Speech-Emotion-Recognition.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "54bd99a7-5312-488b-a8be-af2bb95f55a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61db40e6-91b0-4bdd-b245-0d1de800b867",
   "metadata": {},
   "outputs": [],
   "source": [
    "with zipfile.ZipFile(\"speech-emotion-recognition-ravdess-data.zip\", \"r\") as zip_ref:\n",
    "    zip_ref.extractall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49725254-b685-4377-851f-142a957fa3d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install librosa soundfile numpy scikit-learn pyaudio\n",
    "#Import libraries\n",
    "import librosa\n",
    "import soundfile\n",
    "import os, glob, pickle\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a624272-8ed4-4b93-b493-379ea0f22f86",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define extract_feature. Follow the steps below.\n",
    "\"\"\"Define a function extract feature that takes in mfcc(Mel Frequency Ceptral Coefficient), chroma and mel.\n",
    "a) open soundfile with soundfile.SoundFile so that it is automatically closed once we're done.\n",
    "b) Read from it and name it X\n",
    "c) Get sample rate\n",
    "d) If chroma is True, get the Short-Time Fourier Transform of X\n",
    "e) Result is an empty np.array\n",
    "f) For each feature out of the three, make a corresponding feature from librosa.feature.\n",
    "g) Get the mean value\n",
    "h) Call the function hstack() from numpy with result and feature value\n",
    "i) Store in results=np.hstack()\n",
    "j) Return the result\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2fe75d66-2cd0-4a6f-bd6e-f6885fd73584",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract_feature function\n",
    "def extract_feature(file_name, mfcc, chroma, mel):\n",
    "    with soundfile.SoundFile(file_name) as sound_file:\n",
    "        X = sound_file.read(dtype=\"float32\")\n",
    "        sample_rate = sound_file.samplerate\n",
    "        if chroma:\n",
    "            stft = np.abs(librosa.stft(X))\n",
    "            result = np.array([])\n",
    "        if mfcc:\n",
    "            mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)\n",
    "            result = np.hstack((result, mfccs))\n",
    "        if chroma:\n",
    "            chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T, axis=0)\n",
    "            result = np.hstack((result, chroma))\n",
    "        if mel:\n",
    "            mel = np.mean(librosa.feature.melspectrogram(y=X, sr=sample_rate).T, axis=0)\n",
    "            result = np.hstack((result, mel))\n",
    "    return result\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5b0e2ae-0669-41b8-98d5-0ab9b9827e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "emotions = {\n",
    "    \"01\": \"Neutral\",\n",
    "    \"02\": \"Calm\",\n",
    "    \"03\": \"Happy\",\n",
    "    \"04\": \"Sad\",\n",
    "    \"05\": \"Angry\",\n",
    "    \"06\": \"Fearful\",\n",
    "    \"07\": \"Disgust\",\n",
    "    \"08\": \"Surprised\"\n",
    "\n",
    "\n",
    "    \n",
    "}\n",
    "observed_emotions = [\"Calm\", \"Happy\", \"Fearful\", \"Disgust\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4f71bdf1-ea33-4115-97bb-597322635fd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Load data with a load_data function that takes test_size(0.2)\n",
    "use glob to get all the pathnames for the sound file in the dataset\n",
    "X, y are empty lists\n",
    "Using the emotions dict, the number is turned into an emotion and then checked to see whether it is on the observed_emotions list. \n",
    "If not, it moves to the next file\n",
    "and calls to extract_feature stored in the feature.\n",
    "Append feature to X and emotion to y\n",
    "Call the function train_test_split of X and y, test_size, and random_state\"\"\"\n",
    "\n",
    "def load_data(test_size=0.2):\n",
    "    x, y = [], []\n",
    "    for file in glob.glob(\"C:\\\\Users\\\\Admin\\\\Downloads\\\\speech-emotion-recognition-ravdess-data\\\\Actor_*\\\\*.wav*\"):\n",
    "        file_name = os.path.basename(file)\n",
    "        emotion = emotions[file_name.split(\"-\")[2]]\n",
    "        if emotion not in observed_emotions:\n",
    "            continue\n",
    "        feature = extract_feature(file, mfcc=True, chroma=True, mel=True)\n",
    "        x.append(feature)\n",
    "        y.append(emotion)\n",
    "        \n",
    "    return train_test_split(np.array(x), y, test_size=test_size, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ddbed0ea-b311-4ef5-8ff9-fecbd01d58e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "576 192\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = load_data(test_size=0.25)\n",
    "print(x_train.shape[0], x_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "509336c0-a7d8-4ae1-b646-e6b6ce28b63a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features Extracted: 180\n"
     ]
    }
   ],
   "source": [
    "print(f\"Features Extracted: {x_train.shape[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8793a3f6-07e2-4beb-a2cf-38558ee4f97c",
   "metadata": {},
   "outputs": [
    {
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       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),\n",
       "              learning_rate=&#x27;adaptive&#x27;, max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),\n",
       "              learning_rate=&#x27;adaptive&#x27;, max_iter=1000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),\n",
       "              learning_rate='adaptive', max_iter=1000)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = MLPClassifier(alpha=0.01, batch_size=256, epsilon=1e-08, hidden_layer_sizes=(300,), learning_rate=\"adaptive\", max_iter=1000)\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "640b8c23-8c7a-42ad-b32f-0ed39864d7f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Disgust', 'Calm', 'Fearful', 'Disgust', 'Happy', 'Happy', 'Calm',\n",
       "       'Calm', 'Disgust', 'Fearful', 'Disgust', 'Fearful', 'Calm',\n",
       "       'Fearful', 'Disgust', 'Calm', 'Fearful', 'Calm', 'Fearful',\n",
       "       'Fearful', 'Disgust', 'Calm', 'Fearful', 'Fearful', 'Calm',\n",
       "       'Fearful', 'Calm', 'Disgust', 'Fearful', 'Fearful', 'Calm',\n",
       "       'Disgust', 'Fearful', 'Happy', 'Fearful', 'Fearful', 'Calm',\n",
       "       'Calm', 'Calm', 'Happy', 'Disgust', 'Happy', 'Disgust', 'Calm',\n",
       "       'Calm', 'Disgust', 'Calm', 'Disgust', 'Disgust', 'Disgust',\n",
       "       'Fearful', 'Calm', 'Fearful', 'Calm', 'Disgust', 'Fearful',\n",
       "       'Disgust', 'Fearful', 'Fearful', 'Fearful', 'Fearful', 'Disgust',\n",
       "       'Calm', 'Calm', 'Calm', 'Disgust', 'Fearful', 'Happy', 'Fearful',\n",
       "       'Fearful', 'Fearful', 'Fearful', 'Calm', 'Fearful', 'Happy',\n",
       "       'Calm', 'Fearful', 'Fearful', 'Fearful', 'Fearful', 'Calm', 'Calm',\n",
       "       'Happy', 'Fearful', 'Fearful', 'Happy', 'Fearful', 'Fearful',\n",
       "       'Calm', 'Calm', 'Calm', 'Calm', 'Fearful', 'Fearful', 'Fearful',\n",
       "       'Fearful', 'Calm', 'Disgust', 'Disgust', 'Disgust', 'Calm', 'Calm',\n",
       "       'Fearful', 'Fearful', 'Fearful', 'Fearful', 'Disgust', 'Fearful',\n",
       "       'Calm', 'Disgust', 'Fearful', 'Fearful', 'Fearful', 'Fearful',\n",
       "       'Calm', 'Disgust', 'Calm', 'Happy', 'Calm', 'Calm', 'Disgust',\n",
       "       'Calm', 'Fearful', 'Disgust', 'Disgust', 'Disgust', 'Calm', 'Calm',\n",
       "       'Calm', 'Calm', 'Calm', 'Fearful', 'Disgust', 'Calm', 'Disgust',\n",
       "       'Fearful', 'Fearful', 'Fearful', 'Disgust', 'Calm', 'Fearful',\n",
       "       'Fearful', 'Disgust', 'Disgust', 'Disgust', 'Calm', 'Fearful',\n",
       "       'Fearful', 'Fearful', 'Calm', 'Happy', 'Calm', 'Disgust', 'Happy',\n",
       "       'Disgust', 'Calm', 'Calm', 'Calm', 'Calm', 'Happy', 'Happy',\n",
       "       'Fearful', 'Happy', 'Calm', 'Disgust', 'Fearful', 'Fearful',\n",
       "       'Fearful', 'Calm', 'Calm', 'Happy', 'Calm', 'Calm', 'Calm', 'Calm',\n",
       "       'Calm', 'Calm', 'Happy', 'Calm', 'Calm', 'Disgust', 'Calm', 'Calm',\n",
       "       'Disgust', 'Fearful', 'Calm', 'Fearful', 'Calm', 'Disgust',\n",
       "       'Fearful', 'Calm', 'Calm'], dtype='<U7')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da611711-0961-4f83-bb71-01c00dcab504",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 60.94%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_true=y_test, y_pred=y_pred)\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fa2888f4-e0f5-41ab-a1bf-e3acb28cd018",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 72.40%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Calm       0.86      0.78      0.82        54\n",
      "     Disgust       0.74      0.81      0.78        43\n",
      "     Fearful       0.77      0.51      0.62        47\n",
      "       Happy       0.58      0.79      0.67        48\n",
      "\n",
      "    accuracy                           0.72       192\n",
      "   macro avg       0.74      0.72      0.72       192\n",
      "weighted avg       0.74      0.72      0.72       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from imblearn.over_sampling import ADASYN\n",
    "\n",
    "\"\"\"Preprocessing\"\"\"\n",
    "scaler = StandardScaler()\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_test_scaled = scaler.transform(x_test)\n",
    "\n",
    "\n",
    "\"\"\"Model training\"\"\"\n",
    "model = MLPClassifier(alpha=0.01, batch_size=128, hidden_layer_sizes=(300,150), learning_rate=\"adaptive\", max_iter=2000, \n",
    "                      early_stopping=True, random_state=42)\n",
    "model.fit(x_train_scaled, y_train)\n",
    "\n",
    "\"\"\"Evaluation\"\"\"\n",
    "y_pred = model.predict(x_test_scaled)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy*100))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca6d4555-4ad2-4544-b084-6eaf40996395",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"The accuracy score is not as high as I want. Let me try other models. Lets reduce dimensionality first\"\"\"\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=10)\n",
    "xpca_train = pca.fit_transform(x_train_scaled)\n",
    "xpca_test = pca.transform(x_test_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbcaca24-4817-4eed-ac73-0543b1cc7890",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Hyperparameter tuning\"\"\"\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid={\n",
    "    \"hidden_layer_sizes\": [(100,), (100,50), (300,)],\n",
    "    \"alpha\": [0.0001, 0.001, 0.01],\n",
    "    \"learning_rate\": [\"constant\", \"adaptive\"],\n",
    "    \"batch_size\": [64, 128, 256],\n",
    "    \"max_iter\": [2000, 10_000]\n",
    "}\n",
    "grid = GridSearchCV(MLPClassifier(random_state=42), param_grid, cv=3, scoring=\"accuracy\")\n",
    "grid.fit(x_train, y_train)\n",
    "grid.best_params_\n",
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0d7614e2-1e23-4e38-8b58-2d5fc80a65c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 72.40%\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Try different algorithms\"\"\"\n",
    "\"\"\"RandomForest\"\"\"\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "random = RandomForestClassifier(random_state=42)\n",
    "random.fit(x_train_scaled, y_train)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4557f481-db68-4a87-9700-29a1105a0dfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes: ['Calm' 'Disgust' 'Fearful' 'Happy']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "y_train_encoded = label_encoder.fit_transform(y_train)\n",
    "y_test_encoded = label_encoder.fit(y_test)\n",
    "print(\"Classes:\", label_encoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f3a4403b-5be9-499b-a0ed-feb8906bcce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 72.40%\n"
     ]
    }
   ],
   "source": [
    "\"\"\"XGBClassifier\"\"\"\n",
    "from xgboost import XGBClassifier\n",
    "xgb = XGBClassifier(eval_metric=\"mlogloss\", random_state=42)\n",
    "xgb.fit(x_train_scaled, y_train_encoded)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dae42c56-2e9f-4cd1-8365-87b80f0b7bea",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Make predictions and convert predictions back to normal\"\"\"\n",
    "y_pred_encoded = xgb.predict(x_test)\n",
    "y_pred = label_encoder.inverse_transform(y_pred_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "943cc9df-ccc7-45e5-8ea3-ac4c3b33c716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 72.40%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "model_ens = VotingClassifier(estimators=[\n",
    "    (\"mlp\", MLPClassifier(batch_size=128, hidden_layer_sizes=(300,), max_iter=500, random_state=42)),\n",
    "    (\"random\", RandomForestClassifier(n_estimators=100, random_state=42)),\n",
    "    (\"xgb\", XGBClassifier(eval_metric=\"mlogloss\", random_state=42))\n",
    "], voting=\"soft\")\n",
    "model_ens.fit(x_train_scaled, y_train)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b99364cf-1882-4776-932a-69ebbc4927e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "744d8178-520b-4c41-8efd-d38b8bda1f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;mlp&#x27;,\n",
       "                                MLPClassifier(batch_size=128,\n",
       "                                              hidden_layer_sizes=(300,),\n",
       "                                              max_iter=1000, random_state=42)),\n",
       "                               (&#x27;random&#x27;,\n",
       "                                RandomForestClassifier(random_state=42)),\n",
       "                               (&#x27;xgb&#x27;,\n",
       "                                XGBClassifier(base_score=None, booster=None,\n",
       "                                              callbacks=None,\n",
       "                                              colsample_bylevel=None,\n",
       "                                              colsample_bynode=None,\n",
       "                                              colsample_bytree=None,\n",
       "                                              device=None,\n",
       "                                              early_stopping_rounds=None,\n",
       "                                              ena...\n",
       "                                              interaction_constraints=None,\n",
       "                                              learning_rate=None, max_bin=None,\n",
       "                                              max_cat_threshold=None,\n",
       "                                              max_cat_to_onehot=None,\n",
       "                                              max_delta_step=None,\n",
       "                                              max_depth=None, max_leaves=None,\n",
       "                                              min_child_weight=None,\n",
       "                                              missing=nan,\n",
       "                                              monotone_constraints=None,\n",
       "                                              multi_strategy=None,\n",
       "                                              n_estimators=None, n_jobs=None,\n",
       "                                              num_parallel_tree=None,\n",
       "                                              random_state=42, ...))],\n",
       "                   final_estimator=LogisticRegression())</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;StackingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>StackingClassifier(estimators=[(&#x27;mlp&#x27;,\n",
       "                                MLPClassifier(batch_size=128,\n",
       "                                              hidden_layer_sizes=(300,),\n",
       "                                              max_iter=1000, random_state=42)),\n",
       "                               (&#x27;random&#x27;,\n",
       "                                RandomForestClassifier(random_state=42)),\n",
       "                               (&#x27;xgb&#x27;,\n",
       "                                XGBClassifier(base_score=None, booster=None,\n",
       "                                              callbacks=None,\n",
       "                                              colsample_bylevel=None,\n",
       "                                              colsample_bynode=None,\n",
       "                                              colsample_bytree=None,\n",
       "                                              device=None,\n",
       "                                              early_stopping_rounds=None,\n",
       "                                              ena...\n",
       "                                              interaction_constraints=None,\n",
       "                                              learning_rate=None, max_bin=None,\n",
       "                                              max_cat_threshold=None,\n",
       "                                              max_cat_to_onehot=None,\n",
       "                                              max_delta_step=None,\n",
       "                                              max_depth=None, max_leaves=None,\n",
       "                                              min_child_weight=None,\n",
       "                                              missing=nan,\n",
       "                                              monotone_constraints=None,\n",
       "                                              multi_strategy=None,\n",
       "                                              n_estimators=None, n_jobs=None,\n",
       "                                              num_parallel_tree=None,\n",
       "                                              random_state=42, ...))],\n",
       "                   final_estimator=LogisticRegression())</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>mlp</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(batch_size=128, hidden_layer_sizes=(300,), max_iter=1000,\n",
       "              random_state=42)</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 fitted sk-toggleable\"><label>random</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</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 fitted sk-toggleable\"><label>xgb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;mlogloss&#x27;,\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
       "              n_jobs=None, num_parallel_tree=None, random_state=42, ...)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "StackingClassifier(estimators=[('mlp',\n",
       "                                MLPClassifier(batch_size=128,\n",
       "                                              hidden_layer_sizes=(300,),\n",
       "                                              max_iter=1000, random_state=42)),\n",
       "                               ('random',\n",
       "                                RandomForestClassifier(random_state=42)),\n",
       "                               ('xgb',\n",
       "                                XGBClassifier(base_score=None, booster=None,\n",
       "                                              callbacks=None,\n",
       "                                              colsample_bylevel=None,\n",
       "                                              colsample_bynode=None,\n",
       "                                              colsample_bytree=None,\n",
       "                                              device=None,\n",
       "                                              early_stopping_rounds=None,\n",
       "                                              ena...\n",
       "                                              interaction_constraints=None,\n",
       "                                              learning_rate=None, max_bin=None,\n",
       "                                              max_cat_threshold=None,\n",
       "                                              max_cat_to_onehot=None,\n",
       "                                              max_delta_step=None,\n",
       "                                              max_depth=None, max_leaves=None,\n",
       "                                              min_child_weight=None,\n",
       "                                              missing=nan,\n",
       "                                              monotone_constraints=None,\n",
       "                                              multi_strategy=None,\n",
       "                                              n_estimators=None, n_jobs=None,\n",
       "                                              num_parallel_tree=None,\n",
       "                                              random_state=42, ...))],\n",
       "                   final_estimator=LogisticRegression())"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Lets try stacking classifier and see it's performance\"\"\"\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "stacks = StackingClassifier(\n",
    "    estimators=[\n",
    "        (\"mlp\", MLPClassifier(batch_size=128, hidden_layer_sizes=(300,), max_iter=1000, random_state=42)),\n",
    "        (\"random\", RandomForestClassifier(n_estimators=100, random_state=42)),\n",
    "        (\"xgb\", XGBClassifier(eval_metric=\"mlogloss\", random_state=42))\n",
    "], \n",
    "    final_estimator=LogisticRegression()\n",
    ")\n",
    "stacks.fit(x_train, y_train_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f52fd815-86ba-40bb-bb98-2327a112cb01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 72.40%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf0b50f8-5fa8-48f8-ad41-88e00769e47c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"To my conclusion, preprocessing data using the StandardScaler(), using the MLPClassifier gives the best accuracy score at 72.40% for this dataset. \n",
    "Other models have proven to give me the same accuracy scores even if I use them together.\"\"\""
   ]
  }
 ],
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