diff --git a/notebooks/02_01_b.ipynb b/notebooks/02_01_b.ipynb
index f7d4637..c828524 100644
--- a/notebooks/02_01_b.ipynb
+++ b/notebooks/02_01_b.ipynb
@@ -9,45 +9,134 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import sklearn\n",
+    "\n",
+    "from pylab import rcParams\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.preprocessing import scale"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "%matplotlib inline\n",
+    "rcParams['figure.figsize'] = 10, 8"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2.84884416],\n",
+       "       [3.12830115],\n",
+       "       [3.85274322],\n",
+       "       [3.09730542],\n",
+       "       [1.14883245],\n",
+       "       [7.47831303],\n",
+       "       [5.31171512],\n",
+       "       [4.09584508],\n",
+       "       [4.26109216],\n",
+       "       [4.72142214]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rooms = 2*np.random.randn(100,1)+3\n",
+    "rooms[0:10]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[273.11183584],\n",
+       "       [276.02890594],\n",
+       "       [279.61560297],\n",
+       "       [275.28644444],\n",
+       "       [263.5207815 ],\n",
+       "       [301.65215922],\n",
+       "       [288.5387018 ],\n",
+       "       [281.61306579],\n",
+       "       [282.54865954],\n",
+       "       [284.93961803]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price = 256 +6*rooms + abs(np.random.randn(100,1))\n",
+    "price[0:10]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(rooms,price, 'r^')\n",
+    "plt.xlabel('# of rooms, 2024 Average')\n",
+    "plt.ylabel('2024 average home price , $1000s USD')\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[256.7704585] [[5.98760393]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = rooms\n",
+    "y = price\n",
+    "\n",
+    "LinReg = LinearRegression()\n",
+    "LinReg.fit(X,y)\n",
+    "print(LinReg.intercept_, LinReg.coef_)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -55,18 +144,28 @@
    "source": [
     "*Simple Algebra*\n",
     "- y = mx + b\n",
-    "- b = intercept = 265.7\n",
+    "- b = intercept = 256.7\n",
     "\n",
     "*Estimated Coefficients*\n",
-    "- LinReg.coef_ = [5.99] Estimated coefficients for the terms in the linear regression problem."
+    "- LinReg.coef_ = [5.98] Estimated coefficients for the terms in the linear regression problem."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9970921136250733\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(LinReg.score(X,y))"
+   ]
   },
   {
    "cell_type": "code",
@@ -92,7 +191,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.1"
   },
   "nav_menu": {
    "height": "279px",
diff --git a/notebooks/02_02_b.ipynb b/notebooks/02_02_b.ipynb
index 2389c30..741e528 100644
--- a/notebooks/02_02_b.ipynb
+++ b/notebooks/02_02_b.ipynb
@@ -48,45 +48,206 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "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>year</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>unem</th>\n",
+       "      <th>hgrad</th>\n",
+       "      <th>inc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5501</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>9552</td>\n",
+       "      <td>1923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5945</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9680</td>\n",
+       "      <td>1961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6629</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>9731</td>\n",
+       "      <td>1979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>7556</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>11666</td>\n",
+       "      <td>2030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>8716</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>14675</td>\n",
+       "      <td>2112</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year  roll  unem  hgrad   inc\n",
+       "0     1  5501   8.1   9552  1923\n",
+       "1     2  5945   7.0   9680  1961\n",
+       "2     3  6629   7.3   9731  1979\n",
+       "3     4  7556   7.5  11666  2030\n",
+       "4     5  8716   7.0  14675  2112"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "address = '/workspaces/python-for-data-science-and-machine-learning-essential-training-part-2-3016934/data/enrollment_forecast.csv'\n",
+    "enroll = pd.read_csv(address)\n",
+    "enroll.columns = ['year','roll','unem','hgrad','inc']\n",
+    "enroll.head()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x7b5bb7036300>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1250x1250 with 30 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(enroll)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           year      roll      unem     hgrad       inc\n",
+      "year   1.000000  0.900934  0.378305  0.670300  0.944287\n",
+      "roll   0.900934  1.000000  0.391344  0.890294  0.949876\n",
+      "unem   0.378305  0.391344  1.000000  0.177376  0.282310\n",
+      "hgrad  0.670300  0.890294  0.177376  1.000000  0.820089\n",
+      "inc    0.944287  0.949876  0.282310  0.820089  1.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(enroll.corr())"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "enroll_date = enroll[['unem','hgrad']].values\n",
+    "enroll_target = enroll['roll'].values\n",
+    "enrool_data_names = ['unem','hgrad']\n",
+    "X,y = scale(enroll_date), enroll_target"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([], dtype=float64)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_values = X==np.nan\n",
+    "X[missing_values == True]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8488812666133723\n"
+     ]
+    }
+   ],
+   "source": [
+    "LinReg = LinearRegression()\n",
+    "LinReg.fit(X,y)\n",
+    "print(LinReg.score(X,y))"
+   ]
   },
   {
    "cell_type": "code",
@@ -113,7 +274,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/02_03_b.ipynb b/notebooks/02_03_b.ipynb
index b1fc382..64547a3 100644
--- a/notebooks/02_03_b.ipynb
+++ b/notebooks/02_03_b.ipynb
@@ -51,17 +51,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   PassengerID  Survived  Pclass  \\\n",
+      "0            1         0       3   \n",
+      "1            2         1       1   \n",
+      "2            3         1       3   \n",
+      "3            4         1       1   \n",
+      "4            5         0       3   \n",
+      "\n",
+      "                                                Name     Sex   Age  SibSp  \\\n",
+      "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+      "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+      "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+      "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+      "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+      "\n",
+      "   Parch            Ticket     Fare Cabin Embarked  \n",
+      "0      0         A/5 21171   7.2500   NaN        S  \n",
+      "1      0          PC 17599  71.2833   C85        C  \n",
+      "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+      "3      0            113803  53.1000  C123        S  \n",
+      "4      0            373450   8.0500   NaN        S  \n"
+     ]
+    }
+   ],
+   "source": [
+    "url = '/workspaces/python-for-data-science-and-machine-learning-essential-training-part-2-3016934/data/titanic-training-data.csv'\n",
+    "titanic_training = pd.read_csv(url)\n",
+    "titanic_training.columns = ['PassengerID','Survived','Pclass','Name','Sex','Age','SibSp','Parch','Ticket','Fare','Cabin','Embarked']\n",
+    "print(titanic_training.head())"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   PassengerID  891 non-null    int64  \n",
+      " 1   Survived     891 non-null    int64  \n",
+      " 2   Pclass       891 non-null    int64  \n",
+      " 3   Name         891 non-null    object \n",
+      " 4   Sex          891 non-null    object \n",
+      " 5   Age          714 non-null    float64\n",
+      " 6   SibSp        891 non-null    int64  \n",
+      " 7   Parch        891 non-null    int64  \n",
+      " 8   Ticket       891 non-null    object \n",
+      " 9   Fare         891 non-null    float64\n",
+      " 10  Cabin        204 non-null    object \n",
+      " 11  Embarked     889 non-null    object \n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.7+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(titanic_training.info())"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -87,7 +148,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -101,7 +162,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/02_04_b.ipynb b/notebooks/02_04_b.ipynb
index ab00927..73a07a5 100644
--- a/notebooks/02_04_b.ipynb
+++ b/notebooks/02_04_b.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,18 +51,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'pd' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/workspaces/python-for-data-science-and-machine-learning-essential-training-part-2-3016934/data/titanic-training-data.csv\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 2\u001b[0m titanic_training \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mread_csv(url)\n\u001b[1;32m      3\u001b[0m titanic_training\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPassengerId\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSurvived\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPclass\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mName\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSex\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAge\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSibSp\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mParch\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTicket\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFare\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCabin\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEmbarked\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(titanic_training\u001b[38;5;241m.\u001b[39mhead())\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   PassengerId  Survived  Pclass  \\\n",
+      "0            1         0       3   \n",
+      "1            2         1       1   \n",
+      "2            3         1       3   \n",
+      "3            4         1       1   \n",
+      "4            5         0       3   \n",
+      "\n",
+      "                                                Name     Sex   Age  SibSp  \\\n",
+      "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+      "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+      "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+      "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+      "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+      "\n",
+      "   Parch            Ticket     Fare Cabin Embarked  \n",
+      "0      0         A/5 21171   7.2500   NaN        S  \n",
+      "1      0          PC 17599  71.2833   C85        C  \n",
+      "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+      "3      0            113803  53.1000  C123        S  \n",
+      "4      0            373450   8.0500   NaN        S  \n"
      ]
     }
    ],
@@ -75,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -132,10 +147,337 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Survived', ylabel='count'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='Survived',hue='Survived',data=titanic_training,palette='hls',legend=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      0\n",
+       "Survived         0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age            177\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             0\n",
+       "Cabin          687\n",
+       "Embarked         2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_training.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>32.204208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>49.693429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>223.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>20.125000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.910400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>668.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
+       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
+       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
+       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
+       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
+       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
+       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
+       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
+       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
+       "\n",
+       "            Parch        Fare  \n",
+       "count  891.000000  891.000000  \n",
+       "mean     0.381594   32.204208  \n",
+       "std      0.806057   49.693429  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.000000    7.910400  \n",
+       "50%      0.000000   14.454200  \n",
+       "75%      0.000000   31.000000  \n",
+       "max      6.000000  512.329200  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_training.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
+       "0            1         0       3    male  22.0      1      0   7.2500        S\n",
+       "1            2         1       1  female  38.0      1      0  71.2833        C\n",
+       "2            3         1       3  female  26.0      0      0   7.9250        S\n",
+       "3            4         1       1  female  35.0      1      0  53.1000        S\n",
+       "4            5         0       3    male  35.0      0      0   8.0500        S"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_data = titanic_training.drop(['Name','Ticket','Cabin'], axis=1)\n",
+    "titanic_data.head()"
+   ]
   },
   {
    "cell_type": "code",
@@ -147,7 +489,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -161,7 +503,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/02_05_b.ipynb b/notebooks/02_05_b.ipynb
index 73cddc0..9aed9e3 100644
--- a/notebooks/02_05_b.ipynb
+++ b/notebooks/02_05_b.ipynb
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -156,13 +156,13 @@
        "<Axes: xlabel='Survived', ylabel='count'>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +198,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -349,7 +349,7 @@
        "max      6.000000  512.329200  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -360,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -469,7 +469,7 @@
        "4            5         0       3    male  35.0      0      0   8.0500        S"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -479,6 +479,275 @@
     "titanic_data.head()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Parch', ylabel='Age'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='Parch',y='Age',data=titanic_training)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parch</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>445.255162</td>\n",
+       "      <td>0.343658</td>\n",
+       "      <td>2.321534</td>\n",
+       "      <td>32.178503</td>\n",
+       "      <td>0.237463</td>\n",
+       "      <td>25.586774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>465.110169</td>\n",
+       "      <td>0.550847</td>\n",
+       "      <td>2.203390</td>\n",
+       "      <td>24.422000</td>\n",
+       "      <td>1.084746</td>\n",
+       "      <td>46.778180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>416.662500</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2.275000</td>\n",
+       "      <td>17.216912</td>\n",
+       "      <td>2.062500</td>\n",
+       "      <td>64.337604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>579.200000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>2.600000</td>\n",
+       "      <td>33.200000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>25.951660</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>384.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>44.500000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>84.968750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>435.200000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>39.200000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>32.550000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>679.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>43.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>46.900000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId  Survived    Pclass        Age     SibSp       Fare\n",
+       "Parch                                                                 \n",
+       "0       445.255162  0.343658  2.321534  32.178503  0.237463  25.586774\n",
+       "1       465.110169  0.550847  2.203390  24.422000  1.084746  46.778180\n",
+       "2       416.662500  0.500000  2.275000  17.216912  2.062500  64.337604\n",
+       "3       579.200000  0.600000  2.600000  33.200000  1.000000  25.951660\n",
+       "4       384.000000  0.000000  2.500000  44.500000  0.750000  84.968750\n",
+       "5       435.200000  0.200000  3.000000  39.200000  0.600000  32.550000\n",
+       "6       679.000000  0.000000  3.000000  43.000000  1.000000  46.900000"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Parch_group = titanic_training.groupby(titanic_training['Parch'])\n",
+    "Parch_group.mean(numeric_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def age_approx(cols):\n",
+    "  Age = cols[0]\n",
+    "  Parch = cols[1]\n",
+    "\n",
+    "  if pd.isnull(Age):\n",
+    "    if Parch == 0:\n",
+    "      return 32\n",
+    "    elif Parch == 1:\n",
+    "      return 24\n",
+    "    elif Parch == 2:\n",
+    "      return 17\n",
+    "    elif Parch == 3:\n",
+    "      return 33\n",
+    "    elif Parch == 4:\n",
+    "      return 45\n",
+    "    else:\n",
+    "      return 30\n",
+    "    \n",
+    "  else:\n",
+    "    return Age\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_22473/1825011687.py:2: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  Age = cols[0]\n",
+      "/tmp/ipykernel_22473/1825011687.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  Parch = cols[1]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId    0\n",
+       "Survived       0\n",
+       "Pclass         0\n",
+       "Sex            0\n",
+       "Age            0\n",
+       "SibSp          0\n",
+       "Parch          0\n",
+       "Fare           0\n",
+       "Embarked       2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_data['Age'] = titanic_data[['Age','Parch']].apply(age_approx, axis=1)\n",
+    "titanic_data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 889 entries, 0 to 888\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   index        889 non-null    int64  \n",
+      " 1   PassengerId  889 non-null    int64  \n",
+      " 2   Survived     889 non-null    int64  \n",
+      " 3   Pclass       889 non-null    int64  \n",
+      " 4   Sex          889 non-null    object \n",
+      " 5   Age          889 non-null    float64\n",
+      " 6   SibSp        889 non-null    int64  \n",
+      " 7   Parch        889 non-null    int64  \n",
+      " 8   Fare         889 non-null    float64\n",
+      " 9   Embarked     889 non-null    object \n",
+      "dtypes: float64(2), int64(6), object(2)\n",
+      "memory usage: 69.6+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "titanic_data.dropna(inplace=True)\n",
+    "titanic_data.reset_index(inplace=True)\n",
+    "\n",
+    "print(titanic_data.info())"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -489,7 +758,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -503,7 +772,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/02_06_b.ipynb b/notebooks/02_06_b.ipynb
index 414b1ca..69d89b6 100644
--- a/notebooks/02_06_b.ipynb
+++ b/notebooks/02_06_b.ipynb
@@ -162,7 +162,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -481,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -490,13 +490,13 @@
        "<Axes: xlabel='Parch', ylabel='Age'>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -511,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -632,7 +632,7 @@
        "6       679.000000  0.000000  3.000000  43.000000  1.000000  46.900000"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -644,7 +644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -672,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -690,7 +690,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -702,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -718,12 +718,12 @@
       " 1   Survived     889 non-null    int64  \n",
       " 2   Pclass       889 non-null    int64  \n",
       " 3   Sex          889 non-null    object \n",
-      " 4   Age          889 non-null    int64  \n",
+      " 4   Age          889 non-null    float64\n",
       " 5   SibSp        889 non-null    int64  \n",
       " 6   Parch        889 non-null    int64  \n",
       " 7   Fare         889 non-null    float64\n",
       " 8   Embarked     889 non-null    object \n",
-      "dtypes: float64(1), int64(6), object(2)\n",
+      "dtypes: float64(2), int64(5), object(2)\n",
       "memory usage: 62.6+ KB\n",
       "None\n"
      ]
@@ -736,6 +736,587 @@
     "print(titanic_data.info())"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, 0, 1])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "label_encoder = LabelEncoder()\n",
+    "gender_cat = titanic_data['Sex']\n",
+    "gender_encoded = label_encoder.fit_transform(gender_cat)\n",
+    "gender_encoded[0:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
+       "0            1         0       3    male  22.0      1      0   7.2500        S\n",
+       "1            2         1       1  female  38.0      1      0  71.2833        C\n",
+       "2            3         1       3  female  26.0      0      0   7.9250        S\n",
+       "3            4         1       1  female  35.0      1      0  53.1000        S\n",
+       "4            5         0       3    male  35.0      0      0   8.0500        S"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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>male_gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   male_gender\n",
+       "0            1\n",
+       "1            0\n",
+       "2            0\n",
+       "3            0\n",
+       "4            1"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gender_df = pd.DataFrame(gender_encoded,columns=['male_gender'])\n",
+    "gender_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 0, 2, 2, 2, 1, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2,\n",
+       "       1, 2, 2, 2, 0, 2, 1, 2, 0, 0, 1, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0,\n",
+       "       1, 2, 1, 1, 0, 2, 2, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 2, 2, 0, 0, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "embarked_cat = titanic_data['Embarked']\n",
+    "embarked_encoded = label_encoder.fit_transform(embarked_cat)\n",
+    "embarked_encoded[:100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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>C</th>\n",
+       "      <th>Q</th>\n",
+       "      <th>S</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     C    Q    S\n",
+       "0  0.0  0.0  1.0\n",
+       "1  1.0  0.0  0.0\n",
+       "2  0.0  0.0  1.0\n",
+       "3  0.0  0.0  1.0\n",
+       "4  0.0  0.0  1.0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "binary_encoder = OneHotEncoder(categories='auto')\n",
+    "embarked_1hot = binary_encoder.fit_transform(embarked_encoded.reshape(-1,1))\n",
+    "embarked_1hot_mat = embarked_1hot.toarray()\n",
+    "embarked_df = pd.DataFrame(embarked_1hot_mat, columns=['C','Q','S'])\n",
+    "embarked_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
+       "0            1         0       3  22.0      1      0   7.2500\n",
+       "1            2         1       1  38.0      1      0  71.2833\n",
+       "2            3         1       3  26.0      0      0   7.9250\n",
+       "3            4         1       1  35.0      1      0  53.1000\n",
+       "4            5         0       3  35.0      0      0   8.0500"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_data.drop(['Sex','Embarked'],axis=1,inplace=True)\n",
+    "titanic_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>male_gender</th>\n",
+       "      <th>C</th>\n",
+       "      <th>Q</th>\n",
+       "      <th>S</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male_gender  \\\n",
+       "0          1.0       0.0     3.0  22.0    1.0    0.0   7.2500          1.0   \n",
+       "1          2.0       1.0     1.0  38.0    1.0    0.0  71.2833          0.0   \n",
+       "2          3.0       1.0     3.0  26.0    0.0    0.0   7.9250          0.0   \n",
+       "3          4.0       1.0     1.0  35.0    1.0    0.0  53.1000          0.0   \n",
+       "4          5.0       0.0     3.0  35.0    0.0    0.0   8.0500          1.0   \n",
+       "\n",
+       "     C    Q    S  \n",
+       "0  0.0  0.0  1.0  \n",
+       "1  1.0  0.0  0.0  \n",
+       "2  0.0  0.0  1.0  \n",
+       "3  0.0  0.0  1.0  \n",
+       "4  0.0  0.0  1.0  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_dmy = pd.concat([titanic_data, gender_df, embarked_df], axis=1,verify_integrity=True).astype(float)\n",
+    "titanic_dmy[0:5]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -746,7 +1327,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -760,7 +1341,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/02_07_b.ipynb b/notebooks/02_07_b.ipynb
index 03463e6..cfee2d1 100644
--- a/notebooks/02_07_b.ipynb
+++ b/notebooks/02_07_b.ipynb
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -162,7 +162,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -360,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -496,7 +496,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -981,7 +981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1054,7 +1054,7 @@
        "4  0.0  0.0  1.0"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1071,7 +1071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1168,7 +1168,7 @@
        "4            5         0       3  35.0      0      0   8.0500"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1180,7 +1180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1308,7 +1308,7 @@
        "4  0.0  0.0  1.0  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1318,6 +1318,203 @@
     "titanic_dmy[0:5]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(titanic_dmy.corr())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>male_gender</th>\n",
+       "      <th>C</th>\n",
+       "      <th>Q</th>\n",
+       "      <th>S</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass   Age  SibSp  Parch  male_gender    C    Q  \\\n",
+       "0          1.0       0.0     3.0  22.0    1.0    0.0          1.0  0.0  0.0   \n",
+       "1          2.0       1.0     1.0  38.0    1.0    0.0          0.0  1.0  0.0   \n",
+       "2          3.0       1.0     3.0  26.0    0.0    0.0          0.0  0.0  0.0   \n",
+       "3          4.0       1.0     1.0  35.0    1.0    0.0          0.0  0.0  0.0   \n",
+       "4          5.0       0.0     3.0  35.0    0.0    0.0          1.0  0.0  0.0   \n",
+       "\n",
+       "     S  \n",
+       "0  1.0  \n",
+       "1  0.0  \n",
+       "2  1.0  \n",
+       "3  1.0  \n",
+       "4  1.0  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_dmy.drop(['Fare'], axis=1, inplace=True)\n",
+    "titanic_dmy.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 889 entries, 0 to 888\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   PassengerId  889 non-null    float64\n",
+      " 1   Survived     889 non-null    float64\n",
+      " 2   Pclass       889 non-null    float64\n",
+      " 3   Age          889 non-null    float64\n",
+      " 4   SibSp        889 non-null    float64\n",
+      " 5   Parch        889 non-null    float64\n",
+      " 6   male_gender  889 non-null    float64\n",
+      " 7   C            889 non-null    float64\n",
+      " 8   Q            889 non-null    float64\n",
+      " 9   S            889 non-null    float64\n",
+      "dtypes: float64(10)\n",
+      "memory usage: 69.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "titanic_dmy.info()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1328,7 +1525,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1342,7 +1539,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/02_08_b.ipynb b/notebooks/02_08_b.ipynb
index 0e3d0b4..994416a 100644
--- a/notebooks/02_08_b.ipynb
+++ b/notebooks/02_08_b.ipynb
@@ -162,7 +162,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -496,7 +496,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -1335,7 +1335,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 2 Axes>"
       ]
@@ -1515,6 +1515,598 @@
     "titanic_dmy.info()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(titanic_dmy.drop('Survived', axis=1),\n",
+    "                                                    titanic_dmy['Survived'],test_size=0.20,\n",
+    "                                                                random_state=200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(711, 9)\n",
+      "(711,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_train.shape)\n",
+    "print(y_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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>PassengerId</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>male_gender</th>\n",
+       "      <th>C</th>\n",
+       "      <th>Q</th>\n",
+       "      <th>S</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>719</th>\n",
+       "      <td>721.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>167.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>879</th>\n",
+       "      <td>882.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>451</th>\n",
+       "      <td>453.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>183.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId  Pclass   Age  SibSp  Parch  male_gender    C    Q    S\n",
+       "719        721.0     2.0   6.0    0.0    1.0          0.0  0.0  0.0  1.0\n",
+       "165        167.0     1.0  24.0    0.0    1.0          0.0  0.0  0.0  1.0\n",
+       "879        882.0     3.0  33.0    0.0    0.0          1.0  0.0  0.0  1.0\n",
+       "451        453.0     1.0  30.0    0.0    0.0          1.0  1.0  0.0  0.0\n",
+       "181        183.0     3.0   9.0    4.0    2.0          1.0  0.0  0.0  1.0"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train[0:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression(solver='liblinear')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "LogReg = LogisticRegression(solver='liblinear')\n",
+    "LogReg.fit(X_train.values, y_train.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = LogReg.predict(X_test.values)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1525,7 +2117,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1539,7 +2131,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/02_09_b.ipynb b/notebooks/02_09_b.ipynb
index cdcee59..1f401ed 100644
--- a/notebooks/02_09_b.ipynb
+++ b/notebooks/02_09_b.ipynb
@@ -162,7 +162,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -360,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -496,7 +496,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -981,7 +981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1180,7 +1180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1335,7 +1335,7 @@
     },
     {
      "data": {
-      "image/png": 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fP4/Vq1ejU6dOssQvbx9FDd/Q0BCGhoY4fvw4nJycKvUeZcWKFZg5cyasrKxQvXp17N+/H4cOHcLUqVNl5/z222/o3bt3iWuNjIzQsWNHHD58WNCT8tChQ/jss89QpUoVFBYWwsfHB05OTti9ezd0dHTw448/YvTo0fjtt9+gp6eHbdu2ISIiAt9//z1sbGywbds2/Pnnn2jTpk2Fn4uIiN6gxJn2evbsiczMTKxZswbp6elo0qQJtmzZImvST0tLE9Tox48fD5FIhB9++AGPHj2CqakpOnXqhGnTpimsjB9FwtfR0UFISAjmzZuHvXv3omnTpmjVqhV69uwJOzu7ct1r8uTJcHd3l3329PTE7t27ZQn/7t27iI2NxbJly0q9vk+fPvjuu++Qn5+PKlWqICcnB6dPn8a6desAvJx8QSKRYPHixRCJRACA4OBgtGzZEpcuXUK7du2wY8cOjB07Ft26dQMABAYG4ty5c+X9sRAR0Qdk2LBhb23C37Vrl+Czjo4OJk6ciIkTJyqjaAA+kiZ9AOjevTvOnj2LDRs2oH379rh06RL69u2LAwcOlOs+Dg4Ogs+9evXC/fv3cfXqVQAva+vNmjWDjY1Nqdd36NABurq6OHnyJADg2LFjMDIyko2/jIuLQ3JyMlxcXODs7AxnZ2e0bt0aBQUFSE5OxvPnz5Geno7mzZvL7qmjowN7e/tyPQcREb2HkqfW/dB9FDX8V/T19eHu7g53d3dMmDABc+bMwdq1a7F7924Awo4ORUVFpd6jSpUqgs8WFhZo06YNDh8+DCcnJxw+fBiDBw9+axn09PTQvXt3HDp0CL169cLhw4fRs2dPWee/vLw8NGvWDMuXLy9xrampabmfmYiIKkhNE3dFfTQ1/NI0atQIeXl5skSanp4uO/Z6B7738fT0RFRUFGJiYpCSkoKePXu+9/xz587hv//+w99//w1PT0/ZsWbNmuHevXswMzODtbW1YKtWrRqqVasGCwsL/Pvvv7JrioqKEBsbW+byEhFRGVRiHL46+igS/tOnTzFixAgcPHgQcXFxSElJwdGjR7FlyxZ06dIFBgYGcHJywqZNm5CQkIBLly7hhx9+KPP9u3btitzcXAQEBKB169aoVavWO89v2bIlzM3NMWPGDFhaWgqa5z09PWFiYoLx48fj8uXLSElJwcWLFxEUFISHDx8CAEaMGIHNmzfj+PHjSEhIQGBgoMIn9iEi0jhs0hf4KJr0q1atiubNm2PHjh1ITk5GUVERateujf79+8umIfz+++8xZ84c9O3bFw0aNMB3332HUaNGlen+RkZG6NSpE44ePYrvv//+veeLRCL06tULW7ZswYQJEwTHqlSpgp9//hnLly/HxIkTkZubi1q1aqFt27YwMjICAIwaNQrp6emYOXMmtLS00K9fP3Tt2hXPnz8v50+GiIjeSk1r6hUlknIA+EfpYO0hSo/Z80aQUuP5uvopNV7HQgOlxqsiUe7/evXwQqnxAMD6k6dKjfdFUqFS4+0yUu4MnInPjJUaL05fMePB32Z68s9yvV/+waUVvrbKF8r990cZPooaPhERUbmpadN8RTHhExGRemKTvgATPhERqSfW8AWY8ImISD0x4Qsw4RMRkXpin3SBj2IcPhEREVUOa/hERKSe2KQvwIRPRETqiQlfgAn/IyX+/0vvKpOyJ8IJvVzxSTMqYrySn29UgXLfL2ZJdJUaDwAmJomVGm+rrplS4/1UaKjUeOPrPFFqvBoPlTvRj9xxWJ4AEz4REakn1vAFmPCJiEg9sZe+AHvpExERaQDW8ImISD2xSV+ACZ+IiNQTE74AEz4REakn9tIXYMInIiK1JJWw097r2GmvjIYPH47FixeruhhERFRWEknFNzWkUQnf398ftra2sLW1hb29Pbp27Yp169ahqKhI1UUjIqKP3O7du9G5c2c4ODigf//+uHbt2jvPf/bsGQIDA9GuXTvY29uje/fuOHPmjMLKp3FN+u3bt0dwcDDEYjHOnDmDhQsXQldXF76+vqouGhERyZMS3+FHRUUhODgYgYGBaN68OXbs2AEfHx/8/vvvMDMrOQOkWCyGt7c3zMzMsHr1atSqVQsPHjxA9erVFVZGjarhA4Cenh4sLCxQr149DBkyBG5ubjh58iQA4MqVKxg+fDiaN2+Oli1bwsfHB9nZ2aXeJzIyEn379oWzszPc3d0xffp0ZGRkyI5nZ2dj+vTpaNOmDRwdHdGtWzeEh4cDePkXvXDhQrRr1w4ODg7o1KkTQkNDFf/wRESaRCKt+FZO27dvx4ABA9CvXz80atQIgYGBMDAwkP27/6bw8HBkZ2dj/fr1aNGiBSwtLdGqVSvY2dlV9qnfSuNq+G/S19dHVlYWbt26hZEjR6Jfv36YM2cOtLW1cfHiRRQXF5d6XVFREaZMmYKGDRsiIyMDISEh8Pf3x+bNmwEAq1evRkJCAjZv3gwTExMkJyfjxYsXAIBdu3bh5MmT+OGHH1CnTh2kpaXh4cOHSntmIiKNUIl38WKxGGKxcC0IPT096OnplXpubGysoKVYS0sLbm5uiImJKfX+J0+ehJOTExYuXIgTJ07A1NQUvXv3xpgxY6CtrV3hcr+LxiZ8qVSK6OhonDt3DsOGDcOWLVtgb2+PgIAA2TmffvrpW6//6quvZH+2srLCnDlz8NVXXyE3NxdVq1bFgwcP0KRJEzg4OAAALC0tZeenpaXB2toaLVq0gEgkQr169eT/gEREmq4SCT80NBTr1q0T7Js4cSImTZpU4tynT5+iuLi4RNO9mZkZEhMTS71/SkoK/v77b3h6emLTpk1ITk5GYGAgioqKMHHixAqX+100LuGfPn0azs7OKCwshFQqRe/evTFp0iR89dVX+Pzzz8t8nxs3bmDdunWIi4tDdnY2pP9/zua0tDQ0atQIgwcPxuTJk3Hz5k24u7vjs88+g4uLCwDAy8sLo0aNwueff4727dujY8eOaNeunUKel4iIys/X1xfe3t6CfaXV7itKKpXCzMwMixYtgra2Nuzt7fHo0SNs3bqVCV9eWrdujYCAAOjq6qJmzZrQ0Xn5IzAwMCjzPfLy8uDj44N27dph+fLlMDExQVpaGnx8fFBYWAgA8PDwwKlTp3DmzBmcP38eI0eOxNChQzFz5kw0a9YMJ06cwF9//YULFy5g6tSpcHNzw5o1axTyzEREGqkSi+e8rfm+NCYmJtDW1hb04wKAjIwMmJubl3qNhYUFdHR0BM33DRs2RHp6OsRisVy/XLyicZ32qlSpAmtra9StW1eW7AHA1tYW0dHRZbpHYmIisrKyMGPGDLi6usLGxqbEXzQAmJqawsvLC8uXL8fs2bOxb98+2TEjIyP07NkTQUFBWLVqFY4dO4asrKxKPx8REf1/ShqHr6enh2bNmglyiEQiQXR0NJydnUu9xsXFBcnJyZC8FispKQkWFhYKSfaABib8txk7diyuX7+OgIAAxMXFISEhAb/88gsyMzNLnFu3bl3o6upi165dSElJwYkTJ/Djjz8Kzlm9ejWOHz+Oe/fu4b///sPp06dhY2MD4GVvzsOHDyMhIQF3797F77//DgsLC4UOxyAi0jhK7KXv7e2N/fv3IyIiAgkJCQgICEB+fj769u0LAPDz88OKFStk5w8ePBhZWVlYvHgx7t69i9OnTyM0NBRDhw6V2+O/SeOa9N+mQYMG2LZtG1auXIn+/fvDwMAAjo6O6N27d4lzTU1NERISgpUrV2LXrl1o1qwZZs6cifHjx8vO0dXVxcqVK3H//n0YGBigRYsWWLlyJQCgatWq2LJlC+7duwctLS04ODhg06ZN0NLi9y8iIrlR4jj8nj17IjMzE2vWrEF6ejqaNGmCLVu2yJr009LSBP/G16lTB1u3bkVwcDD69OmDWrVqYcSIERgzZozCyiiSSivxkoNU5tc6ivsW+DZH9QuUGi/08lKlxhvv6qfUeKMKlPu/XpZEV6nxAGCuVqpS423VLTnBiSLt1TJUarzxNZ4oNd79h8ZKjef+MEyu98tb4v3+k97CcOZ2OZbkw8AqJRERkQZgkz4REaklqZouglNRTPhERKSeuDyuABM+ERGpJyV22vsYMOETEZF6Yg1fgAmfiIjUE9/hC7CXPhERkQZgDZ+IiNQTm/QFmPA/UsaSYqXH7FhY9gWG5EHZE+FsUPJEP1Nc/ZUaz1QFDXprX9RWaryiYuX+f/FZgXKbjG/mK3dioedailmXXWnYaU+ACZ+IiNQTa/gCTPhERKSWOPGOEBM+ERGpJ9bwBdhLn4iISAOwhk9EROqJNXwBJnwiIlJP7KUvwIRPRETqiTV8ASZ8IiJSS1ImfAF22quAmJgYNGnSBGPHjlV1UYiI6G0k0opvaogJvwLCwsIwbNgw/PPPP3j06JGqi0NERPReTPjllJubi6ioKAwePBgdO3ZERESE4PiJEyfQrVs3ODg4YPjw4YiIiICtrS2ePXsmO+fy5csYMmQIHB0d4eHhgaCgIOTl5Sn7UYiI1JtEUvFNDTHhl9PRo0fRsGFDNGzYEH369EF4eDik0pfNPykpKZgyZQq6dOmCgwcPYtCgQVi1apXg+uTkZIwZMwbdunXDb7/9hlWrVuHKlStYtGiRKh6HiEh9sUlfgAm/nMLCwtCnTx8AQPv27fH8+XNcunQJALBv3z40aNAAM2fORMOGDdGrVy94eXkJrg8NDYWnpydGjhyJTz75BC4uLpgzZw4iIyNRUFCg9OchIlJbTPgC7KVfDomJibh+/TrWr18PANDR0UHPnj0RFhaG1q1b4+7du7C3txdc4+joKPgcFxeH+Ph4HDp0SLZPKpVCIpEgNTUVNjY2in8QIiIN8Kr1lV5iwi+HsLAwFBUVoX379rJ9UqkUenp6mD9/fpnukZeXh0GDBmH48OEljtWpU0duZSUi0nhqWlOvKCb8MioqKsLBgwfh7+8Pd3d3wbEJEybg8OHDaNCgAc6cOSM4dv36dcHnpk2b4s6dO7C2tlZ4mYmISHl2796NrVu3Ij09HXZ2dpg3b16JVt7SHDlyBN9++y26dOmCH3/8UWHl4zv8Mjp9+jSys7Px1VdfoXHjxoKtW7duCAsLw8CBA3H37l0sW7YMd+/eRVRUlKwXv0gkAgCMGTMGMTExWLhwIW7duoWkpCQcP34cCxcuVOXjERGpHyW+w4+KikJwcDAmTJiAiIgI2NnZwcfHBxkZGe+8LjU1FUuWLIGrq2tFn7LMmPDLKCwsDG5ubqhWrVqJY927d8eNGzeQm5uL1atX488//0SfPn2wZ88ejBs3DgCgp6cHALCzs8OuXbuQlJSEIUOGwMvLC2vWrEHNmjWV+jxEROpOKpFWeBOLxcjJyRFsYrH4rbG2b9+OAQMGoF+/fmjUqBECAwNhYGCA8PDwt15TXFyMGTNmYNKkSbCyslLEj0CATfpltHHjxrcec3R0RHx8PICXCb1Lly6yYxs2bEDt2rWhr68vOH/btm2KKywREVXqHX5oaCjWrVsn2Ddx4kRMmjSpxLlisRixsbHw9fWV7dPS0oKbmxtiYmLeGmP9+vUwMzND//79ceXKlQqXtayY8OVs9+7dcHBwgImJCa5cuYKtW7di6NChqi4WEZHmqcT8Ob6+vvD29hbse9VS+6anT5+iuLgYZmZmgv1mZmZITEws9ZrLly8jLCwMkZGRFS9kOTHhy9m9e/ewYcMGZGdno27duvD29hZ86yMiIuWozOI5enp6b03wlZWTkwM/Pz8sWrQIpqamColRGiZ8OZs9ezZmz56t6mIQEZGSmJiYQFtbu0QHvYyMDJibm5c4PyUlBffv38f48eNl+yT/fzrfpk2b4vfff0f9+vXlXk4mfCIiUk9KGoevp6eHZs2aITo6Gp999tnL0BIJoqOjMWzYsBLnN2zYUDD5GgD88MMPyM3NxZw5c1C7dm2FlJMJn4iI1JMS18Dx9vbGzJkzYW9vD0dHR+zYsQP5+fno27cvAMDPzw+1atXC9OnToa+vj8aNGwuur169OgCU2C9PTPhERKSWKvMOv7x69uyJzMxMrFmzBunp6WjSpAm2bNkia9JPS0uDlpZqR8Iz4RMRkXpS8iq3w4YNK7UJHwB27dr1zmtDQkIUUSQBJnwiIlJLyqzhfwyY8D9S6drK/6urouT/eUYVKDfeFFd/pcZbfVnx3+hflz9T+cNDU08rZljT25wWmyg1XnVtpYaDgZJXf6sr5ZLd6oQJn4iI1JOSm/Q/dEz4RESklqRM+AJM+EREpJ6Y8AWY8ImISC2xhi/EhE9EROqJCV9AtbMAEBERkVKwhk9ERGqJTfpCrOG/g62tLY4fPw4ASE1Nha2tLW7duqXiUhERUVlIJRXf1JFG1/AzMzOxevVqnDlzBk+ePIGxsTHs7OzwzTffoEWLFjh37hyMjY3Ldc8///wTmzdvRkJCAiQSCerWrQs3NzfMmTNHQU9BRESlUdfEXVEanfAnTZqEwsJChISEwMrKChkZGYiOjkZWVhYAwMLColz3i46OxrRp0zB16lR07twZIpEICQkJOH/+vAJKT0RE7yQVqboEHxSNTfjPnj3D5cuXsWvXLrRq1QoAUK9ePTg6OsrOsbW1xfr162XrGwNAYmIiAgMDERsbC2tra8yfP192/cmTJ+Hs7IzRo0fLzm/QoIHg+rVr1+L48eMYPHgwNmzYgKysLHTs2BFBQUGoVq2aoh+biEhjsIYvpLHv8A0NDWFoaIjjx49DLBaX+bqlS5fC29sbkZGRcHJywrhx4/D06VMAL1sE7ty5g9u3b7/zHsnJyTh69Cg2btyILVu24NatWwgICKjM4xAREb2TxiZ8HR0dhISEIDIyEq6urhg0aBBWrlyJuLi4d143dOhQdO/eHTY2NggICEC1atUQFhYG4OXSiA4ODvD09ETnzp0xbdo0hIWFlfhCUVBQgKVLl6JJkyZo2bIl5s6di6ioKKSnpyvseYmINI1UIqrwpo40NuEDQPfu3XH27Fls2LAB7du3x6VLl9C3b18cOHDgrdc4OzvL/qyjowN7e3skJiYCeNlqsGnTJvz5558YP348DA0NsWTJEnz11VfIz8+XXVenTh3UqlVLcE+JRIK7d+8q4CmJiDQTe+kLaXTCBwB9fX24u7tjwoQJ2Lt3L7y8vLB27dpK3bN+/fro378/Fi9ejAMHDiAhIQFRUVFyKjEREZWFVCqq8KaOND7hv6lRo0bIy8t76/GrV6/K/lxUVITY2Fg0bNjwredbWlrCwMBAUMNPS0vDo0ePBPfU0tJCgwYNKld4IiKSYQ1fSGN76T99+hRTpkxBv379YGtri6pVq+LGjRvYsmULunTp8tbrfvnlF3zyySdo2LAhduzYgezsbPTr1w/Ayx74+fn58PDwQN26dfH8+XPs2rULRUVFcHNzk91DX18f/v7+mDlzJnJychAUFIQePXqUexggERG9nbq+i68ojU34VatWRfPmzbFjxw4kJyejqKgItWvXRv/+/TFu3Li3Xjd9+nRs2rQJt27dgrW1NTZs2ABTU1MAQMuWLfHLL79g5syZsol8mjRpgq1btwpaAerXr4+uXbtizJgxyM7ORseOHbFgwQKFPzMREWkujU34enp6mD59OqZPn/7Wc+Lj42V/trS0lH3u3bt3qee3adMGbdq0KVP8IUOGYMiQIeUoMRERlYdUquoSfFg0NuETEZF6Y5O+EBM+ERGpJSZ8IfbSV7JJkybh4MGDqi4GEZHak0orvlXE7t270blzZzg4OKB///64du3aW8/dv38/hgwZgpYtW6Jly5YYOXLkO8+XByZ8IiJSS8qcaS8qKgrBwcGYMGECIiIiYGdnBx8fH2RkZJR6/sWLF9GrVy/s3LkTe/fuRZ06dTBq1CjBkG15Y8InIiKqpO3bt2PAgAHo168fGjVqhMDAQBgYGCA8PLzU81esWIGhQ4eiSZMmsLGxQVBQECQSCaKjoxVWRr7DJyIitVSZGfPEYnGJdVD09PSgp6dX6rmxsbHw9fWV7dPS0oKbmxtiYmLKFC8/Px9FRUUwNjaucJnfhzV8IiJSS5WZaS80NBQtWrQQbKGhoaXGefr0KYqLi2FmZibYb2ZmhidPnpSprMuXL0fNmjUFk7TJG2v4RESkliSVqOH7+vrC29tbsK+02r08bNq0CVFRUdi5cyf09fUVEgNgwv9oVZcUKz2mmUj8/pPkKEuiq9R4pkpu8Mqf6fv+k+SoypLSaycKjdlFuc+YlK3c/y/cCpU77OuptnJ/R2sWf9zD2irTpP+25vvSmJiYQFtbu0QHvYyMDJibm7/z2q1bt2LTpk3Yvn077OzsKlzesmCTPhERqSVl9dLX09NDs2bNBB3uXnXAe31J9Tdt3rwZP/74I7Zs2QIHB4cKP2dZsYZPRERUSd7e3pg5cybs7e3h6OiIHTt2ID8/H3379gUA+Pn5oVatWrLp3Ddt2oQ1a9ZgxYoVqFevHtLT0wEAhoaGqFq1qkLKyIRPRERqSZlz6ffs2ROZmZlYs2YN0tPT0aRJE2zZskXWpJ+WlgYtrf9rVN+7dy8KCwsxefJkwX0mTpyISZMmKaSMTPhERKSWlD217rBhwzBs2LBSj+3atUvw+eTJk8ookgATPhERqaXK9NJXR0z4RESklirTS18dsZc+ERGRBmDCV7KLFy/C1tYWz549U3VRiIjUmrJXy/vQaXyTvr+/PyIiIgAAurq6qFOnDr744guMGzcOOjoa/+MhIvpo8R2+EDMagPbt2yM4OBhisRhnzpzBwoULoaurK1gIoSyKi4shEokEQy+IiEg1+A5fiJkJL2dJsrCwQL169TBkyBC4ubnh5MmT2L59Ozw9PeHk5AQPDw8EBAQgNzdXdt2BAwfg6uqKEydOoGfPnnBwcMCDBw8gFouxbNkyeHh4wN7eHl27dsWvv/4qiBkbG4u+ffuiefPmGDRoEBITE5X92EREao1N+kKs4ZdCX18fWVlZEIlEmDNnDiwtLZGSkoLAwEAsW7YMAQEBsnNfvHiBzZs3IygoCDVq1ICZmRn8/Pxw9epVzJ07F3Z2dkhNTcXTp08FMVatWgV/f3+YmppiwYIFmD17Nvbu3avkJyUiUl9s0hdiwn+NVCpFdHQ0zp07h2HDhmHkyJGyY5aWlpg6dSoWLFggSPiFhYUICAiQLXpw9+5dHD16FNu3b5ctc2hlZVUi1rRp09CqVSsAwNixYzF27FgUFBQodKUkIiLSXEz4AE6fPg1nZ2cUFhZCKpWid+/emDRpEi5cuIDQ0FAkJiYiJycHxcXFKCgoQH5+PqpUqQLgZUc/W1tb2b1u3boFbW1ttGzZ8p0xX7/GwsICwMuVlerWrauAJyQi0jx8hy/EhA+gdevWCAgIgK6uLmrWrAkdHR2kpqbC19cXgwcPxrRp02BsbIwrV65gzpw5KCwslCV8AwMDiET/90tlYGBQppivjwB4db1EIpHjUxERaTY26Qsx4QOoUqUKrK2tBftiY2MhlUrh7+8v63V/9OjR996rcePGkEgk+Oeff2RN+kREpHxq2veuwthL/y2sra1RWFiIXbt2ISUlBZGRkWXqVGdpaQkvLy/Mnj0bx48fR0pKCi5evIioqCgllJqIiF6RSEUV3tQRE/5b2NnZYdasWdi8eTN69+6NQ4cO4dtvvy3TtQEBAejevTsCAgLQo0cPzJs3D/n5+QouMRERvU4qFVV4U0ciqVRdRxyqt0O1Bys9pplIrNR4WRJdpca7UEW533/9uqQrNV6VJaFKjQcAqV3KN3lVZW3INldqPLcXyk0MT7WV+ztqU1yg1HgdHv76/pPK4Xztryp8rfvDMDmW5MPAd/hERKSW2A1aiAmfiIjUkhTq2TRfUUz4RESkliR8YS3AhE9ERGpJwhq+ABM+ERGpJTbpC3FYHhERkQZgDZ+IiNQSe+kLMeETEZFaYpO+EBP+R0pbBfMlWTd4qtR4E5OUO9HP2he1lRov9bSeUuNVUfIkOABgeUK5k/2YtJiv1HitG91XaryIe/WUGu9vHeUu191BzvdjDV+I7/CJiEgtSSqxVcTu3bvRuXNnODg4oH///rh27do7zz969Cg+//xzODg4wNPTE2fOnKlg5LJhwiciIrUkhajCW3lFRUUhODgYEyZMQEREBOzs7ODj44OMjIxSz//f//6H6dOn46uvvkJkZCS6dOmCCRMm4Pbt25V97LdiwiciInqDWCxGTk6OYBOL3/6acfv27RgwYAD69euHRo0aITAwEAYGBggPDy/1/J07d6J9+/YYPXo0bGxsMHXqVDRt2hQ///yzoh6JCZ+IiNSTRFTxLTQ0FC1atBBsoaGl90kRi8WIjY2Fm5ubbJ+Wlhbc3NwQExNT6jVXr15F27ZtBfvatWuHq1evyu3538ROe0REpJYqM9Oer68vvL29Bfv09ErvaPv06VMUFxfDzMxMsN/MzAyJiYmlXvPkyROYm5uXOP/JkycVLvP7MOETEZFaqsxYJj09vbcm+I8VEz4REaklZQ3LMzExgba2dokOehkZGSVq8a+Ym5uXqM2/63x54Dt8IiJSSxKRqMJbeejp6aFZs2aIjo7+v9gSCaKjo+Hs7FzqNU5OTvj7778F+y5cuAAnJ6dyP2dZMeGXgb+/P2xtbUts9+7dU3XRiIjoA+Dt7Y39+/cjIiICCQkJCAgIQH5+Pvr27QsA8PPzw4oVK2TnjxgxAmfPnsW2bduQkJCAtWvX4saNGxg2bJjCysgm/TJq3749goODBftMTU3LdY/i4mKIRCJoafF7FhGRoilzPtKePXsiMzMTa9asQXp6Opo0aYItW7bImujT0tIE//a7uLhg+fLl+OGHH7By5Up88sknWL9+PRo3bqywMjLhl5Genh4sLCwE+7Zv344DBw4gJSUFxsbG6NSpE7777jtUrVoVAHDgwAF8//33WLJkCVasWIGkpCT88ccfqFmzJlatWoXDhw/j+fPn+PTTTzFjxgy0bt1aFY9GRKSWlD217rBhw95aQ9+1a1eJfT169ECPHj0UXSwZJvxKEIlEmDNnDiwtLZGSkoLAwEAsW7YMAQEBsnNevHiBzZs3IygoCDVq1ICZmRkWLlyIO3fuYNWqVahZsyb+/PNPjB49GocOHcInn3yisuchIlInEq6dI8CEX0anT58WdL5o37491qxZI/tsaWmJqVOnYsGCBYKEX1hYiICAANjZ2QEAHjx4gAMHDuDUqVOoVasWAMDHxwdnz57FgQMH8O233yrngYiI1FxlxuGrIyb8MmrdurUgkVepUgUXLlxAaGgoEhMTkZOTg+LiYhQUFCA/Px9VqlQBAOjq6sLW1lZ23e3bt1FcXIzPP/9ccH+xWIwaNWoo41GIiDSC8tcU/bAx4ZdRlSpVYG1tLfucmpoKX19fDB48GNOmTYOxsTGuXLmCOXPmoLCwUJbwDQwMIHptiEdeXh60tbURHh4ObW1tQQxDQ0PlPAwREWkcJvwKio2NhVQqhb+/v6zn5dGjR997XZMmTVBcXIzMzEy4uroquphERBqL7/CFOD6sgqytrVFYWIhdu3YhJSUFkZGR2Lt373uva9CgATw9PeHn54c//vgDKSkpuHbtGkJDQ3H69GnFF5yISEOUZd37t23qiAm/guzs7DBr1ixs3rwZvXv3xqFDh8rc4S44OBhffvklQkJC0KNHD3zzzTe4fv066tSpo+BSExFpDmklNnUkkkql6vpsai2q1iClx2ze8LFS43kmvX3taUVYK62t1HgmVfOVGq+KkXJ/ngBgeaL05UQVZXmL+UqNN9LqvlLjRdyrp9R4OUquEvrdk+9a8FstKz5rnU+q4talVxW+wyciIrWkrk3zFcUmfSIiIg3AGj4REakl1vCFmPCJiEgtSTksT4AJn4iI1BJr+EJM+EREpJaY8IWY8ImISC1xzLkQe+kTERFpANbwP1L6Kvju+kVSoVLjbdU1U2q8ouJipcY7LTZRarykbOU+HwCYKHkinBlXFio13s/Nlft8yl5eqw2eKzmifHEufSEmfCIiUkt8hy/EhE9ERGqJCV+ICZ+IiNQSO+0JMeETEZFa4jt8IfbSJyIi0gCs4RMRkVriO3wh1vCJiEgtSSuxKUpWVhamT58OFxcXuLq6Yvbs2cjNzX3n+YsWLUL37t3h6OiIjh07IigoCM+fl3/IpMoTvr+/P7755htVF6PSUlNTYWtri1u3bqm6KEREBEACaYU3RZkxYwbu3LmD7du3Y+PGjbh8+TLmz3/7fA6PHz/G48ePMXPmTBw+fBjBwcE4e/Ys5syZU+7YbNInIiK19KE16SckJODs2bMICwuDg4MDAGDu3LkYO3Ys/Pz8UKtWrRLXNG7cGGvXrpV9rl+/PqZOnYrvvvsORUVF0NEpexpXeQ2f/o9YLFZ1EYiI1EZlmvTFYjFycnIEW2X/jY6JiUH16tVlyR4A3NzcoKWlhWvXrpX5Pjk5OTAyMipXsgfKmfCHDx+ORYsWYfHixWjZsiXc3Nywf/9+5OXlYdasWXB2dkbXrl1x5swZAEBxcTFmz56Nzp07w9HREd27d8eOHTveGUMikSA0NFR2TZ8+ffD777+XuYwnTpxAt27d4ODggOHDhyMiIgK2trZ49uyZ7JzLly9jyJAhcHR0hIeHB4KCgpCXlyc73rlzZ2zcuFH2TB07dsS+ffsEca5du4Yvv/wSDg4O6Nu3b6lN+bdv38bo0aPh7OwMNzc3fPfdd8jMzBT8PBcuXIjFixejdevW8PHxKfNzEhGR4oSGhqJFixaCLTQ0tFL3fPLkCUxNTQX7dHR0YGxsjPT09DLdIzMzEz/++CMGDhxY7vjlruFHRETAxMQEv/76K4YNG4aAgABMmTIFzs7OiIiIgLu7O/z8/JCfnw+JRILatWtj9erVOHLkCCZMmIBVq1YhKirqrfcPDQ1FZGQkAgMDceTIEYwcORLfffcdLl269N6ypaSkYMqUKejSpQsOHjyIQYMGYdWqVYJzkpOTMWbMGHTr1g2//fYbVq1ahStXrmDRokWC87Zv3w57e3tERkZiyJAhCAgIQGJiIgAgNzcXvr6+sLGxwYEDBzBp0iQsWbJEcP2zZ8/w9ddfo2nTpggLC8OWLVuQkZGBqVOnlvh56urqYs+ePQgMDHzvMxIRUdlIKrH5+vriypUrgs3X17fUOMuXL4etre07t4SEhEo/T05Ojiz3TJw4sdzXl/sdvp2dnayTna+vLzZv3gwTExMMGDAAADBhwgTs2bMH8fHxcHJywuTJk2XXWllZ4erVq/j999/Rs2fPEvcWi8UIDQ3F9u3b4ezsLLvmypUr2LdvH1q1avXOsu3btw8NGjTAzJkzAQANGzbE7du3sXHjRtk5oaGh8PT0xMiRIwEAn3zyCebMmYPhw4cjICAA+vr6AIAOHTpg6NChAIAxY8bgp59+wsWLF9GwYUMcPnwYEokE33//PfT19fHpp5/i4cOHCAgIkMX5+eef0bRpU3z77beyfd9//z08PDxw9+5dNGjQQBbfz8/v/T94IiIql8pMvKOnpwc9Pb0ynTtq1Ch4eXm98xwrKyuYm5sLWnkBoKioCNnZ2bCwsHjn9Tk5ORg9ejSqVq2K9evXQ1dXt0xle125E76tra3sz9ra2qhRowYaN24s22dubg4AyMjIAADs3r0b4eHhePDgAQoKClBYWAg7O7tS733v3j3k5+dj1KhRgv2FhYVo0qTJe8t29+5d2NvbC/Y5OjoKPsfFxSE+Ph6HDh2S7ZNKpZBIJEhNTYWNjU2J5xSJRDA3N5c9U0JCAmxtbWVfDgDIvqC8HufixYsl9gMvWxleJfxmzZq997mIiKj8FNnb/nWmpqYlmupL4+zsjGfPnuHGjRuyXPX3339DIpGUyFWvy8nJgY+PD/T09LBhwwZB7imPcif8NzsJiEQiwT6R6OVXKqlUiiNHjmDJkiWYOXMmnJ2dUbVqVWzduhX//vtvqfd+9R49NDS0RG/Fsn7Tep+8vDwMGjQIw4cPL3GsTp06sj+X9pxSadl/efLy8tCpUyfMmDGjxLHXv8lVqVKlzPckIqKy+9Dm0rexsUH79u0xb948BAYGorCwEIsWLUKvXr1kOe/Ro0f4+uuvsXTpUjg6OiInJwejRo1Cfn4+li1bJutACLz8oqGtrV3m+Aodlve///0Pzs7OsqZx4GXt9m1sbGygp6eHBw8evLf5vjQNGjSQdRh85fr164LPTZs2xZ07d2BtbV3u+79ezoMHD6KgoED2Tevq1auCc5o1a4Zjx46hXr165e5JSURElfehDcsDXr7vX7RoEb7++mtoaWmhW7dumDt3rux4YWEh7t69i/z8fABAbGysrJLctWtXwb1OnDgBS0vLMsdWaCaytrZGZGQkzp49C0tLSxw8eBDXr19/awGNjIwwatQoBAcHQyqVokWLFnj+/Dn+97//wcjI6L3vSAYOHIiffvoJy5Ytw1dffYVbt24hIiICwP+1PIwZMwYDBw7EwoUL0b9/f1SpUgV37tzBhQsX3jn5wet69+6NVatWYe7cufD19cX9+/exbds2wTlDhgzB/v378e2332L06NGoUaMG7t27h6ioKAQFBZXrWxkREamHGjVqYMWKFW89bmlpifj4eNnn1q1bCz5XhkLH4Q8aNAjdunXDtGnTMGDAAGRlZWHIkCHvvGbq1Kn45ptvEBoaip49e2L06NE4ffp0mb7FWFlZYfXq1fjzzz/Rp08f7NmzB+PGjQPwf68E7OzssGvXLiQlJWHIkCHw8vLCmjVrULNmzTI/V9WqVbFx40bcvn0bX375JVatWlWi6b5WrVrYs2cPJBIJfHx84Onpie+//x7VqlWDlhanPyAiUrQPcaY9VRJJy/Ni+iO0YcMG7N27t0RT/8fuRK3yj8GsrJlaD5Qab7Pu+zvByFNRsXK/iP2jVVWp8ZK0i5UaDwBMpMptyZpxZaFS4/3cvGytgvKi7NVeHbXKP197ZbikHJTr/fw+GVzha5cm7ZFjST4Mavdyeffu3XBwcICJiQmuXLmCrVu3CvoQEBGRZvgQ3+Gr0keV8OfPny8YTvc6T09PLFy4EPfu3cOGDRuQnZ2NunXrwtvb+62TJRARkfpS16b5ivqoEv6UKVPeOv2skZERAGD27NmYPXu2MotFREQfIKZ7oY8q4ZuZmcHMzEzVxSAiIvrofFQJn4iIqKz4Dl+ICZ+IiNSSlI36Akz4RESklljDF2LCJyIitcRe+kJM+B+pPJHyZ+vbZVRNqfF+KjRUarzPCpRbH6iu5NmV3QqVPW0L0LrRfaXGU/ZEOMP+Ve5EP+tclPt8rR5eVmq8IqVG0zxM+EREpJZYvxdiwiciIrXEJn0hJnwiIlJL7LQnxIRPRERqicPyhJjwiYhILbGGL8SF2YmIiDQAa/hERKSW2KQvxIRPRERqiU36Qkz4KpKeno6NGzfi9OnTePToEczMzNCkSRN8/fXXaNu2raqLR0T00ZNIWcN/HRO+CqSmpmLw4MGoXr06/Pz80LhxYxQVFeHcuXMIDAzE77//ruoiEhF99JjuhZjwVSAwMBAikQi//vorDA3/b/rYTz/9FP369VNhyYiI1Acn3hFiL30ly8rKwtmzZzF06FBBsn+levXqKigVERGpO9bwlSw5ORlSqRQNGzZUdVGIiNQae+kLsYavZFJ2IiEiUgpJJTZFycrKwvTp0+Hi4gJXV1fMnj0bubm5ZbpWKpVi9OjRsLW1xfHjx8sdmwlfyaytrSESiZCYmKjqohARqTUJpBXeFGXGjBm4c+cOtm/fjo0bN+Ly5cuYP79syx7v2LEDIlHFl7lmwleyGjVqoF27dti9ezfy8vJKHH/27JkKSkVEpH6klfhPLBYjJydHsInF4kqVJyEhAWfPnkVQUBCaN28OV1dXzJ07F0eOHMGjR4/eee2tW7ewbds2fP/99xWOz4SvAgsWLIBEIkH//v1x7NgxJCUlISEhATt37sTAgQNVXTwiIrVQmSb90NBQtGjRQrCFhoZWqjwxMTGoXr06HBwcZPvc3NygpaWFa9euvfW6/Px8TJ8+HfPnz4eFhUWF47PTngpYWVnhwIED2LhxI5YsWYLHjx/D1NQUzZo1Q0BAgKqLR0Sk8Xx9feHt7S3Yp6enV6l7PnnyBKampoJ9Ojo6MDY2Rnp6+luvCw4OhrOzMz777LNKxWfCV5GaNWti/vz5ZX53Q0RE5VOZTtJ6enplTvDLly/H5s2b33lOVFRUhcpx4sQJ/P3334iIiKjQ9a9jwiciIrWkrIl3Ro0aBS8vr3eeY2VlBXNzc2RmZgr2FxUVITs7+61N9X///TeSk5PRsmVLwf5JkybB1dUVu3btKnM5mfCJiEgtKWvxHFNT0xJN9aVxdnbGs2fPcOPGDdjb2wN4mdAlEgkcHR1LvWbs2LHo37+/YJ+npydmzZqFTp06lauc7LRHRERqqTK99BXBxsYG7du3x7x583Dt2jVcuXIFixYtQq9evVCrVi0AwKNHj/D555/LOvFZWFigcePGgg0A6tatCysrq3LFZw2fiIjU0oc4l/7y5cuxaNEifP3119DS0kK3bt0wd+5c2fHCwkLcvXsX+fn5co/NhE9ERKQkNWrUwIoVK9563NLSEvHx8e+8x/uOvw0TPhERqSVOZS7EhE9ERGpJWZ32PhZM+EREpJa4Wp4QE/5H6oVI+QMsEp8ZKzXe+DpPlBrvZr6ZUuMZKLm58am28n9nIu7VU2o8Q6VGA9a5KHfirIn/W6jUeAeaj1FqPHn7EDvtqRITPhERqSW+wxfiOHwiIiINwBo+ERGpJTbpCzHhExGRWmKnPSEmfCIiUksSvsMXYMInIiK1xHQvxIRPRERqie/whdhLn4iISAOwhq8CaWlpWLNmDc6ePYusrCxYWFigS5cumDBhAkxMTFRdPCIitcAavhATvpKlpKRg4MCB+OSTT7By5UpYWlriv//+w7Jly3D27Fns27cPNWrUUHUxiYg+epx4R4gJX8kCAwOhq6uLbdu2wcDAAABQt25dNG3aFF27dsWqVasQGBio4lISEX38WMMX4jt8JcrKysK5c+cwZMgQWbJ/xcLCAp6enjh69Ci/lRIRyYG0Ev+pIyZ8Jbp37x6kUilsbGxKPW5jY4Ps7GxkZmYquWREROpHKpVWeFNHTPgq8L5fJl1dXSWVhIiINAUTvhLVr18fIpEICQkJpR5PSEiAqakpqlevruSSERGpHwmkFd7UERO+EpmYmMDd3R2//PILXrx4ITiWnp6OQ4cOwcvLS0WlIyJSL2zSF2LCV7J58+ZBLBbDx8cH//zzD9LS0vDXX39h1KhR+OSTTzBhwgRVF5GISC2whi/EYXlK9sknnyAsLAzr1q3D1KlTkZGRAalUim7dumHp0qWoUqWKqotIRKQW1LW3fUUx4auApaUlQkJCZJ/XrFmD7du3Iz4+Hk5OTqorGBGRGuFqeUJs0v8ATJ48GXPnzsXVq1chkUhUXRwiIlKQrKwsTJ8+HS4uLnB1dcXs2bORm5v73utiYmIwYsQIODk5wcXFBUOHDi3RF+x9WMP/QPTr10/VRSAiUisfYpP+jBkzkJ6eju3bt6OwsBCzZ8/G/PnzsWLFirdeExMTg9GjR8PX1xfz5s2DtrY24uLioKVVvjo7Ez4REamlD61JPyEhAWfPnkVYWBgcHBwAAHPnzsXYsWPh5+eHWrVqlXpdcHAwhg8fjrFjx8r2NWzYsNzx2aRPRERqqTJT64rFYuTk5Ag2sVhcqfLExMSgevXqsmQPAG5ubtDS0sK1a9dKvSYjIwP//vsvzMzMMGjQILi5uWHYsGG4fPlyueMz4RMRkVqSSKUV3kJDQ9GiRQvBFhoaWqnyPHnyBKampoJ9Ojo6MDY2Rnp6eqnXpKSkAADWrVuH/v37Y8uWLWjatClGjhyJpKSkcsVnkz4REamlyrzD9/X1hbe3t2Cfnp5eqecuX74cmzdvfuf9oqKiKlSOVx25Bw4cKOvr1bRpU0RHRyM8PBzTp08v872Y8ImIiN6gp6f31gT/plGjRr13llQrKyuYm5uXWBytqKgI2dnZsLCwKPW6V/vfXHTNxsYGDx48KFP5XmHCJyIitaSsTnumpqYlmupL4+zsjGfPnuHGjRuwt7cHAPz999+QSCRwdHQs9RpLS0vUrFkTd+/eFexPSkpChw4dylVOJvyPVDVJsdJjxuor99elxkNjpcZ7rqWt1Hh1pQVKjVezWKTUeADwt46+UuO1wXOlxmv1sPwdpyrjQPMxSo138t93N1N/6D60YXk2NjZo37495s2bh8DAQBQWFmLRokXo1auXrIf+o0eP8PXXX2Pp0qVwdHSESCSCj48P1q5dCzs7OzRp0gQRERFITEzEmjVryhWfCZ+IiNSSVPrhTWS2fPlyLFq0CF9//TW0tLTQrVs3zJ07V3a8sLAQd+/eRX5+vmzfyJEjIRaLERwcjOzsbNjZ2WHbtm2oX79+uWIz4RMRkVr6EBfBqVGjxjsn2bG0tER8fHyJ/WPHjhWMw68IJnwiIlJL6rrMbUVxHD4REZEGYA2fiIjU0ofYpK9KTPhERKSW2KQvxIRPRERq6UNbPEfVmPCJiEgtfWjj8FWNCV9FMjMzsXr1apw5cwZPnjyBsbEx7Ozs8M0336BFixaqLh4R0UePTfpCTPgqMmnSJBQWFiIkJARWVlbIyMhAdHQ0srKyVF00IiJSQ0z4KvDs2TNcvnwZu3btQqtWrQAA9erVe+tcykREVH7spS/EcfgqYGhoCENDQxw/fhxisVjVxSEiUktSqbTCmzpiwlcBHR0dhISEIDIyEq6urhg0aBBWrlyJuLg4VReNiEhtSKTSCm/qiAlfRbp3746zZ89iw4YNaN++PS5duoS+ffviwIEDqi4aEZFaYA1fiAlfhfT19eHu7o4JEyZg79698PLywtq1a1VdLCIitSCBtMKbOmLC/4A0atQIeXl5qi4GERGpIfbSV4GnT59iypQp6NevH2xtbVG1alXcuHEDW7ZsQZcuXVRdPCIitaCuTfMVxYSvAlWrVkXz5s2xY8cOJCcno6ioCLVr10b//v0xbtw4VRePiEgtqGvnu4piwlcBPT09TJ8+HdOnT1d1UYiI1Ban1hViwiciIrXEGr4QO+0RERFpANbwiYhILbHTnhATPhERqSW+wxdiwiciIrXEGr4QEz4REaklJnwhJnwiIlJLTPdC7KVPRESkAURStnkQERGpPdbwiYiINAATPhERkQZgwiciItIATPhEREQagAmfiIhIAzDhExERaQAmfCIiIg3AhE9ERKQBmPCJiIg0ABM+ERGRBmDCJyJSU5mZmcjJyVF1MegDwYRPVAFisRiJiYkoKipSWsyMjAxcvnwZly9fRkZGhtLiUsVFR0ejZ8+epSbd58+fo1evXrh8+bJcYz579gyBgYFo3bo13N3d0bJlS7i7u2PFihXIz8+Xa6yYmBicOnVKsC8yMhKdO3dG27ZtMW/ePIjFYrnGpIrj8rgkFxMnTizzuevWrVNgSV569uwZqlevLvf75ufnY9GiRYiMjAQAHDt2DFZWVli0aBFq1aqFsWPHyj1mTk4OAgMDERUVheLiYgCAtrY2evTogQULFqBatWpyj/mKWCxGZmYmJBKJYH/dunUVFlPRCgsLMXr0aAQGBuKTTz5RaKwdO3ZgwIABMDIyKnGsWrVqGDhwILZv3w5XV1e5xMvKysLAgQPx+PFjeHp6omHDhgCAhIQE/Pzzzzh//jz27NmD+Ph4XL16FSNGjKhUvPXr16NVq1bo1KkTACA+Ph5z5syBl5cXbGxssHXrVtSsWROTJk2q9LNR5THhq6ng4OAynztr1qxKx3s96UilUvz555+oVq0a7O3tAQCxsbF49uwZunXrVulYb9q0aRMsLS3Rs2dPAMCUKVPwxx9/wNzcHJs3b4adnZ3cYq1YsQJxcXHYuXMnxowZI9vftm1brFu3TiEJf+7cubh16xY2btwIZ2dnAC9rVosXL8b8+fOxatUqucdMSkrC7NmzERMTI9gvlUohEolw69YtucdMTk5GeHg4UlJSMGfOHJiZmeHMmTOoW7cuPv30U7nF0dXVRXx8vNzu9y7x8fH47rvv3nrc3d0d27Ztk1u89evXQ09PD3/++SfMzc0FxyZPnoxRo0bhu+++w/nz5zF37txKx4uLi8OUKVNkn6OiouDo6IigoCAAQO3atbF27Vom/A8EE76aunnzZonPxcXFaNCgAYCX/6BraWmhWbNmcon3+heMZcuWoUePHggMDIS2tjYAoLi4GIGBgahatapc4r1u7969WL58OQDg/PnzuHDhAjZv3oyjR49i6dKlcv0H9cSJE1i1ahWcnJwE+z/99FMkJyfLLc7rTp8+jS1btghqge3bt0dQUBBGjx6tkJj+/v7Q0dHBxo0bUbNmTYhEIoXEeeXSpUsYM2YMXFxc8M8//2DatGkwMzNDfHw8wsPDsWbNGrnG69OnD8LCwjBjxgy53vdNT548gY7O2/+Z1dHRQWZmptzinThxAoGBgSWSPQBYWFjgu+++w9ixYzFx4kR4eXlVOl52drYg1qVLl9ChQwfZZwcHB6SlpVU6DskHE76a2rVrl+zP27dvR9WqVbFkyRIYGxsDePk/6qxZs+TWlPi68PBw/PLLL7JkD7xsgh45ciQGDx6MmTNnyjXekydPUKdOHQDAqVOn0KNHD7Rr1w716tXDgAED5BorMzMTZmZmJfbn5+crLCnWqFGj1GZ7IyMjhby2AF7W3MLDw2FjY6OQ+79pxYoVmDp1Kry9vWWtGADQpk0b/Pzzz3KPV1xcjD179uDChQuwt7dHlSpVBMfl0eoFALVq1cJ///0Ha2vrUo/Hx8fDwsJCLrEA4PHjx+9sDWncuDG0tLTK9QruXczNzZGamoo6depALBbj5s2bmDx5sux4bm4udHV15RKLKo+d9jTAtm3bMH36dFmyBwBjY2NMnTpVrrXfV4qLi5GYmFhif2JiYol3wfJQvXp1WS3i7NmzaNu2LYCXzc+v3nnLi729PU6fPl1i/6+//lqi1i8v48ePR0hICNLT02X70tPTsWzZMnzzzTcKiWljY4OnT58q5N6luX37Nj777LMS+01NTRVSjtu3b6Np06aoWrUq7t69i5s3b8o2eb6u8PDwwOrVq1FQUFDi2IsXL7B27VrZ+295MDExQWpq6luPp6amwtTUVG7xOnTogBUrVuDy5ctYuXIlDAwM0KJFC9nx+Ph4WFlZyS0eVQ5r+BogJyen1GbDzMxM5Obmyj1e3759MWfOHKSkpMDBwQEAcO3aNWzatAl9+/aVe7xu3bphxowZsLa2RlZWlqxJ8datW2+tWVXUtGnTMGbMGNy5cwfFxcXYuXMnEhISEBMTI2hVkac9e/bg3r176NSpk6wlIy0tDbq6usjMzMS+fftk50ZERFQ4zus9yWfMmIHly5dj2rRpaNy4cYlaWmmd0CqjWrVqSE9PL5Ecbt26hVq1ask1FgCF/V29afz48fjjjz/QvXt3DB06VPZKLTExEb/88guKi4sxbtw4ucVr164dfvjhB2zbtg16enqCY2KxGKtXr0b79u3lFm/KlCmYNGkShg0bBkNDQyxZskQQNzw8HO3atZNbPKockVQqlaq6EKRYfn5+uHz5Mvz9/eHo6AgA+Pfff7F06VK4urpiyZIlco0nkUiwdetW7Ny5U1YrtbCwwIgRIzBq1ChBU788FBYWYufOnUhLS0Pfvn3RtGlTAMBPP/2EqlWron///nKNl5ycjE2bNiEuLg55eXlo2rQpxowZA1tbW7nGeaU8oxoq01RrZ2cneC3xqoPe6xTVaW/JkiX4999/sXr1anTv3h0RERF48uQJZs6ciS+//FJuTdBvunfvHpKTk9GyZUsYGBiU+syVdf/+fQQEBODcuXN49c+tSCRCu3btMH/+fLnWgB8+fIh+/fpBT08PQ4YMQcOGDSGVSmVfMMRiMcLCwuQ+yuL58+cwNDQs8f92VlYWDA0NS3z5INVgwtcA+fn5WLJkCcLDw2XjxrW1tfHVV1/Bz88PhoaGCov9qtYo7xohyd+lS5fKfG6rVq3kGlssFmPhwoWIiIhAcXExdHR0UFxcjN69eyMkJETuXxKfPn2KqVOn4uLFixCJRPjjjz9gZWWFWbNmwdjYGP7+/nKNB7zsN3Pv3j0AgLW1teAVmzylpKQgMDAQ58+fF3zBcHNzw/z58+Xe6kUfDyZ8DZKXlyfrSV6/fn2FJvqioiJcunQJycnJ6N27N4yMjPDo0SMYGRnJvad+REQETExM0LFjRwDA0qVLsX//fjRq1AgrVqxAvXr15BbrXbOW6enpKbwmU1BQgKioKOTl5cHd3V3h48iVQSqVIi0tTfa+/vbt28jNzUXTpk0V9nx+fn7IyMjA4sWL0aNHD/z222+wsrLC2bNnERISgiNHjigkrjK9/gWjfv36qFGjhmoLRCrHhE9yd//+fYwePRppaWkQi8WyyWmCgoJkNTl56t69OwICAtC2bVvExMTA29sbs2bNwqlTp6CjoyPXiX7ebPZ+U+3ateHl5YWJEydCS6tyfWKDg4NRVFSEefPmAXhZC+7fvz8SEhJgYGCA4uJibN26FS4uLpWKU5rw8HAYGhqiR48egv1Hjx7Fixcv5DKk6xWJRAJHR0ccPnxYaV9g3N3dsXXrVtjZ2cHZ2VmW8FNSUtCnT58S8w8QqQN22lNjZX3vKe+Z7xYvXgx7e3scPHgQrVu3lu3v2rWrLHnJ08OHD2XNlMePH0e3bt0wcOBAuLi4YPjw4XKNFRISglWrVsHLy0vWH+LatWuIjIzE+PHjkZmZKeswVdnOWOfPn8e0adNknw8dOoS0tDQcO3YMdevWxezZs7Fx40Zs2rSpUnFKs2nTJgQGBpbYb2Zmhnnz5sk14Wtpack6XCpLXl4eDAwMSuzPysri+2ZSW0z4akyRU66+y5UrV7Bnz54S/3DWq1cPjx49kns8Q0NDZGVloW7dujh//jxGjhwJANDX1y91OFRlREREYObMmbJZ/QCgc+fOaNy4Mfbt24cdO3agTp062LhxY6UT/oMHD9CoUSPZ5/Pnz6N79+6yVxQjRowQzPYnTw8ePIClpWWJ/XXr1lXIRCrTp0/H0qVLERAQgMaNG8v9/m9ydXVFZGQkpk6dKtsnkUiwZcsWwZdUInXChK/GyjO9rjxJJJJSx9s/fPhQITPtubm5Ye7cuWjSpAmSkpLg4eEBAPjvv//k+v4eeDmlbWk136ZNm+Lq1asAgBYtWsglKWppaeH1N25Xr14VjLuvVq0anj17Vuk4pXk1y92bST8uLk4h74JnzpyJ/Px8fPHFF9DV1S1R+y5Ph8Ky+O677zBy5EjcuHEDhYWFWLZsGe7cuYPs7Gzs2bNHrrGIPhRM+GqusLAQzZs3R2RkpFJqTsDL96M7duzAokWLZPtyc3Oxdu1aWTKWpwULFuCHH35AWloa1qxZAxMTEwAv5+/v1auXXGPVqVOn1ClZw8LCULt2bQAvm4XlMQOejY0NTp06BW9vb/z3339IS0sT1D4fPHhQ6hSq8tCrVy8sXrwYVatWRcuWLQG8TLrff/+93H+mADB79my53/NdGjdujGPHjuHnn39G1apVkZeXh65du2Lo0KGoWbOmUstCpCzstKcBunTpgvXr18t1EZl3efjwIXx8fCCVSnHv3j3Y29sjKSkJJiYm2L17d6lT034sTpw4gSlTpqBhw4aySYVu3LiBhIQE2axpv/zyC+7du1fp6Vn//PNPTJs2DS1atMCdO3fg4OCAjRs3yo4vW7YMqampWL16daXilEYsFsPPzw+///67bC54iUSCL774AoGBgXzPTfQRYsLXAL/++iv+/PNPLF26VGlDc4qKinDkyBHEx8cjLy8PzZo1g6enZ6kdpeQlPz8fDx48QGFhoWC/vL/opKamYu/evUhKSgIANGjQAAMHDkReXp7cW1Gio6Nx6tQpmJubY/jw4YI539etW4eWLVvK/Z3z68PkHj58iFu3bsHAwACNGzeW+yuS0hQUFJT4O5THPA5xcXFlPldZX46JlIkJXwN8+eWXuHfvHoqKilC3bt0S4+8rMx1raQoKCqCvry/Xe75LZmYm/P39cfbs2VKPK2Ip11dycnJw+PBhhIeH48aNGwqNpSyqGCaXl5eH5cuX4+jRo6X21pfHz/XVkMo3Z9N7fXIaecYj+tDwHb4GKG1REkVq27Ytunbtij59+qBt27aVHo/+PosXL8bz58+xf/9+jBgxAuvWrcOTJ0+wYcMGhcyYBgD//PMPwsLC8Mcff6BmzZoKG3L4SnZ2NsLCwpCQkADg5fv9fv36KaTFRhXD5JYtW4aLFy8iICAAfn5+mD9/Ph49eoR9+/Zh+vTpcolx4sQJ2Z9v3bqFJUuWwMfHR7bo0dWrV7F9+/Z3rl9P9FGTEsnZH3/8IZ00aZLU0dFR6u7uLg0KCpJeu3ZNYfHc3d2l//77r1QqlUqdnZ2liYmJUqlUKj1+/Lh00KBBcovz+PFjaWhoqLRr167Stm3bShcuXCht2rSp9L///pNbjNJcunRJ6uLiIvXw8JBOmDBBOmHCBGnHjh2lLi4u0kuXLikk5okTJ6SDBw+WxsfHK+T+b/Lw8JD+/fffUqn05d9hUlKSVCqVSiMiIqSjR4+We7x+/fpJT58+XWL/6dOnpV5eXnKPR/QhYA1fQzx79gzHjh1DcnIyfHx8UKNGDcTGxsLc3Fzuq5F17doVXbt2RU5ODo4dO4YjR45g4MCBsLKygqenp9wXQsnLy5Mt+WlsbIzMzEw0aNAAjRs3xs2bN+USY9y4cfjnn3/QsWNHzJ49G+3bt4e2tjb27t0rl/u/y8KFC9GzZ08EBATI5pQvLi5GYGAgFi5ciEOHDsk9prKHyWVnZ8sWkTEyMkJ2djaAl0McSxsGWVm3b98udZ4BS0tL3LlzR+7xiD4ETPgaIC4uDt7e3qhWrRru37+PAQMGoEaNGvjjjz+QlpaGpUuXKiSukZER+vXrh379+uHOnTuYMWMG1q9fL/eE36BBA9y9exeWlpawtbXFvn37YGlpib1798LCwkIuMf766y8MHz4cgwcPVvr89ffu3cPq1asFC8hoa2tj5MiRiIyMVEhMZQ+Ts7S0RGpqKurWrYuGDRvi6NGjcHR0xKlTpxQygZSNjQ1CQ0MRFBQkG3EgFosRGhoKGxsbuccj+hAw4WuAkJAQeHl5wc/PD87OzrL9Hh4eJcaTy1NBQQFOnDiBw4cP4+zZszA3N4ePj4/c44wYMUK2DO/EiRMxevRoHDp0CLq6uggJCZFLjF9++QVhYWHo27cvbGxs8MUXXwhm21Okpk2bIjExEQ0bNhTsT0xMVFhvcnlOnfsuKSkpqFevHvr164e4uDi0atUKY8eOxbhx4/Dzzz+jqKhIIf0wAgMDMW7cOHh4eMiWNY6Pj4dIJBIMfSRSJ+ylrwFatGiBiIgI1K9fX7BQyP379/H555/j+vXrco139uxZHD58GMePH4eOjg66d+8OT09P2QQuipafn4/ExETUqVNH1tQvL3l5eYiKikJ4eDiuX7+O4uJi+Pv7o1+/fnJdAvj1IWSJiYlYtmwZhg0bhubNmwMA/v33X+zevRszZsxQ+BcPRQ2TA4AmTZrg3LlzsrkZpk6dirlz56KgoACxsbGoX7++wr7U5OXl4dChQ0hMTATwstbfu3dvha4iSaRKTPgaoG3btti6dSuaNm0qSPjnz5/H7NmzcebMGbnGa968OTp27AhPT094eHhAV1dXrvf/UCQmJiIsLAy//fYbnj17Bjc3N7nVDl8fQvYuIpFIIUPIlDFMDnj5nOfPn5cl/Nd/P4lIvtikrwE6d+6M9evX44cffpDte/DgAZYvX45u3brJPd758+flWtstTXnWCajsjHdv07BhQ/j5+WH69Ok4deoUwsLC5Hbv14eQqYIyhsmpWlJSEi5evIiMjIwSaz/Iu58J0YeACV8D+Pv7Y/LkyXBzc0NBQQGGDx+OJ0+ewMnJSbD8amXk5OQIknxOTs5bz5XHl4Gy9r5/19r18qKtrY3PPvtMrvMdKGNGu3c5deoUlixZgtatW2PWrFlwdXWFtbU16tati0OHDqFPnz5yiSMSiZTyd/Sm/fv3IyAgACYmJjA3NxeUQSQSMeGTWmKTvga5fPmyYKpbNzc3ud379Xexr5qj3yT9/zOccRaz9ztx4gQ6dOgAXV3d99b2u3TpIvf4zs7OOHLkCOrWrYsOHTpg3bp1cHR0REpKCvr06YOYmBi5xLGzs0OHDh1kPeVPnTqFNm3aCKYQBl5OIyxPnTp1wuDBgzF27Fi53pfoQ8YavgZxdXWFq6urQu69Y8cOGBsby/6szFrb8+fPUVxcXGLWuaysLOjo6Cj89YIiTJgwQfZue8KECW89T1FfoJQ1TO7N0QDyajl4n+zsbPTo0UMpsYg+FKzha4CdO3eWul8kEkFfXx/169dHy5YtBeO8PyajR49Gp06dMHToUMH+PXv24OTJk9i8ebOKSvbx+umnn6ClpYURI0bgwoULGDduHKRSqWyY3Ndff63qIlbK7Nmz4eDggMGDB6u6KERKwxq+Bvjpp5/w9OlT5Ofny2rh2dnZqFKlCgwNDZGRkQErKyvs3LkTderUqXS8bt26wdPTE56enkqZpObatWuldsxr1aqVoKPixyYmJgZZWVno1KmTbF9kZCTWrFmD/Px8fPbZZ5g3b55cl6qVSCTYsmULTp48icLCQjx+/BgTJ07E0aNHFT5MTpmsra2xevVq/Pvvv2jcuLFsCeBXRowYoaKSESkOE74G+Pbbb7Fv3z4sXrwY9evXB/By9rb58+dj4MCBcHFxwbRp0xAcHIw1a9ZUOt6QIUNw6NAh/Pjjj2jatCn69OmDnj17ym3WuzeJxWIUFRWV2F9UVIQXL14oJKYyrF+/Hq1atZIl/Pj4eMyZMwdeXl6wsbHB1q1bUbNmTUyaNEluMTds2IB169bBzc0N+vr62LlzJzIyMhAcHKzyjoTytG/fPhgaGuLSpUslpgkWiURM+KSeVDSHPylRly5dpDdv3iyxPzY2Vtq5c2epVCqVXrlyReru7i7XuImJidLVq1dLu3XrJm3atKnU29tbGhERIdcYUqlUOmzYMOnChQtL7A8ICJAOHjxY7vGUxd3dXbDo0MqVKwWLAUVFRUl79Ogh15hdu3aV7tmzR/b5/Pnz0mbNmkmLi4vlGoeIlI81fA2Qnp7+1hrwkydPAAA1a9ZEbm6uXOM2aNAAkydPxuTJk3H16lUEBARg1qxZ+PLLL+UaZ+rUqfD29kZcXBzatm0LAIiOjsb169exbds2ucZSpuzsbJibm8s+X7p0CR06dJB9dnBwQFpamlxjPnjwAB4eHrLPbm5uEIlEePz4MWrXri3XWB8CsViM1NRU1K9fv0SzPpG6UexC5fRBaN26NRYsWCAYu37z5k0EBASgTZs2AN6+elhlXbt2DYsXL8bEiRORlJSEzz//XO4xWrRogf3796N27do4evQoTp48ifr16+O3335T2KgEZTA3N0dqaiqAl4np5s2bsrXbASA3N1fusxgWFxdDX19fsE9HR6fE1Lofu/z8fMyePRtOTk7o3bu37IvTokWLsGnTJhWXjkgx+JVWAyxevBh+fn7o27evrBZTXFyMtm3bYvHixQAAQ0NDzJw5Uy7x7t69i0OHDuHIkSNITU1FmzZtMGPGDHTt2hVVq1aVSwygZAezNm3aYPHixSWWcv1YdejQAStWrMCMGTNw/PhxGBgYoEWLFrLj8fHxcp+CViqVwt/fX9ARUCwWIyAgQDA2Xt7j4pVtxYoViIuLw86dOzFmzBjZ/rZt22LdunUcn09qiQlfA1hYWGD79u1ISEhAUlISgJfN7a+vvvaqpi8PPXr0gIODA4YMGYJevXoJmqXl6c0OZrt27UJmZma5pt39kE2ZMgWTJk3CsGHDYGhoiCVLlggScXh4ONq1ayfXmKWtkqessfHKdOLECaxatUrQYgIAn376KZKTk1VTKCIF4zh8kqvi4mKEh4eje/fusiGAitKtWzeMGjUKgwYNAgBcuHABY8eOxbVr16ClpT5vq54/fw5DQ8MS8yRkZWXB0NBQrsPyNEXz5s1x+PBhWFlZCRbsiYuLw9ChQ3HlyhVVF5FI7ljD1wDFxcU4cOAA/v7771IXCnnbxDwVoa2tjUWLFqFt27YKT/ia0sHsbTPbvTmzIJWdvb09Tp8+jeHDhwv2//rrryVq/UTqgglfAyxevBgRERHw8PDAp59+qvBpbz/99FOkpqYqfIlTTelgRvI3bdo0jBkzBnfu3EFxcTF27tyJhIQExMTEYNeuXaouHpFCsElfA7Ru3RpLly4V1IYV6a+//sLKlSsxZcoUNGvWDIaGhoLj8prb/s2FV4DSF1/52DuYkWIkJydj06ZNiIuLQ15eHpo2bYoxY8bA1tZW1UUjUggmfA3Qrl077Nq1Cw0aNFBKvNenXn29NUEq59XyyrrOvbp04iMiqgwmfA2wbds2pKSkYP78+UpZxe7NqUrf1KpVK4WXgehdcnJy3npMT0+PHSFJLTHha4AJEybg4sWLMDY2xqefflpiRjE2eZOmsbOze+eX39q1a8PLywsTJ05UqxEfpNnYaU8DVK9eHV27dlVavH/++eedx1u2bKmkkhCVLiQkBKtWrYKXlxccHR0BvJwVMjIyEuPHj0dmZia2bdsGPT09jBs3TsWlJZIP1vBJ7kpbPvX12pS83uETVdTXX3+NgQMHomfPnoL9UVFR2LdvH3bs2IHIyEhs3LgRv//+u4pKSSRfrOFriKKiIly6dAnJycno3bs3jIyM8OjRIxgZGcl1ulugZA2/sLAQt27dwurVqzFt2jS5xiKqiJiYGAQGBpbY37RpU1y9ehXAyzUa5L04EZEqMeFrgPv372P06NFIS0uDWCyGu7s7jIyMsHnzZojFYixcuFCu8UqbKMbd3R26uroICQnBgQMH5BqPqLzq1KmDsLAwzJgxQ7A/LCxMNmlTVlYWqlevroriESkEE74GWLx4Mezt7XHw4EG0bt1atr9r166YN2+e0sphZmaGu3fvKi0e0dv4+flhypQp+Ouvv+Dg4AAAuHHjBhITE7FmzRoAwPXr10s0+RN9zJjwNcCVK1ewZ8+eEkON6tWrh0ePHsk9XlxcXIl9jx8/xubNm0t9v0+kbF26dMHvv/+Offv2yb6EdujQAevXr5ctEz1kyBBVFpFI7pjwNYBEIikxfz4APHz4UO7v7wHgyy+/hEgkwpv9QZ2cnGTL8RKpmqWlJaZPn/7OcwICAjB58mSYmpoqqVREisOErwHc3d2xY8cOLFq0SLYvNzcXa9euVch0uydOnBB81tLSgqmpaYl574k+dL/99ht8fHyY8EktcFieBnj48CF8fHwglUpx79492NvbIykpCSYmJti9ezfMzMzkEicmJgZZWVno1KmTbF9kZCTWrFmD/Px8fPbZZ5g3bx5nMaOPxutL5xJ97FjD1wC1a9fGwYMHERUVJVso5KuvvoKnpycMDAzkFmf9+vVo1aqVLOHHx8djzpw58PLygo2NDbZu3YqaNWti0qRJcotJRERlw4SvIXR0dNCnTx/06dNHYTHi4uIwZcoU2eeoqCg4OjoiKCgIwMsvHmvXrmXCJyJSAU4SrQEiIiJw+vRp2eelS5fC1dUVgwYNwv379+UWJzs7G+bm5rLPly5dQocOHWSfHRwcOJEJEZGKMOFrgI0bN8o6zMXExGD37t347rvvUKNGDbkuHWtubo7U1FQAgFgsxs2bN+Hk5CQ7npubC11dXbnFIyKismOTvgZ4+PAhrK2tAQDHjx9H9+7dMXDgQLi4uGD48OFyi9OhQwesWLECM2bMwPHjx2FgYIAWLVrIjsfHx7PzE31U+vTpo5Chq0SqwBq+BjA0NERWVhYA4Pz583BzcwMA6Ovro6CgQG5xpkyZAm1tbQwbNgz79+9HUFCQoEd+eHg42rVrJ7d4RJVx+fJlzJgxAwMHDpRNQBUZGYnLly/LzgkMDOSQPFIbrOFrADc3N8ydOxdNmjRBUlKSbOz9f//9h3r16sktjqmpKXbv3o3nz5/D0NAQ2traguOrV6+GoaGh3OIRVdSxY8fg5+cHT09P3Lx5E2KxGACQk5OD0NBQuLq6qriERPLHGr4GWLBgAZycnJCZmYk1a9bAxMQEABAbG4tevXrJPV61atVKJHsAqFGjBsfg0wdhw4YNCAwMRFBQEHR0/q/e4+Ligps3b6qwZESKwxq+BqhevTrmz59fYv/kyZNVUBoi1bt7926ptfhq1arh2bNnKigRkeKxhq8B/vrrL8F7yd27d+OLL77A9OnTkZ2drcKSEamGubk5kpOTS+y/cuUKO5aS2mLC1wDLli1Dbm4ugJc95UNCQuDh4YHU1FSEhISouHREyjdgwAAsXrwY//77L0QiER49eoTffvsNS5YsweDBg1VdPCKFYJO+BkhNTYWNjQ0A4I8//kCnTp3w7bffIjY2FmPHjlVx6YiUb+zYsZBIJBg5ciTy8/MxbNgw6OnpYdSoUXIdqkr0IWHC1wC6urp48eIFAODChQv48ssvAQDGxsbIyclRYcmIVEMkEmH8+PHw8fFBcnIy8vLyYGNjwzH3pNaY8DWAi4sLgoOD4eLiguvXr+OHH34AACQlJaF27dqqLRyRCunp6aFRo0aqLgaRUjDha4D58+cjMDAQx44dw4IFC1CrVi0ALzvztW/fXsWlI1KOiRMnlvncdevWKbAkRKohkkqlUlUXgohI0WbNmlXmc+W5xgTRh4IJX8MUFBSgsLBQsM/IyEhFpSEiImVhk74GyMvLw/Lly3H06FHZnPqvu3XrlvILRURESsWErwGWLVuGixcvIiAgAH5+fpg/fz4ePXqEffv2Yfr06aouHpFK/P777zh69CjS0tJKtHpFRESoqFREisOJdzTAqVOnsGDBAnTv3h3a2tpwdXXFN998g2nTpuHQoUOqLh6R0u3cuROzZs2Cubk5bt68CQcHB9SoUQMpKSno0KGDqotHpBBM+BogOztbNl2okZGRbDrdFi1aCKbcJdIUv/zyCxYtWoR58+ZBV1cXY8aMwfbt2zF8+HA8f/5c1cUjUggmfA1gaWmJ1NRUAEDDhg1x9OhRAC9r/tWqVVNl0YhUIi0tDc7OzgAAAwMD2dTTX3zxBY4cOaLKohEpDBO+BujXrx/i4uIAvJxSdPfu3XBwcEBwcDB8fHxUXDoi5TM3N5e1dNWpUwdXr14F8HIaag5cInXFTntqTCKRYMuWLTh58iQKCwvx+PFjTJw4EUePHkVsbCzq168POzs7VReTSOnatGmDkydPomnTpujXrx+Cg4Nx7Ngx3LhxA127dlV18YgUguPw1dj69euxbt06uLm5QV9fH+fOnUOvXr04qQhpPIlEAolEAh2dl3WeqKgo/O9//4O1tTUGDRoEXV1dFZeQSP6Y8NVYt27dMGrUKAwaNAjAy4Vzxo4di2vXrkFLi29zSLMVFBQgPj4eGRkZkEgksv0ikQidO3dWYcmIFINN+mrswYMH8PDwkH12c3ODSCTC48ePuWgOabS//voLfn5+pU5EJRKJOBkVqSUmfDVWXFwMfX19wT4dHZ0Sk4wQaZqgoCD06NEDEyZMgLm5uaqLQ6QUbNJXY3Z2dujQoQP09PRk+06dOoU2bdqgSpUqsn1cGYw0jYuLCyIjI1G/fn1VF4VIaVjDV2NeXl4l9vXp00cFJSH6sHTv3h0XL15kwieNwho+EWmc/Px8TJkyBaampmjcuLGst/4rI0aMUFHJiBSHNXwi0jiHDx/G+fPnoaenh0uXLgmOiUQiJnxSS6zhE5HGcXd3x/DhwzF27FgOUSWNwd90ItI4hYWF6NmzJ5M9aRT+thORxvnyyy8RFRWl6mIQKRXf4RORxnm1zsS5c+dga2tbotPerFmzVFQyIsVhwicijRMfH48mTZoAAG7fvi04JhKJVFEkIoVjpz0iIiINwHf4REREGoAJn4iISAMw4RMREWkAJnwiIiINwIRPRESkAZjwiYiINAATPhERkQb4f1A/nqRQVCpGAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 500x400 with 2 Axes>"
       ]
@@ -1517,7 +1517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1528,7 +1528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1547,7 +1547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1656,7 +1656,7 @@
        "181        183.0     3.0   9.0    4.0    2.0          1.0  0.0  0.0  1.0"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1667,7 +1667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -2077,13 +2077,13 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "LogisticRegression(solver='liblinear')"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2095,13 +2095,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
     "y_pred = LogReg.predict(X_test.values)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       0.83      0.92      0.87       109\n",
+      "         1.0       0.84      0.70      0.76        69\n",
+      "\n",
+      "    accuracy                           0.83       178\n",
+      "   macro avg       0.83      0.81      0.82       178\n",
+      "weighted avg       0.83      0.83      0.83       178\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[384,  56],\n",
+       "       [ 90, 181]])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train_pred = cross_val_predict(LogReg, X_train, y_train, cv=5)\n",
+    "confusion_matrix(y_train, y_train_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.7637130801687764)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(y_train, y_train_pred)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -2112,7 +2179,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -2126,7 +2193,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/02_10_b.ipynb b/notebooks/02_10_b.ipynb
index bab1f21..272e2c9 100644
--- a/notebooks/02_10_b.ipynb
+++ b/notebooks/02_10_b.ipynb
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -162,7 +162,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -496,7 +496,7 @@
     },
     {
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",
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",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -702,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1335,7 +1335,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 2 Axes>"
       ]
@@ -1547,7 +1547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2077,7 +2077,7 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "LogisticRegression(solver='liblinear')"
@@ -2129,7 +2129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2157,7 +2157,7 @@
     {
      "data": {
       "text/plain": [
-       "0.7637130801687764"
+       "np.float64(0.7637130801687764)"
       ]
      },
      "execution_count": 31,
@@ -2169,6 +2169,99 @@
     "precision_score(y_train, y_train_pred)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>male_gender</th>\n",
+       "      <th>C</th>\n",
+       "      <th>Q</th>\n",
+       "      <th>S</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>863</th>\n",
+       "      <td>866.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId  Survived  Pclass   Age  SibSp  Parch  male_gender    C    Q  \\\n",
+       "863        866.0       1.0     2.0  42.0    0.0    0.0          0.0  0.0  0.0   \n",
+       "\n",
+       "       S  \n",
+       "863  1.0  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic_dmy[863:864]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.]\n",
+      "[[0.24588769 0.75411231]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_passenger = np.array(['866','2','40','0','0','0','0','0','1']).astype(float).reshape(1,-1)\n",
+    "print(LogReg.predict(test_passenger))\n",
+    "print(LogReg.predict_proba(test_passenger))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -2179,7 +2272,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -2193,7 +2286,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,