spotify_classifier_raw_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n"
     ]
    },
    {
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       "      <td>2.3</td>\n",
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       "   Unnamed: 0                              session_id  \\\n",
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       "\n",
       "                                 track_id  skip_1  skip_2  skip_3  \\\n",
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       "4  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
       "\n",
       "   not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0            1               0                              1.0   \n",
       "1            0               0                              0.3   \n",
       "2            1               0                              1.0   \n",
       "3            1               0                              0.3   \n",
       "4            0               0                              1.0   \n",
       "\n",
       "   hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  skip_score  \\\n",
       "0                            1.0                              1         1.0   \n",
       "1                            0.4                              0         0.2   \n",
       "2                            1.0                              0         1.0   \n",
       "3                            1.0                              0         1.0   \n",
       "4                            0.3                              1         0.2   \n",
       "\n",
       "   liking  rating  \n",
       "0     3.0     4.0  \n",
       "1     0.7     0.9  \n",
       "2     2.0     3.0  \n",
       "3     1.3     2.3  \n",
       "4     2.3     2.5  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy as sp\n",
    "\n",
    "df = pd.read_csv(r'F:\\AppliedDataScience\\data\\ratingdf_likescore.csv')\n",
    "print(len(df))\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10000.000000\n",
       "mean         1.326170\n",
       "std          1.472203\n",
       "min         -1.100000\n",
       "25%          0.300000\n",
       "50%          1.200000\n",
       "75%          2.900000\n",
       "max          4.000000\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rating_round'] = df['rating'].round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "   Unnamed: 0                              session_id  \\\n",
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       "4           4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290   \n",
       "\n",
       "                                 track_id  skip_1  skip_2  skip_3  \\\n",
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       "\n",
       "   not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0            1               0                              1.0   \n",
       "1            0               0                              0.3   \n",
       "2            1               0                              1.0   \n",
       "3            1               0                              0.3   \n",
       "4            0               0                              1.0   \n",
       "\n",
       "   hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  skip_score  \\\n",
       "0                            1.0                              1         1.0   \n",
       "1                            0.4                              0         0.2   \n",
       "2                            1.0                              0         1.0   \n",
       "3                            1.0                              0         1.0   \n",
       "4                            0.3                              1         0.2   \n",
       "\n",
       "   liking  rating  rating_round  \n",
       "0     3.0     4.0           4.0  \n",
       "1     0.7     0.9           1.0  \n",
       "2     2.0     3.0           3.0  \n",
       "3     1.3     2.3           2.0  \n",
       "4     2.3     2.5           2.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'session_id', 'track_id', 'skip_1', 'skip_2', 'skip_3',\n",
       "       'not_skipped', 'context_switch', 'hist_user_behavior_reason_start',\n",
       "       'hist_user_behavior_reason_end', 'hist_user_behavior_is_shuffle',\n",
       "       'skip_score', 'liking', 'rating', 'rating_round'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Import Decision Tree Classifier\n",
    "from sklearn.model_selection import train_test_split # Import train_test_split function\n",
    "from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation\n",
    "df = df.dropna()\n",
    "len(df)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.   0.   0.   1.   1.   1. ]\n",
      " [ 0.   0.   0.2  0.3  0.4  0. ]\n",
      " [ 0.   0.   0.   1.   1.   0. ]\n",
      " ...\n",
      " [ 0.  -0.5  0.2  0.2  0.3  0. ]\n",
      " [ 0.  -0.5  0.2  1.   0.3  0. ]\n",
      " [-1.  -0.5  0.2  0.2  0.4  0. ]]\n"
     ]
    }
   ],
   "source": [
    "X = df[['skip_1','skip_2','skip_3','hist_user_behavior_reason_start','hist_user_behavior_reason_end','hist_user_behavior_is_shuffle']]\n",
    "X = X.values\n",
    "print(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4.]\n",
      " [ 1.]\n",
      " [ 3.]\n",
      " ...\n",
      " [ 0.]\n",
      " [ 1.]\n",
      " [-1.]]\n"
     ]
    }
   ],
   "source": [
    "y = df[['rating_round']]\n",
    "y = y.values\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2. 2. 0. ... 0. 1. 2.]\n",
      "Accuracy: 0.9726666666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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+qeGxMsLmFTeGHUJK5WQ1DDuEtBepQoTOmhMRyTiRuo5IZ82JiGSeSBWiUsFt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\nEREJlQqRiIiESoVIRERCpUIkIiKh+n+Ng3UH2LTy1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9726666666666667\n",
      "Precision: [1.         0.99666667 0.94871795 0.94925373 0.96732026 0.96341463]\n",
      "Recall: [1.         0.99006623 0.93908629 0.95209581 0.9704918  0.9875    ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:40: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2. 2. 0. ... 0. 1. 2.]\n",
      "Accuracy: 0.9773333333333334\n"
     ]
    },
    {
     "data": {
      "image/png": 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IhGRi15wKkYhIlKhFJCIiYVLXnIiIhCrTbsAFFSIRkUjRNSIREQmVuuZERCRc6poTEZFQ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\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9773333333333334\n",
      "Precision: [1.         0.99666667 0.95431472 0.95820896 0.97689769 0.96385542]\n",
      "Recall: [1.         0.99006623 0.95431472 0.96107784 0.9704918  1.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2. 2. 0. ... 0. 1. 2.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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RCZW65kREJFzqmhMRkVClX4NIhUhEJFLUIhIRkVCpEImISKjUNSciImFytYhERCRU6VeH\nVIhERCIlI/0qUcoWoqg9v6flPg+FHULCLZ9zadghiMiO1DUnIiKhSr86pEIkIhIp6poTEZFQqWtO\nRERClX51SIVIRCRS1DUnIiKhSr86pEIkIhIlGllBRETCpa45EREJVfrVIRUiEZFIUdeciIiESl1z\nIiISqvSrQypEIiKRkpF+T8ZTIRIRiZL0q0MqRCIikaKLFUREJFTpV4dUiEREosTT8Kq5NOxNTJwJ\nE6bRr99Qjjsun4KC58MOp1rq18ti7PPX8O7o65k45kZuuLI/ALs13ZXnHx/KpLG/5fnHh9K0SQMA\nevfqzH9evJb3Xv0N/3nxWo44dO8ww49bOv6MKhO1fCB6OaV9PmbxTVXuztqb2btmNtvMPjezq3d4\n/3ozczPLKbPsZjObb2ZzzKxfVcfYaQtRcXExI0Y8zMiRwyksfIAxYyYwf/7CsMOq0qbNW/nl+Q9y\nzMC7OGbQnfQ5sgsHde/IVfnHMvHDefTs9ycmfjiPq/KPBWDV6g2cfelIep9yJ1fc9DQP/uXskDOo\nvnT9GVUkavlA9HKKRD4W51S1rcB17v4z4FDgcjPbF2JFCjgO2PZNCt47A9gP6A88aGaZlR1gpy1E\nM2fOo2PHNrRvn0u9etkMGHAU48ZNCjusatmwcTMA2VmZZGdl4u6ccGxXRr0yBYBRr0zhxL7dAPh0\n9jcULVsLwBfzllK/Xjb1siv9nUgZ6fwzKk/U8oHo5RSJfDIsvqkK7v6tu08PXq8DZgNtg7fvAW4A\nvMwmA4Fn3X2Tu38NzAcOqTTk+LOsHTO7sK6PWZ6iopXk5m5rSdK6dQuKilaGGFH1ZWQY775yPbP/\n+0fG/3cO02cupGWLxhQtjxWcouVryWne6CfbndyvO5/O/obNW4rrOuQaSeefUXmilg9EL6dI5BNn\n15yZ5ZvZ1DJTfsW7tj2AA4FJZnYK8I27f7LDam2BRWXmF/Nj4SpXGC2iWyt6o+w3pKBgVFKDcPef\nLLM0ueyxpMQ5ZtBd7N97OD/fvwNd8nKr3GafvXP5w/Uncf2w5+ogwsRI559ReaKWD0Qvp0jkE2fX\nnLsXuPvBZaaCcndr1gh4EbiGWHfd74BhFUSwo59+Y8tIylVzZjazoreA1hVtF3wDgm/C3EoDr63c\n3ByWLl2xbb6oaCWtWjVP5iETbu26H/hg0pf0ObILy1euo3XLJhQtX0vrlk1YsWr9tvXatG7KE/df\nyBU3Ps2CRenzv7so/IzKilo+EL2cIpFPEq6aM7NsYkXoKXd/ycy6AXsCnwSFuh0w3cwOIdYCal9m\n83bAkkpDTnjEMa2B84CTy5lS4pOwW7c8FixYwqJFS9m8eQuFhRPo06fSbsyU0KJZQ5o03gWAXepn\n07tXZ+Z9tYw33/mM0wf1AOD0QT14Y9xnADRpvAtPF1zCbXcXMnn616HFXRPp+jOqSNTygejlFIl8\nEnyOyGKV5jFgtrvfDeDun7p7K3ffw933IFZ8fu7uS4FXgTPMrL6Z7QnkAZMrO0ay7iMaAzRy9xk7\nvmFm45N0zLhkZWUybNhQhgy5heLiEk49tS95eR3DDqtKrVs14f47ziIjM4MMM0a/OYO3x89i6owF\njLz3fM4+rSeLv13NxVc/AcCQc45kzw45XHfZ8Vx32fEADL7o4e1aTKkqXX9GFYlaPhC9nKKQjye+\nQXQ4cC7wqZmVfqb/1t1fL/f47p+b2XPALGJdeJe7e6Unpq28PtHUkNyuubrWcp+Hwg4h4ZbPuTTs\nEEQioHNCS0en/Bfi+uz8quC00E+CaWQFEZEoSbeLK1AhEhGJljQc4keFSEQkStJwmAIVIhGRKFHX\nnIiIhEpdcyIiEiZXi0hEREKlc0QiIhIqdc2JiEio1DUnIiKhUotIRERClX51SIVIRCRKXC0iEREJ\nlQqRiIiEShcriIhIqHQfkVQkis/u2euhpWGHkFDzL20ZdggJZ2SGHYLUNbWIREQkVDpHJCIioVIh\nEhGRMGnQUxERCZcuVhARkVCpRSQiIqHSOSIREQmVCpGIiIQq/eqQCpGISJR4ZvpdraBCJCISJeqa\nExGRUKVfHVIhEhGJkoz065lTIRIRiZI0vI1IhUhEJEoiVYjMbB3gpbPBVw9eu7s3SXJsIiISJ0vD\nSlRhIXL3xnUZiIiI1F4a1qHqDY9nZkeY2YXB6xwz2zO5YdWNCROm0a/fUI47Lp+CgufDDqfW0jWf\nO47uzOQLDuWN0w/atqxLi4Y8/4sDeP1XB1Fwwn40yt7+AW9tGtVn5pDDGdK9XV2HW2ObNm1m8Gm/\nYeAp13DSgCv529+eCTukhEjX37uKpHs+ZvFNqaDKQmRmtwA3AjcHi+oBTyYzqLpQXFzMiBEPM3Lk\ncAoLH2DMmAnMn78w7LBqLJ3zeXFOEReO+Wy7ZX8+ujN3fvQ1Jz43jbe+XsElB2xfcH5/eCfeW7iq\nLsOstXr1svnnEyMY/eq9vPzKPbw/cTozZswJO6xaSeffu/JEIR/LiG9KBdUJ4xfAKcAGAHdfAlTZ\nbWdmXczsWDNrtMPy/jUJNNFmzpxHx45taN8+l3r1shkw4CjGjZsUdlg1ls75TPn2O9Zs2rLdsj13\na8Dkb78D4INFa+jXKWfbe8ft0YJFa39g3qqNdRpnbZkZDRs2AGDr1mK2bi1Oy/78stL59648Ucgn\nki0iYLO7O8GFC2bWsKoNzOwqYDRwJfCZmQ0s8/afahJoohUVrSQ398cPt9atW1BUtDLEiGonavnM\nW7WBvnu0AOCEvXJo06g+AA2yMsg/sD1/m/K/MMOrseLiYgYNvIbDe51Pr17d6d69c9gh1UrUfu+i\nkE+GxTelguoUoufM7BFgNzO7BPgP8GgV21wCHOTug4CjgT+Y2dXBexWmbmb5ZjbVzKYWFIyqRmg1\nF6utPzl+Uo+ZTFHL58Z353JO190ZfdqBNKyXyZaSWH7X9OjIP2YuZuPWkpAjrJnMzExeGX0v498b\nycyZ85g7Nz0Laqmo/d5FIZ90bBFVeR+Ru99lZscBa4HOwDB3f7uKzTLdfX2w/QIzOxp4wcw6Ukkh\ncvcCoCA2N/envxEJlJubw9KlK7bNFxWtpFWr5sk8ZFJFLZ+v1nzPBWM+BWCPpg04pkMsl+6tm9C/\nU0tuPLQTTepnUeLOpuIS/v3ZkjDDjVuTJo04pGdXJk78mM6dO4YdTo1F7fcuCvmkSnGJR3VPVX0K\nTAQmBK+rstTMDiidCYrSSUAO0C3eIJOhW7c8FixYwqJFS9m8eQuFhRPo0+eQsMOqsajl06JBNhD7\nX8sVB3Xg6VnfAnDGK5/Q+6nJ9H5qMv+Y+Q0PTV+UNkVo1arvWLt2PQA//LCJD//7CZ06tQ05qtqJ\n2u9dFPIxs7imauzvcTNbZmaflVl2gJl9ZGYzgl6sQ8q8d7OZzTezOWbWrzoxV9kiMrMhwDDgHWKf\nC383sxHu/nglm50HbC27wN23AucF3Xyhy8rKZNiwoQwZcgvFxSWcempf8vLS93+m6ZzPvX270HP3\npjTbJZv3z+3JfVP+R8PsDM7pujsAY79awQtfFIUcZe0tX7aam266j+LiEtyd/v0P55hjeoQdVq2k\n8+9deaKQTxKuhPsncD/wrzLL/gLc6u5vmNmJwfzRZrYvcAawH7A78B8z6+zuxZXGXF6f6HYrmM0B\nern7ymC+BfBfd9+nZjlVV3K75qT29npoadghJNT8S1uGHULCGZlVryQh65zQzrT9/z0xrs/Omece\nWeXxzWwPYIy7dw3mxwKPu/soMzsTONndzzKzmwHc/c9l1hvu7h9Wtv/qjDW3GFhXZn4dsKga24mI\nSB2L9xyRmeUD+WUWFQTn6ytzDTDWzO4idoqnV7C8LfBRmfUWB8sqVdlYc9cGL78BJpnZaGKXcA8E\nJle1YxERqXvxFqLtLxKrtkuBX7v7i2b2K+AxoC/lX4xWZQutshZR6U2rXwZTqdHVDFREROpYHd0b\ndD5QekvO88DI4PVioH2Z9doBVV5NVNmgp7fWMEAREQlJHV2+vQToDYwH+gDzguWvAk+b2d3ELlbI\noxo9aNW5aq4lcAOxqyB2KV3u7n3iDFxERJIs0YXIzJ4hNjBBjpktBm4hNmjBfWaWBfxAcI7J3T83\ns+eAWcSunL68qivmoHoXKzwFjCJ2H9BQYk2y5XFnIyIiSWcJ7ptz9zMreOug8ha6++3A7fEcozpX\nnLdw98eALe7+nrtfBBwaz0FERKRuRHKIH6B0WORvzWwAsb7B9HkIjIjITiRViks8qlOIbjOzpsB1\nwN+BJsCvkxqViIjUSCQLkbuPCV5+BxyT3HBERKQ2UuXRDvGo7IbWv1PJjUjuflVSIhIRkRqLWoto\nap1FISIiCZEqj/+OR2U3tD5Rl4GIiEjtRa1FJCIiaSbdnigLKkQiIpGShnVIhUhEJEoiVYh01ZxU\nZd7QFmGHkFC7dhgRdggJt3bBjWGHkFDZGbuGHULKi1QhQlfNiYiknUjdR6Sr5kRE0k+kClGp4DEQ\nNwL7osdAiIiktAyr8oGoKac6tz49BcwG9gRuBRYAU5IYk4iI1FCWxTelAj0GQkQkQjLM45pSgR4D\nISISIZE8R4QeAyEikjbScKg5PQZCRCRKItkiMrN/UM6NrcG5IhERSSGWIud94lGdrrkxZV7vAvyC\n2HkiERFJMZFsEbn7i2XnzewZ4D9Ji0hERGoskueIypEHdEh0ICIiUnupckl2PKpzjmgd258jWkps\npAUREUkxUe2aa1wXgYiISO2lY9dclTGb2bjqLBMRkfBlWHxTKqjseUS7ALsCOWbWDCgNuQmwex3E\nJiIicYraOaL/A64hVnSm8WMhWgs8kOS46sSECdO4/fZHKSkpYfDg48jPHxx2SLUStXwAju3zfzRs\n2IDMzAwyMzN54cU7ww6pSvXrZ/Of54dRr142WVmZvPz6JG67+wV+OaAnv/v1aXTZe3eOPOUPTJ/5\nFQDZ2Znc/+ch/Hz/TpSUONcPf4KJH80OOYuK/f53DzFh/HSaN2/CK6/9FYDv1qznumvvZck3y9m9\nbUv+es81NG3aKORIaybd/45SpZUTjwq75tz9PnffE7je3Tu5+57B1N3d76/DGJOiuLiYESMeZuTI\n4RQWPsCYMROYP39h2GHVWNTyKeuJf43g5VfuTosiBLBp0xb6n3EbPfvfRM/+N3F87+4ccuDefD5n\nEWfk3837k77Ybv2Lzow9UaXH8Tdy0tl/4o4/nIOl8GM2Bw3qzcMFN2+3bOSjr3DoYV15fex9HHpY\nVx57dHRI0dVOFP6OMuKcUkF14igxs91KZ8ysmZldVtVGZnaImfUIXu9rZtea2Ym1iDWhZs6cR8eO\nbWjfPpd69bIZMOAoxo2bFHZYNRa1fNLdho2bAMjOyiQrKxN3Z878Jcz76tufrNslrx3vfvA5AMtX\nruW7tRs5aP9OdRpvPA7usS9Nd9u+tfPuO1MZOLA3AAMH9uadcen5pJgo/B2l4+jb1SlEl7j7mtIZ\nd18NXFLZBmZ2C/A34CEz+zNwP9AIuMnMfleLeBOmqGglubk52+Zbt25BUdHKECOqnajlU8rMuPji\nWzn1l9fz3Ki3wg6n2jIyjI/e+DMLP36Ed97/lCkzvqxw3U9n/4+Tjz+IzMwMOrZvyYFd96Td7i3q\nMNraW7nyO1q2agZAy1bNWLVqbcgR1UwU/o4idbFCGRlmZu7uAGaWCdSrYpvTgAOA+sTuO2rn7mvN\n7E5gEnB7eRuZWT6QD/DIIyPIzz+9elnUQJDOjsdP2vGSLWr5lHr66T/RqnVzVq5cw8UX3cqendrS\no8d+YYdVpZIS59ATbqZpk10ZVXAt+3Zux6y5i8td94lR4+myd1s+GHM7C79ZwUfT5rJ1a3EdRywQ\njb+jVCku8ahOIRoLPGdmDxO7sXUo8GYV22x192Jgo5l96e5rAdz9ezMrqWgjdy8ACmJzc5PaZszN\nzWHp0hXb5ouKVtKqVfNkHjKpopZPqVatYzm0aLEbffv25NOZ89KiEJX6bu1GJnw0m+OP7l5hISou\nLuGGEf/eNv/uS7cyf8HSugoxIVq0aMryZatp2aoZy5etpnnzJmGHVCNR+DtKlfM+8ahOzDcC44BL\ngcuD17+pYpvNZrZr8Pqg0oXBc40qLER1qVu3PBYsWMKiRUvZvHkLhYUT6NPnkLDDqrGo5QOwceMP\nbFj//bbXH3zwCXmdU390qT2Fuj4AABFLSURBVJzmjWnaJPbrv0v9bPoc0ZU5X1Y8TnCDXeqxa4P6\nAPQ5shtbi4v5Yt43dRJrohzd52BGj34PgNGj3+OYPgeHHFHNROHvKB3PEVVnZIUS4OFgwsyOIPaA\nvMsr2ewod99UZvtS2cD5NY42gbKyMhk2bChDhtxCcXEJp57al7y8jmGHVWNRywdg5co1XHnF/wNg\na3EJJ510JEce+fOQo6pabqtmPHr3pWRmZpCRYbw45iPeGPcxp/Q7mLtHXEBO8ya89I8bmDlrAaec\newctc5rw2r9vpqTEWVK0iouveTDsFCr1m+vuY8rkWaxZs45jj76Uy64YzJAhA7nu2nt56YV3abN7\nDnffk57PzozC31E6ds1ZeX2iP1nJ7ADgTOB04GvgJXf/e3JDS27XnNReiW+peqU00rDjbWGHkHBr\nF0RrWMjsjF2rXintdE5o6bh+0jtxfXbe1bNP6KWrspEVOgNnECtAK4FRxAqXntIqIpKi0rFFVFnX\n3BfAROBkd58PYGbp2d4WEdlJpOMTWiu7WOFUYpdev2tmj5rZsfw4zI+IiKSgRN9HZGaPm9kyM/us\nzLI7zewLM5tpZi/vMOjBzWY238zmmFm/asVc0Rvu/rK7nw50AcYDvwZam9lDZnZ8dXYuIiJ1KwlD\n/PwT6L/DsreBru6+PzAXuBlio+gQO6WzX7DNg8G9p1XGXCl33+DuT7n7SUA7YAZwU/XiFxGRupTo\ny7fdfQKwaodlb7n71mD2I2K1AWAg8Ky7b3L3r4H5QJXXv8d175O7r3L3R9y9TzzbiYhI3Yi3a87M\n8s1sapkpP85DXgS8EbxuCywq897iYFmlqjOygoiIpIl4r5rbfkSb+ARjh24FnipdVN4hqtqPCpGI\nSIRUeUImQczsfOAk4Fj/8YbUxUD7Mqu1AyoeViSQjsMSiYhIBepiiB8z609s+LdT3H1jmbdeBc4w\ns/pmtieQB0yuan9qEYmIREiib2g1s2eAo4EcM1sM3ELsKrn6wNvB6OQfuftQd//czJ4DZhHrsrs8\nGAC7UipEIiIRkuhC5O5nlrP4sUrWv50KHvVTERUiEZEIyUzDYQdUiEREIiRqY82JiEiaSZVnDMVD\nhUhEJELUIpKdSoZlhx1CQn2/8NawQ0i4lvs8FHYICbVsztCwQ0i4RNeNurqPKJFUiEREIiQrQ11z\nIiISIl01JyIiodI5IhERCZUKkYiIhEqFSEREQpWp+4hERCRM6fhIBRUiEZEIUdeciIiESoVIRERC\npXNEIiISKrWIREQkVCpEIiISKhUiEREJlcaaExGRUOnBeCIiEird0JpmJkyYxu23P0pJSQmDBx9H\nfv7gsEOqlajlA9HL6eab72P8+Cm0aNGUMWMeCDucaqlfL4tXn7qCevWyyMrM5LWxn/CXv7/Jbk13\n5dF7zqND2+Ys/GYVQ655gu/Wfk/vXp35w3UnkZ2dyZYtxQy/81Xe/2h+2GlUy7ffLufGG+5lxYrV\nZGQYv/pVP847/5Sww4pLOp4jSsfimRDFxcWMGPEwI0cOp7DwAcaMmcD8+QvDDqvGopYPRDOnX/7y\nWEaOHB52GHHZtHkrvzz/QY4ZeBfHDLqTPkd24aDuHbkq/1gmfjiPnv3+xMQP53FV/rEArFq9gbMv\nHUnvU+7kipue5sG/nB1yBtWXmZnJjTddxOtvPMizo+7kqadfT7vfuUyLb0oFO20hmjlzHh07tqF9\n+1zq1ctmwICjGDduUthh1VjU8oFo5tSjR1eaNm0cdhhx27BxMwDZWZlkZ2Xi7pxwbFdGvTIFgFGv\nTOHEvt0A+HT2NxQtWwvAF/OWUr9eNvWy0+MB1q1aNWe//fYCoFGjXdmrUzuKilaGHFV8MszjmlJB\nnRUiM/tXXR2rOoqKVpKbm7NtvnXrFmn3C1dW1PKBaOaUrjIyjHdfuZ7Z//0j4/87h+kzF9KyRWOK\nlscKTtHyteQ0b/ST7U7u151PZ3/D5i3FdR1yrS1eXMTs2V/Rvfs+YYcSlwyLb0oFSTlHZGav7rgI\nOMbMdgNw93I7Xc0sH8gHeOSREeTnn56M8AhiKO/4STteskUtH4hmTumqpMQ5ZtBdNGm8C088cBFd\n8nKr3GafvXP5w/Un8auLHq6DCBNrw4bvueqqO7j5t0No1GjXsMOJS6oUl3gk62KFdsAsYCTgxArR\nwcBfK9vI3QuAgtjc3KS2GXNzc1i6dMW2+aKilbRq1TyZh0yqqOUD0cwp3a1d9wMfTPqSPkd2YfnK\ndbRu2YSi5Wtp3bIJK1at37Zem9ZNeeL+C7nixqdZsCi9WrFbtmzlqqvu4OSTe3P88b3CDidu6Xi+\nJVkxHwxMA34HfOfu44Hv3f09d38vSceMS7dueSxYsIRFi5ayefMWCgsn0KfPIWGHVWNRyweimVM6\natGsIU0a7wLALvWz6d2rM/O+Wsab73zG6YN6AHD6oB68Me4zAJo03oWnCy7htrsLmTz969Dirgl3\n5/e/+zt7dWrHhRcOCjucGjGLb0oFSWkRuXsJcI+ZPR98LUrWsWoqKyuTYcOGMmTILRQXl3DqqX3J\ny+sYdlg1FrV8IJo5XXvtnUye/CmrV6/lqKMu4Morz2Lw4OPDDqtSrVs14f47ziIjM4MMM0a/OYO3\nx89i6owFjLz3fM4+rSeLv13NxVc/AcCQc45kzw45XHfZ8Vx3WSy3wRc9vF2LKVVNnzab0aPfpXPn\njgwaeDUAv772XHr3PjjkyKovRWpLXKy8fviEH8RsAHC4u/+2+lslt2tOZGfQcp+Hwg4hoZbNGRp2\nCAln7JPQ2jF1RWFcn50H5wwIvXbVSSvF3QuBwro4lojIziwdzxGlVHeZiIjUjqXIvUHxUCESEYmQ\n0PvZakCFSEQkQlLlSrh4qBCJiERIGtYhFSIRkSjRyAoiIhKqNKxDKkQiIlGSjueI0vGScxERqYDF\nOVVrn2a7mdkLZvaFmc02s8PMrLmZvW1m84KvzWoaswqRiEiEJKMQAfcBb7p7F6A7MBu4CRjn7nnA\nuGC+RlSIREQiJNHPIzKzJsBRwGMA7r7Z3dcAA4EngtWeAGo8SqwKkYhIhMTbIjKzfDObWmbK32GX\nnYDlwD/M7GMzG2lmDYHW7v4tQPC1VU1j1sUKIiIREu8QP9s/B65cWcDPgSvdfZKZ3UctuuHKoxaR\niEiEJOFR4YuBxe4+KZh/gVhhKjKzNgDB12U1jrmmG4qISOrJiHOqirsvBRaZ2T7BomOJPYH7VeD8\nYNn5wOiaxqyuORGRCEnSfURXAk+ZWT3gK+BCYnXsOTO7GFgIDK7pzuvkwXg1owfjicj2Plw2L+wQ\nEu6wVol9MN3C9a/F9dnZodHJod8CqxaRiEiEpOPICipEIiIRkoZ1SIVIRCRKNPq2iIiEKg3rkAqR\niEiUxHtDaypQIRIRiRC1iEREJFS6ak5EREKVhnVIhUhEJErScdw2FSIRkQhR15yIiIQs/SqRCpGI\nSISYCpGIiITJLP3OEqkQiYhEilpEIiISInXNiYhIyFSI0sqECdO4/fZHKSkpYfDg48jPr/EDBlNC\n1PKB6OUUtXwgGjmNHfUe7435CDOjXac2XHzzGWzetJmHbvk3K5auIie3OZeNOI+GjXcNO9QqpeM5\novSLOEGKi4sZMeJhRo4cTmHhA4wZM4H58xeGHVaNRS0fiF5OUcsHopHT6uVrePvFiQwf+Wtu/9cN\nlJSUMGncxxQ++Q4/OyiP//fMb/nZQXkUPjku7FCryeKcwlcnhcjMjjCza83s+Lo4XnXMnDmPjh3b\n0L59LvXqZTNgwFGMGzcp7LBqLGr5QPRyilo+EJ2cSopL2LxpC8Vbi9n8wxaa5TTl4/c/44j+PQA4\non8Ppk/8LOQoq8fi/JcKklKIzGxymdeXAPcDjYFbzOymZBwzXkVFK8nNzdk237p1C4qKVoYYUe1E\nLR+IXk5RyweikVOzlrvR/4yjue60P3LNoOE0aLQLXQ/Zh+9Wr2O3nCYA7JbThLWr14ccafWoEP0o\nu8zrfOA4d78VOB44u6KNzCzfzKaa2dSCglFJCi3G/afP7LB0HBsjELV8IHo5RS0fiEZOG9Zt5OP3\nP+POUb/nnleGs+n7zfx37NSww6qFjDin8CXrYoUMM2tGLEtz9+UA7r7BzLZWtJG7FwAFsbm5SX26\nU25uDkuXrtg2X1S0klatmifzkEkVtXwgejlFLR+IRk6fT51LTpvmNGnWCICDe3dj/mcLaNqsMWtW\nrGW3nCasWbF22/upLt3+IwDJK4dNgWnAVKC5meUCmFkjUuTsWLdueSxYsIRFi5ayefMWCgsn0KfP\nIWGHVWNRyweil1PU8oFo5NSiVTO+/Px/bPphM+7OrGnzaNOxNQccvh/vvzkFgPffnMKBR3QNOdLq\nSr+LFZLSInL3PSp4qwT4RTKOGa+srEyGDRvKkCG3UFxcwqmn9iUvr2PYYdVY1PKB6OUUtXwgGjnt\ntV9HehzdnVsuvpvMzAw65LXl6FMOY9P3m3hg2L+YWDiJ5q2acfkfzws71GpJlfM+8bDy+nhTQ3K7\n5kQk/Xy4bF7YISTcYa0GJLRybNz6QVyfnbtmHR565dqpb2gVEYmadGwRqRCJiERIOl6soEIkIhIp\nKkQiIhIiS5F7g+KhQiQiEilqEYmISIh0jkhEREKmQiQiIiHSOSIREQmZWkQiIhKijDR8QqsKkYhI\npKgQiYhIiDTEj4iIhCz9ClH6teFERKRCZhbXVM199jezOWY238xuSnTMKkQiIpGS2EeFm1km8ABw\nArAvcKaZ7ZvoiEVEJCIszn/VcAgw392/cvfNwLPAwETGnMLniDrXSUenmeW7e0FdHKuuRC2nqOUD\n0cuprvI5rFXnZB9im/T9GcX32Wlm+UB+mUUFO+TdFlhUZn4x0LPm8f2UWkTb/wCiImo5RS0fiF5O\nUcsHopnTT7h7gbsfXGbasfiWV9gS+gRtFSIREanMYqB9mfl2wJJEHkCFSEREKjMFyDOzPc2sHnAG\n8GoiD5DC54jqTBr2AVcpajlFLR+IXk5RyweimVPc3H2rmV0BjAUygcfd/fNEHsPcE9rVJyIiEhd1\nzYmISKhUiEREJFQ7dSFK9rAVdc3MHjezZWb2WdixJIKZtTezd81stpl9bmZXhx1TbZjZLmY22cw+\nCfK5NeyYEsHMMs3sYzMbE3YsiWBmC8zsUzObYWZTw45nZ7DTniMKhq2YCxxH7PLEKcCZ7j4r1MBq\nwcyOAtYD/3L3rmHHU1tm1gZo4+7TzawxMA0YlK4/I4sN7NXQ3debWTbwPnC1u38Ucmi1YmbXAgcD\nTdz9pLDjqS0zWwAc7O4rwo5lZ7Ezt4iSPmxFXXP3CcCqsONIFHf/1t2nB6/XAbOJ3eWdljxmfTCb\nHUxp/T9BM2sHDABGhh2LpK+duRCVN2xF2n7IRZ2Z7QEcCEwKN5LaCbqxZgDLgLfdPa3zAe4FbgBK\nwg4kgRx4y8ymBcPfSJLtzIUo6cNWSGKYWSPgReAad18bdjy14e7F7n4AsbvTDzGztO1CNbOTgGXu\nPi3sWBLscHf/ObHRpi8PurwliXbmQpT0YSuk9oJzKS8CT7n7S2HHkyjuvgYYD/QPOZTaOBw4JTin\n8izQx8yeDDek2nP3JcHXZcDLxLrxJYl25kKU9GErpHaCk/uPAbPd/e6w46ktM2tpZrsFrxsAfYEv\nwo2q5tz9Zndv5+57EPv7ecfdzwk5rFoxs4bBhTGYWUPgeCASV6Gmsp22ELn7VqB02IrZwHOJHrai\nrpnZM8CHwD5mttjMLg47plo6HDiX2P+0ZwTTiWEHVQttgHfNbCax/wi97e6RuOQ5QloD75vZJ8Bk\noNDd3ww5psjbaS/fFhGR1LDTtohERCQ1qBCJiEioVIhERCRUKkQiIhIqFSIREQmVCpGIiIRKhUhE\nREL1/wHKZ4qEnf+kSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.986\n",
      "Precision: [1.         1.         0.97487437 0.9876161  0.96794872 0.97560976]\n",
      "Recall: [1.         1.         0.98477157 0.95508982 0.99016393 1.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Xk/wqo//wtw7bflFVP924qW+6Va/dojFvAL5dVd9f7wk3sta12wI8P8kWRn/xPu17kM9h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\nWdGUxGTqOnsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Decision Tree Classifier \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier(max_depth=None, max_leaf_nodes=20)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(y_pred)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "cnf_matrix\n",
    "class_names=[0,1] # name  of classes\n",
    "fig, ax = plt.subplots()\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "# create heatmap\n",
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()\n",
    "dtree_accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "dtree_precision_score = metrics.precision_score(y_test, y_pred, average=None)\n",
    "dtree_recall_score = metrics.recall_score(y_test, y_pred, average=None)\n",
    "print(\"Accuracy:\",dtree_accuracy)\n",
    "print(\"Precision:\",dtree_precision_score)\n",
    "print(\"Recall:\",dtree_recall_score)\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.hist(dtree_precision_score)\n",
    "plt.hist(dtree_recall_score)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "### RandomForestClassifier ####\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf2 = RandomForestClassifier(max_depth=None, max_leaf_nodes=20)\n",
    "clf2.fit(X_train, y_train)\n",
    "y_pred = clf2.predict(X_test)\n",
    "print(y_pred)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "cnf_matrix\n",
    "class_names=[0,1] # name  of classes\n",
    "fig, ax = plt.subplots()\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "# create heatmap\n",
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()\n",
    "\n",
    "rftree_accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "rftree_precision_score = metrics.precision_score(y_test, y_pred, average=None)\n",
    "rftree_recall_score = metrics.recall_score(y_test, y_pred, average=None)\n",
    "print(\"Accuracy:\",rftree_accuracy)\n",
    "print(\"Precision:\",rftree_precision_score)\n",
    "print(\"Recall:\",rftree_recall_score)\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.hist(rftree_precision_score)\n",
    "plt.hist(rftree_recall_score)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#### SVM Classifier\n",
    "\n",
    "from sklearn import svm\n",
    "model = svm.SVC(kernel='rbf', C=1E6)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print(y_pred)\n",
    "from sklearn import metrics\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "cnf_matrix\n",
    "class_names=[0,1] # name  of classes\n",
    "fig, ax = plt.subplots()\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "# create heatmap\n",
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()\n",
    "\n",
    "svm_accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "svm_precision_score = metrics.precision_score(y_test, y_pred, average=None)\n",
    "svm_recall_score = metrics.recall_score(y_test, y_pred, average=None)\n",
    "print(\"Accuracy:\",svm_accuracy)\n",
    "print(\"Precision:\",svm_precision_score)\n",
    "print(\"Recall:\",svm_recall_score)\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.hist(svm_precision_score)\n",
    "plt.hist(svm_recall_score)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WUCSpUS4jlKRGTU/4NoBLGhhLKJLUqNkpysEN4JIGxQxckhpVZuCS1CYzcElqlMsIJalR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\nZQCXpEYZwCWpUQZwSWqUAVySGvX/AOw1gANtkJi7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.972667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.977333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.986000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.972667\n",
       "1  0.977333\n",
       "2  0.986000"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_accuracy = [dtree_accuracy,rftree_accuracy,svm_accuracy]\n",
    "model_accuracy = pd.DataFrame(model_accuracy)\n",
    "sns.heatmap(model_accuracy)\n",
    "plt.show()\n",
    "\n",
    "model_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZJO8GPp7kIsYk5O558H0Ar75wor1/akmDNeQN6r+U5O2vvemS8/uAbwb+7ekMTJJORp9b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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.996667</td>\n",
       "      <td>0.948718</td>\n",
       "      <td>0.949254</td>\n",
       "      <td>0.967320</td>\n",
       "      <td>0.963415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.996667</td>\n",
       "      <td>0.954315</td>\n",
       "      <td>0.958209</td>\n",
       "      <td>0.976898</td>\n",
       "      <td>0.963855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974874</td>\n",
       "      <td>0.987616</td>\n",
       "      <td>0.967949</td>\n",
       "      <td>0.975610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0         1         2         3         4         5\n",
       "0  1.0  0.996667  0.948718  0.949254  0.967320  0.963415\n",
       "1  1.0  0.996667  0.954315  0.958209  0.976898  0.963855\n",
       "2  1.0  1.000000  0.974874  0.987616  0.967949  0.975610"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_precision = [dtree_precision_score,rftree_precision_score,svm_precision_score]\n",
    "model_precision = pd.DataFrame(model_precision)\n",
    "sns.heatmap(model_precision)\n",
    "plt.show()\n",
    "\n",
    "model_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.990066</td>\n",
       "      <td>0.939086</td>\n",
       "      <td>0.952096</td>\n",
       "      <td>0.970492</td>\n",
       "      <td>0.9875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.990066</td>\n",
       "      <td>0.954315</td>\n",
       "      <td>0.961078</td>\n",
       "      <td>0.970492</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.984772</td>\n",
       "      <td>0.955090</td>\n",
       "      <td>0.990164</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0         1         2         3         4       5\n",
       "0  1.0  0.990066  0.939086  0.952096  0.970492  0.9875\n",
       "1  1.0  0.990066  0.954315  0.961078  0.970492  1.0000\n",
       "2  1.0  1.000000  0.984772  0.955090  0.990164  1.0000"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_recall = [dtree_recall_score,rftree_recall_score,svm_recall_score]\n",
    "model_recall = pd.DataFrame(model_recall)\n",
    "sns.heatmap(model_recall)\n",
    "plt.show()\n",
    "\n",
    "model_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}