diff --git a/Chapter11/Exercise150-155/Exercise150-155.ipynb b/Chapter11/Exercise150-155/Exercise150-155.ipynb
deleted file mode 100644
index 4ec1969..0000000
--- a/Chapter11/Exercise150-155/Exercise150-155.ipynb
+++ /dev/null
@@ -1,827 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>140.5625</th>\n",
-       "      <th>55.68378214</th>\n",
-       "      <th>-0.234571412</th>\n",
-       "      <th>-0.699648398</th>\n",
-       "      <th>3.199832776</th>\n",
-       "      <th>19.11042633</th>\n",
-       "      <th>7.975531794</th>\n",
-       "      <th>74.24222492</th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>102.507812</td>\n",
-       "      <td>58.882430</td>\n",
-       "      <td>0.465318</td>\n",
-       "      <td>-0.515088</td>\n",
-       "      <td>1.677258</td>\n",
-       "      <td>14.860146</td>\n",
-       "      <td>10.576487</td>\n",
-       "      <td>127.393580</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>103.015625</td>\n",
-       "      <td>39.341649</td>\n",
-       "      <td>0.323328</td>\n",
-       "      <td>1.051164</td>\n",
-       "      <td>3.121237</td>\n",
-       "      <td>21.744669</td>\n",
-       "      <td>7.735822</td>\n",
-       "      <td>63.171909</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>136.750000</td>\n",
-       "      <td>57.178449</td>\n",
-       "      <td>-0.068415</td>\n",
-       "      <td>-0.636238</td>\n",
-       "      <td>3.642977</td>\n",
-       "      <td>20.959280</td>\n",
-       "      <td>6.896499</td>\n",
-       "      <td>53.593661</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>88.726562</td>\n",
-       "      <td>40.672225</td>\n",
-       "      <td>0.600866</td>\n",
-       "      <td>1.123492</td>\n",
-       "      <td>1.178930</td>\n",
-       "      <td>11.468720</td>\n",
-       "      <td>14.269573</td>\n",
-       "      <td>252.567306</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>93.570312</td>\n",
-       "      <td>46.698114</td>\n",
-       "      <td>0.531905</td>\n",
-       "      <td>0.416721</td>\n",
-       "      <td>1.636288</td>\n",
-       "      <td>14.545074</td>\n",
-       "      <td>10.621748</td>\n",
-       "      <td>131.394004</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     140.5625  55.68378214  -0.234571412  -0.699648398  3.199832776  \\\n",
-       "0  102.507812    58.882430      0.465318     -0.515088     1.677258   \n",
-       "1  103.015625    39.341649      0.323328      1.051164     3.121237   \n",
-       "2  136.750000    57.178449     -0.068415     -0.636238     3.642977   \n",
-       "3   88.726562    40.672225      0.600866      1.123492     1.178930   \n",
-       "4   93.570312    46.698114      0.531905      0.416721     1.636288   \n",
-       "\n",
-       "   19.11042633  7.975531794  74.24222492  0  \n",
-       "0    14.860146    10.576487   127.393580  0  \n",
-       "1    21.744669     7.735822    63.171909  0  \n",
-       "2    20.959280     6.896499    53.593661  0  \n",
-       "3    11.468720    14.269573   252.567306  0  \n",
-       "4    14.545074    10.621748   131.394004  0  "
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "df = pd.read_csv('HTRU_2.csv')\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>Mean of integrated profile</th>\n",
-       "      <th>Standard deviation of integrated profile</th>\n",
-       "      <th>Excess kurtosis of integrated profile</th>\n",
-       "      <th>Skewness of integrated profile</th>\n",
-       "      <th>Mean of DM-SNR curve</th>\n",
-       "      <th>Standard deviation of DM-SNR curve</th>\n",
-       "      <th>Excess kurtosis of DM-SNR curve</th>\n",
-       "      <th>Skewness of DM-SNR curve</th>\n",
-       "      <th>Class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>140.562500</td>\n",
-       "      <td>55.683782</td>\n",
-       "      <td>-0.234571</td>\n",
-       "      <td>-0.699648</td>\n",
-       "      <td>3.199833</td>\n",
-       "      <td>19.110426</td>\n",
-       "      <td>7.975532</td>\n",
-       "      <td>74.242225</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>102.507812</td>\n",
-       "      <td>58.882430</td>\n",
-       "      <td>0.465318</td>\n",
-       "      <td>-0.515088</td>\n",
-       "      <td>1.677258</td>\n",
-       "      <td>14.860146</td>\n",
-       "      <td>10.576487</td>\n",
-       "      <td>127.393580</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>103.015625</td>\n",
-       "      <td>39.341649</td>\n",
-       "      <td>0.323328</td>\n",
-       "      <td>1.051164</td>\n",
-       "      <td>3.121237</td>\n",
-       "      <td>21.744669</td>\n",
-       "      <td>7.735822</td>\n",
-       "      <td>63.171909</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>136.750000</td>\n",
-       "      <td>57.178449</td>\n",
-       "      <td>-0.068415</td>\n",
-       "      <td>-0.636238</td>\n",
-       "      <td>3.642977</td>\n",
-       "      <td>20.959280</td>\n",
-       "      <td>6.896499</td>\n",
-       "      <td>53.593661</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>88.726562</td>\n",
-       "      <td>40.672225</td>\n",
-       "      <td>0.600866</td>\n",
-       "      <td>1.123492</td>\n",
-       "      <td>1.178930</td>\n",
-       "      <td>11.468720</td>\n",
-       "      <td>14.269573</td>\n",
-       "      <td>252.567306</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  Mean of integrated profile Standard deviation of integrated profile  \\\n",
-       "0                 140.562500                                55.683782   \n",
-       "1                 102.507812                                58.882430   \n",
-       "2                 103.015625                                39.341649   \n",
-       "3                 136.750000                                57.178449   \n",
-       "4                  88.726562                                40.672225   \n",
-       "\n",
-       "  Excess kurtosis of integrated profile Skewness of integrated profile  \\\n",
-       "0                             -0.234571                      -0.699648   \n",
-       "1                              0.465318                      -0.515088   \n",
-       "2                              0.323328                       1.051164   \n",
-       "3                             -0.068415                      -0.636238   \n",
-       "4                              0.600866                       1.123492   \n",
-       "\n",
-       "  Mean of DM-SNR curve Standard deviation of DM-SNR curve  \\\n",
-       "0             3.199833                          19.110426   \n",
-       "1             1.677258                          14.860146   \n",
-       "2             3.121237                          21.744669   \n",
-       "3             3.642977                          20.959280   \n",
-       "4             1.178930                          11.468720   \n",
-       "\n",
-       "  Excess kurtosis of DM-SNR curve Skewness of DM-SNR curve Class  \n",
-       "0                        7.975532                74.242225     0  \n",
-       "1                       10.576487               127.393580     0  \n",
-       "2                        7.735822                63.171909     0  \n",
-       "3                        6.896499                53.593661     0  \n",
-       "4                       14.269573               252.567306     0  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv('HTRU_2.csv', header = None)\n",
-    "df.columns = [['Mean of integrated profile', 'Standard deviation of integrated profile', \n",
-    "               'Excess kurtosis of integrated profile', 'Skewness of integrated profile',\n",
-    "               'Mean of DM-SNR curve', 'Standard deviation of DM-SNR curve',\n",
-    "               'Excess kurtosis of DM-SNR curve', 'Skewness of DM-SNR curve', 'Class' ]]\n",
-    "\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 17898 entries, 0 to 17897\n",
-      "Data columns (total 9 columns):\n",
-      "(Mean of integrated profile,)                  17898 non-null float64\n",
-      "(Standard deviation of integrated profile,)    17898 non-null float64\n",
-      "(Excess kurtosis of integrated profile,)       17898 non-null float64\n",
-      "(Skewness of integrated profile,)              17898 non-null float64\n",
-      "(Mean of DM-SNR curve,)                        17898 non-null float64\n",
-      "(Standard deviation of DM-SNR curve,)          17898 non-null float64\n",
-      "(Excess kurtosis of DM-SNR curve,)             17898 non-null float64\n",
-      "(Skewness of DM-SNR curve,)                    17898 non-null float64\n",
-      "(Class,)                                       17898 non-null int64\n",
-      "dtypes: float64(8), int64(1)\n",
-      "memory usage: 1.2 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "17898"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Exercise152 begins from here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.model_selection import cross_val_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = df.iloc[:, 0:8]\n",
-    "y = df.iloc[:, 8]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def clf_model(model):\n",
-    "    clf = model\n",
-    "\n",
-    "    scores = cross_val_score(clf, X, y)\n",
-    "\n",
-    "    print('Scores:', scores)\n",
-    "    print('Mean score:', scores.mean())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scores: [0.9740238  0.98223265 0.97686505]\n",
-      "Mean score: 0.9777071651161332\n"
-     ]
-    }
-   ],
-   "source": [
-    "clf_model(LogisticRegression(random_state = 0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Exercise153 begins from here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scores: [0.95692978 0.92474019 0.94836547]\n",
-      "Mean score: 0.9433451467026904\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.naive_bayes import GaussianNB\n",
-    "clf_model(GaussianNB())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scores: [0.96899615 0.97200805 0.97082984]\n",
-      "Mean score: 0.9706113439320188\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "clf_model(KNeighborsClassifier())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scores: [0.96782303 0.96161582 0.9673093 ]\n",
-      "Mean score: 0.9655827179728252\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "clf_model(DecisionTreeClassifier(random_state = 0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scores: [0.97586727 0.97820986 0.97720034]\n",
-      "Mean score: 0.9770924870413066\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "clf_model(RandomForestClassifier(random_state = 0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Exercise154 begins from here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Class    17898\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.Class.count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Class    1639\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[df.Class == 1].Class.count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Class    0.091574\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[df.Class == 1].Class.count()/df.Class.count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import classification_report\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "from sklearn.model_selection import train_test_split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def confusion(model):\n",
-    "    clf = model\n",
-    "    clf.fit(X_train, y_train)\n",
-    "    y_pred = clf.predict(X_test)\n",
-    "    print('Confusion Matrix:', confusion_matrix(y_test, y_pred))\n",
-    "    print('Classification Report:', classification_report(y_test, y_pred))\n",
-    "    return clf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Confusion Matrix: [[4094   21]\n",
-      " [  63  297]]\n",
-      "Classification Report:              precision    recall  f1-score   support\n",
-      "\n",
-      "          0       0.98      0.99      0.99      4115\n",
-      "          1       0.93      0.82      0.88       360\n",
-      "\n",
-      "avg / total       0.98      0.98      0.98      4475\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
-       "          penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
-       "          verbose=0, warm_start=False)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion(LogisticRegression(random_state = 0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Confusion Matrix: [[4077   38]\n",
-      " [  69  291]]\n",
-      "Classification Report:              precision    recall  f1-score   support\n",
-      "\n",
-      "          0       0.98      0.99      0.99      4115\n",
-      "          1       0.88      0.81      0.84       360\n",
-      "\n",
-      "avg / total       0.98      0.98      0.98      4475\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
-       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
-       "           weights='uniform')"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion(KNeighborsClassifier())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Confusion Matrix: [[3946  169]\n",
-      " [  52  308]]\n",
-      "Classification Report:              precision    recall  f1-score   support\n",
-      "\n",
-      "          0       0.99      0.96      0.97      4115\n",
-      "          1       0.65      0.86      0.74       360\n",
-      "\n",
-      "avg / total       0.96      0.95      0.95      4475\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "GaussianNB(priors=None)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion(GaussianNB())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Confusion Matrix: [[4093   22]\n",
-      " [  61  299]]\n",
-      "Classification Report:              precision    recall  f1-score   support\n",
-      "\n",
-      "          0       0.99      0.99      0.99      4115\n",
-      "          1       0.93      0.83      0.88       360\n",
-      "\n",
-      "avg / total       0.98      0.98      0.98      4475\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
-       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
-       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
-       "            min_samples_leaf=1, min_samples_split=2,\n",
-       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
-       "            oob_score=False, random_state=0, verbose=0, warm_start=False)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion(RandomForestClassifier(random_state = 0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Exercise156 begins from here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scores: [0.97519692 0.98122695 0.97652976]\n",
-      "Mean score: 0.9776512086736252\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.ensemble import AdaBoostClassifier\n",
-    "clf_model(AdaBoostClassifier(random_state = 0))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Confusion Matrix: [[4094   21]\n",
-      " [  63  297]]\n",
-      "Classification Report:              precision    recall  f1-score   support\n",
-      "\n",
-      "          0       0.98      0.99      0.99      4115\n",
-      "          1       0.93      0.82      0.88       360\n",
-      "\n",
-      "avg / total       0.98      0.98      0.98      4475\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
-       "          learning_rate=1.0, n_estimators=50, random_state=0)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion(AdaBoostClassifier(random_state = 0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python_Workshop",
-   "language": "python",
-   "name": "python_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}