07_classification Metrics _Precision_Recall_f1_roc_auc.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "e912b23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "912abc98",
   "metadata": {},
   "source": [
    "## Customer Churn prediction example : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a93d2dae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "8f5b2388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"churn_logistic (1).csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "d4d7b98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"State\",\"Area Code\",\"Phone\",\"Intl Mins\",\"Intl Charge\",\"Intl Plan\",\"VMail Plan\",\"Day Calls\",\"Night Mins\",\"Night Charge\",\"Account Length\",\"Eve Calls\",\"Night Calls\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "0eb56021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Eve Mins</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>Eve Charge</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>197.4</td>\n",
       "      <td>1</td>\n",
       "      <td>45.07</td>\n",
       "      <td>16.78</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>195.5</td>\n",
       "      <td>1</td>\n",
       "      <td>27.47</td>\n",
       "      <td>16.62</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>121.2</td>\n",
       "      <td>0</td>\n",
       "      <td>41.38</td>\n",
       "      <td>10.30</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>61.9</td>\n",
       "      <td>2</td>\n",
       "      <td>50.90</td>\n",
       "      <td>5.26</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>148.3</td>\n",
       "      <td>3</td>\n",
       "      <td>28.34</td>\n",
       "      <td>12.61</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>0</td>\n",
       "      <td>171.5</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.16</td>\n",
       "      <td>13.60</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5696</th>\n",
       "      <td>0</td>\n",
       "      <td>131.6</td>\n",
       "      <td>179.3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.37</td>\n",
       "      <td>15.24</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>0</td>\n",
       "      <td>291.2</td>\n",
       "      <td>234.2</td>\n",
       "      <td>1</td>\n",
       "      <td>49.50</td>\n",
       "      <td>19.91</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5698</th>\n",
       "      <td>0</td>\n",
       "      <td>113.3</td>\n",
       "      <td>197.9</td>\n",
       "      <td>4</td>\n",
       "      <td>19.26</td>\n",
       "      <td>16.82</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5699</th>\n",
       "      <td>0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>231.8</td>\n",
       "      <td>0</td>\n",
       "      <td>46.58</td>\n",
       "      <td>19.70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5700 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      VMail Message  Day Mins  Eve Mins  CustServ Calls  Day Charge  \\\n",
       "0                25     265.1     197.4               1       45.07   \n",
       "1                26     161.6     195.5               1       27.47   \n",
       "2                 0     243.4     121.2               0       41.38   \n",
       "3                 0     299.4      61.9               2       50.90   \n",
       "4                 0     166.7     148.3               3       28.34   \n",
       "...             ...       ...       ...             ...         ...   \n",
       "5695              0     171.5     160.0               1       29.16   \n",
       "5696              0     131.6     179.3               1       22.37   \n",
       "5697              0     291.2     234.2               1       49.50   \n",
       "5698              0     113.3     197.9               4       19.26   \n",
       "5699              0     274.0     231.8               0       46.58   \n",
       "\n",
       "      Eve Charge  Intl Calls  Churn  \n",
       "0          16.78           3      0  \n",
       "1          16.62           3      0  \n",
       "2          10.30           5      0  \n",
       "3           5.26           7      0  \n",
       "4          12.61           3      0  \n",
       "...          ...         ...    ...  \n",
       "5695       13.60           2      1  \n",
       "5696       15.24           3      1  \n",
       "5697       19.91           3      1  \n",
       "5698       16.82           2      1  \n",
       "5699       19.70           1      1  \n",
       "\n",
       "[5700 rows x 8 columns]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "1f379263",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"Churn\"],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "da80569d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "714c2692",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"Churn\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "b5d342b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2850\n",
       "1    2850\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "ad95ab4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "17ae7f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(C = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "49824848",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "ae77d0b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "be52993d",
   "metadata": {},
   "outputs": [],
   "source": [
    "StandardScaler = StandardScaler()\n",
    "X = StandardScaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "747a41ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_test, y_train , y_test = train_test_split(X,y,\n",
    "                                                     test_size=0.2,\n",
    "                                                     random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "979ce740",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "737bd8eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "8f4e5401",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LogisticRegression(C=1000)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "648568b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import recall_score,precision_score,f1_score,precision_recall_curve\n",
    "from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "dbcc5f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[392, 160],\n",
       "       [178, 410]], dtype=int64)"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,model.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "e8a5de46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1663,  635],\n",
       "       [ 666, 1596]], dtype=int64)"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train,model.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae40ffb1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "a6b717ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[392, 160],\n",
       "       [178, 410]], dtype=int64)"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,model.predict(X_test))\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "0f8a3173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2562943f0d0>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATgAAAEGCAYAAADxD4m3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbNUlEQVR4nO3de5RU1Zn38e+PpmkUBUEuQgOCChpQgQzBaxy8rAETJ8b3ja9oYlyJszB5NXGiE6OOSUwyZJI30SSO0cSoE2OiBpcm4g0vRLzFKyoqIMqICtJylZtg0931vH/UaSyUrq6SLqrq9O+z1lldtWufc57uph/2PvvsfRQRmJmlUZdyB2BmVipOcGaWWk5wZpZaTnBmllpOcGaWWl3LHUCu3n26xMDBFRWStWPpS7uVOwQrwvu8x5Zo1I4cY9LRPWL1mpaC6s55sfG+iJi8I+fbERWVTQYO7spNdw0odxhWhG8PO7TcIVgRnopZO3yMVWtaeOq+wQXVrR34P313+IQ7oKISnJlVg6AlMuUOoiBOcGZWlAAyVMcEASc4MytaBrfgzCyFgqCpSrqovk3EzIoSQAtR0FYISTWSnpd0V/K+j6QHJL2WfO2dU/ciSYskLZQ0qb1jO8GZWdEyREFbgc4FFuS8vxCYFREjgFnJeySNAqYAo4HJwFWSavId2AnOzIoSQEtEQVt7JA0GPgtcm1N8InBD8voG4PM55bdERGNELAYWARPyHd8JzsyKlilwA/pKejZnm/qhQ/0SuOCD6gAMiIgGgORr/6S8HliSU29pUtYmDzKYWVGiiOtrwKqIGL+9DySdAKyIiDmSJhZwrO3NwMgbiBOcmRUlApo65ja4I4DPSfoM0B3oKemPwHJJAyOiQdJAYEVSfykwJGf/wcCyfCdwF9XMiiRaCtzyiYiLImJwRAwjO3jwt4j4EjADOCOpdgZwR/J6BjBFUp2k4cAI4Ol853ALzsyKEkCmtBMZfgJMl3Qm8BZwMkBEzJM0HZgPNANnR0TeWf9OcGZWtPZaZ8WKiNnA7OT1auDYNupNA6YVelwnODMrSvZG345NcKXiBGdmRQmgKarj8r0TnJkVJRAtVTI+6QRnZkXLhLuoZpZCvgZnZikmWnwNzszSKLuirxOcmaVQhNgSeVcpqhhOcGZWtIyvwZlZGmUHGdxFNbNU8iCDmaWUBxnMLNVafKOvmaVRIJqiOlJHdURpZhXDgwxmllqB3EU1s/TyIIOZpVIEvk3EzNIpO8jgqVpmllIeZDCzVArkBS/NLL3cgjOzVMo+F9UJzsxSqf2n1lcKJzgzK0r2sYEeRTWzFIqQu6hmll6+0dfMUim7HpyvwZlZKnlFXzNLqextIm7BmVkKeS6qmaWal0sys1TKLpfkLqqZpVS1XIOrjnammVWM7GoiXQra8pHUXdLTkuZKmifpB0n5pZLelvRCsn0mZ5+LJC2StFDSpPZidQvOzIqSnarVIW2jRuCYiNgoqRZ4TNK9yWe/iIif51aWNAqYAowGBgEPShoZES1tncAJbgc1vS+uPmU0zY0i0yIOOn4Nk85byrL5u3Lbvw9ny6Yaeg9u5LRfLqL77i28+mgv7vnpEFqaulBTm+GEi99iv8PXl/vb6FTOu/wtDjluA2tXdeWsY/bfWv65r67kc19ZTaYZnprVk+v+YxAAp5yznMmnrqElI66+ZBBzHu5ZrtArRMdM1YqIADYmb2uTLfLsciJwS0Q0AoslLQImAE+0tUNJE5ykycCvgBrg2oj4SSnPVw5d64KzbppPXY8MLU3i118YzQET1/LXS4dxwsVvsu+hG3h6ej9mXzOQyecvpUfvJr5y3UJ6DWjinYW78Lsvf4LvPvVcub+NTuX+P/dhxn/35du/WrK1bMzhGzl80nq+fuxImrZ0odeeTQAMHfE+E09cy9Sj96fPgCZ+8ufXOfPI3clkquMaVKl01EwGSTXAHGA/4NcR8ZSk44FzJH0ZeBY4PyLeBeqBJ3N2X5qUtalk1+CSwH8NHA+MAk5NmpipIkFdjwwALc0i0ywkWPl6d/Y5ZAMAI49cx0v39gGg/sBN9BqQ/eMZMHIzzY2iubFz/7HsbC8/tRsb3t32//YTvryKP1/Zn6Yt2T+JdatrAThs0jpm37EHTVu6sHxJHcve6Mb+4zbt9JgrSesoaiEb0FfSsznb1G2PFS0RMRYYDEyQdCBwNbAvMBZoAC5Lqm/vDyVfi6+kgwwTgEUR8XpEbAFuIdvETJ1MC1x+/EH84B/+gRFHrmPouI3sNXIz8x7oDcDce/qwrqHuI/u9dG8fBo3eRNe6vL8j2wnq923kwEPe41d3vcbPblvEyDHZJNZ3YBMrl3XbWm9VQzf23KupXGFWjCIGGVZFxPic7ZrtHS8i1gKzgckRsTxJfBngd2RzCWRbbENydhsMLMsXZykTXD2wJOf9dpuTkqa2Zve1azIlDKd0utTAefe+xCVPPMeSuT14Z+Eu/J//9z/8/cYB/PKEA2ncWENN7bbf2zuv7sLdPxnK//7x62WK2nLV1MBuvVo494T9uPZHg/j3374JxMdoM6Rf6zMZCtnykdRP0h7J612A44BXJA3MqXYS8HLyegYwRVKdpOHACODpfOco5TW4gv5pJBn9GoBRB3er6n86u/RqYZ9D1/PKw3swcWoDU298Bch2V195qPfWemsbunHDWSOZcvki+u7dWK5wLceqhloev6cXIBa+sCuZDPTq08KqZbX0G7Rla72+A7ewenlt+QKtAAE0d8wo6kDghuRyVhdgekTcJelGSWOTU70BnAUQEfMkTQfmA83A2flGUKG0Lbiim5PVaOPqrmxel52X1/S+WPR4L/rvu5mNq7L/d2Qy8OCV9Rz6xeUAbF5Xw/Vf2Z/jL1jC8PEb2zyu7Vx/n9mTsUdmfx/1+zRS2y1Yt6aGJ+/vxcQT11LbLcOAIY3UD9/Cwud3LXO05dcR98FFxIsRMS4iDo6IAyPih0n56RFxUFL+uYhoyNlnWkTsGxH7R8S9bR89q5QtuGeAEUlT8m2y96+cVsLzlcX6Fd348/n7kslAZMSYz65m1LFrefT6vfj7jQMAOGjSGj518koAHv/DXqx6szsPXlHPg1dke+xTb1zAbn2by/Y9dDYXXvUmBx+2kV59mvnjs/O58bIB3HdLH867fAm//dtCmprEz84dAog3X+3OI3fuwTWzF9LSIq68uL7Tj6BSQPezUih7K0qJDp69A/mXZG8TuT4ipuWrP+rgbnHTXQNKFo91vG8PO7TcIVgRnopZrI81O5Sdeh/QP465/gsF1b39iKvnRMT4HTnfjijpfXARcQ9wTynPYWY7X7W04DyTwcyK4gUvzSy1AtGcqY51OpzgzKxofuiMmaVTuItqZinla3BmlmpOcGaWSoFo8SCDmaWVBxnMLJXCgwxmlmbhBGdm6VQ9k+2d4MysaG7BmVkqRUBLlSwZ5QRnZkXzKKqZpVLgLqqZpZYHGcwsxUq4EHiHcoIzs6K5i2pmqZQdRfVcVDNLKXdRzSy13EU1s1QK5ARnZulVJT1UJzgzK1JAeKqWmaWVu6hmllpVP4oq6b/I09WOiG+WJCIzq2hpmYv67E6LwsyqRwDVnuAi4obc95J6RMR7pQ/JzCpdtXRR251vIekwSfOBBcn7MZKuKnlkZlahRGQK28qtkAllvwQmAasBImIucFQJYzKzShcFbmVW0ChqRCyRtsnGLaUJx8wqXlTPIEMhLbglkg4HQlI3Sf9G0l01s06qA1pwkrpLelrSXEnzJP0gKe8j6QFJryVfe+fsc5GkRZIWSprUXpiFJLivAWcD9cDbwNjkvZl1Wipwy6sROCYixpDNK5MlHQpcCMyKiBHArOQ9kkYBU4DRwGTgKkk1+U7Qbhc1IlYBX2yvnpl1IpkdP0REBLAxeVubbAGcCExMym8AZgPfScpviYhGYLGkRcAE4Im2zlHIKOo+ku6UtFLSCkl3SNrn431LZlb1Wu+DK2SDvpKezdmm5h5KUo2kF4AVwAMR8RQwICIaAJKv/ZPq9cCSnN2XJmVtKmSQ4Sbg18BJyfspwM3AIQXsa2YpVMR9cKsiYnzbx4kWYKykPYC/SDowz7G21+fNG0kh1+AUETdGRHOy/bG9g5pZynXwbSIRsZZsV3QysFzSQIDk64qk2lJgSM5ug4Fl+Y7bZoJLRjL6AA9JulDSMEl7S7oAuLvw0M0sdQrvorZJUr+k5YakXYDjgFeAGcAZSbUzgDuS1zOAKZLqJA0HRgBP5ztHvi7qHLI5uDXKs3K/PeBHeaM3s9RSx/ThBgI3JCOhXYDpEXGXpCeA6ZLOBN4CTgaIiHmSpgPzgWbg7KSL26Z8c1GHd8i3YGbpEoIOmIYVES8C47ZTvho4to19pgHTCj1HQTMZkgt/o4DuOSf6Q6EnMbOUqZKr8O0mOEnfJ3tPyijgHuB44DHACc6ss6qSBFfIKOoXyDYX34mIrwBjgLqSRmVmlS1Fk+03R0RGUrOknmSHbH2jr1lnlYYFL3M8mwzl/o7syOpG2hmaNbN066BR1JIrZC7q/01e/kbSTKBnMvphZp1VtSc4SZ/M91lEPFeakMys0qWhBXdZns8COKaDY+HtV/bgosM+39GHtRK6b9nMcodgRZgwaVPHHKjar8FFxNE7MxAzqxIVMkJaCD/42cyK5wRnZmmlDljwcmdwgjOz4lVJC66QFX0l6UuSvpe8HyppQulDM7NKpCh8K7dCpmpdBRwGnJq830B2hV8z66w6YD24naGQLuohEfFJSc8DRMS7krqVOC4zq2QV0DorRCEJrilZkC4guwonHfJMHTOrVpXQ/SxEIQnuCuAvQH9J08iuLnJJSaMys8oVKRpFjYg/SZpDdskkAZ+PCD/Z3qwzS0sLTtJQYBNwZ25ZRLxVysDMrIKlJcGRfYJW68NnugPDgYXA6BLGZWYVLDXX4CLioNz3ySojZ7VR3cysYhQ9kyEinpP0qVIEY2ZVIi0tOEnn5bztAnwSWFmyiMyssqVpFBXYPed1M9lrcreVJhwzqwppaMElN/juFhHf3knxmFmFEykYZJDUNSKa8y1dbmadVLUnOLJPzvok8IKkGcCtwHutH0bE7SWOzcwqUYWsFFKIQq7B9QFWk30GQ+v9cAE4wZl1VikYZOifjKC+zAeJrVWV5G8zK4U0tOBqgN3YNrG1qpJvz8xKokoyQL4E1xARP9xpkZhZdUjJU7XKvxynmVWkNHRRj91pUZhZdan2BBcRa3ZmIGZWPaplqlYhD50xM/tAFLHlIWmIpIckLZA0T9K5Sfmlkt6W9EKyfSZnn4skLZK0UNKk9kL1c1HNrCiiwy7QNwPnJysU7Q7MkfRA8tkvIuLn25xXGgVMIbsW5SDgQUkjI6KlrRO4BWdmxeuAFlxENETEc8nrDcACoD7PLicCt0REY0QsBhYBeZ/R7ARnZkXr6Ac/SxoGjAOeSorOkfSipOsl9U7K6oElObstJX9CdIIzs4+h8BZcX0nP5mxTP3woSbuRXYLtXyNiPXA1sC8wFmgALmut2kYkbfI1ODMrTnELXq6KiPFtfSiplmxy+1PrAh4RsTzn898BdyVvlwJDcnYfDCzLd3K34MyseB0ziirgOmBBRFyeUz4wp9pJZOfDA8wApkiqkzQcGEF21aM2uQVnZkXroJkMRwCnAy9JeiEpuxg4VdJYsinyDZKHXEXEPEnTgflkR2DPzjeCCk5wZvZxdECCi4jH2P51tXvy7DMNmFboOZzgzKxoaZiLamb2UUEqFrw0M/uIVDx0xsysTU5wZpZWiurIcE5wZlaclKzoa2a2Xb4GZ2apVS0LXjrBmVnx3IIzs1RK2ZPtzcy25QRnZmnkG33NLNWUqY4M5wRnZsXxfXCdx7nfe5kJn17J2jXdOPuUIwD4zn/OZfDe7wHQY/cm3ttQyzdOO5yarhm++d157HfAempqgll3D+LW/96nnOF3ai0t8I3JI9lzYBM/+sNiHrmzFzdethdLXuvOFfe8ysgxm7fWveW/+jPz5j2p6RJ8/T/eZvzEDWWMvPw6/W0ikq4HTgBWRMSBpTpPuT145yDumj6U837w0tayn140ZuvrM7/1Cps2Zn/MRx73DrW1Gc4+5Qjqurdw9a2P8fDMgaxo2GWnx23w12v7MWREI5s2Zhe2HnbA+3zv2je44jtDtqn35qt1zL6jN9c89Aprltdy4Sn7ct1jC6ipKUfUFaJKWnClXLL898DkEh6/Isx7vg8b1tW28Wnw6eOW8/DMZAXmEN13aaFLTYZudS00N3Vh03ud+a+kfFYuq+XpWT05/rTVW8uGjmhkyH6NH6n7xH29mHjiu3SrC/YauoVBwxpZ+PyuOzPcitPRT9UqlZIluIh4BFhTquNXg9Hj3mXtmm4sW9IDgMdmDeD9zTX88b7Z/P7uR7j9xmFsXN+tzFF2Tr/5fj3/cskyVMBfwKqGWvoNatr6vu/AJla/09Z/ap1AABGFbWVW9ofOSJra+kixLZnN7e9QRf5x8js8fN8Hz88YOXodmYw4ffJEvvrPn+akL73BXvWbyhhh5/TkAz3Zo28zIw4u8N/b9v5OO+jR7tVKmcK2cit7gouIayJifESM79YlPdeiutRkOPzo5Txy/15byyZObmDO3/vS0tyFde/WMX9ub/Ybtb6MUXZO85/pwZP39+TLE0bxn1/fm7mP7c5PzxnaZv2+g5pYueyDFtuqhlr2HNDUZv20a70PrlN3UTu7cRNWs/SNHqxe0X1r2cp3ujPmU6uBoK57MwcctJali3uUL8hO6qsXN/CnOfP5w9PzuejqNxlz5Aa+c+VbbdY/9J/WM/uO3mxpFO+81Y23F9ex/7hO3PIutHtaAV1U3yaygy6YNpeDxq+h5x5N3HDPbP702/24/47BHDVp2+4pwF3Th/KtS1/mqumPI8EDM+p5Y9HuZYrcPuzxe3tx1SX1rFvdle+evg/7jt7Mj29+nWH7v89R/7yWqRMPoKYmOOfHSzv3CCqV0TorhKJEWVbSzcBEoC+wHPh+RFyXb59e3frH4f1OKUk8Vhp3z5lZ7hCsCBMmLeHZue/v0BXE3fcYHOOOOreguo/eecGcfE+2L7WSteAi4tRSHdvMyqtaWnDuoppZcQJoqY4M5wRnZkVzC87M0qsCRkgL4QRnZkVzC87M0snLJZlZWgmQBxnMLK38ZHszSyd3Uc0svSpjnmkhnODMrGgeRTWz9KqSFpyXSzKz4kR2FLWQLR9JQyQ9JGmBpHmSzk3K+0h6QNJrydfeOftcJGmRpIWSJrUXqhOcmRUvCtzyawbOj4hPAIcCZ0saBVwIzIqIEcCs5D3JZ1OA0WSf93KVpLwLVznBmVnRFFHQlk9ENETEc8nrDcACoB44EbghqXYD8Pnk9YnALRHRGBGLgUXAhHzncIIzs+IVvqJv39ZnriTb1O0dTtIwYBzwFDAgIhqyp4kGoH9SrR5YkrPb0qSsTR5kMLPiBFD4A2VWtbfgpaTdgNuAf42I9VKb63Fu74O8zUS34MysKKKw7mkhsx0k1ZJNbn+KiNuT4uWSBiafDwRWJOVLgdyncg8GluU7vhOcmRUvkylsy0PZptp1wIKIuDznoxnAGcnrM4A7csqnSKqTNBwYATyd7xzuoppZcYrrouZzBHA68JKkF5Kyi4GfANMlnQm8BZwMEBHzJE0H5pMdgT07IlryncAJzsyK1hGT7SPiMdp+hPaxbewzDZhW6Dmc4MyseFUyk8EJzsyK5Mn2ZpZWfqqWmaWZF7w0s/RygjOzVAog4wRnZqnkQQYzSzMnODNLpQBaOmYqQ6k5wZlZkQLCCc7M0spdVDNLJY+imlmquQVnZqnlBGdmqRQBLXmXYasYTnBmVjy34MwstZzgzCydwqOoZpZSAeEbfc0stTxVy8xSKaLdRwJWCic4MyueBxnMLK3CLTgzSycveGlmaeXJ9maWVgGEp2qZWSqFF7w0sxQLd1HNLLWqpAWnqKDREEkrgTfLHUcJ9AVWlTsIK0paf2d7R0S/HTmApJlkfz6FWBURk3fkfDuiohJcWkl6NiLGlzsOK5x/Z+nQpdwBmJmVihOcmaWWE9zOcU25A7Ci+XeWAr4GZ2ap5RacmaWWE5yZpZYTXAlJmixpoaRFki4sdzzWPknXS1oh6eVyx2I7zgmuRCTVAL8GjgdGAadKGlXeqKwAvwfKdmOqdSwnuNKZACyKiNcjYgtwC3BimWOydkTEI8CacsdhHcMJrnTqgSU575cmZWa2kzjBlY62U+Z7csx2Iie40lkKDMl5PxhYVqZYzDolJ7jSeQYYIWm4pG7AFGBGmWMy61Sc4EokIpqBc4D7gAXA9IiYV96orD2SbgaeAPaXtFTSmeWOyT4+T9Uys9RyC87MUssJzsxSywnOzFLLCc7MUssJzsxSywmuikhqkfSCpJcl3Spp1x041u8lfSF5fW2+hQAkTZR0+Mc4xxuSPvL0pbbKP1RnY5HnulTSvxUbo6WbE1x12RwRYyPiQGAL8LXcD5MVTIoWEf8SEfPzVJkIFJ3gzMrNCa56PQrsl7SuHpJ0E/CSpBpJP5P0jKQXJZ0FoKwrJc2XdDfQv/VAkmZLGp+8nizpOUlzJc2SNIxsIv1W0nr8tKR+km5LzvGMpCOSffeUdL+k5yX9lu3Px92GpL9KmiNpnqSpH/rssiSWWZL6JWX7SpqZ7POopAM65KdpqeQn21chSV3JrjM3MymaABwYEYuTJLEuIj4lqQ54XNL9wDhgf+AgYAAwH7j+Q8ftB/wOOCo5Vp+IWCPpN8DGiPh5Uu8m4BcR8ZikoWRna3wC+D7wWET8UNJngW0SVhu+mpxjF+AZSbdFxGqgB/BcRJwv6XvJsc8h+zCYr0XEa5IOAa4CjvkYP0brBJzgqssukl5IXj8KXEe26/h0RCxOyv8JOLj1+hrQCxgBHAXcHBEtwDJJf9vO8Q8FHmk9VkS0tS7accAoaWsDraek3ZNz/K9k37slvVvA9/RNSSclr4cksa4GMsCfk/I/ArdL2i35fm/NOXddAeewTsoJrrpsjoixuQXJH/p7uUXANyLivg/V+wztL9ekAupA9tLGYRGxeTuxFDz3T9JEssnysIjYJGk20L2N6pGcd+2HfwZmbfE1uPS5D/i6pFoASSMl9QAeAaYk1+gGAkdvZ98ngH+UNDzZt09SvgHYPafe/WS7iyT1xiYvHwG+mJQdD/RuJ9ZewLtJcjuAbAuyVRegtRV6Gtmu73pgsaSTk3NI0ph2zmGdmBNc+lxL9vrac8mDU35LtqX+F+A14CXgauDhD+8YESvJXje7XdJcPugi3gmc1DrIAHwTGJ8MYszng9HcHwBHSXqObFf5rXZinQl0lfQi8CPgyZzP3gNGS5pD9hrbD5PyLwJnJvHNw8vAWx5eTcTMUsstODNLLSc4M0stJzgzSy0nODNLLSc4M0stJzgzSy0nODNLrf8PB0OsjX03Nn4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay(cm).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "b0aa6e33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7146929824561403, 0.7035087719298245)"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train,y_train),model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "93feb45b",
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = cm[1][1]\n",
    "TN = cm[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "070b3670",
   "metadata": {},
   "outputs": [],
   "source": [
    "FP = cm[0][1]\n",
    "FN =  cm[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "565e28b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = (TP + TN) /(TP+TN+FP+FN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "93dccc77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7035087719298245"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "22ecf5bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6972789115646258"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test,model.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "bf11c398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7192982456140351"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test,model.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "67d3a713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7081174438687392"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,model.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "6b6f16ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.25982607,  0.37190708,  0.12675763,  0.76939735,  0.3718793 ,\n",
       "         0.12663576, -0.16998068]])"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "0715b947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "9d9e0b3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(data = model.coef_,columns = cols).T.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "723ae27b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "06b73bf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "f6669273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "569     1\n",
       "3458    1\n",
       "3491    1\n",
       "5124    1\n",
       "4874    1\n",
       "       ..\n",
       "589     0\n",
       "3620    1\n",
       "481     0\n",
       "1645    0\n",
       "4438    1\n",
       "Name: Churn, Length: 1140, dtype: int64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "ac1b6a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "probabilities = y_pred[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "972e5392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.55825052, 0.73948257, 0.65938542, ..., 0.32158678, 0.82331112,\n",
       "       0.6286494 ])"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probabilities # 0.5 threshold "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79a3e386",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1db70f5a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "73321e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.55825052, 0.73948257, 0.65938542, ..., 0.32158678, 0.82331112,\n",
       "       0.6286494 ])"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c9a6d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a513df1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d904d532",
   "metadata": {},
   "source": [
    "## Receiver operating characteristic \n",
    "    AU-ROC : area under the ROC :  \n",
    "    \n",
    "https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "8eb0d550",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)\n",
    "An ROC curve plots TPR vs. FPR at different classification thresholds. Lowering the classification threshold classifies more items as positive, thus increasing both False Positives and True Positives. The following figure shows a typical ROC curve.\n",
    "\n",
    "ROC Curve showing TP Rate vs. FP Rate at different classification thresholds.\n",
    "\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "182c4e84",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score,RocCurveDisplay,roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "444e7fb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7502002612639258"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,probabilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "b3c4acb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ideal model is when TPR is 1 and FPR is 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "16da9ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr,tpr,threshold = roc_curve(y_test, probabilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "b67ce7fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.plot(fpr,tpr)\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.plot([0,1],[0,1],\"h--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "64c68a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# roc-auc is impacted by imbalanced data . \n",
    "# random model has AUC of 0.5\n",
    "   # it doent depends on y^ values . \n",
    "        #rahter it depends on the order of probability.\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42f138cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for imbalanced data : prefer PR curve, precisison , recall , f1 score \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "81140374",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve,precision_recall_fscore_support,auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "0c759084",
   "metadata": {},
   "outputs": [],
   "source": [
    "precision, recall, thresholds = precision_recall_curve(y_test, probabilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "71793063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2562b18de80>]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6wSGeXLcPgD++sQuAz00eohkYJSwE0+WSDwxrtj0U2N9Km+edc5XOuUJgFTCxY0oUOXlD+vXhMxMH85svT2LXbRdySmocAP1ie7GnuIraBnXBSPcXTKCvAbLMbLiZRQOXAE+3aPMvYI6ZRZlZLP4uma0dW6pIxzAz6n0+AI5U1XP2r19l9I+fZ+0eLYwh3VubXS7OuQYzux54AYgEHnLObTazpU3H73PObTWz54H3AR/woHNuUygLFzkZD10+jQ35pQDc+PcNgH/+9MXT0+ndK5KvzsygT3SklyWKtJvmQ5cezTnHgrveYMuBsk8cS42P5tKZmSycNJjMpi4aEa8dbz50rVgkPZqZ8ewNc6htaMTngwdW5fLo27sprqyjsKKO3768ncKKWm797HivSxVpk67QRVqx81AF5/3m4xuep6T3Y8GEwaTERbNw0mDMNCpGvKEVi0TaaXhqHHctmhzYfm/vEW799xa+/8T7VGlxDOmi1OUi0orICOMzEwdz0YRB5BZW8rVH1pBfUs3diyYTF6MfG+madIUuchxmxm9e3M6eoioafY5vPr6OitoGr8sSaZUCXaQNXz97BFn94wGobfAx/qcv8KfVe7TQtHQ5CnSRNowbnMifr57Bjy4cE9j346c2cf8qrUsqXYsCXSQI/fv2ZsmZI3j6+tmBfeeO0SzR0rUo0EXa4f7XPr4qH9nUDSPSVejjepF22F1UGXh87Z/e4/yxA6hv9DGoXx/mjEzVrI3iKQW6SDvcNG8Mlz30LgDPby7g+c0FgWPZGUn849rTvSpNRIEu0h5njkpj97IFFFXUUlnbSHV9IxfcsQrwLxLwr/X7yEyJY+Kwfp7WKT2TAl3kBKTEx5ASD2/sKAzsW7unhLV7SjglNY6VN57lXXHSY+lDUZGTcEZWKm/cdDZ3LZpMTFQEyXHR3HHJJK/Lkh5KV+giJym/pJofPbmR+kYfV50xnH0l1ewrqcbnoE90BGdmpREVqWsnCT0FushJunvljsB0ALe/sK3VNt+7YDTjBvdlTlYakRoJIyGi6XNFTlJ5TT37jlR/Yv/uwiqW/mntUfv+/Y0zGD8ksbNKkzB0vOlzFegiIVRcWccPnnifF7ccDOybMTyZa+acAvgXqc7OTPaqPOmGtGKRiEeS46L55rlZJMdFs3xNHgDv7CrmnV0fL0i97ubzSYqL9qpECSO6QhfpJHUNPrYfLOdIVT03/n0DB8tr+Plnx/OVGRlelybdiFYsEukCoqMiSEuI4dZ/b6GgrIYvZw/j3DEDvC5LwogCXaQTPfrWbrYdLAdg+Zo8Zt62gi37yzyuSsKFAl2kE33jnCyeuHYW3zovK7BvywEFunQMfSgq0on6REdy2pB+3PbsBwD0T4hhfV4J6/NKjmrnc7Bu7xG+nD2UiyYOJiU+xotypZvRh6Iinay4so7P//4tyqrrj9mmqLLuqO2oCOMv18xstW1UpDFhSKLuRu0hNA5dpJvx+Rwb8o/w3b9vIPdwZZvt7/3KFC48bVAnVCZe0zh0kW4mIsKYnJ7Eiu/M5f38UsprGrAWMwY8/OYuXt56iHPH9OccLYcnKNBFujQza3Vu9btW7ODlrYcA+PUXJ9K7V2QnVyZdkTrdRLqh17YfDjxu2d8uPZcCXaSb2VNUyeHyWvr0iuSRK6dpsWoJUJeLSDeyaV8pVzy8hiNVddyycByjBiR4XZJ0IUFdoZvZPDPbZmY7zewHx2k3zcwazewLHVeiiHzkR//cSGFFLQ0+x3//cxOnL1vJur0lfFBQRkOjz+vyxGNtBrqZRQL3APOBscAiMxt7jHa/BF7o6CJFxO83X5rE/ZdOZe6otMC+i+99i3l3vM79q3I9rEy6gmC6XKYDO51zuQBmthxYCGxp0e4bwBPAtA6tUEQCRvaPZ2T/eGaPTOX17YdZn3+E+1/zB/nfcvL41NjWJ/vKSIkjOkofmYW7YAJ9CJDXbDsfmNG8gZkNAS4GzuE4gW5mS4AlAOnp6e2tVUSaxMdEMf+0QZw6qC9/fH0XDT7HnqIqzv/tqmN+z8NXTuPs0RqvHs6CCfTWFkBseXvpHcBNzrlGa3n3Q/Nvcu4B4AHw3ykaZI0icgyZqXFs+dk8Vn5wiPpG3yduPvrH2nxe3eYf4njlw2sYMzCBDwrKmTsqjT9enq3pAsJMMIGeDwxrtj0U2N+iTTawvCnMU4ELzazBOfdURxQpIscWHRXBvPEDWz326QmDOVBazY1/30B1XSPv7T0C+Mex5xZWapRMmAnm1/MaIMvMhptZNHAJ8HTzBs654c65TOdcJvAP4OsKc5GuYVBiH/589Uye/Ppszjv14y6Xp9e3vC6T7q7NQHfONQDX4x+9shX4m3Nus5ktNbOloS5QRDqGz+fISIkDYHJ6Pz4/dSjFuss0rGi2RZEe4sXNBSz5v7VH7TOD179/NkOTYj2qStpLsy2KCLNHpvLbL0+kus7HXSt2UFBWwxenDmVwYh+vS5MOoo+4RXqIuJgozh87kOc3F1BQVsO3zsvil5+fQETEsUemSfeiK3SRHuJAaTXXPJbD1gPl/PLzp/HlaboXJNwo0EV6gPf2lvBf/7eWqtoGHrwsm7O1IEZYUqCLhLl/rM3nR09uZGBib/589QyNPQ9jCnSRMFXf6GPZcx/wxzd2cfqIFO5ZPIWkuGivy5IQUqCLhKGC0hq+9sgathwo4/JZGfz402Pppdv8w54CXSQMXf7Qu2w7WA5Afkm1wryH0LssEoayM5MCj7+YPew4LSWc6ApdJIzU1DfyP//Zyp/f2cvEoYncvWgK6Sm6C7SnUKCLhIkdB8v55vL1bD1QxpIzT+HGT43WohY9jN5tkTDwwuYCzv/tKrYeKANgUGJv3th5GK/mahJvKNBFwkBlbcNR27c8s4WvPZLD8jV5+HwK9Z5CgS4SBj43ZSi7ly1g28/nMXFYv8D+Hz65kX1Hqr0rTDqV+tBFwkhMVCSXzsxgQ96RwL5Xth2if0JMYHtgYh8mNQt9CR8KdJEwU15TzylpceQergTgJ//afNTxhJgo1v/0U0RqlsWwo0AXCTNXzh7OlbOHU9fg48PDFVTXN3L789t4O7eI4alx/PqLExXmYUp96CJhKjoqglMH9eWOl3fwdm4RAIXltcTFRHpcmYSKAl0kzM0Ynhx4PCm9n5abC2PqchEJU3UNPu577UN+t3In/WJ7cfOCsXxuyhDM1N0SrhToImFob1EVZ97+SmD7m2eM5PNTh3pYkXQGdbmIhKGWY8/XNxvGKOFLV+giYaa0up4XtxQQGWHEx0Rx4wWjWTxd64f2BAp0kTBSU9/IvDtWcaC0BvCvWnTHS9u546XtREUady+awvRmH5JKeFGgi4SRmKgIvjxtGIUVtQBs2lcW6G6Jj4kiJV5L0IUzBbpIGDEzvnXeqMD27GUrA48rahv48+q9/OSisV6UJp1AH4qKhLEvNVutKMKgpKqOW/+9hasfzeEn/9pETX2jh9VJR9MVukgY21tcRXzMxz/m/1y376jj88YN5PSRqZ1dloSIeTUBfnZ2tsvJyfHktUV6qp89s4WH3tx11L6V353LKWnxHlUk7WVma51z2a0dU5eLSA9ywbgB9O7l/7HvnxDDss+dxvDUOI+rko4SVJeLmc0D7gQigQedc8taHP8KcFPTZgVwrXNuQ0cWKiInbltBOXeu2M6zGwtIiIniGxdkceXsTGKj1esaTtp8N80sErgHOB/IB9aY2dPOuS3Nmu0C5jrnSsxsPvAAMCMUBYtI8DbvL+XeVz7kPxsPEB8TxTfOGclVZwynX6yGL4ajYH49Twd2OudyAcxsObAQCAS6c+6tZu1XA5o0QsQjzjne3FnE/as+5PUdhcTHRHH92SO5eo6CPNwFE+hDgLxm2/kc/+r7KuC51g6Y2RJgCUB6um5FFulIDY0+/rPxAPe/lsuWA2WkJcRw07wxLJ6RTmKfXl6XJ50gmEBvba7NVofGmNnZ+AP9jNaOO+cewN8dQ3Z2tpYiF+kAh8pqePzdPP7y7h4OltUysn88v/r8BBZOHkxMlBaz6EmCCfR8YFiz7aHA/paNzGwC8CAw3zlX1DHliUhrnHOs2V3CY2/v5vlNBTT4HHNHpfGLizM4e3R/IrTEXI8UTKCvAbLMbDiwD7gEWNy8gZmlA08Clzrntnd4lSICwKHyGp58bx9/y8kj93AlfXtHccXpmXx1ZgaZGn7Y47UZ6M65BjO7HngB/7DFh5xzm81sadPx+4CfACnAvU2roTQca+C7iLRPbUMjr3xwmH+szeeVbYdo9DmmZSaxdO4ILpowmD7R6lYRP90pKtJFFVbUcv5vXqOkqj6wL8LgxW+fycj+CR5WJl7SnaIi3dCbOwuPCnMAn4OUuBiPKpKuToEu0kWdPaZ/q/vvXLGjkyuR7kL3/Yp0UQkxUZw6qC9bD5Qdtf8/Gw+weX/pMb9v8/4yquoamT48mQvHDzxmu4gIY/74QaQl6Io/XCjQRbooM+OyWRn88MmNAEwfnkxUEMMRq+r8c5y/u6uYd3cVH7dt76hIvjRt2HHbSPehQBfpwhZNT2dROxd49vkcR6rrMfx3AK7PK+Hp9ft5YfNBqusb6Rfbi09PGMTFk4cwJT0pJHWLNxToImEmIsJIjotmdW4Rlzyw+hPHs/rHk3u4kv99cTs+51id67+K/94Fo/nqzIzjPndUhBEXo9joqvTOiISp5iOSRw9IoG+fj3/c6xt9AKzZXRLYd/sL27j9hW1BPfecrI9XOTIzvnnOSLIzk0+yYjlZCnSRMDVrRAq7ly04bpv9R6r5zUvbyUiOJbaNK+9b/+2fYDUmKoLXdxQG9ifHRVPb4Dv5guWkKdBFerDB/frw6y9ObLNdfkkVNfWN/Gv9PrYfrAD8V+lfmDqUeeMHahKwLkKBLiLHtbeoijNvf+UT+1/fUcjrOwo5Z0x/HrpimgeVSUu6sUhEjmtAYgxL545gzMAE4prNGxMVYcwdlcbXZg/3sDppTlfoItKqytoG3thZyMqth1i57RCHy2uJjDDmZKWy4LRBXDBuIElxWgGpK1Ggiwjgn2N928Fy3thRyKodhaz+sIi6Rh8JMVGcOSqNc8b056zRaaTE687SrkqBLtJDOefYcaiCd3KLWJ1bzDu7iimsqAXglLQ4Lp2Vwblj+pOdmUx0lHpnuwMFukgPU13XyJxfvRII75Ymp/cjPTmWS6YNI2uApuntTvRrV6SHqff5jhnmAOv2HuGNHYUUV9Z1YlXSEXSFLtID1DY0suNgBZv2lbJ5fxnjh/Rl64FyGn0OMxjVP4EpGUlkZyQxNSOJjJRYmlYfk25EgS4SZkoq69h+sJytB8rYvN//teNQOfWN/rkAEmKiOG1oItedNYIpGUlMTk8isU8vj6uWjqBAF+mmSirr2HGogu0Hy9lxsLzpccVR3SkpcdGMG5LIWaPTGDc4kfFD+jIsKZaIIKbhle5HgS7SDX3pvrd5d/fx5zoH/9zoObuLyWnRtqa+EV8rywl/67wsvnXeqI4qUzqZAl2kG7p0Vgbv7i5m1IB4sgYkMKhvb4Lt8l6+Jq/VMAe44+UdPPj6rnbVUl3fSGMrT5j7iwv1l0AnU6CLdEMXTRzMRRMHn9D3fm7KUBb/YTV9ekUyamACmSlxRAYRvHUNPnIL/d06h8uPPUoGYOGkE6tNTo4CXaSHOXVQX9b95FOtHqupb2RvcRV7iqrYU1Tp/2+x/3F+SXXgSjwywshIiWVU/wRGDUxg3OC+jB+SyODE3hod4yEFukgP4ZyjuLKOgrIaDpbVUFBaS0FZDQeOVLOnuIq9RVUUlNUc9T19e0eRmRrHaUMS+czEwWQNSGDUgHiGp8ZpytwuSIEuEgZq6hs5VOYP6IKyGg6W1nzi8aGyWuoaj16IwgzS4mPISIll9shUMlNiSU+JJSMljsyUWPrFavKt7kSBLtKNfXi4gq8++A4HSmvabtyKfn164YDdRVXsLqpi1Y7gv9c5KK6sPeYHrAA/WziOy2ZlnlBt0n4KdJFuLDk2mgtPG0RVXWPIX+vxd/e2+3v+lpPH7sKqEFRz4g6W17Axv5Sa+kb+76oZjB4YPvPVKNBFurGkuGhu/vTYTnmtNbuL2Xmo4rht4qIjiWj2oeiewir2eBTo5bUNbbbZuK9UgS4iPc/L35nrdQkBNfWNHC6v5VB5LYfLazlcXsOh8lryiqvYVVTF7sLKo9pHGJySFs+EIYmMH5LIhKGJjB3cl9jo8IrA8DobEQkrZTX1fOH3bwUWpm6PXpFGZkocmalxDEuKDczpfqi8lpe3HuLlrYc6utygRBh8KXsYmalxHf7cCnQR6bIOHKkJOsyjIuwTd6buKfaPo+9K6hr8I42+P29Mhz93UIFuZvOAO4FI4EHn3LIWx63p+IVAFXCFc+69Dq5VRHqY0QMT2L1sgddldKhRP37uuCODTkabC1yYWSRwDzAfGAssMrOWn8LMB7KavpYAv+/gOkVEpA3BrFg0HdjpnMt1ztUBy4GFLdosBB5zfquBfmY2qINrFRGR4wgm0IcAec2285v2tbcNZrbEzHLMLOfw4cPtrVVEpNubN24gY0I0VDKYPvTWZtpp2QMUTBuccw8ADwBkZ2eHqBdJRKTrumvR5JA9dzBX6PnAsGbbQ4H9J9BGRERCKJhAXwNkmdlwM4sGLgGebtHmaeAy85sJlDrnDnRwrSIichxtdrk45xrM7HrgBfzDFh9yzm02s6VNx+8DnsU/ZHEn/mGLV4auZBERaU1Q49Cdc8/iD+3m++5r9tgB13VsaSIi0h7BdLmIiEg3oEAXEQkTCnQRkTChQBcRCRPm/zzTgxc2OwzsabE7FSj0oJyuQOfeM+nce6aTOfcM51xaawc8C/TWmFmOcy7b6zq8oHPXufc0OveOP3d1uYiIhAkFuohImOhqgf6A1wV4SOfeM+nce6aQnHuX6kMXEZET19Wu0EVE5AQp0EVEwoQngW5m88xsm5ntNLMftHL8LDMrNbP1TV8/8aLOUGjr3JvanNV03pvN7LXOrjFUgnjfv9fsPd9kZo1mluxFrR0tiHNPNLNnzGxD0/seNjOWBnHuSWb2TzN738zeNbPxXtTZ0czsITM7ZGabjnHczOyupn+X981sykm/qHOuU7/wT8H7IXAKEA1sAMa2aHMW8O/Orq2LnHs/YAuQ3rTd3+u6O+vcW7S/CFjpdd2d+L7/CPhl0+M0oBiI9rr2Tjr324GfNj0eA6zwuu4OOvczgSnApmMcvxB4Dv+KbzOBd072Nb24Qg9m0elwFcy5LwaedM7tBXDOHerkGkOlve/7IuDxTqks9II5dwckmJkB8fgDvaFzywyJYM59LLACwDn3AZBpZgM6t8yO55xbhf99PJaFwGPObzXQz8wGncxrehHoQS0oDcxq+vPzOTMb1zmlhVww5z4KSDKzV81srZld1mnVhVaw7ztmFgvMA57ohLo6QzDn/jvgVPxLN24EbnDO+TqnvJAK5tw3AJ8DMLPpQAb+ZSzDXdA/E8EKaoGLDhbMgtLv4Z+voMLMLgSeArJCXVgnCObco4CpwLlAH+BtM1vtnNse6uJCLKiFxJtcBLzpnDve1U13Esy5XwCsB84BRgAvmdnrzrmyENcWasGc+zLgTjNbj/+X2TrC46+TtrTnZyIoXlyht7mgtHOuzDlX0fT4WaCXmaV2XokhE+yC28875yqdc4XAKmBiJ9UXSu1ZSPwSwqe7BYI79yvxd7U559xOYBf+/uTuLtif9yudc5OAy/B/hrCr0yr0Tnt+JoLiRaC3uei0mQ1s6kv86E+wCKCo0yvteMEsuP0vYI6ZRTV1PcwAtnZynaEQzLljZonAXPz/DuEimHPfi/+vMpr6j0cDuZ1aZWgE8/Per+kYwNXAqjD4yyQYTwOXNY12mQmUOucOnMwTdnqXiwtu0ekvANeaWQNQDVzimj4W7s6COXfn3FYzex54H/ABDzrnWh321J0E+b4DXAy86Jyr9KjUDhfkud8KPGJmG/H/KX5T019o3VqQ534q8JiZNeIf4XWVZwV3IDN7HP+IvVQzywd+CvSCwHk/i3+ky06gCv9faSf3mmGQkyIigu4UFREJGwp0EZEwoUAXEQkTCnQRkTChQBcRCRMKdBGRMKFAFxEJE/8fR6IPeMiq+88AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(precision,recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "8e22554b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7575126880529139"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc(recall,precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43b5c974",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "b8346467",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, roc_auc_score\n",
    "\n",
    "def evaluate(model, X_train, X_test, y_train, y_test):\n",
    "    y_test_pred = model.predict(X_test)\n",
    "    y_train_pred = model.predict(X_train)\n",
    "\n",
    "    print(\"TRAINIG RESULTS: \\n===============================\")\n",
    "    clf_report = pd.DataFrame(classification_report(y_train, y_train_pred, output_dict=True))\n",
    "    print(f\"CONFUSION MATRIX:\\n{confusion_matrix(y_train, y_train_pred)}\")\n",
    "    print(f\"ACCURACY SCORE:\\n{accuracy_score(y_train, y_train_pred):.4f}\")\n",
    "    print(f\"CLASSIFICATION REPORT:\\n{clf_report}\")\n",
    "\n",
    "    print(\"TESTING RESULTS: \\n===============================\")\n",
    "    clf_report = pd.DataFrame(classification_report(y_test, y_test_pred, output_dict=True))\n",
    "    print(f\"CONFUSION MATRIX:\\n{confusion_matrix(y_test, y_test_pred)}\")\n",
    "    print(f\"ACCURACY SCORE:\\n{accuracy_score(y_test, y_test_pred):.4f}\")\n",
    "    print(f\"CLASSIFICATION REPORT:\\n{clf_report}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "0d1d0246",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINIG RESULTS: \n",
      "===============================\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFUSION MATRIX:\n",
      "[[1663  635]\n",
      " [ 666 1596]]\n",
      "ACCURACY SCORE:\n",
      "0.7147\n",
      "CLASSIFICATION REPORT:\n",
      "                     0            1  accuracy    macro avg  weighted avg\n",
      "precision     0.714040     0.715374  0.714693     0.714707      0.714702\n",
      "recall        0.723673     0.705570  0.714693     0.714622      0.714693\n",
      "f1-score      0.718824     0.710438  0.714693     0.714631      0.714664\n",
      "support    2298.000000  2262.000000  0.714693  4560.000000   4560.000000\n",
      "TESTING RESULTS: \n",
      "===============================\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONFUSION MATRIX:\n",
      "[[392 160]\n",
      " [178 410]]\n",
      "ACCURACY SCORE:\n",
      "0.7035\n",
      "CLASSIFICATION REPORT:\n",
      "                    0           1  accuracy    macro avg  weighted avg\n",
      "precision    0.687719    0.719298  0.703509     0.703509      0.704007\n",
      "recall       0.710145    0.697279  0.703509     0.703712      0.703509\n",
      "f1-score     0.698752    0.708117  0.703509     0.703435      0.703583\n",
      "support    552.000000  588.000000  0.703509  1140.000000   1140.000000\n"
     ]
    }
   ],
   "source": [
    "evaluate(model,X_train,X_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35b9a8db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "8d8ede29",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve, roc_curve\n",
    "\n",
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\")\n",
    "    plt.plot(thresholds, recalls[:-1], \"g--\", label=\"Recall\")\n",
    "    plt.xlabel(\"Threshold\")\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.title(\"Precision/Recall Tradeoff\")\n",
    "    \n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], \"k--\")\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC Curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "8daa7c9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x1800 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precisions, recalls, thresholds = precision_recall_curve(y_test, model.predict(X_test))\n",
    "plt.figure(figsize=(14, 25))\n",
    "plt.subplot(4, 2, 1)\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "\n",
    "plt.subplot(4, 2, 2)\n",
    "plt.plot(precisions, recalls)\n",
    "plt.xlabel(\"Precision\")\n",
    "plt.ylabel(\"Recall\")\n",
    "plt.title(\"PR Curve: precisions/recalls tradeoff\");\n",
    "\n",
    "plt.subplot(4, 2, 3)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, model.predict(X_test))\n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cba67831",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "85fcce30",
   "metadata": {},
   "source": [
    "https://medium.com/@sayahfares19/machine-learning-project-in-python-step-by-step-predicting-employee-attrition-eef6afc0959b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27b688d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e28932e3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}