeJUjKSmTS3b0Vg4NPrEA1Mdze0I0dE73eZcgidXRsQm/vgbK9vsavmJRLPN6ZZOIM1fDwsHcJkrISmSTNlNXQAwFq0GOxj3I39Rq/YlIu8XhnkomGqr293bsESVEmIqXRsRKTconHO5NMNFRDQ0PeJUiKMhEpjY6VmJRLPN6ZZKKhMrOFN5KKUiYipdGxEpNyicc7k0w0VN6nAWU2ZSJSGh0rMSmXeLwzyURDdfrpp3uXICkDAwPeJYhUBR0rMSmXeLw/60tqqEheR/JBkveTPEDyF0iuIvkxko+SfIDkv5C8orB9C8nPk/x/JPeT3Evy6vK+FakmLS0t3iWIVAUdKzEpF0lbcB4qki8H8DoAF5jZ0yTbADQA+DCADQDOKyzvBHBp4Wl/BuDHAM42s2dJtgP47bK8AxERERFnpZyh2gDgiJk9DQBmdgTAUwCuBvCuouUDZvZVkj8H4GUArjezZwvr8mb28bK8A6lKIyMj3iWIVAUdKzEpF0krpaH6NoAzSB4k+TmSlwI4C8ATZjbXLFovBvDD6WZKZC6dnZ3eJYhUBR0rMSkXSVvwkp+ZjZC8EMA2AJcBuAPAR0rdAcnrAPwagA4zm3XH2ODgIHbu3In6+npMTU1h+/bt2LVrF/r7+9Hc3Iy6ujoMDw+jvb0dQ0NDMDO0t7djYGBg5hr2yMgIOjs7kc/nQRK5XA75fB6tra2YmpoCAORyuVJLFhFZFI0vIjEcPnwYk5OT6OrqWtE+YnR0dOY158PFzttA8k0A3gngAgBnmtmJ1PqzkJzVOqv4LBXJETObdRffnj17rLu7e1E1LFYul3Of8Eue69ChQ9i4ceOyXiP5EKuduWBubzgXOyYe9i5DFo1lHV9W4liRladc4qnUZ/2+ffv29vT0XJRevuAlP5LnkDy7aNFWAI8AuAXAzSQbCtttIHmlmT0G4AcAbiRZV1jXCIAr8D6kRuhf9CKl0bESk3KRtFLuoWoB8GWSD5G8H8CLANwA4HoAeQAPkXwAwF2FnwHgdwCsB/AYyb0A/h7AB1a4dqli+Xx+4Y1ERMdKUMpF0kq5h2ovgEvmWf0HhUf6OcNILguKzKm1tdW7BJGqoGMlJuUiaZmYKV3imf6ygIicmo6VmJSLpKmhEhejo6PeJYhUBR0rMSkXSctEQ/Xkk096lyApXV1d3iWIVAUdKzEpl3i8P+sz0VCdat4I8aFMREqjYyUm5RKPdyaZaKhWrVrlXYKkKBOR0uhYiUm5xOOdSSYaqrVr13qXICnKRKQ0OlZiUi7xeGeSiYbqyJEj3iVIykpk0tGxCcl8sbXyQIAa9FjsI/n/sHw0fsWkXOLxzmTBeahqgXfXKrOtRCa9vQdWoJJYvH9B0lNPPYXnP//5zlVIMY1fMSmXeLwzycQZqomJCe8SJEWZxKRc4lEmMSmXeLwzyURDNTY25l2CpCiTmJRLPMokJuUSj3cmmWioNF9IPMokJuUSjzKJSbnE451JJhoq77kpZDZlEpNyiUeZxKRc4vHOJBMNVUNDg3cJkqJMYlIu8SiTmJRLPN6ZZKKhWrNmjXcJkqJMYlIu8SiTmJRLPN6ZZKKhOnr0qHcJkqJMYlIu8SiTmJRLPN6ZZKKhWrdunXcJkqJMYlIu8SiTmJRLPN6ZZKKh8v4qpcymTGJSLvEok5iUSzzemWSioRofH/cuQVKUSUzKJR5lEpNyicc7k0w0VN5zU8hsyiQm5RKPMolJucTjnUkmGirvuSlkNmUSk3KJR5nEpFzi8c4kEw1VY2OjdwmSokxiUi7xKJOYlEs83plkoqFqamryLkFSlElMyiUeZRKTconHO5NMNFTHjh3zLkFSlElMyiUeZRKTconHO5NMNFTr16/3LkFSlElMyiUeZRKTconHO5NMNFQnTpzwLkFSlElMyiUeZRKTconHO5NMNFQTExPeJUiKMolJucSjTGJSLvF4Z5KJhsp7bgqZTZnEpFziUSYxKZd4vDPJREPlPTeFzKZMYlIu8SiTmJRLPN6Z1LvuvUK8v0ops5Urk+7urRgcfKIsr10Otzd0Y8dEr3cZNamjYxN6ew94l7FsGr9iUi7xeGeSiYaqoaHBuwRJKVcmSTNlZXnt8jgX1VVv9RgcpHcJK0LjV0zKJR7vTDJxye/48ePeJUiKMhEpjY6VmJRLPN6ZZKKhamtr8y5BUpSJSGl0rMSkXOLxziQTDZV31yqzKROR0uhYiUm5xOOdSSYaqvPOO8+7BEmZnJz0LkGkKuhYickjl1wuV/F9VhPvYyUTDZXE4z1fiEi10LESk3KJxzuTkhsqkteRfJDk/SQPkLyn8N/HSB4v/PkAyUtI1pP8CMlHi5ZfV843ItXFe74QkWqhYyUm5RKPdyYlNVQkXw7gdQAuMLOXAng1gLeY2VYAvwNgt5ltLTz+GcCNAE4H8JLCNtsArCrLO5Cq1Nzc7F2CSFXQsRJTNeVy55134pJLLkFbWxsuueQS3HnnnctaFs10jRdeeKFrjaXOQ7UBwBEzexoAzOzIfBuSXA3gagBnmtl4YfsTAG5YXqlSS+rq6rxLEKkKOlZiqpZc7rzzTtx44424+ebhAWo+AAAJa0lEQVSbcfHFF+O+++7Dzp07AQC33HLLopddc801AIA3vvGNPm8opfj9nXPOOXjkkUfcaiz1kt+3AZxB8iDJz5G89BTbngXgiUITJTKn4eFh7xJEqoKOlZiqJZebbroJN998M7Zt24ZVq1Zh27ZtWL16NZqamp6zrKmpCatXr15wu5tvvhk33XST99uaUfz+xsbGXGss6QyVmY2QvBDJpbvLANxB8oNmdttCzyX5dgDvBrAewCVm9pPi9YODg9i5cyfq6+sxNTWF7du3Y9euXejv70dzczPq6uowPDyM9vZ2DA0NwczQ3t6OgYEBtLS0AABGRkbQ2dmJfD4Pksjlcsjn82htbcXU1BQAfTtCJIt03EutGRkZwdGjR7Fu3TqMjY1hfHwcXV1d6O/vR2NjI5qamnDs2DGsX78eJ06cwMGDB3H++eejr68PTU1NaGhowKFDh2BmOHz4MCYnJ9HV1YVDhw6BJI4dOzbzmTu93fj4+Mxn7tlnn42DBw9ifHx83s/c0dHRmZpWrVqFtWvX4siRI1i7di0mJiYwNjY2s76hoQFr1qxZ1HuamJiYWX/w4EF0d3ejr68PLS0tOHz4MDZs2ICDBw+ir69vxfqI4vc0n5J/9YyZTQG4F8C9JH8E4G0Abptj08cAbCK5xsxOmNmtAG4l+QCAWedIOzo68L3vfW/Wi2zevHnmz+vWrQMArF69es7169evBwCcccYZc64HgKGhoVO/QamoQ4cOYePGjSv+uvoAlWK1cNyX61iR5fHIJZfLoaWlZaYJaG1tnVlX/Jk3vbylpQVbtmzB/v37sW3btpn1GzduhJnh9NNPf84ykli3bt3MZ+70do2NjTOvv3v3bmzZsgWNjY3zfuZOT7BZvGyue86K1y/mPRWv37JlC3p7e7Ft27aZTKZrXMk+ovg9HT16dNZ7AUq/Kf0ckmcXLdoKoG+ubc3sJIBbAHyWZGPh+XUA9IuPZIaZfn+dSCl0rMRULblce+21uOaaa7B7925MTk5i9+7dOHnyJMbGxp6zbGxsDCdPnlxwu2uuuQbXXnut99uaUfz+JiYmXGss9QxVC4DPkHw+gGeQnIV6xym2vw7AhwE8QPIEgDEAXwZweBm1Sg1pb2/3LkGkKuhYialacpm+MfsDH/gADh48iC1btuCjH/3okpddf/31YW5IB+Z+f1410rvL3rNnj3V3d5d1H7lcriZO/deSvr6+WZdlV0Jyya86/uUIALc3nIsdEw97l1GjWBPHfbmOFVkej1z0WXZqlcpk3759e3t6ei5KL9dM6eKi+Dq4iMxPx0pMyiUe70wy0VA9+uij3iWIiIgsi85OxZaJhmpkZMS7BElRJiKl0bESk3KJxzuTTDRUnZ2d3iVIijIRKY2OlZiUSzzemWSiocrn894lSIoyESmNjpWYlEs83pmUPLFnNSPpXYKklCuTjo5NGByspry7AVRTvdWjo2OTdwkrQuNXTMolHu9MMtFQafbseMqVSW/vgbK8bjlFus305MmTz5lJWPxp/IpJucTjnYku+YkLZRKTcolHmcSkXOLxziQTDVXx7waSGJRJTMolHmUSk3KJxzuTTDRUU1NT3iVIijKJSbnEo0xiUi7xeGeSiYZqdHTUuwRJUSYxKZd4lElMyiUe70wy0VB1dXV5lyApyiQm5RKPMolJucTjnUkmGqr+/n7vEiRFmcSkXOJRJjEpl3i8M8lEQ3XXXXd5lyApyiQm5RKPMolJucTjnUkmGqqvf/3r3iVIijKJSbnEo0xiUi7xeGeSiYbqmWee8S5BUpRJTMolHmUSk3KJxzsTmplrAd/5znfyAPrKuY+hoaG2XC53pJz7kMVRJjEpl3iUSUzKJZ4KZrK5p6enPb3QvaESERERqXaZuOQnIiIiUk5qqERERESWqaYaKpKXk3yE5GMkPzjH+tNI3lFY/32SZ1a+ymwpIZP3knyI5P0kv0Nys0edWbNQLkXbvYmkkbyokvVlUSmZkPz1wvHyIMnbK11jFpUwhm0ieQ/J/YVx7LUedWYFyS+RHCT5wDzrSfLmQl73k7ygUrXVTENFsg7AHwO4AsCLALyZ5ItSm+0EcMzMzgLwaQAfr2yV2VJiJvsBXGRmLwXwNQCfqGyV2VNiLiC5BsA1AL5f2Qqzp5RMSJ4N4L8AeIWZvRjA71e80Iwp8Vi5HsBXzex8AL8J4HOVrTJzbgNw+SnWXwHg7MLjHQA+X4GaANRQQwXgZQAeM7Mfm9kEgL8E8IbUNm8A8OXCn78GoIckK1hj1iyYiZndY2YnCz/eB2BjhWvMolKOFQD4MJIGd7ySxWVUKZlcDeCPzewYAJjZYIVrzKJScjEArYU/rwVwuIL1ZY6ZfRfA0Ck2eQOAr1jiPgDPJ7mhErXVUkP1AgA/Kfr5UGHZnNuY2TMAjgNYX5HqsqmUTIrtBPC3Za1IgBJyIXk+gDPM7P9UsrAMK+VY2QJgC8nvkbyP5Kn+lS4ro5RcbgBwJclDAP4GwLsqU5rMY7GfOyumvhI7qZC5zjSl54QoZRtZOSX/fZO8EsBFAC4ta0UCLJALyechuSR+VaUKkpKOlXoklzFeheRM7m6S55nZU2WuLctKyeXNAG4zs5tIvhzAnxdyebb85ckc3D7na+kM1SEAZxT9vBGzT73ObEOyHsnp2VOdOpTlKSUTkHw1gOsAvN7Mnq5QbVm2UC5rAJwH4F6SjwO4GMDdujG9rEodv/7azCbN7N8APIKkwZLyKSWXnQC+CgBmtgdAI4C2ilQncynpc6ccaqmh+lcAZ5N8IckGJDcH3p3a5m4Abyv8+U0A/sE0s2k5LZhJ4dLSF5A0U7onpDJOmYuZHTezNjM708zORHJv2+vN7Ac+5WZCKePXXQAuAwCSbUguAf64olVmTym5PAGgBwBInoukocpXtEopdjeAtxa+7XcxgONm9mQldlwzl/zM7BmS/xnA3wGoA/AlM3uQ5IcA/MDM7gZwC5LTsY8hOTP1m34V174SM/kkgBYAf1X4fsATZvZ6t6IzoMRcpIJKzOTvAPwSyYcATAF4v5kd9au69pWYy7UA/pTke5BcWrpK/1AvH5L/C8ll77bCfWt/CGAVAJjZnyC5j+21AB4DcBLA2ytWm3IXERERWZ5auuQnIiIi4kINlYiIiMgyqaESERERWSY1VCIiIiLLpIZKREREZJnUUImIiIgskxoqERERkWVSQyUiIiKyTP8fntb6u3zkVeMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,4))\n",
    "plt.style.use('bmh')\n",
    "\n",
    "# Without scaling\n",
    "plt.boxplot(x,vert=False,labels=['STG','SCG','STR','LPR','PEG'],patch_artist=True)\n",
    "plt.title('Without Scaling')\n",
    "plt.show()\n",
    "\n",
    "# Normalisation\n",
    "fig=plt.figure(figsize=(10,4))\n",
    "plt.boxplot(x_norm,vert=False,labels=['STG','SCG','STR','LPR','PEG'],patch_artist=True)\n",
    "plt.title('Normalisation(0-1)')\n",
    "plt.show()\n",
    "\n",
    "# Standard scaling\n",
    "fig=plt.figure(figsize=(10,4))\n",
    "plt.boxplot(x_sc,vert=False,labels=['STG','SCG','STR','LPR','PEG'],patch_artist=True)\n",
    "plt.title('Standard Scaling(Z-score)')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fig=plt.figure(figsize=(8,6))\n",
    "plt.scatter(x[:,0],x[:,1],c=y)\n",
    "\n",
    "\n",
    "plt.xlabel('Sepal Width') , plt.ylabel('Petal Width')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((206, 5), (206,), (52, 5), (52,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x_sc,y,test_size=0.2,random_state=0)\n",
    "x_train.shape,y_train.shape,x_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">**Building machine learning model**\n",
    "- LogisticRegression\n",
    "- KNeighborsClassifier\n",
    "- Support Vector Machine\n",
    "- DecisionTreeClassifier\n",
    "- RandomForestClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_log= LogisticRegression(C=10.0) # class\n",
    "model_knn= KNeighborsClassifier(n_neighbors=3)\n",
    "model_svm= SVC(kernel='rbf')\n",
    "model_dt= DecisionTreeClassifier()\n",
    "model_rf= RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model trained successfully\n"
     ]
    }
   ],
   "source": [
    "model_log.fit(x_train,y_train)\n",
    "model_knn.fit(x_train,y_train)\n",
    "model_svm.fit(x_train,y_train)\n",
    "model_dt.fit(x_train,y_train)\n",
    "model_rf.fit(x_train,y_train)\n",
    "print('Model trained successfully')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Model Evaluation(How good is my model)\n",
    "- Confusion Matrix\n",
    "- Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_log=model_log.predict(x_test)\n",
    "y_pred_knn=model_knn.predict(x_test)\n",
    "y_pred_svm=model_svm.predict(x_test)\n",
    "y_pred_dt=model_dt.predict(x_test)\n",
    "y_pred_rf=model_rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_log= confusion_matrix(y_test,y_pred_log)\n",
    "cm_knn= confusion_matrix(y_test,y_pred_knn)\n",
    "cm_svm= confusion_matrix(y_test,y_pred_svm)\n",
    "cm_dt= confusion_matrix(y_test,y_pred_dt)\n",
    "cm_rf= confusion_matrix(y_test,y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1296 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(30,18))\n",
    "\n",
    "plt.subplot(2,3,1)\n",
    "sns.heatmap(cm_log,annot=True,cmap='summer')\n",
    "plt.title('Logistic Regression')\n",
    "\n",
    "plt.subplot(2,3,2)\n",
    "sns.heatmap(cm_knn,annot=True,cmap='ocean')\n",
    "plt.title('K Nearest Neighbor ')\n",
    "\n",
    "plt.subplot(2,3,3)\n",
    "sns.heatmap(cm_svm,annot=True,cmap='brg',)\n",
    "plt.title('Support Vector Machine')\n",
    "\n",
    "plt.subplot(2,3,4)\n",
    "sns.heatmap(cm_dt,annot=True,cmap='jet',)\n",
    "plt.title('Decision Tree')\n",
    "\n",
    "plt.subplot(2,3,5)\n",
    "sns.heatmap(cm_rf,annot=True,cmap='gnuplot',)\n",
    "plt.title('Random Forest Tree')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cr_log=classification_report(y_test,y_pred_log)\n",
    "cr_knn=classification_report(y_test,y_pred_knn)\n",
    "cr_svm=classification_report(y_test,y_pred_svm)\n",
    "cr_dt=classification_report(y_test,y_pred_dt)\n",
    "cr_rf=classification_report(y_test,y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************Logistic Regression********************\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.20      0.33         5\n",
      "          1       0.46      1.00      0.63        11\n",
      "          2       1.00      0.50      0.67        24\n",
      "          3       0.80      1.00      0.89        12\n",
      "\n",
      "avg / total       0.84      0.69      0.68        52\n",
      "\n",
      "********************K Nearest Neighbor********************\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.50      0.40      0.44         5\n",
      "          1       0.32      0.55      0.40        11\n",
      "          2       0.79      0.62      0.70        24\n",
      "          3       1.00      0.83      0.91        12\n",
      "\n",
      "avg / total       0.71      0.63      0.66        52\n",
      "\n",
      "********************Support Vector Machine********************\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.20      0.33         5\n",
      "          1       0.53      0.82      0.64        11\n",
      "          2       0.86      0.79      0.83        24\n",
      "          3       0.92      0.92      0.92        12\n",
      "\n",
      "avg / total       0.82      0.77      0.76        52\n",
      "\n",
      "********************Decision tree********************\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.71      1.00      0.83         5\n",
      "          1       0.67      0.73      0.70        11\n",
      "          2       0.95      0.79      0.86        24\n",
      "          3       0.92      1.00      0.96        12\n",
      "\n",
      "avg / total       0.86      0.85      0.85        52\n",
      "\n",
      "********************Random Forest********************\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.60      0.75         5\n",
      "          1       0.69      1.00      0.81        11\n",
      "          2       1.00      0.83      0.91        24\n",
      "          3       0.92      1.00      0.96        12\n",
      "\n",
      "avg / total       0.92      0.88      0.89        52\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"*\"*20+'Logistic Regression'+\"*\"*20)\n",
    "print(cr_log)\n",
    "\n",
    "print(\"*\"*20+'K Nearest Neighbor'+\"*\"*20)\n",
    "print(cr_knn)\n",
    "\n",
    "print(\"*\"*20+'Support Vector Machine'+\"*\"*20)\n",
    "print(cr_svm)\n",
    "\n",
    "print(\"*\"*20+'Decision tree'+\"*\"*20)\n",
    "print(cr_dt)\n",
    "\n",
    "print(\"*\"*20+'Random Forest'+\"*\"*20)\n",
    "print(cr_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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