Sklearn_Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c6d6a368-c0df-4516-a826-741fb45f4784",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic data handling and numerical processing\n",
    "import pandas as pd                 # For data manipulation and analysis\n",
    "import time                         # For timing code execution\n",
    "\n",
    "# Scikit-learn - Data preprocessing and feature selection\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler  # For feature scaling\n",
    "from sklearn.feature_selection import SelectKBest, f_classif    # For feature selection\n",
    "\n",
    "# Scikit-learn - Model selection and evaluation\n",
    "from sklearn.model_selection import train_test_split            # For splitting data into training and testing sets\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score    # For model evaluation\n",
    "\n",
    "# Scikit-learn - Machine learning algorithms\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier  # Ensemble methods\n",
    "from sklearn.svm import SVC                                          # Support Vector Machine classifier\n",
    "\n",
    "# Scikit-learn - Pipeline utilities\n",
    "from sklearn.pipeline import Pipeline          # For creating machine learning pipelines\n",
    "from sklearn.compose import ColumnTransformer  # For applying transformers to columns of Pandas dataframes\n",
    "\n",
    "# Scikit-learn - Dataset generation\n",
    "from sklearn.datasets import make_classification  # For generating a random n-class classification problem\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2ef8068a-775c-4085-89c8-4c400249c208",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a fake an complex dataset\n",
    "X, y = make_classification(n_samples=100000,# Large number of samples\n",
    "                           n_classes=5,\n",
    "                           n_features=100,    # Large number of features\n",
    "                           n_informative=50,  # Number of informative features\n",
    "                           n_redundant=10,    # Number of redundant features\n",
    "                           n_clusters_per_class=3,  # Complexity in data\n",
    "                           random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6eedee5e-99d6-4cb4-9057-06d06af4a04c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
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       "      <td>-6.555128</td>\n",
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       "      <td>1.179597</td>\n",
       "      <td>...</td>\n",
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       "      <td>-3.118910</td>\n",
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       "      <td>0.298360</td>\n",
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       "      <td>...</td>\n",
       "      <td>-0.632791</td>\n",
       "      <td>5.784838</td>\n",
       "      <td>-20.767853</td>\n",
       "      <td>-4.961945</td>\n",
       "      <td>-0.168336</td>\n",
       "      <td>2.492596</td>\n",
       "      <td>1.263648</td>\n",
       "      <td>5.675441</td>\n",
       "      <td>-1.008551</td>\n",
       "      <td>0.616330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>100000 rows × 100 columns</p>\n",
       "</div>"
      ],
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       "             0         1         2         3         4         5         6   \\\n",
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       "1     -1.487609  1.086423 -7.084398  1.701420 -3.667561  1.396777 -1.884257   \n",
       "2      1.748358  2.259034  2.756386 -0.550596  6.249146  2.389290 -3.277544   \n",
       "3      0.619607  0.492218 -5.005010 -3.630852  0.573500 -2.928691 -2.063202   \n",
       "4      0.290803 -3.608448  5.608295 -0.046357  4.531516 -0.187625  1.207627   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "99995 -0.010659  1.777973 -0.312340  1.357472  4.885210 -0.498488  1.305339   \n",
       "99996 -1.378788  3.989405 -2.016169  0.713512  2.141328  0.051475  1.988437   \n",
       "99997 -0.013823  0.224935  4.193758  0.330235  8.345118  0.370281  2.696467   \n",
       "99998 -0.155785  2.437995  7.001220  0.670684  2.294471 -1.061331 -1.080551   \n",
       "99999  0.189790 -1.898268 -1.282874 -0.274220  0.099891 -0.616771  1.118017   \n",
       "\n",
       "             7         8         9   ...        90        91         92  \\\n",
       "0     -3.347722 -0.671021 -0.165953  ...  2.274136 -7.732157  19.526272   \n",
       "1      6.509376 -0.008781  1.748435  ... -1.294777  2.874897 -20.886530   \n",
       "2      1.760280  0.169276  0.365308  ... -0.243318  7.236897   0.866962   \n",
       "3      1.430140 -0.013358  0.353652  ...  0.730538 -4.858785  15.227895   \n",
       "4      0.766376 -0.864373 -1.172826  ... -0.709476 -0.595337   7.506311   \n",
       "...         ...       ...       ...  ...       ...       ...        ...   \n",
       "99995 -2.630185  0.181239  0.708548  ...  0.418166  1.670129   0.053675   \n",
       "99996  2.865841  0.578019 -0.334300  ...  0.095726 -2.281722 -24.816425   \n",
       "99997  6.244126 -0.283730  0.084403  ...  0.184015  2.677937 -10.257050   \n",
       "99998  8.998209  0.645305  1.179597  ...  2.487604  1.204669 -34.725457   \n",
       "99999  0.298360  1.090821 -0.034147  ... -0.632791  5.784838 -20.767853   \n",
       "\n",
       "             93        94        95        96         97        98        99  \n",
       "0     -3.670708  0.689832  8.363257  2.605347  46.894223 -5.705433  0.007832  \n",
       "1     -2.582731  2.119685 -4.562733  2.455793 -20.112751  1.963437  0.727768  \n",
       "2     -3.191125  1.413882 -1.946430  1.856227   0.799218  3.892423  1.440825  \n",
       "3      7.877092  0.515281  4.949889  2.680786  14.099886  7.892850  0.220872  \n",
       "4      5.039526  0.462610 -4.940043 -2.834191  26.225681  0.928784  1.398901  \n",
       "...         ...       ...       ...       ...        ...       ...       ...  \n",
       "99995  2.274703 -0.910542 -6.214790 -2.025914  -6.356494  3.496883 -0.631609  \n",
       "99996 -4.251613  1.120953 -2.698653 -2.879288 -14.415476 -0.074965 -0.597848  \n",
       "99997 -3.806622 -0.555677 -3.534904 -5.756449  -3.938316 -6.555128  1.286027  \n",
       "99998 -3.118910 -0.503067 -5.817525  2.007334  17.565355  0.602449  0.069982  \n",
       "99999 -4.961945 -0.168336  2.492596  1.263648   5.675441 -1.008551  0.616330  \n",
       "\n",
       "[100000 rows x 100 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c71eaa0c-7d8d-4210-ae52-b8a39b2bb1a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split your Dataset\n",
    "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": 39,
   "id": "6b640b60-7656-4059-8535-d74814ab87ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80000, 100)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ca6439d9-ccc0-4125-826b-814d25bb369c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 0, ..., 4, 2, 3])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebed990a-54b4-4f18-970d-ef8adf3f1928",
   "metadata": {},
   "source": [
    "# Chat GPT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9af7ab9f-5427-416e-8ba5-438337c8840f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a pipeline with a fast solver and limited iterations\n",
    "logistic_pipeline = Pipeline([\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('logistic', LogisticRegression(solver='saga', max_iter=100, n_jobs=-1))\n",
    "])\n",
    "\n",
    "# Fit the pipeline on your training data\n",
    "logistic_pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "predictions = logistic_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de37e6d5-3d22-4d15-b314-9a02b5ff77df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# yen Usage Limits"
   ]
  },
  {
   "attachments": {
    "51a2d1ed-0f2d-46c6-879c-4112847aa13f.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "c54742e1-8266-4bc3-8ca1-dfda1f87c659",
   "metadata": {},
   "source": [
    "![image.png](attachment:51a2d1ed-0f2d-46c6-879c-4112847aa13f.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a33d77-cf6a-4d42-b3c2-a0ffca2856a9",
   "metadata": {},
   "source": [
    "# Why Cores Matter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b70c2e5a-9408-4483-90eb-65192b24a3df",
   "metadata": {},
   "source": [
    "## 10 Cores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a716fd57-365c-4a4c-8ea4-ab2e2f65908c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Time: 12.71 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;classifier&#x27;,\n",
       "                 RandomForestClassifier(n_jobs=10, random_state=42))])</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-63\" type=\"checkbox\" ><label for=\"sk-estimator-id-63\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;classifier&#x27;,\n",
       "                 RandomForestClassifier(n_jobs=10, random_state=42))])</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-64\" type=\"checkbox\" ><label for=\"sk-estimator-id-64\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=10, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('classifier',\n",
       "                 RandomForestClassifier(n_jobs=10, random_state=42))])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pipeline\n",
    "time_start=time.time()\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('scaler', StandardScaler()),  # Feature scaling\n",
    "    ('classifier', RandomForestClassifier(random_state=42,n_jobs=10))  # Classifier\n",
    "])\n",
    "# Train the pipeline on the training data\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "time_end=time.time()\n",
    "total_time=(time_end-time_start)\n",
    "print(f'Total Time: {round(total_time,2)} seconds',)\n",
    "pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ceea52-2e02-41e7-9470-89bbedefe3ec",
   "metadata": {},
   "source": [
    "## 40 Cores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5d1babe7-13b3-4130-8b0b-a47c878fa837",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Time: 4.08 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 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-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 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-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 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-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 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-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;classifier&#x27;,\n",
       "                 RandomForestClassifier(n_jobs=40, random_state=42))])</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-66\" type=\"checkbox\" ><label for=\"sk-estimator-id-66\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;classifier&#x27;,\n",
       "                 RandomForestClassifier(n_jobs=40, random_state=42))])</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-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=40, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('classifier',\n",
       "                 RandomForestClassifier(n_jobs=40, random_state=42))])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pipeline\n",
    "time_start=time.time()\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('scaler', StandardScaler()),  # Feature scaling\n",
    "    ('classifier', RandomForestClassifier(random_state=42,n_jobs=40))  # Classifier\n",
    "])\n",
    "# Train the pipeline on the training data\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "time_end=time.time()\n",
    "total_time=(time_end-time_start)\n",
    "print(f'Total Time: {round(total_time,2)} seconds')\n",
    "pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c27d0809-71ba-4353-82b5-34630b360490",
   "metadata": {},
   "outputs": [],
   "source": [
    "# More Advanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "053b9ce0-e75e-4bcb-834c-b8f52f9e1293",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Time: 19.6 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {color: black;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 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-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 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-17 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-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 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-17 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-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 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-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 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-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 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-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                 ColumnTransformer(n_jobs=48,\n",
       "                                   transformers=[(&#x27;standard_scaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                   10, 11, 12, 13, 14, 15, 16,\n",
       "                                                   17, 18, 19, 20, 21, 22, 23,\n",
       "                                                   24, 25, 26, 27, 28, 29, ...]),\n",
       "                                                 (&#x27;min_max_scaler&#x27;,\n",
       "                                                  MinMaxScaler(),\n",
       "                                                  [51, 52, 53, 54, 55, 56, 57,\n",
       "                                                   58, 59, 60, 61, 62, 63, 64,\n",
       "                                                   65, 66, 67, 68, 69, 70, 71,\n",
       "                                                   72, 73, 74, 75, 76, 77, 78,\n",
       "                                                   79, 80, ...])])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=50)),\n",
       "                (&#x27;logistic&#x27;,\n",
       "                 LogisticRegression(max_iter=500, n_jobs=48, solver=&#x27;sag&#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-78\" type=\"checkbox\" ><label for=\"sk-estimator-id-78\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                 ColumnTransformer(n_jobs=48,\n",
       "                                   transformers=[(&#x27;standard_scaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                   10, 11, 12, 13, 14, 15, 16,\n",
       "                                                   17, 18, 19, 20, 21, 22, 23,\n",
       "                                                   24, 25, 26, 27, 28, 29, ...]),\n",
       "                                                 (&#x27;min_max_scaler&#x27;,\n",
       "                                                  MinMaxScaler(),\n",
       "                                                  [51, 52, 53, 54, 55, 56, 57,\n",
       "                                                   58, 59, 60, 61, 62, 63, 64,\n",
       "                                                   65, 66, 67, 68, 69, 70, 71,\n",
       "                                                   72, 73, 74, 75, 76, 77, 78,\n",
       "                                                   79, 80, ...])])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=50)),\n",
       "                (&#x27;logistic&#x27;,\n",
       "                 LogisticRegression(max_iter=500, n_jobs=48, solver=&#x27;sag&#x27;))])</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-79\" type=\"checkbox\" ><label for=\"sk-estimator-id-79\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=48,\n",
       "                  transformers=[(&#x27;standard_scaler&#x27;, StandardScaler(),\n",
       "                                 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
       "                                  14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,\n",
       "                                  25, 26, 27, 28, 29, ...]),\n",
       "                                (&#x27;min_max_scaler&#x27;, MinMaxScaler(),\n",
       "                                 [51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,\n",
       "                                  62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,\n",
       "                                  73, 74, 75, 76, 77, 78, 79, 80, ...])])</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-80\" type=\"checkbox\" ><label for=\"sk-estimator-id-80\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">standard_scaler</label><div class=\"sk-toggleable__content\"><pre>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]</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-81\" type=\"checkbox\" ><label for=\"sk-estimator-id-81\" 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 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-82\" type=\"checkbox\" ><label for=\"sk-estimator-id-82\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">min_max_scaler</label><div class=\"sk-toggleable__content\"><pre>[51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]</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-83\" type=\"checkbox\" ><label for=\"sk-estimator-id-83\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</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-84\" type=\"checkbox\" ><label for=\"sk-estimator-id-84\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=50)</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-85\" type=\"checkbox\" ><label for=\"sk-estimator-id-85\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=500, n_jobs=48, solver=&#x27;sag&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('transformer',\n",
       "                 ColumnTransformer(n_jobs=48,\n",
       "                                   transformers=[('standard_scaler',\n",
       "                                                  StandardScaler(),\n",
       "                                                  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                   10, 11, 12, 13, 14, 15, 16,\n",
       "                                                   17, 18, 19, 20, 21, 22, 23,\n",
       "                                                   24, 25, 26, 27, 28, 29, ...]),\n",
       "                                                 ('min_max_scaler',\n",
       "                                                  MinMaxScaler(),\n",
       "                                                  [51, 52, 53, 54, 55, 56, 57,\n",
       "                                                   58, 59, 60, 61, 62, 63, 64,\n",
       "                                                   65, 66, 67, 68, 69, 70, 71,\n",
       "                                                   72, 73, 74, 75, 76, 77, 78,\n",
       "                                                   79, 80, ...])])),\n",
       "                ('feature_selection', SelectKBest(k=50)),\n",
       "                ('logistic',\n",
       "                 LogisticRegression(max_iter=500, n_jobs=48, solver='sag'))])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_start=time.time()\n",
    "column_transformer = ColumnTransformer(n_jobs=48,\n",
    "    transformers=[\n",
    "        ('standard_scaler', StandardScaler(), list(range(0, 50))),  # Apply StandardScaler to the first two features\n",
    "        ('min_max_scaler', MinMaxScaler(), list(range(51, 100)))     # Apply MinMaxScaler to the last two features\n",
    "    ])\n",
    "\n",
    "feature_selection = SelectKBest(score_func=f_classif, k=50)\n",
    "pipeline_complex = Pipeline([\n",
    "    ('transformer', column_transformer),\n",
    "    ('feature_selection', feature_selection),\n",
    "    #('classifier', RandomForestClassifier(random_state=42,n_jobs=48)),\n",
    "    ('logistic', LogisticRegression(solver='sag',max_iter=500, n_jobs=48))\n",
    "])\n",
    "\n",
    "pipeline_complex.fit(X_train,y_train)\n",
    "\n",
    "time_end=time.time()\n",
    "total_time=(time_end-time_start)\n",
    "print(f'Total Time: {round(total_time,2)} seconds')\n",
    "pipeline_complex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9a0a4317-5a05-411c-8b18-0dd789be0b33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-18 {color: black;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 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-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 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-18 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-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 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-18 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-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 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-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 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-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 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-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=500, n_jobs=48, solver=&#x27;sag&#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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-86\" type=\"checkbox\" checked><label for=\"sk-estimator-id-86\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=500, n_jobs=48, solver=&#x27;sag&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=500, n_jobs=48, solver='sag')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_complex['logistic']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8186762d-c37a-47e0-a08d-b554e288bc09",
   "metadata": {},
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9d6a16fe-e0f2-45de-a21a-b4b95c749020",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_simple = pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f3d9a103-6dfb-49da-8450-2ac583e92449",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_complex= pipeline_complex.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5097dd3-ca02-4c46-adbe-5ae3ec7193b8",
   "metadata": {},
   "source": [
    "# Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bc1f3035-6e25-452a-864a-b6a0050d5526",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.74825"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predictions_simple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c5146390-7536-483f-bdca-98692be81601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47095"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predictions_complex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a1e4275-f153-417b-b052-f14f02d10bb3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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