Mushroom dataset.ipynb

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      ]
     },
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   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "import xgboost\n",
    "import pickle\n",
    "\n",
    "df_mushroom = pd.read_csv(r'data\\Mushroom\\agaricus-lepiota.data.txt', header=None)\n",
    "df_mushroom.head(10)"
   ]
  },
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   "execution_count": 3,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8124 entries, 0 to 8123\n",
      "Data columns (total 23 columns):\n",
      "0     8124 non-null object\n",
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      "22    8124 non-null object\n",
      "dtypes: object(23)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_mushroom.info()"
   ]
  },
  {
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0   1   2   3   4   5   6   7   8   9   ...  13  14  15  16  17  18  19  \\\n",
       "0   1   5   2   4   1   6   1   0   1   4  ...   2   7   7   0   2   1   4   \n",
       "1   0   5   2   9   1   0   1   0   0   4  ...   2   7   7   0   2   1   4   \n",
       "2   0   0   2   8   1   3   1   0   0   5  ...   2   7   7   0   2   1   4   \n",
       "3   1   5   3   8   1   6   1   0   1   5  ...   2   7   7   0   2   1   4   \n",
       "4   0   5   2   3   0   5   1   1   0   4  ...   2   7   7   0   2   1   0   \n",
       "\n",
       "   20  21  22  \n",
       "0   2   3   5  \n",
       "1   3   2   1  \n",
       "2   3   2   3  \n",
       "3   2   3   5  \n",
       "4   3   0   1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder=LabelEncoder()\n",
    "for col in df_mushroom.columns:\n",
    "    df_mushroom[col] = labelencoder.fit_transform(df_mushroom[col])\n",
    " \n",
    "df_mushroom.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_mushroom.iloc[:,1:23]  # all rows, all the features and no labels\n",
    "y = df_mushroom.iloc[:, 0]  # all rows, label only\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>8124.000000</td>\n",
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       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
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       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.348104</td>\n",
       "      <td>1.827671</td>\n",
       "      <td>4.504677</td>\n",
       "      <td>0.415559</td>\n",
       "      <td>4.144756</td>\n",
       "      <td>0.974151</td>\n",
       "      <td>0.161497</td>\n",
       "      <td>0.309207</td>\n",
       "      <td>4.810684</td>\n",
       "      <td>0.567208</td>\n",
       "      <td>...</td>\n",
       "      <td>1.603644</td>\n",
       "      <td>5.816347</td>\n",
       "      <td>5.794682</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965534</td>\n",
       "      <td>1.069424</td>\n",
       "      <td>2.291974</td>\n",
       "      <td>3.596750</td>\n",
       "      <td>3.644018</td>\n",
       "      <td>1.508616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.604329</td>\n",
       "      <td>1.229873</td>\n",
       "      <td>2.545821</td>\n",
       "      <td>0.492848</td>\n",
       "      <td>2.103729</td>\n",
       "      <td>0.158695</td>\n",
       "      <td>0.368011</td>\n",
       "      <td>0.462195</td>\n",
       "      <td>3.540359</td>\n",
       "      <td>0.495493</td>\n",
       "      <td>...</td>\n",
       "      <td>0.675974</td>\n",
       "      <td>1.901747</td>\n",
       "      <td>1.907291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.242669</td>\n",
       "      <td>0.271064</td>\n",
       "      <td>1.801672</td>\n",
       "      <td>2.382663</td>\n",
       "      <td>1.252082</td>\n",
       "      <td>1.719975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                1            2            3            4            5   \\\n",
       "count  8124.000000  8124.000000  8124.000000  8124.000000  8124.000000   \n",
       "mean      3.348104     1.827671     4.504677     0.415559     4.144756   \n",
       "std       1.604329     1.229873     2.545821     0.492848     2.103729   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       2.000000     0.000000     3.000000     0.000000     2.000000   \n",
       "50%       3.000000     2.000000     4.000000     0.000000     5.000000   \n",
       "75%       5.000000     3.000000     8.000000     1.000000     5.000000   \n",
       "max       5.000000     3.000000     9.000000     1.000000     8.000000   \n",
       "\n",
       "                6            7            8            9            10  ...  \\\n",
       "count  8124.000000  8124.000000  8124.000000  8124.000000  8124.000000  ...   \n",
       "mean      0.974151     0.161497     0.309207     4.810684     0.567208  ...   \n",
       "std       0.158695     0.368011     0.462195     3.540359     0.495493  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       1.000000     0.000000     0.000000     2.000000     0.000000  ...   \n",
       "50%       1.000000     0.000000     0.000000     5.000000     1.000000  ...   \n",
       "75%       1.000000     0.000000     1.000000     7.000000     1.000000  ...   \n",
       "max       1.000000     1.000000     1.000000    11.000000     1.000000  ...   \n",
       "\n",
       "                13           14           15      16           17  \\\n",
       "count  8124.000000  8124.000000  8124.000000  8124.0  8124.000000   \n",
       "mean      1.603644     5.816347     5.794682     0.0     1.965534   \n",
       "std       0.675974     1.901747     1.907291     0.0     0.242669   \n",
       "min       0.000000     0.000000     0.000000     0.0     0.000000   \n",
       "25%       1.000000     6.000000     6.000000     0.0     2.000000   \n",
       "50%       2.000000     7.000000     7.000000     0.0     2.000000   \n",
       "75%       2.000000     7.000000     7.000000     0.0     2.000000   \n",
       "max       3.000000     8.000000     8.000000     0.0     3.000000   \n",
       "\n",
       "                18           19           20           21           22  \n",
       "count  8124.000000  8124.000000  8124.000000  8124.000000  8124.000000  \n",
       "mean      1.069424     2.291974     3.596750     3.644018     1.508616  \n",
       "std       0.271064     1.801672     2.382663     1.252082     1.719975  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       1.000000     0.000000     2.000000     3.000000     0.000000  \n",
       "50%       1.000000     2.000000     3.000000     4.000000     1.000000  \n",
       "75%       1.000000     4.000000     7.000000     4.000000     2.000000  \n",
       "max       2.000000     4.000000     8.000000     5.000000     6.000000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>0.107965</td>\n",
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       "      <td>-0.016603</td>\n",
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       "      <td>-0.106407</td>\n",
       "      <td>0.230364</td>\n",
       "      <td>0.021555</td>\n",
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       "      <td>0.070228</td>\n",
       "      <td>-0.000764</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.061825</td>\n",
       "      <td>0.137359</td>\n",
       "      <td>-0.299473</td>\n",
       "      <td>-0.369596</td>\n",
       "      <td>0.527120</td>\n",
       "      <td>...</td>\n",
       "      <td>0.458983</td>\n",
       "      <td>0.083538</td>\n",
       "      <td>0.092874</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.119770</td>\n",
       "      <td>0.056788</td>\n",
       "      <td>0.692973</td>\n",
       "      <td>-0.285008</td>\n",
       "      <td>0.088137</td>\n",
       "      <td>-0.075095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.093552</td>\n",
       "      <td>-0.021935</td>\n",
       "      <td>0.045233</td>\n",
       "      <td>-0.387121</td>\n",
       "      <td>-0.061825</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059590</td>\n",
       "      <td>0.063936</td>\n",
       "      <td>0.310495</td>\n",
       "      <td>-0.129213</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061820</td>\n",
       "      <td>0.174532</td>\n",
       "      <td>0.169407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.057747</td>\n",
       "      <td>0.111905</td>\n",
       "      <td>-0.281387</td>\n",
       "      <td>0.469055</td>\n",
       "      <td>-0.043623</td>\n",
       "      <td>-0.026610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.129200</td>\n",
       "      <td>0.078865</td>\n",
       "      <td>-0.034180</td>\n",
       "      <td>0.041436</td>\n",
       "      <td>0.137359</td>\n",
       "      <td>-0.059590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.071489</td>\n",
       "      <td>0.108984</td>\n",
       "      <td>-0.128567</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.116177</td>\n",
       "      <td>0.099299</td>\n",
       "      <td>0.097160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.897518</td>\n",
       "      <td>0.093236</td>\n",
       "      <td>-0.146689</td>\n",
       "      <td>-0.029524</td>\n",
       "      <td>0.165575</td>\n",
       "      <td>-0.030304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.348387</td>\n",
       "      <td>0.013196</td>\n",
       "      <td>-0.282306</td>\n",
       "      <td>0.144259</td>\n",
       "      <td>-0.299473</td>\n",
       "      <td>0.063936</td>\n",
       "      <td>0.071489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.108333</td>\n",
       "      <td>0.100193</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.213775</td>\n",
       "      <td>0.274574</td>\n",
       "      <td>0.253505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.073363</td>\n",
       "      <td>0.243014</td>\n",
       "      <td>-0.195897</td>\n",
       "      <td>0.047323</td>\n",
       "      <td>-0.529253</td>\n",
       "      <td>-0.154680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.540024</td>\n",
       "      <td>0.054050</td>\n",
       "      <td>0.208100</td>\n",
       "      <td>-0.169464</td>\n",
       "      <td>-0.369596</td>\n",
       "      <td>0.310495</td>\n",
       "      <td>0.108984</td>\n",
       "      <td>-0.108333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.516736</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010894</td>\n",
       "      <td>0.296548</td>\n",
       "      <td>0.278708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.103809</td>\n",
       "      <td>-0.171362</td>\n",
       "      <td>-0.460872</td>\n",
       "      <td>0.622991</td>\n",
       "      <td>0.147682</td>\n",
       "      <td>0.161418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.530566</td>\n",
       "      <td>-0.006039</td>\n",
       "      <td>-0.161017</td>\n",
       "      <td>0.084659</td>\n",
       "      <td>0.527120</td>\n",
       "      <td>-0.129213</td>\n",
       "      <td>-0.128567</td>\n",
       "      <td>0.100193</td>\n",
       "      <td>-0.516736</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.257224</td>\n",
       "      <td>-0.058299</td>\n",
       "      <td>-0.074781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.097583</td>\n",
       "      <td>0.096054</td>\n",
       "      <td>0.629398</td>\n",
       "      <td>-0.416135</td>\n",
       "      <td>-0.034090</td>\n",
       "      <td>-0.202972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.102019</td>\n",
       "      <td>0.063794</td>\n",
       "      <td>-0.014123</td>\n",
       "      <td>-0.456496</td>\n",
       "      <td>0.099364</td>\n",
       "      <td>0.459766</td>\n",
       "      <td>0.186485</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>0.214576</td>\n",
       "      <td>-0.175699</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.034399</td>\n",
       "      <td>0.223439</td>\n",
       "      <td>0.235794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.162604</td>\n",
       "      <td>-0.293221</td>\n",
       "      <td>-0.291444</td>\n",
       "      <td>0.258831</td>\n",
       "      <td>0.087383</td>\n",
       "      <td>-0.269216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.379361</td>\n",
       "      <td>0.030191</td>\n",
       "      <td>-0.126245</td>\n",
       "      <td>0.321274</td>\n",
       "      <td>0.244188</td>\n",
       "      <td>-0.205215</td>\n",
       "      <td>0.144063</td>\n",
       "      <td>0.350548</td>\n",
       "      <td>-0.344345</td>\n",
       "      <td>0.315080</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087454</td>\n",
       "      <td>0.157140</td>\n",
       "      <td>0.159805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.156213</td>\n",
       "      <td>-0.247357</td>\n",
       "      <td>0.210155</td>\n",
       "      <td>-0.536996</td>\n",
       "      <td>-0.306747</td>\n",
       "      <td>-0.007668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.334593</td>\n",
       "      <td>-0.030417</td>\n",
       "      <td>0.089090</td>\n",
       "      <td>-0.060837</td>\n",
       "      <td>0.460824</td>\n",
       "      <td>0.118617</td>\n",
       "      <td>-0.088916</td>\n",
       "      <td>-0.212359</td>\n",
       "      <td>0.056310</td>\n",
       "      <td>0.224287</td>\n",
       "      <td>...</td>\n",
       "      <td>0.437164</td>\n",
       "      <td>0.132708</td>\n",
       "      <td>0.142835</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.090591</td>\n",
       "      <td>0.107904</td>\n",
       "      <td>0.390091</td>\n",
       "      <td>0.100764</td>\n",
       "      <td>0.079604</td>\n",
       "      <td>-0.058076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.298801</td>\n",
       "      <td>-0.032591</td>\n",
       "      <td>0.107965</td>\n",
       "      <td>-0.047710</td>\n",
       "      <td>0.458983</td>\n",
       "      <td>0.061820</td>\n",
       "      <td>-0.116177</td>\n",
       "      <td>-0.213775</td>\n",
       "      <td>0.010894</td>\n",
       "      <td>0.257224</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.106933</td>\n",
       "      <td>0.110656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.077284</td>\n",
       "      <td>0.040006</td>\n",
       "      <td>0.394644</td>\n",
       "      <td>0.130974</td>\n",
       "      <td>0.046797</td>\n",
       "      <td>-0.039628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.154003</td>\n",
       "      <td>-0.031659</td>\n",
       "      <td>0.066050</td>\n",
       "      <td>0.002364</td>\n",
       "      <td>0.083538</td>\n",
       "      <td>0.174532</td>\n",
       "      <td>0.099299</td>\n",
       "      <td>0.274574</td>\n",
       "      <td>0.296548</td>\n",
       "      <td>-0.058299</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106933</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.491510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.067377</td>\n",
       "      <td>0.084917</td>\n",
       "      <td>-0.048878</td>\n",
       "      <td>0.271533</td>\n",
       "      <td>-0.240261</td>\n",
       "      <td>0.042561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.146730</td>\n",
       "      <td>-0.030390</td>\n",
       "      <td>0.068885</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.092874</td>\n",
       "      <td>0.169407</td>\n",
       "      <td>0.097160</td>\n",
       "      <td>0.253505</td>\n",
       "      <td>0.278708</td>\n",
       "      <td>-0.074781</td>\n",
       "      <td>...</td>\n",
       "      <td>0.110656</td>\n",
       "      <td>0.491510</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.065567</td>\n",
       "      <td>0.087580</td>\n",
       "      <td>-0.034284</td>\n",
       "      <td>0.254518</td>\n",
       "      <td>-0.242792</td>\n",
       "      <td>0.041594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.145142</td>\n",
       "      <td>0.072560</td>\n",
       "      <td>-0.016603</td>\n",
       "      <td>0.036130</td>\n",
       "      <td>0.119770</td>\n",
       "      <td>-0.057747</td>\n",
       "      <td>0.897518</td>\n",
       "      <td>0.073363</td>\n",
       "      <td>0.103809</td>\n",
       "      <td>-0.097583</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.077284</td>\n",
       "      <td>0.067377</td>\n",
       "      <td>0.065567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036380</td>\n",
       "      <td>-0.143673</td>\n",
       "      <td>-0.003600</td>\n",
       "      <td>0.124924</td>\n",
       "      <td>-0.040581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.214366</td>\n",
       "      <td>-0.106534</td>\n",
       "      <td>-0.026147</td>\n",
       "      <td>-0.005822</td>\n",
       "      <td>0.056788</td>\n",
       "      <td>0.111905</td>\n",
       "      <td>0.093236</td>\n",
       "      <td>0.243014</td>\n",
       "      <td>-0.171362</td>\n",
       "      <td>0.096054</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040006</td>\n",
       "      <td>0.084917</td>\n",
       "      <td>0.087580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.036380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058312</td>\n",
       "      <td>0.338417</td>\n",
       "      <td>-0.242020</td>\n",
       "      <td>0.235835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.411771</td>\n",
       "      <td>-0.025457</td>\n",
       "      <td>-0.106407</td>\n",
       "      <td>0.162513</td>\n",
       "      <td>0.692973</td>\n",
       "      <td>-0.281387</td>\n",
       "      <td>-0.146689</td>\n",
       "      <td>-0.195897</td>\n",
       "      <td>-0.460872</td>\n",
       "      <td>0.629398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.394644</td>\n",
       "      <td>-0.048878</td>\n",
       "      <td>-0.034284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.143673</td>\n",
       "      <td>0.058312</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.487048</td>\n",
       "      <td>0.211763</td>\n",
       "      <td>-0.212080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.171961</td>\n",
       "      <td>-0.073416</td>\n",
       "      <td>0.230364</td>\n",
       "      <td>-0.293523</td>\n",
       "      <td>-0.285008</td>\n",
       "      <td>0.469055</td>\n",
       "      <td>-0.029524</td>\n",
       "      <td>0.047323</td>\n",
       "      <td>0.622991</td>\n",
       "      <td>-0.416135</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130974</td>\n",
       "      <td>0.271533</td>\n",
       "      <td>0.254518</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.003600</td>\n",
       "      <td>0.338417</td>\n",
       "      <td>-0.487048</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.126859</td>\n",
       "      <td>0.185954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.298686</td>\n",
       "      <td>0.063413</td>\n",
       "      <td>0.021555</td>\n",
       "      <td>-0.144770</td>\n",
       "      <td>0.088137</td>\n",
       "      <td>-0.043623</td>\n",
       "      <td>0.165575</td>\n",
       "      <td>-0.529253</td>\n",
       "      <td>0.147682</td>\n",
       "      <td>-0.034090</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046797</td>\n",
       "      <td>-0.240261</td>\n",
       "      <td>-0.242792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.124924</td>\n",
       "      <td>-0.242020</td>\n",
       "      <td>0.211763</td>\n",
       "      <td>-0.126859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.174529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.217179</td>\n",
       "      <td>-0.042221</td>\n",
       "      <td>0.163887</td>\n",
       "      <td>0.033925</td>\n",
       "      <td>-0.075095</td>\n",
       "      <td>-0.026610</td>\n",
       "      <td>-0.030304</td>\n",
       "      <td>-0.154680</td>\n",
       "      <td>0.161418</td>\n",
       "      <td>-0.202972</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.039628</td>\n",
       "      <td>0.042561</td>\n",
       "      <td>0.041594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.040581</td>\n",
       "      <td>0.235835</td>\n",
       "      <td>-0.212080</td>\n",
       "      <td>0.185954</td>\n",
       "      <td>-0.174529</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6   \\\n",
       "0   1.000000  0.052951  0.178446 -0.031384 -0.501530 -0.093552  0.129200   \n",
       "1   0.052951  1.000000 -0.050454 -0.048203 -0.035374 -0.021935  0.078865   \n",
       "2   0.178446 -0.050454  1.000000 -0.019402  0.070228  0.045233 -0.034180   \n",
       "3  -0.031384 -0.048203 -0.019402  1.000000 -0.000764 -0.387121  0.041436   \n",
       "4  -0.501530 -0.035374  0.070228 -0.000764  1.000000 -0.061825  0.137359   \n",
       "5  -0.093552 -0.021935  0.045233 -0.387121 -0.061825  1.000000 -0.059590   \n",
       "6   0.129200  0.078865 -0.034180  0.041436  0.137359 -0.059590  1.000000   \n",
       "7  -0.348387  0.013196 -0.282306  0.144259 -0.299473  0.063936  0.071489   \n",
       "8   0.540024  0.054050  0.208100 -0.169464 -0.369596  0.310495  0.108984   \n",
       "9  -0.530566 -0.006039 -0.161017  0.084659  0.527120 -0.129213 -0.128567   \n",
       "10 -0.102019  0.063794 -0.014123 -0.456496  0.099364  0.459766  0.186485   \n",
       "11 -0.379361  0.030191 -0.126245  0.321274  0.244188 -0.205215  0.144063   \n",
       "12 -0.334593 -0.030417  0.089090 -0.060837  0.460824  0.118617 -0.088916   \n",
       "13 -0.298801 -0.032591  0.107965 -0.047710  0.458983  0.061820 -0.116177   \n",
       "14 -0.154003 -0.031659  0.066050  0.002364  0.083538  0.174532  0.099299   \n",
       "15 -0.146730 -0.030390  0.068885  0.008057  0.092874  0.169407  0.097160   \n",
       "16       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "17  0.145142  0.072560 -0.016603  0.036130  0.119770 -0.057747  0.897518   \n",
       "18 -0.214366 -0.106534 -0.026147 -0.005822  0.056788  0.111905  0.093236   \n",
       "19 -0.411771 -0.025457 -0.106407  0.162513  0.692973 -0.281387 -0.146689   \n",
       "20  0.171961 -0.073416  0.230364 -0.293523 -0.285008  0.469055 -0.029524   \n",
       "21  0.298686  0.063413  0.021555 -0.144770  0.088137 -0.043623  0.165575   \n",
       "22  0.217179 -0.042221  0.163887  0.033925 -0.075095 -0.026610 -0.030304   \n",
       "\n",
       "          7         8         9   ...        13        14        15  16  \\\n",
       "0  -0.348387  0.540024 -0.530566  ... -0.298801 -0.154003 -0.146730 NaN   \n",
       "1   0.013196  0.054050 -0.006039  ... -0.032591 -0.031659 -0.030390 NaN   \n",
       "2  -0.282306  0.208100 -0.161017  ...  0.107965  0.066050  0.068885 NaN   \n",
       "3   0.144259 -0.169464  0.084659  ... -0.047710  0.002364  0.008057 NaN   \n",
       "4  -0.299473 -0.369596  0.527120  ...  0.458983  0.083538  0.092874 NaN   \n",
       "5   0.063936  0.310495 -0.129213  ...  0.061820  0.174532  0.169407 NaN   \n",
       "6   0.071489  0.108984 -0.128567  ... -0.116177  0.099299  0.097160 NaN   \n",
       "7   1.000000 -0.108333  0.100193  ... -0.213775  0.274574  0.253505 NaN   \n",
       "8  -0.108333  1.000000 -0.516736  ...  0.010894  0.296548  0.278708 NaN   \n",
       "9   0.100193 -0.516736  1.000000  ...  0.257224 -0.058299 -0.074781 NaN   \n",
       "10  0.080895  0.214576 -0.175699  ... -0.034399  0.223439  0.235794 NaN   \n",
       "11  0.350548 -0.344345  0.315080  ...  0.087454  0.157140  0.159805 NaN   \n",
       "12 -0.212359  0.056310  0.224287  ...  0.437164  0.132708  0.142835 NaN   \n",
       "13 -0.213775  0.010894  0.257224  ...  1.000000  0.106933  0.110656 NaN   \n",
       "14  0.274574  0.296548 -0.058299  ...  0.106933  1.000000  0.491510 NaN   \n",
       "15  0.253505  0.278708 -0.074781  ...  0.110656  0.491510  1.000000 NaN   \n",
       "16       NaN       NaN       NaN  ...       NaN       NaN       NaN NaN   \n",
       "17  0.073363  0.103809 -0.097583  ... -0.077284  0.067377  0.065567 NaN   \n",
       "18  0.243014 -0.171362  0.096054  ...  0.040006  0.084917  0.087580 NaN   \n",
       "19 -0.195897 -0.460872  0.629398  ...  0.394644 -0.048878 -0.034284 NaN   \n",
       "20  0.047323  0.622991 -0.416135  ...  0.130974  0.271533  0.254518 NaN   \n",
       "21 -0.529253  0.147682 -0.034090  ...  0.046797 -0.240261 -0.242792 NaN   \n",
       "22 -0.154680  0.161418 -0.202972  ... -0.039628  0.042561  0.041594 NaN   \n",
       "\n",
       "          17        18        19        20        21        22  \n",
       "0   0.145142 -0.214366 -0.411771  0.171961  0.298686  0.217179  \n",
       "1   0.072560 -0.106534 -0.025457 -0.073416  0.063413 -0.042221  \n",
       "2  -0.016603 -0.026147 -0.106407  0.230364  0.021555  0.163887  \n",
       "3   0.036130 -0.005822  0.162513 -0.293523 -0.144770  0.033925  \n",
       "4   0.119770  0.056788  0.692973 -0.285008  0.088137 -0.075095  \n",
       "5  -0.057747  0.111905 -0.281387  0.469055 -0.043623 -0.026610  \n",
       "6   0.897518  0.093236 -0.146689 -0.029524  0.165575 -0.030304  \n",
       "7   0.073363  0.243014 -0.195897  0.047323 -0.529253 -0.154680  \n",
       "8   0.103809 -0.171362 -0.460872  0.622991  0.147682  0.161418  \n",
       "9  -0.097583  0.096054  0.629398 -0.416135 -0.034090 -0.202972  \n",
       "10  0.162604 -0.293221 -0.291444  0.258831  0.087383 -0.269216  \n",
       "11  0.156213 -0.247357  0.210155 -0.536996 -0.306747 -0.007668  \n",
       "12 -0.090591  0.107904  0.390091  0.100764  0.079604 -0.058076  \n",
       "13 -0.077284  0.040006  0.394644  0.130974  0.046797 -0.039628  \n",
       "14  0.067377  0.084917 -0.048878  0.271533 -0.240261  0.042561  \n",
       "15  0.065567  0.087580 -0.034284  0.254518 -0.242792  0.041594  \n",
       "16       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "17  1.000000  0.036380 -0.143673 -0.003600  0.124924 -0.040581  \n",
       "18  0.036380  1.000000  0.058312  0.338417 -0.242020  0.235835  \n",
       "19 -0.143673  0.058312  1.000000 -0.487048  0.211763 -0.212080  \n",
       "20 -0.003600  0.338417 -0.487048  1.000000 -0.126859  0.185954  \n",
       "21  0.124924 -0.242020  0.211763 -0.126859  1.000000 -0.174529  \n",
       "22 -0.040581  0.235835 -0.212080  0.185954 -0.174529  1.000000  \n",
       "\n",
       "[23 rows x 23 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mushroom.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.02971224,  0.14012794, -0.19824983, ..., -0.67019486,\n",
       "        -0.5143892 ,  2.03002809],\n",
       "       [ 1.02971224,  0.14012794,  1.76587407, ..., -0.2504706 ,\n",
       "        -1.31310821, -0.29572966],\n",
       "       [-2.08704716,  0.14012794,  1.37304929, ..., -0.2504706 ,\n",
       "        -1.31310821,  0.86714922],\n",
       "       ...,\n",
       "       [-0.8403434 ,  0.14012794, -0.19824983, ..., -1.50964337,\n",
       "        -2.11182722,  0.28570978],\n",
       "       [-0.21699152,  0.95327039, -0.19824983, ...,  1.42842641,\n",
       "         0.28432981,  0.28570978],\n",
       "       [ 1.02971224,  0.14012794, -0.19824983, ...,  0.16925365,\n",
       "        -2.11182722,  0.28570978]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scale the data to be between -1 and 1\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "x=scaler.fit_transform(x)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.3,random_state=1, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.959025 (0.009430)\n",
      "LDA: 0.951288 (0.009582)\n",
      "KNN: 0.999648 (0.000704)\n",
      "CART: 1.000000 (0.000000)\n",
      "NB: 0.928599 (0.012914)\n",
      "SVM: 1.000000 (0.000000)\n",
      "RF: 0.991383 (0.002883)\n"
     ]
    }
   ],
   "source": [
    "rf_params = {\n",
    "    'n_jobs': -1,\n",
    "    'n_estimators': 1000,\n",
    "#     'warm_start': True, \n",
    "    'max_features': 0.3,\n",
    "    'max_depth': 4,\n",
    "    'min_samples_leaf': 2,\n",
    "    'max_features' : 'sqrt',\n",
    "    'random_state' : 1,\n",
    "    'verbose': 0\n",
    "}\n",
    "# Spot Check Algorithms\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC(gamma='auto')))\n",
    "models.append(('RF', RandomForestClassifier(**rf_params)))\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "\tkfold = model_selection.KFold(n_splits=10, random_state=1)\n",
    "\tcv_results = model_selection.cross_val_score(model, x_train, y_train, cv=kfold, scoring='accuracy')\n",
    "\tresults.append(cv_results)\n",
    "\tnames.append(name)\n",
    "\tmsg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "\tprint(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Compare Algorithms\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Based on above, RF is giving more better performance.  KNN is also good, but it looks like over-fitting and avoiding this model.\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "\n",
    "model_RF= RandomForestClassifier(\n",
    "    n_jobs= -1,\n",
    "    n_estimators= 1000,\n",
    "    #max_features= 0.3,\n",
    "    max_depth= 4,\n",
    "    min_samples_leaf= 2,\n",
    "    max_features= 'sqrt',\n",
    "    random_state= 1,\n",
    "    verbose= 0)\n",
    "model_RF.fit(x_train,y_train)\n",
    "y_pred = model_RF.predict(x_test)\n",
    "model_RF.score(x_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1263,    0],\n",
       "       [  31, 1144]], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix=metrics.confusion_matrix(y_test,y_pred)\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9868085106382979"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc_roc=metrics.roc_auc_score(y_test,y_pred)\n",
    "auc_roc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9859739559643538"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'False Positive Rate')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(false_positive_rate,true_positive_rate, color='red',label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],linestyle='--')\n",
    "plt.axis('tight')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}