diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml
new file mode 100644
index 0000000..94f4cbf
--- /dev/null
+++ b/.github/workflows/main.yaml
@@ -0,0 +1,39 @@
+name: Deploy to Render
+
+on:
+  push:
+    branches:
+      - main  # Adjust this branch as needed
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.11 # Replace with your Python version
+
+    - name: Install dependencies
+      run: pip install -r requirements.txt  # Replace with your requirements file
+      
+    - name: Build Docker image
+      run: |
+        docker build -t company_bankruptcy_predictor:latest .
+        docker login -u ${{ secrets.DOCKER_USERNAME }} -p ${{ secrets.DOCKER_PASSWORD }}
+        docker tag company_bankruptcy_predictor:latest sayam2801/company_bankruptcy_predictor:latest
+        docker push sayam2801/company_bankruptcy_predictor:latest
+
+    - name: Deploy to Render
+      run: |
+        curl -X POST -H "Authorization: Bearer ${{ secrets.RENDER_TOKEN }}" \
+          -H "Content-Type: application/json" \
+          --data '{
+            "image": "company_bankruptcy_predictor:latest",
+            "serviceId": "srv-ckl722rj89us73coe160"  # Replace with your Render service ID
+          }' \
+          https://api.render.com/v1/deploys
\ No newline at end of file
diff --git a/Company Bankruptcy Prediction.ipynb b/Company Bankruptcy Prediction.ipynb
index ebe8bf0..a6b8ff2 100644
--- a/Company Bankruptcy Prediction.ipynb	
+++ b/Company Bankruptcy Prediction.ipynb	
@@ -5276,7 +5276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 169,
    "id": "261d1595",
    "metadata": {},
    "outputs": [
@@ -5321,6 +5321,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=RandomFores...</td>\n",
+       "      <td>0.977273</td>\n",
+       "      <td>0.977445</td>\n",
+       "      <td>0.977387</td>\n",
+       "      <td>0.977273</td>\n",
+       "      <td>0.977387</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
        "      <td>(ExtraTreeClassifier(random_state=191144010), ...</td>\n",
        "      <td>0.976010</td>\n",
        "      <td>0.976301</td>\n",
@@ -5329,7 +5338,7 @@
        "      <td>0.976151</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>3</th>\n",
        "      <td>VotingClassifier(estimators=[('ET', ExtraTrees...</td>\n",
        "      <td>0.975505</td>\n",
        "      <td>0.975772</td>\n",
@@ -5338,7 +5347,25 @@
        "      <td>0.975641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=HistGradien...</td>\n",
+       "      <td>0.975000</td>\n",
+       "      <td>0.975341</td>\n",
+       "      <td>0.975150</td>\n",
+       "      <td>0.974999</td>\n",
+       "      <td>0.975150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>RandomizedSearchCV(cv=5,\\n                   e...</td>\n",
+       "      <td>0.974242</td>\n",
+       "      <td>0.974454</td>\n",
+       "      <td>0.974366</td>\n",
+       "      <td>0.974242</td>\n",
+       "      <td>0.974366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
        "      <td>&lt;catboost.core.CatBoostClassifier object at 0x...</td>\n",
        "      <td>0.974242</td>\n",
        "      <td>0.974521</td>\n",
@@ -5347,7 +5374,7 @@
        "      <td>0.974381</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>7</th>\n",
        "      <td>&lt;catboost.core.CatBoostClassifier object at 0x...</td>\n",
        "      <td>0.974242</td>\n",
        "      <td>0.974521</td>\n",
@@ -5356,7 +5383,16 @@
        "      <td>0.974381</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>8</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=KNeighborsC...</td>\n",
+       "      <td>0.973990</td>\n",
+       "      <td>0.974357</td>\n",
+       "      <td>0.974145</td>\n",
+       "      <td>0.973989</td>\n",
+       "      <td>0.974145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
        "      <td>(DecisionTreeClassifier(max_features='auto', r...</td>\n",
        "      <td>0.972980</td>\n",
        "      <td>0.973270</td>\n",
@@ -5365,7 +5401,7 @@
        "      <td>0.973120</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>10</th>\n",
        "      <td>XGBClassifier(base_score=None, booster=None, c...</td>\n",
        "      <td>0.972475</td>\n",
        "      <td>0.972841</td>\n",
@@ -5374,7 +5410,16 @@
        "      <td>0.972630</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>11</th>\n",
+       "      <td>RandomizedSearchCV(estimator=ExtraTreesClassif...</td>\n",
+       "      <td>0.970202</td>\n",
+       "      <td>0.970831</td>\n",
+       "      <td>0.970398</td>\n",
+       "      <td>0.970198</td>\n",
+       "      <td>0.970398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
        "      <td>HistGradientBoostingClassifier()</td>\n",
        "      <td>0.967677</td>\n",
        "      <td>0.968028</td>\n",
@@ -5383,7 +5428,7 @@
        "      <td>0.967829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
+       "      <th>13</th>\n",
        "      <td>MLPClassifier()</td>\n",
        "      <td>0.967172</td>\n",
        "      <td>0.967382</td>\n",
@@ -5392,7 +5437,16 @@
        "      <td>0.967295</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>14</th>\n",
+       "      <td>RandomizedSearchCV(estimator=LGBMClassifier(),...</td>\n",
+       "      <td>0.967172</td>\n",
+       "      <td>0.967666</td>\n",
+       "      <td>0.967348</td>\n",
+       "      <td>0.967169</td>\n",
+       "      <td>0.967348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
        "      <td>LGBMClassifier()</td>\n",
        "      <td>0.966919</td>\n",
        "      <td>0.967257</td>\n",
@@ -5401,7 +5455,25 @@
        "      <td>0.967069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>16</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=GradientBoo...</td>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.966966</td>\n",
+       "      <td>0.966809</td>\n",
+       "      <td>0.966666</td>\n",
+       "      <td>0.966809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>RandomizedSearchCV(estimator=BaggingClassifier...</td>\n",
+       "      <td>0.966162</td>\n",
+       "      <td>0.966624</td>\n",
+       "      <td>0.966333</td>\n",
+       "      <td>0.966159</td>\n",
+       "      <td>0.966333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
        "      <td>(DecisionTreeClassifier(random_state=157850700...</td>\n",
        "      <td>0.964646</td>\n",
        "      <td>0.964781</td>\n",
@@ -5410,7 +5482,7 @@
        "      <td>0.964750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
+       "      <th>19</th>\n",
        "      <td>KNeighborsClassifier()</td>\n",
        "      <td>0.957828</td>\n",
        "      <td>0.959996</td>\n",
@@ -5419,7 +5491,16 @@
        "      <td>0.958176</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>20</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=SVC(),\\n   ...</td>\n",
+       "      <td>0.956061</td>\n",
+       "      <td>0.956487</td>\n",
+       "      <td>0.956226</td>\n",
+       "      <td>0.956058</td>\n",
+       "      <td>0.956226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
        "      <td>DecisionTreeClassifier()</td>\n",
        "      <td>0.953030</td>\n",
        "      <td>0.953147</td>\n",
@@ -5428,7 +5509,16 @@
        "      <td>0.953128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>22</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=DecisionTre...</td>\n",
+       "      <td>0.946465</td>\n",
+       "      <td>0.946581</td>\n",
+       "      <td>0.946562</td>\n",
+       "      <td>0.946465</td>\n",
+       "      <td>0.946562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
        "      <td>XGBRFClassifier(base_score=None, booster=None,...</td>\n",
        "      <td>0.944192</td>\n",
        "      <td>0.944778</td>\n",
@@ -5437,7 +5527,7 @@
        "      <td>0.944383</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>24</th>\n",
        "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
        "      <td>0.942172</td>\n",
        "      <td>0.942239</td>\n",
@@ -5446,7 +5536,7 @@
        "      <td>0.942251</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>25</th>\n",
        "      <td>SVC()</td>\n",
        "      <td>0.941667</td>\n",
        "      <td>0.942156</td>\n",
@@ -5455,7 +5545,16 @@
        "      <td>0.941843</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>26</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=AdaBoostCla...</td>\n",
+       "      <td>0.929040</td>\n",
+       "      <td>0.929067</td>\n",
+       "      <td>0.929099</td>\n",
+       "      <td>0.929040</td>\n",
+       "      <td>0.929099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
        "      <td>(DecisionTreeClassifier(max_depth=1, random_st...</td>\n",
        "      <td>0.923485</td>\n",
        "      <td>0.923520</td>\n",
@@ -5464,7 +5563,34 @@
        "      <td>0.923548</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
+       "      <th>28</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=PassiveAggr...</td>\n",
+       "      <td>0.906061</td>\n",
+       "      <td>0.906454</td>\n",
+       "      <td>0.906222</td>\n",
+       "      <td>0.906054</td>\n",
+       "      <td>0.906222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=LinearSVC()...</td>\n",
+       "      <td>0.905051</td>\n",
+       "      <td>0.905497</td>\n",
+       "      <td>0.905221</td>\n",
+       "      <td>0.905043</td>\n",
+       "      <td>0.905221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>RandomizedSearchCV(cv=5,\\n                   e...</td>\n",
+       "      <td>0.902778</td>\n",
+       "      <td>0.902843</td>\n",
+       "      <td>0.902854</td>\n",
+       "      <td>0.902778</td>\n",
+       "      <td>0.902854</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
        "      <td>LogisticRegression()</td>\n",
        "      <td>0.902525</td>\n",
        "      <td>0.902596</td>\n",
@@ -5473,7 +5599,7 @@
        "      <td>0.902604</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>32</th>\n",
        "      <td>SGDClassifier()</td>\n",
        "      <td>0.902273</td>\n",
        "      <td>0.902376</td>\n",
@@ -5482,7 +5608,16 @@
        "      <td>0.902363</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>33</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=LogisticReg...</td>\n",
+       "      <td>0.902020</td>\n",
+       "      <td>0.902103</td>\n",
+       "      <td>0.902103</td>\n",
+       "      <td>0.902020</td>\n",
+       "      <td>0.902103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
        "      <td>LinearSVC()</td>\n",
        "      <td>0.901263</td>\n",
        "      <td>0.901328</td>\n",
@@ -5491,7 +5626,16 @@
        "      <td>0.901338</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
+       "      <th>35</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=SGDClassifi...</td>\n",
+       "      <td>0.897727</td>\n",
+       "      <td>0.897891</td>\n",
+       "      <td>0.897837</td>\n",
+       "      <td>0.897726</td>\n",
+       "      <td>0.897837</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
        "      <td>RidgeClassifier()</td>\n",
        "      <td>0.894697</td>\n",
        "      <td>0.895429</td>\n",
@@ -5500,7 +5644,16 @@
        "      <td>0.894913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>37</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=RidgeClassi...</td>\n",
+       "      <td>0.894697</td>\n",
+       "      <td>0.895429</td>\n",
+       "      <td>0.894913</td>\n",
+       "      <td>0.894677</td>\n",
+       "      <td>0.894913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
        "      <td>NuSVC()</td>\n",
        "      <td>0.893434</td>\n",
        "      <td>0.893436</td>\n",
@@ -5509,7 +5662,7 @@
        "      <td>0.893410</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>22</th>\n",
+       "      <th>39</th>\n",
        "      <td>GaussianNB()</td>\n",
        "      <td>0.888131</td>\n",
        "      <td>0.888185</td>\n",
@@ -5518,7 +5671,7 @@
        "      <td>0.888201</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
+       "      <th>40</th>\n",
        "      <td>BernoulliNB()</td>\n",
        "      <td>0.874747</td>\n",
        "      <td>0.874735</td>\n",
@@ -5527,7 +5680,16 @@
        "      <td>0.874765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>24</th>\n",
+       "      <th>41</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=BernoulliNB...</td>\n",
+       "      <td>0.874747</td>\n",
+       "      <td>0.874735</td>\n",
+       "      <td>0.874765</td>\n",
+       "      <td>0.874742</td>\n",
+       "      <td>0.874765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
        "      <td>PassiveAggressiveClassifier()</td>\n",
        "      <td>0.871970</td>\n",
        "      <td>0.871954</td>\n",
@@ -5542,60 +5704,96 @@
       "text/plain": [
        "                                                Model  Accuracy  Precision  \\\n",
        "0   StackingClassifier(estimators=[('ET', ExtraTre...  0.978535   0.978523   \n",
-       "1   (ExtraTreeClassifier(random_state=191144010), ...  0.976010   0.976301   \n",
-       "2   VotingClassifier(estimators=[('ET', ExtraTrees...  0.975505   0.975772   \n",
-       "3   <catboost.core.CatBoostClassifier object at 0x...  0.974242   0.974521   \n",
-       "4   <catboost.core.CatBoostClassifier object at 0x...  0.974242   0.974521   \n",
-       "5   (DecisionTreeClassifier(max_features='auto', r...  0.972980   0.973270   \n",
-       "6   XGBClassifier(base_score=None, booster=None, c...  0.972475   0.972841   \n",
-       "7                    HistGradientBoostingClassifier()  0.967677   0.968028   \n",
-       "8                                     MLPClassifier()  0.967172   0.967382   \n",
-       "9                                    LGBMClassifier()  0.966919   0.967257   \n",
-       "10  (DecisionTreeClassifier(random_state=157850700...  0.964646   0.964781   \n",
-       "11                             KNeighborsClassifier()  0.957828   0.959996   \n",
-       "12                           DecisionTreeClassifier()  0.953030   0.953147   \n",
-       "13  XGBRFClassifier(base_score=None, booster=None,...  0.944192   0.944778   \n",
-       "14  ([DecisionTreeRegressor(criterion='friedman_ms...  0.942172   0.942239   \n",
-       "15                                              SVC()  0.941667   0.942156   \n",
-       "16  (DecisionTreeClassifier(max_depth=1, random_st...  0.923485   0.923520   \n",
-       "17                               LogisticRegression()  0.902525   0.902596   \n",
-       "18                                    SGDClassifier()  0.902273   0.902376   \n",
-       "19                                        LinearSVC()  0.901263   0.901328   \n",
-       "20                                  RidgeClassifier()  0.894697   0.895429   \n",
-       "21                                            NuSVC()  0.893434   0.893436   \n",
-       "22                                       GaussianNB()  0.888131   0.888185   \n",
-       "23                                      BernoulliNB()  0.874747   0.874735   \n",
-       "24                      PassiveAggressiveClassifier()  0.871970   0.871954   \n",
+       "1   RandomizedSearchCV(cv=5, estimator=RandomFores...  0.977273   0.977445   \n",
+       "2   (ExtraTreeClassifier(random_state=191144010), ...  0.976010   0.976301   \n",
+       "3   VotingClassifier(estimators=[('ET', ExtraTrees...  0.975505   0.975772   \n",
+       "4   RandomizedSearchCV(cv=5, estimator=HistGradien...  0.975000   0.975341   \n",
+       "5   RandomizedSearchCV(cv=5,\\n                   e...  0.974242   0.974454   \n",
+       "6   <catboost.core.CatBoostClassifier object at 0x...  0.974242   0.974521   \n",
+       "7   <catboost.core.CatBoostClassifier object at 0x...  0.974242   0.974521   \n",
+       "8   RandomizedSearchCV(cv=5, estimator=KNeighborsC...  0.973990   0.974357   \n",
+       "9   (DecisionTreeClassifier(max_features='auto', r...  0.972980   0.973270   \n",
+       "10  XGBClassifier(base_score=None, booster=None, c...  0.972475   0.972841   \n",
+       "11  RandomizedSearchCV(estimator=ExtraTreesClassif...  0.970202   0.970831   \n",
+       "12                   HistGradientBoostingClassifier()  0.967677   0.968028   \n",
+       "13                                    MLPClassifier()  0.967172   0.967382   \n",
+       "14  RandomizedSearchCV(estimator=LGBMClassifier(),...  0.967172   0.967666   \n",
+       "15                                   LGBMClassifier()  0.966919   0.967257   \n",
+       "16  RandomizedSearchCV(cv=5, estimator=GradientBoo...  0.966667   0.966966   \n",
+       "17  RandomizedSearchCV(estimator=BaggingClassifier...  0.966162   0.966624   \n",
+       "18  (DecisionTreeClassifier(random_state=157850700...  0.964646   0.964781   \n",
+       "19                             KNeighborsClassifier()  0.957828   0.959996   \n",
+       "20  RandomizedSearchCV(cv=5, estimator=SVC(),\\n   ...  0.956061   0.956487   \n",
+       "21                           DecisionTreeClassifier()  0.953030   0.953147   \n",
+       "22  RandomizedSearchCV(cv=5, estimator=DecisionTre...  0.946465   0.946581   \n",
+       "23  XGBRFClassifier(base_score=None, booster=None,...  0.944192   0.944778   \n",
+       "24  ([DecisionTreeRegressor(criterion='friedman_ms...  0.942172   0.942239   \n",
+       "25                                              SVC()  0.941667   0.942156   \n",
+       "26  RandomizedSearchCV(cv=5, estimator=AdaBoostCla...  0.929040   0.929067   \n",
+       "27  (DecisionTreeClassifier(max_depth=1, random_st...  0.923485   0.923520   \n",
+       "28  RandomizedSearchCV(cv=5, estimator=PassiveAggr...  0.906061   0.906454   \n",
+       "29  RandomizedSearchCV(cv=5, estimator=LinearSVC()...  0.905051   0.905497   \n",
+       "30  RandomizedSearchCV(cv=5,\\n                   e...  0.902778   0.902843   \n",
+       "31                               LogisticRegression()  0.902525   0.902596   \n",
+       "32                                    SGDClassifier()  0.902273   0.902376   \n",
+       "33  RandomizedSearchCV(cv=5, estimator=LogisticReg...  0.902020   0.902103   \n",
+       "34                                        LinearSVC()  0.901263   0.901328   \n",
+       "35  RandomizedSearchCV(cv=5, estimator=SGDClassifi...  0.897727   0.897891   \n",
+       "36                                  RidgeClassifier()  0.894697   0.895429   \n",
+       "37  RandomizedSearchCV(cv=5, estimator=RidgeClassi...  0.894697   0.895429   \n",
+       "38                                            NuSVC()  0.893434   0.893436   \n",
+       "39                                       GaussianNB()  0.888131   0.888185   \n",
+       "40                                      BernoulliNB()  0.874747   0.874735   \n",
+       "41  RandomizedSearchCV(cv=5, estimator=BernoulliNB...  0.874747   0.874735   \n",
+       "42                      PassiveAggressiveClassifier()  0.871970   0.871954   \n",
        "\n",
        "      Recall        F1   ROC-AUC  \n",
        "0   0.978555  0.978534  0.978555  \n",
-       "1   0.976151  0.976009  0.976151  \n",
-       "2   0.975641  0.975504  0.975641  \n",
-       "3   0.974381  0.974242  0.974381  \n",
-       "4   0.974381  0.974242  0.974381  \n",
-       "5   0.973120  0.972979  0.973120  \n",
-       "6   0.972630  0.972473  0.972630  \n",
-       "7   0.967829  0.967675  0.967829  \n",
-       "8   0.967295  0.967171  0.967295  \n",
-       "9   0.967069  0.966918  0.967069  \n",
-       "10  0.964750  0.964646  0.964750  \n",
-       "11  0.958176  0.957797  0.958176  \n",
-       "12  0.953128  0.953030  0.953128  \n",
-       "13  0.944383  0.944185  0.944383  \n",
-       "14  0.942251  0.942172  0.942251  \n",
-       "15  0.941843  0.941662  0.941843  \n",
-       "16  0.923548  0.923484  0.923548  \n",
-       "17  0.902604  0.902525  0.902604  \n",
-       "18  0.902363  0.902273  0.902363  \n",
-       "19  0.901338  0.901263  0.901338  \n",
-       "20  0.894913  0.894677  0.894913  \n",
-       "21  0.893410  0.893421  0.893410  \n",
-       "22  0.888201  0.888131  0.888201  \n",
-       "23  0.874765  0.874742  0.874765  \n",
-       "24  0.871975  0.871962  0.871975  "
-      ]
-     },
-     "execution_count": 108,
+       "1   0.977387  0.977273  0.977387  \n",
+       "2   0.976151  0.976009  0.976151  \n",
+       "3   0.975641  0.975504  0.975641  \n",
+       "4   0.975150  0.974999  0.975150  \n",
+       "5   0.974366  0.974242  0.974366  \n",
+       "6   0.974381  0.974242  0.974381  \n",
+       "7   0.974381  0.974242  0.974381  \n",
+       "8   0.974145  0.973989  0.974145  \n",
+       "9   0.973120  0.972979  0.973120  \n",
+       "10  0.972630  0.972473  0.972630  \n",
+       "11  0.970398  0.970198  0.970398  \n",
+       "12  0.967829  0.967675  0.967829  \n",
+       "13  0.967295  0.967171  0.967295  \n",
+       "14  0.967348  0.967169  0.967348  \n",
+       "15  0.967069  0.966918  0.967069  \n",
+       "16  0.966809  0.966666  0.966809  \n",
+       "17  0.966333  0.966159  0.966333  \n",
+       "18  0.964750  0.964646  0.964750  \n",
+       "19  0.958176  0.957797  0.958176  \n",
+       "20  0.956226  0.956058  0.956226  \n",
+       "21  0.953128  0.953030  0.953128  \n",
+       "22  0.946562  0.946465  0.946562  \n",
+       "23  0.944383  0.944185  0.944383  \n",
+       "24  0.942251  0.942172  0.942251  \n",
+       "25  0.941843  0.941662  0.941843  \n",
+       "26  0.929099  0.929040  0.929099  \n",
+       "27  0.923548  0.923484  0.923548  \n",
+       "28  0.906222  0.906054  0.906222  \n",
+       "29  0.905221  0.905043  0.905221  \n",
+       "30  0.902854  0.902778  0.902854  \n",
+       "31  0.902604  0.902525  0.902604  \n",
+       "32  0.902363  0.902273  0.902363  \n",
+       "33  0.902103  0.902020  0.902103  \n",
+       "34  0.901338  0.901263  0.901338  \n",
+       "35  0.897837  0.897726  0.897837  \n",
+       "36  0.894913  0.894677  0.894913  \n",
+       "37  0.894913  0.894677  0.894913  \n",
+       "38  0.893410  0.893421  0.893410  \n",
+       "39  0.888201  0.888131  0.888201  \n",
+       "40  0.874765  0.874742  0.874765  \n",
+       "41  0.874765  0.874742  0.874765  \n",
+       "42  0.871975  0.871962  0.871975  "
+      ]
+     },
+     "execution_count": 169,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7810,7 +8008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 154,
    "id": "219a054c",
    "metadata": {},
    "outputs": [
@@ -7864,6 +8062,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
+       "      <td>StackingClassifier(estimators=[('ET', ExtraTre...</td>\n",
+       "      <td>0.976768</td>\n",
+       "      <td>0.976757</td>\n",
+       "      <td>0.976780</td>\n",
+       "      <td>0.976766</td>\n",
+       "      <td>0.976780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
        "      <td>(ExtraTreeClassifier(random_state=191144010), ...</td>\n",
        "      <td>0.976010</td>\n",
        "      <td>0.976301</td>\n",
@@ -7872,7 +8079,7 @@
        "      <td>0.976151</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>4</th>\n",
        "      <td>VotingClassifier(estimators=[('ET', ExtraTrees...</td>\n",
        "      <td>0.975505</td>\n",
        "      <td>0.975772</td>\n",
@@ -7881,7 +8088,7 @@
        "      <td>0.975641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>5</th>\n",
        "      <td>RandomizedSearchCV(cv=5, estimator=HistGradien...</td>\n",
        "      <td>0.975000</td>\n",
        "      <td>0.975341</td>\n",
@@ -7890,7 +8097,7 @@
        "      <td>0.975150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>6</th>\n",
        "      <td>RandomizedSearchCV(cv=5,\\n                   e...</td>\n",
        "      <td>0.974242</td>\n",
        "      <td>0.974454</td>\n",
@@ -7899,7 +8106,7 @@
        "      <td>0.974366</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>7</th>\n",
        "      <td>&lt;catboost.core.CatBoostClassifier object at 0x...</td>\n",
        "      <td>0.974242</td>\n",
        "      <td>0.974521</td>\n",
@@ -7908,7 +8115,7 @@
        "      <td>0.974381</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>8</th>\n",
        "      <td>&lt;catboost.core.CatBoostClassifier object at 0x...</td>\n",
        "      <td>0.974242</td>\n",
        "      <td>0.974521</td>\n",
@@ -7917,7 +8124,7 @@
        "      <td>0.974381</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
+       "      <th>9</th>\n",
        "      <td>RandomizedSearchCV(cv=5, estimator=KNeighborsC...</td>\n",
        "      <td>0.973990</td>\n",
        "      <td>0.974357</td>\n",
@@ -7926,7 +8133,7 @@
        "      <td>0.974145</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>10</th>\n",
        "      <td>(DecisionTreeClassifier(max_features='auto', r...</td>\n",
        "      <td>0.972980</td>\n",
        "      <td>0.973270</td>\n",
@@ -7935,7 +8142,7 @@
        "      <td>0.973120</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>11</th>\n",
        "      <td>XGBClassifier(base_score=None, booster=None, c...</td>\n",
        "      <td>0.972475</td>\n",
        "      <td>0.972841</td>\n",
@@ -7944,7 +8151,7 @@
        "      <td>0.972630</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
+       "      <th>12</th>\n",
        "      <td>RandomizedSearchCV(estimator=ExtraTreesClassif...</td>\n",
        "      <td>0.970202</td>\n",
        "      <td>0.970831</td>\n",
@@ -7953,7 +8160,7 @@
        "      <td>0.970398</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>13</th>\n",
        "      <td>HistGradientBoostingClassifier()</td>\n",
        "      <td>0.967677</td>\n",
        "      <td>0.968028</td>\n",
@@ -7962,7 +8169,7 @@
        "      <td>0.967829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>14</th>\n",
        "      <td>MLPClassifier()</td>\n",
        "      <td>0.967172</td>\n",
        "      <td>0.967382</td>\n",
@@ -7971,7 +8178,7 @@
        "      <td>0.967295</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>15</th>\n",
        "      <td>RandomizedSearchCV(estimator=LGBMClassifier(),...</td>\n",
        "      <td>0.967172</td>\n",
        "      <td>0.967666</td>\n",
@@ -7980,7 +8187,7 @@
        "      <td>0.967348</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>16</th>\n",
        "      <td>LGBMClassifier()</td>\n",
        "      <td>0.966919</td>\n",
        "      <td>0.967257</td>\n",
@@ -7989,7 +8196,7 @@
        "      <td>0.967069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>17</th>\n",
        "      <td>RandomizedSearchCV(cv=5, estimator=GradientBoo...</td>\n",
        "      <td>0.966667</td>\n",
        "      <td>0.966966</td>\n",
@@ -7998,7 +8205,7 @@
        "      <td>0.966809</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
+       "      <th>18</th>\n",
        "      <td>RandomizedSearchCV(estimator=BaggingClassifier...</td>\n",
        "      <td>0.966162</td>\n",
        "      <td>0.966624</td>\n",
@@ -8007,7 +8214,7 @@
        "      <td>0.966333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>19</th>\n",
        "      <td>(DecisionTreeClassifier(random_state=157850700...</td>\n",
        "      <td>0.964646</td>\n",
        "      <td>0.964781</td>\n",
@@ -8016,7 +8223,7 @@
        "      <td>0.964750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>20</th>\n",
        "      <td>KNeighborsClassifier()</td>\n",
        "      <td>0.957828</td>\n",
        "      <td>0.959996</td>\n",
@@ -8025,7 +8232,7 @@
        "      <td>0.958176</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
+       "      <th>21</th>\n",
        "      <td>RandomizedSearchCV(cv=5, estimator=SVC(),\\n   ...</td>\n",
        "      <td>0.956061</td>\n",
        "      <td>0.956487</td>\n",
@@ -8034,7 +8241,7 @@
        "      <td>0.956226</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>22</th>\n",
        "      <td>DecisionTreeClassifier()</td>\n",
        "      <td>0.953030</td>\n",
        "      <td>0.953147</td>\n",
@@ -8043,7 +8250,7 @@
        "      <td>0.953128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>22</th>\n",
+       "      <th>23</th>\n",
        "      <td>RandomizedSearchCV(cv=5, estimator=DecisionTre...</td>\n",
        "      <td>0.946465</td>\n",
        "      <td>0.946581</td>\n",
@@ -8052,7 +8259,7 @@
        "      <td>0.946562</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
+       "      <th>24</th>\n",
        "      <td>XGBRFClassifier(base_score=None, booster=None,...</td>\n",
        "      <td>0.944192</td>\n",
        "      <td>0.944778</td>\n",
@@ -8061,7 +8268,7 @@
        "      <td>0.944383</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>24</th>\n",
+       "      <th>25</th>\n",
        "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
        "      <td>0.942172</td>\n",
        "      <td>0.942239</td>\n",
@@ -8070,7 +8277,7 @@
        "      <td>0.942251</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25</th>\n",
+       "      <th>26</th>\n",
        "      <td>SVC()</td>\n",
        "      <td>0.941667</td>\n",
        "      <td>0.942156</td>\n",
@@ -8079,7 +8286,7 @@
        "      <td>0.941843</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>27</th>\n",
        "      <td>RandomizedSearchCV(cv=5, estimator=AdaBoostCla...</td>\n",
        "      <td>0.929040</td>\n",
        "      <td>0.929067</td>\n",
@@ -8088,7 +8295,7 @@
        "      <td>0.929099</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
+       "      <th>28</th>\n",
        "      <td>(DecisionTreeClassifier(max_depth=1, random_st...</td>\n",
        "      <td>0.923485</td>\n",
        "      <td>0.923520</td>\n",
@@ -8097,7 +8304,7 @@
        "      <td>0.923548</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28</th>\n",
+       "      <th>29</th>\n",
        "      <td>RandomizedSearchCV(cv=5, estimator=PassiveAggr...</td>\n",
        "      <td>0.906061</td>\n",
        "      <td>0.906454</td>\n",
@@ -8106,7 +8313,7 @@
        "      <td>0.906222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
+       "      <th>30</th>\n",
        "      <td>RandomizedSearchCV(cv=5, estimator=LinearSVC()...</td>\n",
        "      <td>0.905051</td>\n",
        "      <td>0.905497</td>\n",
@@ -8115,7 +8322,7 @@
        "      <td>0.905221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
+       "      <th>31</th>\n",
        "      <td>RandomizedSearchCV(cv=5,\\n                   e...</td>\n",
        "      <td>0.902778</td>\n",
        "      <td>0.902843</td>\n",
@@ -8124,7 +8331,7 @@
        "      <td>0.902854</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>31</th>\n",
+       "      <th>32</th>\n",
        "      <td>LogisticRegression()</td>\n",
        "      <td>0.902525</td>\n",
        "      <td>0.902596</td>\n",
@@ -8133,7 +8340,7 @@
        "      <td>0.902604</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>32</th>\n",
+       "      <th>33</th>\n",
        "      <td>SGDClassifier()</td>\n",
        "      <td>0.902273</td>\n",
        "      <td>0.902376</td>\n",
@@ -8142,7 +8349,7 @@
        "      <td>0.902363</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>33</th>\n",
+       "      <th>34</th>\n",
        "      <td>RandomizedSearchCV(cv=5, estimator=LogisticReg...</td>\n",
        "      <td>0.902020</td>\n",
        "      <td>0.902103</td>\n",
@@ -8151,7 +8358,7 @@
        "      <td>0.902103</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>34</th>\n",
+       "      <th>35</th>\n",
        "      <td>LinearSVC()</td>\n",
        "      <td>0.901263</td>\n",
        "      <td>0.901328</td>\n",
@@ -8160,7 +8367,7 @@
        "      <td>0.901338</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>35</th>\n",
+       "      <th>36</th>\n",
        "      <td>RandomizedSearchCV(cv=5, estimator=SGDClassifi...</td>\n",
        "      <td>0.897727</td>\n",
        "      <td>0.897891</td>\n",
@@ -8169,7 +8376,7 @@
        "      <td>0.897837</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>36</th>\n",
+       "      <th>37</th>\n",
        "      <td>RidgeClassifier()</td>\n",
        "      <td>0.894697</td>\n",
        "      <td>0.895429</td>\n",
@@ -8178,7 +8385,7 @@
        "      <td>0.894913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>37</th>\n",
+       "      <th>38</th>\n",
        "      <td>RandomizedSearchCV(cv=5, estimator=RidgeClassi...</td>\n",
        "      <td>0.894697</td>\n",
        "      <td>0.895429</td>\n",
@@ -8187,7 +8394,7 @@
        "      <td>0.894913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>38</th>\n",
+       "      <th>39</th>\n",
        "      <td>NuSVC()</td>\n",
        "      <td>0.893434</td>\n",
        "      <td>0.893436</td>\n",
@@ -8196,7 +8403,7 @@
        "      <td>0.893410</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>39</th>\n",
+       "      <th>40</th>\n",
        "      <td>GaussianNB()</td>\n",
        "      <td>0.888131</td>\n",
        "      <td>0.888185</td>\n",
@@ -8205,8 +8412,8 @@
        "      <td>0.888201</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>BernoulliNB()</td>\n",
+       "      <th>41</th>\n",
+       "      <td>RandomizedSearchCV(cv=5, estimator=BernoulliNB...</td>\n",
        "      <td>0.874747</td>\n",
        "      <td>0.874735</td>\n",
        "      <td>0.874765</td>\n",
@@ -8214,8 +8421,8 @@
        "      <td>0.874765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>RandomizedSearchCV(cv=5, estimator=BernoulliNB...</td>\n",
+       "      <th>42</th>\n",
+       "      <td>BernoulliNB()</td>\n",
        "      <td>0.874747</td>\n",
        "      <td>0.874735</td>\n",
        "      <td>0.874765</td>\n",
@@ -8223,7 +8430,7 @@
        "      <td>0.874765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>42</th>\n",
+       "      <th>43</th>\n",
        "      <td>PassiveAggressiveClassifier()</td>\n",
        "      <td>0.871970</td>\n",
        "      <td>0.871954</td>\n",
@@ -8239,95 +8446,97 @@
        "                                                Model  Accuracy  Precision  \\\n",
        "0   StackingClassifier(estimators=[('ET', ExtraTre...  0.978535   0.978523   \n",
        "1   RandomizedSearchCV(cv=5, estimator=RandomFores...  0.977273   0.977445   \n",
-       "2   (ExtraTreeClassifier(random_state=191144010), ...  0.976010   0.976301   \n",
-       "3   VotingClassifier(estimators=[('ET', ExtraTrees...  0.975505   0.975772   \n",
-       "4   RandomizedSearchCV(cv=5, estimator=HistGradien...  0.975000   0.975341   \n",
-       "5   RandomizedSearchCV(cv=5,\\n                   e...  0.974242   0.974454   \n",
-       "6   <catboost.core.CatBoostClassifier object at 0x...  0.974242   0.974521   \n",
+       "2   StackingClassifier(estimators=[('ET', ExtraTre...  0.976768   0.976757   \n",
+       "3   (ExtraTreeClassifier(random_state=191144010), ...  0.976010   0.976301   \n",
+       "4   VotingClassifier(estimators=[('ET', ExtraTrees...  0.975505   0.975772   \n",
+       "5   RandomizedSearchCV(cv=5, estimator=HistGradien...  0.975000   0.975341   \n",
+       "6   RandomizedSearchCV(cv=5,\\n                   e...  0.974242   0.974454   \n",
        "7   <catboost.core.CatBoostClassifier object at 0x...  0.974242   0.974521   \n",
-       "8   RandomizedSearchCV(cv=5, estimator=KNeighborsC...  0.973990   0.974357   \n",
-       "9   (DecisionTreeClassifier(max_features='auto', r...  0.972980   0.973270   \n",
-       "10  XGBClassifier(base_score=None, booster=None, c...  0.972475   0.972841   \n",
-       "11  RandomizedSearchCV(estimator=ExtraTreesClassif...  0.970202   0.970831   \n",
-       "12                   HistGradientBoostingClassifier()  0.967677   0.968028   \n",
-       "13                                    MLPClassifier()  0.967172   0.967382   \n",
-       "14  RandomizedSearchCV(estimator=LGBMClassifier(),...  0.967172   0.967666   \n",
-       "15                                   LGBMClassifier()  0.966919   0.967257   \n",
-       "16  RandomizedSearchCV(cv=5, estimator=GradientBoo...  0.966667   0.966966   \n",
-       "17  RandomizedSearchCV(estimator=BaggingClassifier...  0.966162   0.966624   \n",
-       "18  (DecisionTreeClassifier(random_state=157850700...  0.964646   0.964781   \n",
-       "19                             KNeighborsClassifier()  0.957828   0.959996   \n",
-       "20  RandomizedSearchCV(cv=5, estimator=SVC(),\\n   ...  0.956061   0.956487   \n",
-       "21                           DecisionTreeClassifier()  0.953030   0.953147   \n",
-       "22  RandomizedSearchCV(cv=5, estimator=DecisionTre...  0.946465   0.946581   \n",
-       "23  XGBRFClassifier(base_score=None, booster=None,...  0.944192   0.944778   \n",
-       "24  ([DecisionTreeRegressor(criterion='friedman_ms...  0.942172   0.942239   \n",
-       "25                                              SVC()  0.941667   0.942156   \n",
-       "26  RandomizedSearchCV(cv=5, estimator=AdaBoostCla...  0.929040   0.929067   \n",
-       "27  (DecisionTreeClassifier(max_depth=1, random_st...  0.923485   0.923520   \n",
-       "28  RandomizedSearchCV(cv=5, estimator=PassiveAggr...  0.906061   0.906454   \n",
-       "29  RandomizedSearchCV(cv=5, estimator=LinearSVC()...  0.905051   0.905497   \n",
-       "30  RandomizedSearchCV(cv=5,\\n                   e...  0.902778   0.902843   \n",
-       "31                               LogisticRegression()  0.902525   0.902596   \n",
-       "32                                    SGDClassifier()  0.902273   0.902376   \n",
-       "33  RandomizedSearchCV(cv=5, estimator=LogisticReg...  0.902020   0.902103   \n",
-       "34                                        LinearSVC()  0.901263   0.901328   \n",
-       "35  RandomizedSearchCV(cv=5, estimator=SGDClassifi...  0.897727   0.897891   \n",
-       "36                                  RidgeClassifier()  0.894697   0.895429   \n",
-       "37  RandomizedSearchCV(cv=5, estimator=RidgeClassi...  0.894697   0.895429   \n",
-       "38                                            NuSVC()  0.893434   0.893436   \n",
-       "39                                       GaussianNB()  0.888131   0.888185   \n",
-       "40                                      BernoulliNB()  0.874747   0.874735   \n",
+       "8   <catboost.core.CatBoostClassifier object at 0x...  0.974242   0.974521   \n",
+       "9   RandomizedSearchCV(cv=5, estimator=KNeighborsC...  0.973990   0.974357   \n",
+       "10  (DecisionTreeClassifier(max_features='auto', r...  0.972980   0.973270   \n",
+       "11  XGBClassifier(base_score=None, booster=None, c...  0.972475   0.972841   \n",
+       "12  RandomizedSearchCV(estimator=ExtraTreesClassif...  0.970202   0.970831   \n",
+       "13                   HistGradientBoostingClassifier()  0.967677   0.968028   \n",
+       "14                                    MLPClassifier()  0.967172   0.967382   \n",
+       "15  RandomizedSearchCV(estimator=LGBMClassifier(),...  0.967172   0.967666   \n",
+       "16                                   LGBMClassifier()  0.966919   0.967257   \n",
+       "17  RandomizedSearchCV(cv=5, estimator=GradientBoo...  0.966667   0.966966   \n",
+       "18  RandomizedSearchCV(estimator=BaggingClassifier...  0.966162   0.966624   \n",
+       "19  (DecisionTreeClassifier(random_state=157850700...  0.964646   0.964781   \n",
+       "20                             KNeighborsClassifier()  0.957828   0.959996   \n",
+       "21  RandomizedSearchCV(cv=5, estimator=SVC(),\\n   ...  0.956061   0.956487   \n",
+       "22                           DecisionTreeClassifier()  0.953030   0.953147   \n",
+       "23  RandomizedSearchCV(cv=5, estimator=DecisionTre...  0.946465   0.946581   \n",
+       "24  XGBRFClassifier(base_score=None, booster=None,...  0.944192   0.944778   \n",
+       "25  ([DecisionTreeRegressor(criterion='friedman_ms...  0.942172   0.942239   \n",
+       "26                                              SVC()  0.941667   0.942156   \n",
+       "27  RandomizedSearchCV(cv=5, estimator=AdaBoostCla...  0.929040   0.929067   \n",
+       "28  (DecisionTreeClassifier(max_depth=1, random_st...  0.923485   0.923520   \n",
+       "29  RandomizedSearchCV(cv=5, estimator=PassiveAggr...  0.906061   0.906454   \n",
+       "30  RandomizedSearchCV(cv=5, estimator=LinearSVC()...  0.905051   0.905497   \n",
+       "31  RandomizedSearchCV(cv=5,\\n                   e...  0.902778   0.902843   \n",
+       "32                               LogisticRegression()  0.902525   0.902596   \n",
+       "33                                    SGDClassifier()  0.902273   0.902376   \n",
+       "34  RandomizedSearchCV(cv=5, estimator=LogisticReg...  0.902020   0.902103   \n",
+       "35                                        LinearSVC()  0.901263   0.901328   \n",
+       "36  RandomizedSearchCV(cv=5, estimator=SGDClassifi...  0.897727   0.897891   \n",
+       "37                                  RidgeClassifier()  0.894697   0.895429   \n",
+       "38  RandomizedSearchCV(cv=5, estimator=RidgeClassi...  0.894697   0.895429   \n",
+       "39                                            NuSVC()  0.893434   0.893436   \n",
+       "40                                       GaussianNB()  0.888131   0.888185   \n",
        "41  RandomizedSearchCV(cv=5, estimator=BernoulliNB...  0.874747   0.874735   \n",
-       "42                      PassiveAggressiveClassifier()  0.871970   0.871954   \n",
+       "42                                      BernoulliNB()  0.874747   0.874735   \n",
+       "43                      PassiveAggressiveClassifier()  0.871970   0.871954   \n",
        "\n",
        "      Recall        F1   ROC-AUC  \n",
        "0   0.978555  0.978534  0.978555  \n",
        "1   0.977387  0.977273  0.977387  \n",
-       "2   0.976151  0.976009  0.976151  \n",
-       "3   0.975641  0.975504  0.975641  \n",
-       "4   0.975150  0.974999  0.975150  \n",
-       "5   0.974366  0.974242  0.974366  \n",
-       "6   0.974381  0.974242  0.974381  \n",
+       "2   0.976780  0.976766  0.976780  \n",
+       "3   0.976151  0.976009  0.976151  \n",
+       "4   0.975641  0.975504  0.975641  \n",
+       "5   0.975150  0.974999  0.975150  \n",
+       "6   0.974366  0.974242  0.974366  \n",
        "7   0.974381  0.974242  0.974381  \n",
-       "8   0.974145  0.973989  0.974145  \n",
-       "9   0.973120  0.972979  0.973120  \n",
-       "10  0.972630  0.972473  0.972630  \n",
-       "11  0.970398  0.970198  0.970398  \n",
-       "12  0.967829  0.967675  0.967829  \n",
-       "13  0.967295  0.967171  0.967295  \n",
-       "14  0.967348  0.967169  0.967348  \n",
-       "15  0.967069  0.966918  0.967069  \n",
-       "16  0.966809  0.966666  0.966809  \n",
-       "17  0.966333  0.966159  0.966333  \n",
-       "18  0.964750  0.964646  0.964750  \n",
-       "19  0.958176  0.957797  0.958176  \n",
-       "20  0.956226  0.956058  0.956226  \n",
-       "21  0.953128  0.953030  0.953128  \n",
-       "22  0.946562  0.946465  0.946562  \n",
-       "23  0.944383  0.944185  0.944383  \n",
-       "24  0.942251  0.942172  0.942251  \n",
-       "25  0.941843  0.941662  0.941843  \n",
-       "26  0.929099  0.929040  0.929099  \n",
-       "27  0.923548  0.923484  0.923548  \n",
-       "28  0.906222  0.906054  0.906222  \n",
-       "29  0.905221  0.905043  0.905221  \n",
-       "30  0.902854  0.902778  0.902854  \n",
-       "31  0.902604  0.902525  0.902604  \n",
-       "32  0.902363  0.902273  0.902363  \n",
-       "33  0.902103  0.902020  0.902103  \n",
-       "34  0.901338  0.901263  0.901338  \n",
-       "35  0.897837  0.897726  0.897837  \n",
-       "36  0.894913  0.894677  0.894913  \n",
+       "8   0.974381  0.974242  0.974381  \n",
+       "9   0.974145  0.973989  0.974145  \n",
+       "10  0.973120  0.972979  0.973120  \n",
+       "11  0.972630  0.972473  0.972630  \n",
+       "12  0.970398  0.970198  0.970398  \n",
+       "13  0.967829  0.967675  0.967829  \n",
+       "14  0.967295  0.967171  0.967295  \n",
+       "15  0.967348  0.967169  0.967348  \n",
+       "16  0.967069  0.966918  0.967069  \n",
+       "17  0.966809  0.966666  0.966809  \n",
+       "18  0.966333  0.966159  0.966333  \n",
+       "19  0.964750  0.964646  0.964750  \n",
+       "20  0.958176  0.957797  0.958176  \n",
+       "21  0.956226  0.956058  0.956226  \n",
+       "22  0.953128  0.953030  0.953128  \n",
+       "23  0.946562  0.946465  0.946562  \n",
+       "24  0.944383  0.944185  0.944383  \n",
+       "25  0.942251  0.942172  0.942251  \n",
+       "26  0.941843  0.941662  0.941843  \n",
+       "27  0.929099  0.929040  0.929099  \n",
+       "28  0.923548  0.923484  0.923548  \n",
+       "29  0.906222  0.906054  0.906222  \n",
+       "30  0.905221  0.905043  0.905221  \n",
+       "31  0.902854  0.902778  0.902854  \n",
+       "32  0.902604  0.902525  0.902604  \n",
+       "33  0.902363  0.902273  0.902363  \n",
+       "34  0.902103  0.902020  0.902103  \n",
+       "35  0.901338  0.901263  0.901338  \n",
+       "36  0.897837  0.897726  0.897837  \n",
        "37  0.894913  0.894677  0.894913  \n",
-       "38  0.893410  0.893421  0.893410  \n",
-       "39  0.888201  0.888131  0.888201  \n",
-       "40  0.874765  0.874742  0.874765  \n",
+       "38  0.894913  0.894677  0.894913  \n",
+       "39  0.893410  0.893421  0.893410  \n",
+       "40  0.888201  0.888131  0.888201  \n",
        "41  0.874765  0.874742  0.874765  \n",
-       "42  0.871975  0.871962  0.871975  "
+       "42  0.874765  0.874742  0.874765  \n",
+       "43  0.871975  0.871962  0.871975  "
       ]
      },
-     "execution_count": 139,
+     "execution_count": 154,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8352,14 +8561,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 155,
    "id": "91bc2fbb",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;ET&#x27;, ExtraTreesClassifier()),\n",
+       "<style>#sk-container-id-17 {color: black;background-color: white;}#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>StackingClassifier(estimators=[(&#x27;ET&#x27;, ExtraTreesClassifier()),\n",
        "                               (&#x27;XGB&#x27;,\n",
        "                                XGBClassifier(base_score=None, booster=None,\n",
        "                                              callbacks=None,\n",
@@ -8385,7 +8594,7 @@
        "                                              predictor=None, random_state=None, ...)),\n",
        "                               (&#x27;CAT&#x27;,\n",
        "                                &lt;catboost.core.CatBoostClassifier object at 0x000002944CE3C9D0&gt;)],\n",
-       "                   final_estimator=RandomForestClassifier(), verbose=2)</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-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StackingClassifier</label><div class=\"sk-toggleable__content\"><pre>StackingClassifier(estimators=[(&#x27;ET&#x27;, ExtraTreesClassifier()),\n",
+       "                   final_estimator=RandomForestClassifier(), verbose=2)</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-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StackingClassifier</label><div class=\"sk-toggleable__content\"><pre>StackingClassifier(estimators=[(&#x27;ET&#x27;, ExtraTreesClassifier()),\n",
        "                               (&#x27;XGB&#x27;,\n",
        "                                XGBClassifier(base_score=None, booster=None,\n",
        "                                              callbacks=None,\n",
@@ -8411,7 +8620,7 @@
        "                                              predictor=None, random_state=None, ...)),\n",
        "                               (&#x27;CAT&#x27;,\n",
        "                                &lt;catboost.core.CatBoostClassifier object at 0x000002944CE3C9D0&gt;)],\n",
-       "                   final_estimator=RandomForestClassifier(), verbose=2)</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 sk-toggleable\"><label>ET</label></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-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier()</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\"><label>XGB</label></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-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "                   final_estimator=RandomForestClassifier(), verbose=2)</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 sk-toggleable\"><label>ET</label></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-62\" type=\"checkbox\" ><label for=\"sk-estimator-id-62\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier()</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\"><label>XGB</label></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-63\" type=\"checkbox\" ><label for=\"sk-estimator-id-63\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
        "              colsample_bylevel=None, colsample_bynode=None,\n",
        "              colsample_bytree=None, early_stopping_rounds=None,\n",
        "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
@@ -8421,7 +8630,7 @@
        "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
        "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
        "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
-       "              predictor=None, random_state=None, ...)</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\"><label>CAT</label></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-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x000002944CE3C9D0&gt;</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 sk-toggleable\"><label>final_estimator</label></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-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
+       "              predictor=None, random_state=None, ...)</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\"><label>CAT</label></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\">CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x000002944CE3C9D0&gt;</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 sk-toggleable\"><label>final_estimator</label></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-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()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
       ],
       "text/plain": [
        "StackingClassifier(estimators=[('ET', ExtraTreesClassifier()),\n",
@@ -8453,7 +8662,7 @@
        "                   final_estimator=RandomForestClassifier(), verbose=2)"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8612,154 +8821,33 @@
     "## Saving the best performing model for deployment into production"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "97193cc2",
+   "metadata": {},
+   "source": [
+    "It is always preferable to leverage the lightweight models with fast inferential capabilities while deploying. It would be more appropriate to use the third-best performing model, the Extra Trees Classifier, which offers significantly lower latency and comparable efficiency in predictive performance, as the top two models, the Stacking Classifier and the hyperparameter-optimized Random Forest Classifier, are relatively heavyweight and possess more latency despite being highly accurate."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 164,
    "id": "35ab60bf",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;model&#x27;,\n",
-       "                 StackingClassifier(estimators=[(&#x27;ET&#x27;, ExtraTreesClassifier()),\n",
-       "                                                (&#x27;XGB&#x27;,\n",
-       "                                                 XGBClassifier(base_score=None,\n",
-       "                                                               booster=None,\n",
-       "                                                               callbacks=None,\n",
-       "                                                               colsample_bylevel=None,\n",
-       "                                                               colsample_bynode=None,\n",
-       "                                                               colsample_bytree=None,\n",
-       "                                                               early_stopping_rounds=None,\n",
-       "                                                               enable_categorical=False,\n",
-       "                                                               eval_metric=None,\n",
-       "                                                               feature_types=None,\n",
-       "                                                               gamma=None,\n",
-       "                                                               gpu_i...\n",
-       "                                                               max_cat_threshold=None,\n",
-       "                                                               max_cat_to_onehot=None,\n",
-       "                                                               max_delta_step=None,\n",
-       "                                                               max_depth=None,\n",
-       "                                                               max_leaves=None,\n",
-       "                                                               min_child_weight=None,\n",
-       "                                                               missing=nan,\n",
-       "                                                               monotone_constraints=None,\n",
-       "                                                               n_estimators=100,\n",
-       "                                                               n_jobs=None,\n",
-       "                                                               num_parallel_tree=None,\n",
-       "                                                               predictor=None,\n",
-       "                                                               random_state=None, ...)),\n",
-       "                                                (&#x27;CAT&#x27;,\n",
-       "                                                 &lt;catboost.core.CatBoostClassifier object at 0x000002944CE3C9D0&gt;)],\n",
-       "                                    final_estimator=RandomForestClassifier(),\n",
-       "                                    verbose=2))])</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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;model&#x27;,\n",
-       "                 StackingClassifier(estimators=[(&#x27;ET&#x27;, ExtraTreesClassifier()),\n",
-       "                                                (&#x27;XGB&#x27;,\n",
-       "                                                 XGBClassifier(base_score=None,\n",
-       "                                                               booster=None,\n",
-       "                                                               callbacks=None,\n",
-       "                                                               colsample_bylevel=None,\n",
-       "                                                               colsample_bynode=None,\n",
-       "                                                               colsample_bytree=None,\n",
-       "                                                               early_stopping_rounds=None,\n",
-       "                                                               enable_categorical=False,\n",
-       "                                                               eval_metric=None,\n",
-       "                                                               feature_types=None,\n",
-       "                                                               gamma=None,\n",
-       "                                                               gpu_i...\n",
-       "                                                               max_cat_threshold=None,\n",
-       "                                                               max_cat_to_onehot=None,\n",
-       "                                                               max_delta_step=None,\n",
-       "                                                               max_depth=None,\n",
-       "                                                               max_leaves=None,\n",
-       "                                                               min_child_weight=None,\n",
-       "                                                               missing=nan,\n",
-       "                                                               monotone_constraints=None,\n",
-       "                                                               n_estimators=100,\n",
-       "                                                               n_jobs=None,\n",
-       "                                                               num_parallel_tree=None,\n",
-       "                                                               predictor=None,\n",
-       "                                                               random_state=None, ...)),\n",
-       "                                                (&#x27;CAT&#x27;,\n",
-       "                                                 &lt;catboost.core.CatBoostClassifier object at 0x000002944CE3C9D0&gt;)],\n",
-       "                                    final_estimator=RandomForestClassifier(),\n",
-       "                                    verbose=2))])</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-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" 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-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">model: StackingClassifier</label><div class=\"sk-toggleable__content\"><pre>StackingClassifier(estimators=[(&#x27;ET&#x27;, ExtraTreesClassifier()),\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",
-       "                                              early_stopping_rounds=None,\n",
-       "                                              enable_categorical=False,\n",
-       "                                              eval_metric=None,\n",
-       "                                              feature_types=None, gamma=None,\n",
-       "                                              gpu_id=None, grow_policy=None,\n",
-       "                                              importance_type=None,\n",
-       "                                              interact...\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",
-       "                                              n_estimators=100, n_jobs=None,\n",
-       "                                              num_parallel_tree=None,\n",
-       "                                              predictor=None, random_state=None, ...)),\n",
-       "                               (&#x27;CAT&#x27;,\n",
-       "                                &lt;catboost.core.CatBoostClassifier object at 0x000002944CE3C9D0&gt;)],\n",
-       "                   final_estimator=RandomForestClassifier(), verbose=2)</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 sk-toggleable\"><label>ET</label></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-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier()</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\"><label>XGB</label></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-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None,\n",
-       "              colsample_bytree=None, early_stopping_rounds=None,\n",
-       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
-       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
-       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
-       "              predictor=None, random_state=None, ...)</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\"><label>CAT</label></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-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x000002944CE3C9D0&gt;</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 sk-toggleable\"><label>final_estimator</label></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-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
+       "<style>#sk-container-id-23 {color: black;background-color: white;}#sk-container-id-23 pre{padding: 0;}#sk-container-id-23 div.sk-toggleable {background-color: white;}#sk-container-id-23 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-23 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-23 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-23 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-23 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-23 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-23 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-23 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 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-23 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-23 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-23 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-23 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 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-23 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-23 div.sk-item {position: relative;z-index: 1;}#sk-container-id-23 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-23 div.sk-item::before, #sk-container-id-23 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-23 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-23 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-23 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-23 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-23 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-23 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-23 div.sk-label-container {text-align: center;}#sk-container-id-23 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-23 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-23\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;model&#x27;, ExtraTreesClassifier())])</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-101\" type=\"checkbox\" ><label for=\"sk-estimator-id-101\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;model&#x27;, ExtraTreesClassifier())])</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-102\" type=\"checkbox\" ><label for=\"sk-estimator-id-102\" 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-103\" type=\"checkbox\" ><label for=\"sk-estimator-id-103\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier()</pre></div></div></div></div></div></div></div>"
       ],
       "text/plain": [
        "Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                ('model',\n",
-       "                 StackingClassifier(estimators=[('ET', ExtraTreesClassifier()),\n",
-       "                                                ('XGB',\n",
-       "                                                 XGBClassifier(base_score=None,\n",
-       "                                                               booster=None,\n",
-       "                                                               callbacks=None,\n",
-       "                                                               colsample_bylevel=None,\n",
-       "                                                               colsample_bynode=None,\n",
-       "                                                               colsample_bytree=None,\n",
-       "                                                               early_stopping_rounds=None,\n",
-       "                                                               enable_categorical=False,\n",
-       "                                                               eval_metric=None,\n",
-       "                                                               feature_types=None,\n",
-       "                                                               gamma=None,\n",
-       "                                                               gpu_i...\n",
-       "                                                               max_cat_threshold=None,\n",
-       "                                                               max_cat_to_onehot=None,\n",
-       "                                                               max_delta_step=None,\n",
-       "                                                               max_depth=None,\n",
-       "                                                               max_leaves=None,\n",
-       "                                                               min_child_weight=None,\n",
-       "                                                               missing=nan,\n",
-       "                                                               monotone_constraints=None,\n",
-       "                                                               n_estimators=100,\n",
-       "                                                               n_jobs=None,\n",
-       "                                                               num_parallel_tree=None,\n",
-       "                                                               predictor=None,\n",
-       "                                                               random_state=None, ...)),\n",
-       "                                                ('CAT',\n",
-       "                                                 <catboost.core.CatBoostClassifier object at 0x000002944CE3C9D0>)],\n",
-       "                                    final_estimator=RandomForestClassifier(),\n",
-       "                                    verbose=2))])"
-      ]
-     },
-     "execution_count": 134,
+       "                ('model', ExtraTreesClassifier())])"
+      ]
+     },
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8767,14 +8855,14 @@
    "source": [
     "pipeline = Pipeline(steps=[\n",
     "    ('scaler',scaler),\n",
-    "    ('model',best_model)\n",
+    "    ('model',model_perfs.iloc[3]['Model'])\n",
     "])\n",
     "pipeline"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 165,
    "id": "ae594670",
    "metadata": {},
    "outputs": [
@@ -8784,7 +8872,7 @@
        "['pipeline.pkl']"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 165,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8795,152 +8883,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 166,
    "id": "92f0f21f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 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-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 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-10 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-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 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-10 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-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 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-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 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-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 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-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;model&#x27;,\n",
-       "                 StackingClassifier(estimators=[(&#x27;ET&#x27;, ExtraTreesClassifier()),\n",
-       "                                                (&#x27;XGB&#x27;,\n",
-       "                                                 XGBClassifier(base_score=None,\n",
-       "                                                               booster=None,\n",
-       "                                                               callbacks=None,\n",
-       "                                                               colsample_bylevel=None,\n",
-       "                                                               colsample_bynode=None,\n",
-       "                                                               colsample_bytree=None,\n",
-       "                                                               early_stopping_rounds=None,\n",
-       "                                                               enable_categorical=False,\n",
-       "                                                               eval_metric=None,\n",
-       "                                                               feature_types=None,\n",
-       "                                                               gamma=None,\n",
-       "                                                               gpu_i...\n",
-       "                                                               max_cat_threshold=None,\n",
-       "                                                               max_cat_to_onehot=None,\n",
-       "                                                               max_delta_step=None,\n",
-       "                                                               max_depth=None,\n",
-       "                                                               max_leaves=None,\n",
-       "                                                               min_child_weight=None,\n",
-       "                                                               missing=nan,\n",
-       "                                                               monotone_constraints=None,\n",
-       "                                                               n_estimators=100,\n",
-       "                                                               n_jobs=None,\n",
-       "                                                               num_parallel_tree=None,\n",
-       "                                                               predictor=None,\n",
-       "                                                               random_state=None, ...)),\n",
-       "                                                (&#x27;CAT&#x27;,\n",
-       "                                                 &lt;catboost.core.CatBoostClassifier object at 0x0000029454240970&gt;)],\n",
-       "                                    final_estimator=RandomForestClassifier(),\n",
-       "                                    verbose=2))])</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-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;model&#x27;,\n",
-       "                 StackingClassifier(estimators=[(&#x27;ET&#x27;, ExtraTreesClassifier()),\n",
-       "                                                (&#x27;XGB&#x27;,\n",
-       "                                                 XGBClassifier(base_score=None,\n",
-       "                                                               booster=None,\n",
-       "                                                               callbacks=None,\n",
-       "                                                               colsample_bylevel=None,\n",
-       "                                                               colsample_bynode=None,\n",
-       "                                                               colsample_bytree=None,\n",
-       "                                                               early_stopping_rounds=None,\n",
-       "                                                               enable_categorical=False,\n",
-       "                                                               eval_metric=None,\n",
-       "                                                               feature_types=None,\n",
-       "                                                               gamma=None,\n",
-       "                                                               gpu_i...\n",
-       "                                                               max_cat_threshold=None,\n",
-       "                                                               max_cat_to_onehot=None,\n",
-       "                                                               max_delta_step=None,\n",
-       "                                                               max_depth=None,\n",
-       "                                                               max_leaves=None,\n",
-       "                                                               min_child_weight=None,\n",
-       "                                                               missing=nan,\n",
-       "                                                               monotone_constraints=None,\n",
-       "                                                               n_estimators=100,\n",
-       "                                                               n_jobs=None,\n",
-       "                                                               num_parallel_tree=None,\n",
-       "                                                               predictor=None,\n",
-       "                                                               random_state=None, ...)),\n",
-       "                                                (&#x27;CAT&#x27;,\n",
-       "                                                 &lt;catboost.core.CatBoostClassifier object at 0x0000029454240970&gt;)],\n",
-       "                                    final_estimator=RandomForestClassifier(),\n",
-       "                                    verbose=2))])</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-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" 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-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">model: StackingClassifier</label><div class=\"sk-toggleable__content\"><pre>StackingClassifier(estimators=[(&#x27;ET&#x27;, ExtraTreesClassifier()),\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",
-       "                                              early_stopping_rounds=None,\n",
-       "                                              enable_categorical=False,\n",
-       "                                              eval_metric=None,\n",
-       "                                              feature_types=None, gamma=None,\n",
-       "                                              gpu_id=None, grow_policy=None,\n",
-       "                                              importance_type=None,\n",
-       "                                              interact...\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",
-       "                                              n_estimators=100, n_jobs=None,\n",
-       "                                              num_parallel_tree=None,\n",
-       "                                              predictor=None, random_state=None, ...)),\n",
-       "                               (&#x27;CAT&#x27;,\n",
-       "                                &lt;catboost.core.CatBoostClassifier object at 0x0000029454240970&gt;)],\n",
-       "                   final_estimator=RandomForestClassifier(), verbose=2)</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 sk-toggleable\"><label>ET</label></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-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier()</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\"><label>XGB</label></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-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None,\n",
-       "              colsample_bytree=None, early_stopping_rounds=None,\n",
-       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
-       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
-       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
-       "              predictor=None, random_state=None, ...)</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\"><label>CAT</label></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-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x0000029454240970&gt;</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 sk-toggleable\"><label>final_estimator</label></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-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
+       "<style>#sk-container-id-24 {color: black;background-color: white;}#sk-container-id-24 pre{padding: 0;}#sk-container-id-24 div.sk-toggleable {background-color: white;}#sk-container-id-24 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-24 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-24 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-24 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-24 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-24 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-24 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-24 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-24 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 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-24 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-24 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-24 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-24 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 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-24 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-24 div.sk-item {position: relative;z-index: 1;}#sk-container-id-24 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-24 div.sk-item::before, #sk-container-id-24 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-24 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-24 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-24 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-24 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-24 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-24 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-24 div.sk-label-container {text-align: center;}#sk-container-id-24 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-24 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-24\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;model&#x27;, ExtraTreesClassifier())])</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-104\" type=\"checkbox\" ><label for=\"sk-estimator-id-104\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;model&#x27;, ExtraTreesClassifier())])</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-105\" type=\"checkbox\" ><label for=\"sk-estimator-id-105\" 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-106\" type=\"checkbox\" ><label for=\"sk-estimator-id-106\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier()</pre></div></div></div></div></div></div></div>"
       ],
       "text/plain": [
        "Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                ('model',\n",
-       "                 StackingClassifier(estimators=[('ET', ExtraTreesClassifier()),\n",
-       "                                                ('XGB',\n",
-       "                                                 XGBClassifier(base_score=None,\n",
-       "                                                               booster=None,\n",
-       "                                                               callbacks=None,\n",
-       "                                                               colsample_bylevel=None,\n",
-       "                                                               colsample_bynode=None,\n",
-       "                                                               colsample_bytree=None,\n",
-       "                                                               early_stopping_rounds=None,\n",
-       "                                                               enable_categorical=False,\n",
-       "                                                               eval_metric=None,\n",
-       "                                                               feature_types=None,\n",
-       "                                                               gamma=None,\n",
-       "                                                               gpu_i...\n",
-       "                                                               max_cat_threshold=None,\n",
-       "                                                               max_cat_to_onehot=None,\n",
-       "                                                               max_delta_step=None,\n",
-       "                                                               max_depth=None,\n",
-       "                                                               max_leaves=None,\n",
-       "                                                               min_child_weight=None,\n",
-       "                                                               missing=nan,\n",
-       "                                                               monotone_constraints=None,\n",
-       "                                                               n_estimators=100,\n",
-       "                                                               n_jobs=None,\n",
-       "                                                               num_parallel_tree=None,\n",
-       "                                                               predictor=None,\n",
-       "                                                               random_state=None, ...)),\n",
-       "                                                ('CAT',\n",
-       "                                                 <catboost.core.CatBoostClassifier object at 0x0000029454240970>)],\n",
-       "                                    final_estimator=RandomForestClassifier(),\n",
-       "                                    verbose=2))])"
-      ]
-     },
-     "execution_count": 136,
+       "                ('model', ExtraTreesClassifier())])"
+      ]
+     },
+     "execution_count": 166,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8969,6 +8928,107 @@
    "source": [
     "gc.collect()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "4434673b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.rename(renamed_cols,axis=1,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "c3e1d614",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimum value of WorkingCapital/Equity: 0.0\n",
+      "Maximum value of WorkingCapital/Equity: 1.0\n",
+      "Minimum value of PersistentEPSintheLastFourSeasons: 0.0\n",
+      "Maximum value of PersistentEPSintheLastFourSeasons: 1.0\n",
+      "Minimum value of BorrowingDependency: 0.0\n",
+      "Maximum value of BorrowingDependency: 1.0\n",
+      "Minimum value of NetValueGrowthRate: 0.0\n",
+      "Maximum value of NetValueGrowthRate: 9330000000.0\n",
+      "Minimum value of InterestBearingDebtInterestRate: 0.0\n",
+      "Maximum value of InterestBearingDebtInterestRate: 990000000.0\n",
+      "Minimum value of ROA(C)BeforeInterestAndDepreciationBeforeInterest: 0.0\n",
+      "Maximum value of ROA(C)BeforeInterestAndDepreciationBeforeInterest: 1.0\n",
+      "Minimum value of Cash/TotalAssets: 0.0\n",
+      "Maximum value of Cash/TotalAssets: 1.0\n",
+      "Minimum value of NonIndustryIncomeAndExpenditure/Revenue: 0.0\n",
+      "Maximum value of NonIndustryIncomeAndExpenditure/Revenue: 1.0\n",
+      "Minimum value of NetValuePerShare(B): 0.0\n",
+      "Maximum value of NetValuePerShare(B): 1.0\n",
+      "Minimum value of TotalDebt/TotalNetWorth: 0.0\n",
+      "Maximum value of TotalDebt/TotalNetWorth: 9940000000.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in final_X_train.columns:\n",
+    "    print(f\"Minimum value of {col}:\",df[col].min())\n",
+    "    print(f\"Maximum value of {col}:\",df[col].max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "81b1753d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['WorkingCapital/Equity',\n",
+       " 'PersistentEPSintheLastFourSeasons',\n",
+       " 'BorrowingDependency',\n",
+       " 'NetValueGrowthRate',\n",
+       " 'InterestBearingDebtInterestRate',\n",
+       " 'ROA(C)BeforeInterestAndDepreciationBeforeInterest',\n",
+       " 'Cash/TotalAssets',\n",
+       " 'NonIndustryIncomeAndExpenditure/Revenue',\n",
+       " 'NetValuePerShare(B)',\n",
+       " 'TotalDebt/TotalNetWorth']"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(final_X_train.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "83518732",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12660"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "del pipeline\n",
+    "gc.collect()"
+   ]
   }
  ],
  "metadata": {
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..e0ba684
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,6 @@
+FROM python
+COPY . /app
+WORKDIR /app
+RUN pip install -r requirements.txt
+EXPOSE $PORT
+CMD gunicorn --workers=4 --bind 0.0.0.0:$PORT app:app
\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000..01e1a8d
--- /dev/null
+++ b/app.py
@@ -0,0 +1,55 @@
+from flask import Flask, render_template, request
+import joblib
+import pandas as pd
+
+app = Flask(__name__)
+
+pipeline = joblib.load('pipeline.pkl')
+
+cols = ['WorkingCapital/Equity',
+ 'PersistentEPSintheLastFourSeasons',
+ 'BorrowingDependency',
+ 'NetValueGrowthRate',
+ 'InterestBearingDebtInterestRate',
+ 'ROA(C)BeforeInterestAndDepreciationBeforeInterest',
+ 'Cash/TotalAssets',
+ 'NonIndustryIncomeAndExpenditure/Revenue',
+ 'NetValuePerShare(B)',
+ 'TotalDebt/TotalNetWorth']
+
+@app.route("/")
+def home():
+    return render_template('index.html')
+
+@app.route("/predict",methods=['GET', 'POST'])
+def predict():
+    if request.method == 'POST':
+        working_capital_equity = request.form['working_capital_equity']
+        persistent_eps_in_last_4_seasons = request.form['persistent_eps_in_last_4_seasons']
+        borrowing_dependency = request.form['borrowing_dependency']
+        net_value_growth_rate = request.form['net_value_growth_rate']
+        interest_bearing_debt_interest_rate = request.form['interest_bearing_debt_interest_rate']
+        roa_c_before_interest_and_depreciation_before_interest = request.form['roa_c_before_interest_and_depreciation_before_interest']
+        cash_total_assets = request.form['cash_total_assets']
+        non_industry_income_and_expenditure_revenue = request.form['non_industry_income_and_expenditure_revenue']
+        net_value_per_share_b = request.form['net_value_per_share_b']
+        total_debt_total_net_worth = request.form['total_debt_total_net_worth']
+        data = pd.DataFrame([[working_capital_equity,
+                 persistent_eps_in_last_4_seasons,
+                 borrowing_dependency,
+                 net_value_growth_rate,
+                 interest_bearing_debt_interest_rate,
+                 roa_c_before_interest_and_depreciation_before_interest,
+                 cash_total_assets,
+                 non_industry_income_and_expenditure_revenue,
+                 net_value_per_share_b,
+                 total_debt_total_net_worth]],columns=cols)
+        pred = pipeline.predict(data)
+        print(pred)
+        if pred == 0:
+            return render_template('index.html',prediction_text="The company with the specified details will not become bankrupt.")
+        elif pred == 1:
+            return render_template('index.html',prediction_text="The company with the specified details will become bankrupt in the near future.")
+        
+if __name__ == '__main__':
+    app.run(port=8000)
\ No newline at end of file
diff --git a/catboost_info/catboost_training.json b/catboost_info/catboost_training.json
index b4b9225..73d150f 100644
--- a/catboost_info/catboost_training.json
+++ b/catboost_info/catboost_training.json
@@ -1,1004 +1,1004 @@
 {
 "meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"Logloss"}],"launch_mode":"Train","parameters":"","iteration_count":1000,"learn_sets":["learn"],"name":"experiment"},
 "iterations":[
-{"learn":[0.67261931],"iteration":0,"passed_time":0.0145062491,"remaining_time":14.49174285},
-{"learn":[0.6502083136],"iteration":1,"passed_time":0.02701109905,"remaining_time":13.47853843},
-{"learn":[0.630778058],"iteration":2,"passed_time":0.03882768294,"remaining_time":12.9037333},
-{"learn":[0.6108958418],"iteration":3,"passed_time":0.05034872273,"remaining_time":12.53683196},
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diff --git a/catboost_info/learn/events.out.tfevents b/catboost_info/learn/events.out.tfevents
index 20fbd23..4461b45 100644
Binary files a/catboost_info/learn/events.out.tfevents and b/catboost_info/learn/events.out.tfevents differ
diff --git a/catboost_info/learn_error.tsv b/catboost_info/learn_error.tsv
index b4d854a..d5af63b 100644
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diff --git a/catboost_info/time_left.tsv b/catboost_info/time_left.tsv
index 8930b64..9673619 100644
--- a/catboost_info/time_left.tsv
+++ b/catboost_info/time_left.tsv
@@ -1,1001 +1,1001 @@
 iter	Passed	Remaining
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-3	50	12536
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 52	393	7024
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diff --git a/pipeline.pkl b/pipeline.pkl
index 61758cd..d17029c 100644
Binary files a/pipeline.pkl and b/pipeline.pkl differ
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..e97d0ce
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,5 @@
+joblib
+gunicorn
+flask
+scikit-learn
+pandas
\ No newline at end of file
diff --git a/templates/index.html b/templates/index.html
new file mode 100644
index 0000000..77bc84f
--- /dev/null
+++ b/templates/index.html
@@ -0,0 +1,202 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Company Bankruptcy Prediction</title>
+    <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.0/css/bootstrap.min.css"
+        integrity="sha384-9aIt2nRpC12Uk9gS9baDl411NQApFmC26EwAOH8WgZl5MYYxFfc+NcPb1dKGj7Sk" crossorigin="anonymous">
+    <style>
+        body {
+            background-color: #85f2f6;
+        }
+        .navbar-brand {
+            display: block;
+            margin: auto;
+            color: #4c118a;
+            font-weight: bold;
+            font-family: "Helvetica";
+        }
+        .submit-button {
+            display: block;
+            margin: auto;
+        }
+        p {
+            text-align: center;
+        }
+        p:hover {
+            filter: brightness(250%) saturate(100%);
+        }
+        .card {
+            display: block;
+            margin: auto;
+            text-align: center;
+        }
+        h5 {
+            font-weight: bold;
+            font-family: "Helvetica",sans;
+            color: #9c3803;
+            font-size: 1.8rem;
+        }
+        h1{
+            color: #0c6709;
+            font-family: 'Georgia', serif;
+            font-size: 3rem;
+            font-weight: bold;
+        }
+        .first_row {
+            margin-top: 5rem;
+        }
+        .last_row {
+            margin-bottom: 2rem;
+        }
+        .card {
+            margin: 0 auto;
+            float: none;
+            margin-bottom: 10px;
+            background-color: #c4d80d;
+            border: 2px solid #220254;
+            border-width: 0.5rem;
+            size: 6rem;
+        }
+        .copyright{
+            color: #ca0c7b;
+            font-size: 1.5rem;
+            font-family: 'Arial',sans-serif;
+            font-weight: bold;
+        }
+        .predict-dec {
+            text-align: center;
+            font-weight: bold;
+            color: #5c2f03;
+            font-size: 2rem;
+            font-family: 'Helvetica',sans-serif;
+            font-weight: bold;
+        }
+    </style>
+</head>
+<body>
+    <nav class="navbar navbar-light bg-light">
+        <span class="navbar-brand mb-0 h1"><h1>Company Bankruptcy Prediction</h1></span>
+    </nav>
+    <div class="container">
+        <form action="/predict" method="post">
+            <div class="row first_row">
+                <div class="col-sm-6">
+                    <div class="card">
+                        <div class="card-body">
+                            <h5 class="card-title">Working Capital / Equity:</h5>
+                            <input type="number" min="0" max="1" step="0.001" name="working_capital_equity" id="working_capital_equity" required="required">
+                        </div>
+                    </div>
+                </div>
+                <br>
+                <div class="col-sm-6">
+                    <div class="card">
+                        <div class="card-body">
+                            <h5 class="card-title">Persistent EPS(Earnings per Share) in the last four seasons:</h5>
+                            <input type="number" min="0" max="1" step="0.001" name="persistent_eps_in_last_4_seasons" id="persistent_eps_in_last_4_seasons" required="required">
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <br>
+            <div class="row">
+                <div class="col-sm-6">
+                    <div class="card">
+                        <div class="card-body">
+                            <h5 class="card-title">Borrowing Dependency:</h5>
+                            <input type="number" min="0" max="1" step="0.001" name="borrowing_dependency" id="borrowing_dependency" required="required">
+                        </div>
+                    </div>
+                </div>
+                <div class="col-sm-6">
+                    <div class="card">
+                        <div class="card-body">
+                            <h5 class="card-title">Net Value Growth Rate:</h5>
+                            <input type="number" min="0" max="9330000000" step="0.0001" name="net_value_growth_rate" id="net_value_growth_rate" required="required">
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <br>
+            <div class="row"> 
+                <div class="col-sm-6">
+                    <div class="card">
+                        <div class="card-body">
+                            <h5 class="card-title">Interest-Bearing Debt Interest Rate:</h5>
+                            <input type="number" min="0" max="990000000" step="0.0001" name="interest_bearing_debt_interest_rate" id="interest_bearing_debt_interest_rate" required="required">
+                        </div>
+                    </div>
+                </div>
+                <div class="col-sm-6">
+                    <div class="card">
+                        <div class="card-body">
+                            <h5 class="card-title">Return on Assets (ROA) Before Interest and Depreciation Before Interest:</h5>
+                            <input type="number" min="0" max="1" step="0.001" name="roa_c_before_interest_and_depreciation_before_interest" id="roa_c_before_interest_and_depreciation_before_interest" required="required">
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <br>
+            <div class="row">
+                <div class="col-sm-6">
+                    <div class="card">
+                        <div class="card-body">
+                            <div class="card-body">
+                                <h5 class="card-title">Cash / Total Assets:</h5>
+                                <input type="number" min="0" max="1" step="0.001" name="cash_total_assets" id="cash_total_assets" required="required">
+                            </div>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-sm-6">
+                    <div class="card">
+                        <div class="card-body">
+                            <h5 class="card-title">Non-Industry Income and Expenditure / Revenue:</h5>
+                            <input type="number" min="0" max="1" step="0.001" name="non_industry_income_and_expenditure_revenue" id="non_industry_income_and_expenditure_revenue" required="required">
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <br>
+            <div class="row">
+                <div class="col-sm-6">
+                    <div class="card">
+                        <div class="card-body">
+                            <h5 class="card-title">Net Value per Share:</h5>
+                            <input type="number" min="0" max="1" step="0.001" name="net_value_per_share_b" id="net_value_per_share_b" required="required">
+                        </div>
+                    </div>
+                </div>
+                <div class="col-sm-6">
+                    <div class="card">
+                        <div class="card-body">
+                            <h5 class="card-title">Total Debt / Total Net Worth:</h5>
+                            <input type="number" min="0" max="9940000000" step="0.001" name="total_debt_total_net_worth" id="total_debt_total_net_worth" required="required">
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <br><br>
+            <!-- Submit -->
+            <input type="submit" value="Submit" class="btn btn-primary submit-button">
+        </form>
+        <br>
+        <h3 class="predict-dec">{{ prediction_text }}</h3> 
+        <br>
+        <p class="copyright">Copyrights 2023 Sayam Kumar</p>
+    </div>
+
+    <script src="https://code.jquery.com/jquery-3.5.1.slim.min.js"
+        integrity="sha384-DfXdz2htPH0lsSSs5nCTpuj/zy4C+OGpamoFVy38MVBnE+IbbVYUew+OrCXaRkfj"
+        crossorigin="anonymous"></script>
+    <script src="https://cdn.jsdelivr.net/npm/popper.js@1.16.0/dist/umd/popper.min.js"
+        integrity="sha384-Q6E9RHvbIyZFJoft+2mJbHaEWldlvI9IOYy5n3zV9zzTtmI3UksdQRVvoxMfooAo"
+        crossorigin="anonymous"></script>
+    <script src="https://stackpath.bootstrapcdn.com/bootstrap/4.5.0/js/bootstrap.min.js"
+        integrity="sha384-OgVRvuATP1z7JjHLkuOU7Xw704+h835Lr+6QL9UvYjZE3Ipu6Tp75j7Bh/kR0JKI"
+        crossorigin="anonymous"></script>
+</body>
+</html>
\ No newline at end of file