titanic-solution-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **MODEL TRAINING :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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   "outputs": [],
   "source": [
    "# import models needed for the project\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "train_df = pd.read_csv(\"/kaggle/input/titanic/train.csv\")\n",
    "train_df.head()"
   ]
  },
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   "cell_type": "code",
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       "      <td>Wirz, Mr. Albert</td>\n",
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       "      <td>27.0</td>\n",
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       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
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       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
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     "metadata": {},
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   ],
   "source": [
    "test_df = pd.read_csv(\"/kaggle/input/titanic/test.csv\")\n",
    "test_df.head()"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Train</th>\n",
       "      <th>Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>177</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>687</td>\n",
       "      <td>327.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "             Train   Test\n",
       "Age            177   86.0\n",
       "Cabin          687  327.0\n",
       "Embarked         2    0.0\n",
       "Fare             0    1.0\n",
       "Name             0    0.0\n",
       "Parch            0    0.0\n",
       "PassengerId      0    0.0\n",
       "Pclass           0    0.0\n",
       "Sex              0    0.0\n",
       "SibSp            0    0.0\n",
       "Survived         0    NaN\n",
       "Ticket           0    0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "missing_values_train = train_df.isna().sum()\n",
    "missing_values_test = test_df.isna().sum()\n",
    "\n",
    "missing_values = pd.DataFrame({\n",
    "    'Train': missing_values_train,\n",
    "    'Test': missing_values_test\n",
    "})\n",
    "missing_values"
   ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_34/1459666619.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  train_df['Age'].fillna(train_df['Age'].median(), inplace=True)\n",
      "/tmp/ipykernel_34/1459666619.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  train_df['Embarked'].fillna(train_df['Embarked'].mode()[0], inplace=True)\n",
      "/tmp/ipykernel_34/1459666619.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  test_df['Age'].fillna(test_df['Age'].median(), inplace=True)\n",
      "/tmp/ipykernel_34/1459666619.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  test_df['Fare'].fillna(test_df['Fare'].median(), inplace=True)\n"
     ]
    }
   ],
   "source": [
    "train_df['Age'].fillna(train_df['Age'].median(), inplace=True)\n",
    "train_df['Embarked'].fillna(train_df['Embarked'].mode()[0], inplace=True)\n",
    "test_df['Age'].fillna(test_df['Age'].median(), inplace=True)\n",
    "test_df['Fare'].fillna(test_df['Fare'].median(), inplace=True)"
   ]
  },
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Train</th>\n",
       "      <th>Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>687</td>\n",
       "      <td>327.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Train   Test\n",
       "Age              0    0.0\n",
       "Cabin          687  327.0\n",
       "Embarked         0    0.0\n",
       "Fare             0    0.0\n",
       "Name             0    0.0\n",
       "Parch            0    0.0\n",
       "PassengerId      0    0.0\n",
       "Pclass           0    0.0\n",
       "Sex              0    0.0\n",
       "SibSp            0    0.0\n",
       "Survived         0    NaN\n",
       "Ticket           0    0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_train = train_df.isna().sum()\n",
    "missing_values_test = test_df.isna().sum()\n",
    "\n",
    "missing_values = pd.DataFrame({\n",
    "    'Train': missing_values_train,\n",
    "    'Test': missing_values_test\n",
    "})\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:17:29.713764Z",
     "iopub.status.busy": "2024-07-16T15:17:29.713390Z",
     "iopub.status.idle": "2024-07-16T15:17:29.748656Z",
     "shell.execute_reply": "2024-07-16T15:17:29.747640Z",
     "shell.execute_reply.started": "2024-07-16T15:17:29.713703Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>373450</td>\n",
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       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
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       "      <td>B42</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
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       "      <td>0</td>\n",
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       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
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       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>False</td>\n",
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       "      <th>890</th>\n",
       "      <td>891</td>\n",
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       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>32.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name   Age  SibSp  Parch  \\\n",
       "0                              Braund, Mr. Owen Harris  22.0      1      0   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0      1      0   \n",
       "2                               Heikkinen, Miss. Laina  26.0      0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0      1      0   \n",
       "4                             Allen, Mr. William Henry  35.0      0      0   \n",
       "..                                                 ...   ...    ...    ...   \n",
       "886                              Montvila, Rev. Juozas  27.0      0      0   \n",
       "887                       Graham, Miss. Margaret Edith  19.0      0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  28.0      1      2   \n",
       "889                              Behr, Mr. Karl Howell  26.0      0      0   \n",
       "890                                Dooley, Mr. Patrick  32.0      0      0   \n",
       "\n",
       "               Ticket     Fare Cabin  Sex_male  Embarked_Q  Embarked_S  \n",
       "0           A/5 21171   7.2500   NaN      True       False        True  \n",
       "1            PC 17599  71.2833   C85     False       False       False  \n",
       "2    STON/O2. 3101282   7.9250   NaN     False       False        True  \n",
       "3              113803  53.1000  C123     False       False        True  \n",
       "4              373450   8.0500   NaN      True       False        True  \n",
       "..                ...      ...   ...       ...         ...         ...  \n",
       "886            211536  13.0000   NaN      True       False        True  \n",
       "887            112053  30.0000   B42     False       False        True  \n",
       "888        W./C. 6607  23.4500   NaN     False       False        True  \n",
       "889            111369  30.0000  C148      True       False       False  \n",
       "890            370376   7.7500   NaN      True        True       False  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.get_dummies(train_df, columns=['Sex', 'Embarked'], drop_first=True)\n",
    "test_df = pd.get_dummies(test_df, columns=['Sex', 'Embarked'], drop_first=True)\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:17:29.750576Z",
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     "shell.execute_reply.started": "2024-07-16T15:17:29.750540Z"
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>Sex_male</th>\n",
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       "      <th>Family_size</th>\n",
       "      <th>isAlone</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "\n",
       "                                                Name   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0      1      0   \n",
       "\n",
       "      Ticket     Fare Cabin  Sex_male  Embarked_Q  Embarked_S  Family_size  \\\n",
       "0  A/5 21171   7.2500   NaN      True       False        True            2   \n",
       "1   PC 17599  71.2833   C85     False       False       False            2   \n",
       "\n",
       "   isAlone  \n",
       "0        0  \n",
       "1        0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"Family_size\"] = train_df[\"SibSp\"] + train_df[\"Parch\"] + 1\n",
    "test_df[\"Family_size\"] = test_df[\"SibSp\"] + test_df[\"Parch\"] + 1\n",
    "train_df[\"isAlone\"] = 1\n",
    "train_df.loc[train_df[\"Family_size\"]>1, \"isAlone\"] = 0\n",
    "test_df[\"isAlone\"] = 1\n",
    "test_df.loc[test_df[\"Family_size\"]>1, \"isAlone\"] = 0\n",
    "train_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:17:29.776584Z",
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     "shell.execute_reply.started": "2024-07-16T15:17:29.776543Z"
    }
   },
   "outputs": [],
   "source": [
    "features = [\"Pclass\", \"Age\", \"Fare\", \"Sex_male\", \"Embarked_Q\", \"Embarked_S\", \"Family_size\", \"isAlone\"]\n",
    "\n",
    "X = train_df[features]\n",
    "y = train_df[\"Survived\"]\n",
    "\n",
    "X_test_result = test_df[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:17:29.789073Z",
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     "shell.execute_reply.started": "2024-07-16T15:17:29.789030Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:17:29.800312Z",
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     "shell.execute_reply.started": "2024-07-16T15:17:29.800275Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(max_iter=1000)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:17:29.852731Z",
     "iopub.status.busy": "2024-07-16T15:17:29.852411Z",
     "iopub.status.idle": "2024-07-16T15:17:29.873734Z",
     "shell.execute_reply": "2024-07-16T15:17:29.872735Z",
     "shell.execute_reply.started": "2024-07-16T15:17:29.852686Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7988826815642458\n",
      "[[90 15]\n",
      " [21 53]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.86      0.83       105\n",
      "           1       0.78      0.72      0.75        74\n",
      "\n",
      "    accuracy                           0.80       179\n",
      "   macro avg       0.80      0.79      0.79       179\n",
      "weighted avg       0.80      0.80      0.80       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_pred)}\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:31:51.196425Z",
     "iopub.status.busy": "2024-07-16T15:31:51.196018Z",
     "iopub.status.idle": "2024-07-16T15:31:51.205191Z",
     "shell.execute_reply": "2024-07-16T15:31:51.203873Z",
     "shell.execute_reply.started": "2024-07-16T15:31:51.196393Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7932960893854749"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:17:29.875293Z",
     "iopub.status.busy": "2024-07-16T15:17:29.874982Z",
     "iopub.status.idle": "2024-07-16T15:17:29.886895Z",
     "shell.execute_reply": "2024-07-16T15:17:29.885772Z",
     "shell.execute_reply.started": "2024-07-16T15:17:29.875267Z"
    }
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test_result)\n",
    "\n",
    "submission = pd.DataFrame({\n",
    "    'PassengerId': test_df['PassengerId'],\n",
    "    'Survived': predictions\n",
    "})\n",
    "submission.to_csv('submission.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **MODEL TUNING :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:17:29.888693Z",
     "iopub.status.busy": "2024-07-16T15:17:29.888286Z",
     "iopub.status.idle": "2024-07-16T15:17:29.903323Z",
     "shell.execute_reply": "2024-07-16T15:17:29.902190Z",
     "shell.execute_reply.started": "2024-07-16T15:17:29.888656Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "X_test_result_scaled = scaler.transform(X_test_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:17:29.904884Z",
     "iopub.status.busy": "2024-07-16T15:17:29.904454Z",
     "iopub.status.idle": "2024-07-16T15:17:33.826312Z",
     "shell.execute_reply": "2024-07-16T15:17:33.825306Z",
     "shell.execute_reply.started": "2024-07-16T15:17:29.904847Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1181: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
      "500 fits failed out of a total of 1200.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "100 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py\", line 1162, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py\", line 54, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "100 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py\", line 1162, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py\", line 64, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "100 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py\", line 1291, in fit\n",
      "    fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, prefer=prefer)(\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/joblib/parallel.py\", line 1918, in __call__\n",
      "    return output if self.return_generator else list(output)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\n",
      "    res = func(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py\", line 521, in _logistic_regression_path\n",
      "    alpha = (1.0 / C) * (1 - l1_ratio)\n",
      "TypeError: unsupported operand type(s) for -: 'int' and 'NoneType'\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "100 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py\", line 1162, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py\", line 54, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "100 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py\", line 1162, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py\", line 71, in _check_solver\n",
      "    raise ValueError(\"penalty='none' is not supported for the liblinear solver\")\n",
      "ValueError: penalty='none' is not supported for the liblinear solver\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [0.7878755  0.67430316        nan 0.7822614  0.79488821 0.79488821\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.7878755  0.67430316        nan 0.7822614  0.79488821 0.79488821\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.7878755  0.67430316        nan 0.7822614  0.79488821 0.79488821\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.7878755  0.67430316        nan 0.7822614  0.79488821 0.79488821\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.7878952  0.79211071        nan 0.80192061 0.80191077 0.80191077\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.7878952  0.79211071        nan 0.80192061 0.80191077 0.80191077\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.7878952  0.79211071        nan 0.80192061 0.80191077 0.80191077\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.7878952  0.79211071        nan 0.80192061 0.80191077 0.80191077\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.80473752 0.80332907        nan 0.80332907 0.80332907 0.80332907\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.80473752 0.80332907        nan 0.80332907 0.80332907 0.80332907\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.80473752 0.80332907        nan 0.80332907 0.80332907 0.80332907\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.80473752 0.80332907        nan 0.80332907 0.80332907 0.80332907\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.80192061 0.80192061        nan 0.80192061 0.80192061 0.80192061\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.80192061 0.80192061        nan 0.80192061 0.80192061 0.80192061\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.80192061 0.80192061        nan 0.80192061 0.80192061 0.80192061\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.80192061 0.80192061        nan 0.80192061 0.80192061 0.80192061\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.80192061 0.80192061        nan 0.80192061 0.80192061 0.80192061\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.80192061 0.80192061        nan 0.80192061 0.80192061 0.80192061\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.80192061 0.80192061        nan 0.80192061 0.80192061 0.80192061\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061\n",
      " 0.80192061 0.80192061        nan 0.80192061 0.80192061 0.80192061\n",
      "        nan        nan        nan        nan 0.80192061 0.80192061]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1, 10, 100],\n",
       "                         &#x27;max_iter&#x27;: [100, 200, 300, 1000],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, &#x27;none&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;, &#x27;saga&#x27;, &#x27;lbfgs&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1, 10, 100],\n",
       "                         &#x27;max_iter&#x27;: [100, 200, 300, 1000],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, &#x27;none&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;, &#x27;saga&#x27;, &#x27;lbfgs&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [0.01, 0.1, 1, 10, 100],\n",
       "                         'max_iter': [100, 200, 300, 1000],\n",
       "                         'penalty': ['l1', 'l2', 'elasticnet', 'none'],\n",
       "                         'solver': ['liblinear', 'saga', 'lbfgs']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the model\n",
    "m = LogisticRegression()\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'penalty': ['l1', 'l2', 'elasticnet', 'none'],\n",
    "    'C': [0.01, 0.1, 1, 10, 100],\n",
    "    'solver': ['liblinear', 'saga', 'lbfgs'],\n",
    "    'max_iter': [100, 200, 300, 1000]\n",
    "}\n",
    "\n",
    "# Perform grid search with cross-validation\n",
    "grid_search = GridSearchCV(estimator=m, param_grid=param_grid, cv=5, scoring='accuracy')\n",
    "grid_search.fit(X_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:17:33.827898Z",
     "iopub.status.busy": "2024-07-16T15:17:33.827542Z",
     "iopub.status.idle": "2024-07-16T15:17:33.834161Z",
     "shell.execute_reply": "2024-07-16T15:17:33.832946Z",
     "shell.execute_reply.started": "2024-07-16T15:17:33.827863Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found:  {'C': 1, 'max_iter': 100, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "Best cross-validation accuracy:  0.8047375160051218\n"
     ]
    }
   ],
   "source": [
    "# Print the best parameters and best score\n",
    "print(\"Best parameters found: \", grid_search.best_params_)\n",
    "print(\"Best cross-validation accuracy: \", grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:17:33.836113Z",
     "iopub.status.busy": "2024-07-16T15:17:33.835686Z",
     "iopub.status.idle": "2024-07-16T15:17:33.856084Z",
     "shell.execute_reply": "2024-07-16T15:17:33.854955Z",
     "shell.execute_reply.started": "2024-07-16T15:17:33.836079Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy:  0.7932960893854749\n",
      "[[89 16]\n",
      " [21 53]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.85      0.83       105\n",
      "           1       0.77      0.72      0.74        74\n",
      "\n",
      "    accuracy                           0.79       179\n",
      "   macro avg       0.79      0.78      0.78       179\n",
      "weighted avg       0.79      0.79      0.79       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best model on the validation set\n",
    "best_model = grid_search.best_estimator_\n",
    "y_pred = best_model.predict(X_test_scaled)\n",
    "print(\"Validation Accuracy: \", accuracy_score(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:17:33.857737Z",
     "iopub.status.busy": "2024-07-16T15:17:33.857354Z",
     "iopub.status.idle": "2024-07-16T15:17:33.865355Z",
     "shell.execute_reply": "2024-07-16T15:17:33.864235Z",
     "shell.execute_reply.started": "2024-07-16T15:17:33.857689Z"
    }
   },
   "outputs": [],
   "source": [
    "predictions = best_model.predict(X_test_result_scaled)\n",
    "\n",
    "# Create a submission file\n",
    "submission = pd.DataFrame({\n",
    "    'PassengerId': test_df['PassengerId'],\n",
    "    'Survived': predictions\n",
    "})\n",
    "submission.to_csv('submission_.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:33:09.818672Z",
     "iopub.status.busy": "2024-07-16T15:33:09.818280Z",
     "iopub.status.idle": "2024-07-16T15:33:10.071043Z",
     "shell.execute_reply": "2024-07-16T15:33:10.069785Z",
     "shell.execute_reply.started": "2024-07-16T15:33:09.818638Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/base.py:432: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/base.py:432: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "62.36"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_scaled, y_train)\n",
    "Y_pred = random_forest.predict(X_test_scaled)\n",
    "random_forest.score(X_train, y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-16T15:34:08.052297Z",
     "iopub.status.busy": "2024-07-16T15:34:08.051887Z",
     "iopub.status.idle": "2024-07-16T15:34:08.061433Z",
     "shell.execute_reply": "2024-07-16T15:34:08.060112Z",
     "shell.execute_reply.started": "2024-07-16T15:34:08.052268Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7932960893854749"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "databundleVersionId": 26502,
     "sourceId": 3136,
     "sourceType": "competition"
    }
   ],
   "dockerImageVersionId": 30732,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
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  "kernelspec": {
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