Building Pipeline  with feature retrieval after Transformation & using  Logistic Regression for Feature Importance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "43b31536",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "672407f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c340e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "71477cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['embark_town'] = df['embark_town'].fillna(df['embark_town'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9227131a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['embarked'] = df['embarked'].fillna(df['embarked'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dd7df5c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age            177\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         0\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      0\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "355af736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived          int64\n",
       "pclass            int64\n",
       "sex              object\n",
       "age             float64\n",
       "sibsp             int64\n",
       "parch             int64\n",
       "fare            float64\n",
       "embarked         object\n",
       "class          category\n",
       "who              object\n",
       "adult_male         bool\n",
       "deck           category\n",
       "embark_town      object\n",
       "alive            object\n",
       "alone              bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "194920c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['sex','class','embark_town','alone','age','fare']]\n",
    "y = df['survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71b605ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features = ['age','fare']\n",
    "col_features = ['sex','class','embark_town','alone']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90f3ac15",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "add92cdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline(steps = [\n",
    "    ('impute',SimpleImputer(strategy = 'median')),\n",
    "    ('scaler',StandardScaler())    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a672a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pipeline = Pipeline(steps = [\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore',sparse=False))    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e53cbd74",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor= ColumnTransformer(transformers =[ \n",
    "        (\"numeric\",num_pipeline,numeric_features),\n",
    "        (\"categorical\",cat_pipeline,col_features)\n",
    "],\n",
    "    remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e82a49f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "213bcdee",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d38c9eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_pipeline = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('model', clf)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0825c24f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0f3d12a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "154bac3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sex', 'class', 'embark_town', 'alone', 'age', 'fare'], dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "19785503",
   "metadata": {},
   "outputs": [
    {
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       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;numeric&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;impute&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;age&#x27;, &#x27;fare&#x27;]),\n",
       "                                                 (&#x27;categorical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse=False))]),\n",
       "                                                  [&#x27;sex&#x27;, &#x27;class&#x27;,\n",
       "                                                   &#x27;embark_town&#x27;, &#x27;alone&#x27;])])),\n",
       "                (&#x27;model&#x27;, LogisticRegression(random_state=0))])</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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;numeric&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;impute&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;age&#x27;, &#x27;fare&#x27;]),\n",
       "                                                 (&#x27;categorical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse=False))]),\n",
       "                                                  [&#x27;sex&#x27;, &#x27;class&#x27;,\n",
       "                                                   &#x27;embark_town&#x27;, &#x27;alone&#x27;])])),\n",
       "                (&#x27;model&#x27;, LogisticRegression(random_state=0))])</pre></div></div></div><div class=\"sk-serial\"><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\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;numeric&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;impute&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;age&#x27;, &#x27;fare&#x27;]),\n",
       "                                (&#x27;categorical&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse=False))]),\n",
       "                                 [&#x27;sex&#x27;, &#x27;class&#x27;, &#x27;embark_town&#x27;, &#x27;alone&#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\">numeric</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;fare&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">categorical</label><div class=\"sk-toggleable__content\"><pre>[&#x27;sex&#x27;, &#x27;class&#x27;, &#x27;embark_town&#x27;, &#x27;alone&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div></div></div></div></div></div><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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=0)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('numeric',\n",
       "                                                  Pipeline(steps=[('impute',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['age', 'fare']),\n",
       "                                                 ('categorical',\n",
       "                                                  Pipeline(steps=[('onehot',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
       "                                                                                 sparse=False))]),\n",
       "                                                  ['sex', 'class',\n",
       "                                                   'embark_town', 'alone'])])),\n",
       "                ('model', LogisticRegression(random_state=0))])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "58d239d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model score: 0.799\n"
     ]
    }
   ],
   "source": [
    "print(\"model score: %.3f\" % clf_pipeline.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c31b1cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_cols = clf_pipeline.named_steps['preprocessor'].named_transformers_['numeric'].named_steps['scaler'].get_feature_names_out(numeric_features)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "92641fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot_cols = clf_pipeline.named_steps['preprocessor'].named_transformers_['categorical'].named_steps['onehot'].get_feature_names_out(col_features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0acad9f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['age', 'fare'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9f647476",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = numeric_cols.tolist()+onehot_cols.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1e0906a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'fare',\n",
       " 'sex_female',\n",
       " 'sex_male',\n",
       " 'class_First',\n",
       " 'class_Second',\n",
       " 'class_Third',\n",
       " 'embark_town_Cherbourg',\n",
       " 'embark_town_Queenstown',\n",
       " 'embark_town_Southampton',\n",
       " 'alone_False',\n",
       " 'alone_True']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "52da759b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>class_First</th>\n",
       "      <th>class_Second</th>\n",
       "      <th>class_Third</th>\n",
       "      <th>embark_town_Cherbourg</th>\n",
       "      <th>embark_town_Queenstown</th>\n",
       "      <th>embark_town_Southampton</th>\n",
       "      <th>alone_False</th>\n",
       "      <th>alone_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>0.718637</td>\n",
       "      <td>1.023857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>-0.809990</td>\n",
       "      <td>-0.479216</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>-0.045676</td>\n",
       "      <td>-0.481629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>0.489343</td>\n",
       "      <td>-0.288681</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>2.323696</td>\n",
       "      <td>0.142456</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>712 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age      fare  sex_female  sex_male  class_First  class_Second  \\\n",
       "0   -0.045676 -0.331679         1.0       0.0          0.0           0.0   \n",
       "1    0.107186 -0.426405         0.0       1.0          0.0           1.0   \n",
       "2    0.107186  0.102620         0.0       1.0          0.0           1.0   \n",
       "3   -0.733559 -0.555896         0.0       1.0          0.0           0.0   \n",
       "4   -0.657127 -0.491276         0.0       1.0          0.0           0.0   \n",
       "..        ...       ...         ...       ...          ...           ...   \n",
       "707  0.718637  1.023857         1.0       0.0          1.0           0.0   \n",
       "708 -0.809990 -0.479216         1.0       0.0          0.0           0.0   \n",
       "709 -0.045676 -0.481629         0.0       1.0          0.0           0.0   \n",
       "710  0.489343 -0.288681         1.0       0.0          0.0           0.0   \n",
       "711  2.323696  0.142456         0.0       1.0          0.0           1.0   \n",
       "\n",
       "     class_Third  embark_town_Cherbourg  embark_town_Queenstown  \\\n",
       "0            1.0                    1.0                     0.0   \n",
       "1            0.0                    0.0                     0.0   \n",
       "2            0.0                    1.0                     0.0   \n",
       "3            1.0                    1.0                     0.0   \n",
       "4            1.0                    0.0                     0.0   \n",
       "..           ...                    ...                     ...   \n",
       "707          0.0                    1.0                     0.0   \n",
       "708          1.0                    0.0                     0.0   \n",
       "709          1.0                    0.0                     1.0   \n",
       "710          1.0                    0.0                     0.0   \n",
       "711          0.0                    0.0                     0.0   \n",
       "\n",
       "     embark_town_Southampton  alone_False  alone_True  \n",
       "0                        0.0          1.0         0.0  \n",
       "1                        1.0          0.0         1.0  \n",
       "2                        0.0          1.0         0.0  \n",
       "3                        0.0          0.0         1.0  \n",
       "4                        1.0          0.0         1.0  \n",
       "..                       ...          ...         ...  \n",
       "707                      0.0          1.0         0.0  \n",
       "708                      1.0          1.0         0.0  \n",
       "709                      0.0          0.0         1.0  \n",
       "710                      1.0          1.0         0.0  \n",
       "711                      1.0          1.0         0.0  \n",
       "\n",
       "[712 rows x 12 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pp = preprocessor.fit_transform(X_train)\n",
    "pd.DataFrame(X_train_pp, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0cb5b7fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted_survival</th>\n",
       "      <th>actual_survival</th>\n",
       "      <th>sex</th>\n",
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       "      <th>495</th>\n",
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       "      <td>Cherbourg</td>\n",
       "      <td>True</td>\n",
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       "      <td>14.4583</td>\n",
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       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>0</td>\n",
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       "      <td>Southampton</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Third</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>False</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>First</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146.5208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>Third</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>False</td>\n",
       "      <td>29.0</td>\n",
       "      <td>15.2458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>Third</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>True</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.2292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>Third</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>First</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>False</td>\n",
       "      <td>31.0</td>\n",
       "      <td>113.2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>Third</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.8542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>Third</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predicted_survival  actual_survival     sex  class  embark_town  alone  \\\n",
       "495                   0                0    male  Third    Cherbourg   True   \n",
       "648                   0                0    male  Third  Southampton   True   \n",
       "278                   0                0    male  Third   Queenstown  False   \n",
       "31                    1                1  female  First    Cherbourg  False   \n",
       "255                   1                1  female  Third    Cherbourg  False   \n",
       "..                  ...              ...     ...    ...          ...    ...   \n",
       "780                   1                1  female  Third    Cherbourg   True   \n",
       "837                   0                0    male  Third  Southampton   True   \n",
       "215                   1                1  female  First    Cherbourg  False   \n",
       "833                   0                0    male  Third  Southampton   True   \n",
       "372                   0                0    male  Third  Southampton   True   \n",
       "\n",
       "      age      fare  \n",
       "495   NaN   14.4583  \n",
       "648   NaN    7.5500  \n",
       "278   7.0   29.1250  \n",
       "31    NaN  146.5208  \n",
       "255  29.0   15.2458  \n",
       "..    ...       ...  \n",
       "780  13.0    7.2292  \n",
       "837   NaN    8.0500  \n",
       "215  31.0  113.2750  \n",
       "833  23.0    7.8542  \n",
       "372  19.0    8.0500  \n",
       "\n",
       "[179 rows x 8 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df = pd.DataFrame({\n",
    "    \"Predicted_survival\": clf_pipeline.predict(X_test),\n",
    "    \"actual_survival\": y_test\n",
    "})\n",
    "pred_df.join(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4ad6ecbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7988826815642458"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_pipeline.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "16366dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "coefs = clf_pipeline.named_steps[\"model\"].coef_.flatten()\n",
    "coef = pd.DataFrame(zip(columns, coefs), columns=[\"feature\", \"coef\"])\n",
    "coef[\"abs_coef\"] = coef[\"coef\"].apply(lambda x: abs(x))\n",
    "coef[\"colors\"] = coef[\"coef\"].apply(lambda x: \"green\" if x > 0 else \"red\")\n",
    "coef = coef.sort_values(\"abs_coef\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c8a0ae55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Feature Name')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot coef\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(12, 7))\n",
    "sns.barplot(x=\"feature\",\n",
    "            y=\"coef\",\n",
    "            data=coef.head(20),\n",
    "           palette=coef.head(20)[\"colors\"])\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90, fontsize=20)\n",
    "ax.set_title(\"Top 20 Features\", fontsize=25)\n",
    "ax.set_ylabel(\"Coef\", fontsize=22)\n",
    "ax.set_xlabel(\"Feature Name\", fontsize=22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4f125cec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'memory': None,\n",
       " 'steps': [('preprocessor',\n",
       "   ColumnTransformer(remainder='passthrough',\n",
       "                     transformers=[('numeric',\n",
       "                                    Pipeline(steps=[('impute',\n",
       "                                                     SimpleImputer(strategy='median')),\n",
       "                                                    ('scaler', StandardScaler())]),\n",
       "                                    ['age', 'fare']),\n",
       "                                   ('categorical',\n",
       "                                    Pipeline(steps=[('onehot',\n",
       "                                                     OneHotEncoder(handle_unknown='ignore',\n",
       "                                                                   sparse=False))]),\n",
       "                                    ['sex', 'class', 'embark_town', 'alone'])])),\n",
       "  ('model', LogisticRegression(random_state=0))],\n",
       " 'verbose': False,\n",
       " 'preprocessor': ColumnTransformer(remainder='passthrough',\n",
       "                   transformers=[('numeric',\n",
       "                                  Pipeline(steps=[('impute',\n",
       "                                                   SimpleImputer(strategy='median')),\n",
       "                                                  ('scaler', StandardScaler())]),\n",
       "                                  ['age', 'fare']),\n",
       "                                 ('categorical',\n",
       "                                  Pipeline(steps=[('onehot',\n",
       "                                                   OneHotEncoder(handle_unknown='ignore',\n",
       "                                                                 sparse=False))]),\n",
       "                                  ['sex', 'class', 'embark_town', 'alone'])]),\n",
       " 'model': LogisticRegression(random_state=0),\n",
       " 'preprocessor__n_jobs': None,\n",
       " 'preprocessor__remainder': 'passthrough',\n",
       " 'preprocessor__sparse_threshold': 0.3,\n",
       " 'preprocessor__transformer_weights': None,\n",
       " 'preprocessor__transformers': [('numeric',\n",
       "   Pipeline(steps=[('impute', SimpleImputer(strategy='median')),\n",
       "                   ('scaler', StandardScaler())]),\n",
       "   ['age', 'fare']),\n",
       "  ('categorical',\n",
       "   Pipeline(steps=[('onehot',\n",
       "                    OneHotEncoder(handle_unknown='ignore', sparse=False))]),\n",
       "   ['sex', 'class', 'embark_town', 'alone'])],\n",
       " 'preprocessor__verbose': False,\n",
       " 'preprocessor__verbose_feature_names_out': True,\n",
       " 'preprocessor__numeric': Pipeline(steps=[('impute', SimpleImputer(strategy='median')),\n",
       "                 ('scaler', StandardScaler())]),\n",
       " 'preprocessor__categorical': Pipeline(steps=[('onehot',\n",
       "                  OneHotEncoder(handle_unknown='ignore', sparse=False))]),\n",
       " 'preprocessor__numeric__memory': None,\n",
       " 'preprocessor__numeric__steps': [('impute', SimpleImputer(strategy='median')),\n",
       "  ('scaler', StandardScaler())],\n",
       " 'preprocessor__numeric__verbose': False,\n",
       " 'preprocessor__numeric__impute': SimpleImputer(strategy='median'),\n",
       " 'preprocessor__numeric__scaler': StandardScaler(),\n",
       " 'preprocessor__numeric__impute__add_indicator': False,\n",
       " 'preprocessor__numeric__impute__copy': True,\n",
       " 'preprocessor__numeric__impute__fill_value': None,\n",
       " 'preprocessor__numeric__impute__missing_values': nan,\n",
       " 'preprocessor__numeric__impute__strategy': 'median',\n",
       " 'preprocessor__numeric__impute__verbose': 'deprecated',\n",
       " 'preprocessor__numeric__scaler__copy': True,\n",
       " 'preprocessor__numeric__scaler__with_mean': True,\n",
       " 'preprocessor__numeric__scaler__with_std': True,\n",
       " 'preprocessor__categorical__memory': None,\n",
       " 'preprocessor__categorical__steps': [('onehot',\n",
       "   OneHotEncoder(handle_unknown='ignore', sparse=False))],\n",
       " 'preprocessor__categorical__verbose': False,\n",
       " 'preprocessor__categorical__onehot': OneHotEncoder(handle_unknown='ignore', sparse=False),\n",
       " 'preprocessor__categorical__onehot__categories': 'auto',\n",
       " 'preprocessor__categorical__onehot__drop': None,\n",
       " 'preprocessor__categorical__onehot__dtype': numpy.float64,\n",
       " 'preprocessor__categorical__onehot__handle_unknown': 'ignore',\n",
       " 'preprocessor__categorical__onehot__max_categories': None,\n",
       " 'preprocessor__categorical__onehot__min_frequency': None,\n",
       " 'preprocessor__categorical__onehot__sparse': False,\n",
       " 'model__C': 1.0,\n",
       " 'model__class_weight': None,\n",
       " 'model__dual': False,\n",
       " 'model__fit_intercept': True,\n",
       " 'model__intercept_scaling': 1,\n",
       " 'model__l1_ratio': None,\n",
       " 'model__max_iter': 100,\n",
       " 'model__multi_class': 'auto',\n",
       " 'model__n_jobs': None,\n",
       " 'model__penalty': 'l2',\n",
       " 'model__random_state': 0,\n",
       " 'model__solver': 'lbfgs',\n",
       " 'model__tol': 0.0001,\n",
       " 'model__verbose': 0,\n",
       " 'model__warm_start': False}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_pipeline.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "791abd83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7851275485078302"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(clf_pipeline , X_train,y_train, cv = 5, scoring = 'accuracy').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c9e80e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6a836a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_params = {'model__penalty' : ['none', 'l2'],\n",
    "               'model__C' : np.logspace(-4, 4, 20)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c362bb00",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score of train set: 0.7977839062346105\n",
      "Best parameter set: {'model__C': 0.03359818286283781, 'model__penalty': 'l2'}\n",
      "Test Score: 0.7821229050279329\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:1113: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "gs = GridSearchCV(clf_pipeline, grid_params, cv=5, scoring='accuracy')\n",
    "gs.fit(X_train, y_train)\n",
    "\n",
    "print(\"Best Score of train set: \"+str(gs.best_score_))\n",
    "print(\"Best parameter set: \"+str(gs.best_params_))\n",
    "print(\"Test Score: \"+str(gs.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "844969c0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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