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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Before you start:\n",
+    "- Read the README.md file\n",
+    "- Comment as much as you can and use the resources in the README.md file\n",
+    "- Happy learning!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import your libraries:\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
+    "from sklearn.linear_model import Ridge, Lasso, ElasticNet, LinearRegression, BayesianRidge, SGDRegressor, LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: numpy in c:\\users\\mario\\anaconda3\\lib\\site-packages (1.26.4)\n",
+      "Requirement already satisfied: scikit-learn in c:\\users\\mario\\anaconda3\\lib\\site-packages (1.5.0)\n",
+      "Requirement already satisfied: pandas in c:\\users\\mario\\anaconda3\\lib\\site-packages (2.0.3)\n",
+      "Requirement already satisfied: scipy>=1.6.0 in c:\\users\\mario\\anaconda3\\lib\\site-packages (from scikit-learn) (1.11.1)\n",
+      "Requirement already satisfied: joblib>=1.2.0 in c:\\users\\mario\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
+      "Requirement already satisfied: threadpoolctl>=3.1.0 in c:\\users\\mario\\anaconda3\\lib\\site-packages (from scikit-learn) (3.5.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\mario\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\mario\\anaconda3\\lib\\site-packages (from pandas) (2023.3.post1)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\mario\\anaconda3\\lib\\site-packages (from pandas) (2023.3)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\mario\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# pip install --upgrade numpy scikit-learn\n",
+    "# %pip install numpy scikit-learn pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this lab, we will explore a dataset that describes websites with different features and labels them either benign or malicious . We will use supervised learning algorithms to figure out what feature patterns malicious websites are likely to have and use our model to predict malicious websites.\n",
+    "\n",
+    "# Challenge 1 - Explore The Dataset\n",
+    "\n",
+    "Let's start by exploring the dataset. First load the data file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "websites = pd.read_csv('../data/website.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Explore the data from an bird's-eye view.\n",
+    "\n",
+    "You should already been very familiar with the procedures now so we won't provide the instructions step by step. Reflect on what you did in the previous labs and explore the dataset.\n",
+    "\n",
+    "Things you'll be looking for:\n",
+    "\n",
+    "* What the dataset looks like?\n",
+    "* What are the data types?\n",
+    "* Which columns contain the features of the websites?\n",
+    "* Which column contains the feature we will predict? What is the code standing for benign vs malicious websites?\n",
+    "* Do we need to transform any of the columns from categorical to ordinal values? If so what are these columns?\n",
+    "\n",
+    "Feel free to add additional cells for more exploration. Make sure to comment what you find!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>URL</th>\n",
+       "      <th>URL_LENGTH</th>\n",
+       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
+       "      <th>CHARSET</th>\n",
+       "      <th>SERVER</th>\n",
+       "      <th>CONTENT_LENGTH</th>\n",
+       "      <th>WHOIS_COUNTRY</th>\n",
+       "      <th>WHOIS_STATEPRO</th>\n",
+       "      <th>WHOIS_REGDATE</th>\n",
+       "      <th>WHOIS_UPDATED_DATE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
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+       "      <td>16</td>\n",
+       "      <td>7</td>\n",
+       "      <td>iso-8859-1</td>\n",
+       "      <td>nginx</td>\n",
+       "      <td>263.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/10/2015 18:21</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>700</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1153</td>\n",
+       "      <td>832</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B0_2314</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>Apache/2.4.10</td>\n",
+       "      <td>15087.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1230</td>\n",
+       "      <td>17</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1265</td>\n",
+       "      <td>1230</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>B0_911</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>us-ascii</td>\n",
+       "      <td>Microsoft-HTTPAPI/2.0</td>\n",
+       "      <td>324.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B0_113</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "      <td>ISO-8859-1</td>\n",
+       "      <td>nginx</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>AK</td>\n",
+       "      <td>7/10/1997 4:00</td>\n",
+       "      <td>12/09/2013 0:45</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3812</td>\n",
+       "      <td>39</td>\n",
+       "      <td>37</td>\n",
+       "      <td>18784</td>\n",
+       "      <td>4380</td>\n",
+       "      <td>39</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B0_403</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>124140.0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>TX</td>\n",
+       "      <td>12/05/1996 0:00</td>\n",
+       "      <td>11/04/2017 0:00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4278</td>\n",
+       "      <td>61</td>\n",
+       "      <td>62</td>\n",
+       "      <td>129889</td>\n",
+       "      <td>4586</td>\n",
+       "      <td>61</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       URL  URL_LENGTH  NUMBER_SPECIAL_CHARACTERS     CHARSET  \\\n",
+       "0   M0_109          16                          7  iso-8859-1   \n",
+       "1  B0_2314          16                          6       UTF-8   \n",
+       "2   B0_911          16                          6    us-ascii   \n",
+       "3   B0_113          17                          6  ISO-8859-1   \n",
+       "4   B0_403          17                          6       UTF-8   \n",
+       "\n",
+       "                  SERVER  CONTENT_LENGTH WHOIS_COUNTRY WHOIS_STATEPRO  \\\n",
+       "0                  nginx           263.0           NaN            NaN   \n",
+       "1          Apache/2.4.10         15087.0           NaN            NaN   \n",
+       "2  Microsoft-HTTPAPI/2.0           324.0           NaN            NaN   \n",
+       "3                  nginx           162.0            US             AK   \n",
+       "4                    NaN        124140.0            US             TX   \n",
+       "\n",
+       "      WHOIS_REGDATE WHOIS_UPDATED_DATE  ...  DIST_REMOTE_TCP_PORT  REMOTE_IPS  \\\n",
+       "0  10/10/2015 18:21                NaN  ...                     0           2   \n",
+       "1               NaN                NaN  ...                     7           4   \n",
+       "2               NaN                NaN  ...                     0           0   \n",
+       "3    7/10/1997 4:00    12/09/2013 0:45  ...                    22           3   \n",
+       "4   12/05/1996 0:00    11/04/2017 0:00  ...                     2           5   \n",
+       "\n",
+       "   APP_BYTES  SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  SOURCE_APP_BYTES  \\\n",
+       "0        700                   9                  10              1153   \n",
+       "1       1230                  17                  19              1265   \n",
+       "2          0                   0                   0                 0   \n",
+       "3       3812                  39                  37             18784   \n",
+       "4       4278                  61                  62            129889   \n",
+       "\n",
+       "   REMOTE_APP_BYTES  APP_PACKETS  DNS_QUERY_TIMES  Type  \n",
+       "0               832            9              2.0     1  \n",
+       "1              1230           17              0.0     0  \n",
+       "2                 0            0              0.0     0  \n",
+       "3              4380           39              8.0     0  \n",
+       "4              4586           61              4.0     0  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "websites.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1781 entries, 0 to 1780\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column                     Non-Null Count  Dtype  \n",
+      "---  ------                     --------------  -----  \n",
+      " 0   URL                        1781 non-null   object \n",
+      " 1   URL_LENGTH                 1781 non-null   int64  \n",
+      " 2   NUMBER_SPECIAL_CHARACTERS  1781 non-null   int64  \n",
+      " 3   CHARSET                    1774 non-null   object \n",
+      " 4   SERVER                     1605 non-null   object \n",
+      " 5   CONTENT_LENGTH             969 non-null    float64\n",
+      " 6   WHOIS_COUNTRY              1475 non-null   object \n",
+      " 7   WHOIS_STATEPRO             1419 non-null   object \n",
+      " 8   WHOIS_REGDATE              1654 non-null   object \n",
+      " 9   WHOIS_UPDATED_DATE         1642 non-null   object \n",
+      " 10  TCP_CONVERSATION_EXCHANGE  1781 non-null   int64  \n",
+      " 11  DIST_REMOTE_TCP_PORT       1781 non-null   int64  \n",
+      " 12  REMOTE_IPS                 1781 non-null   int64  \n",
+      " 13  APP_BYTES                  1781 non-null   int64  \n",
+      " 14  SOURCE_APP_PACKETS         1781 non-null   int64  \n",
+      " 15  REMOTE_APP_PACKETS         1781 non-null   int64  \n",
+      " 16  SOURCE_APP_BYTES           1781 non-null   int64  \n",
+      " 17  REMOTE_APP_BYTES           1781 non-null   int64  \n",
+      " 18  APP_PACKETS                1781 non-null   int64  \n",
+      " 19  DNS_QUERY_TIMES            1780 non-null   float64\n",
+      " 20  Type                       1781 non-null   int64  \n",
+      "dtypes: float64(2), int64(12), object(7)\n",
+      "memory usage: 292.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "websites.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <th>WHOIS_REGDATE</th>\n",
+       "      <th>WHOIS_UPDATED_DATE</th>\n",
+       "      <th>...</th>\n",
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+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>M0_109</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>Apache</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>US</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>17/09/2008 0:00</td>\n",
+       "      <td>2/09/2016 0:00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>676</td>\n",
+       "      <td>386</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>372</td>\n",
+       "      <td>62</td>\n",
+       "      <td>64</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>56.961258</td>\n",
+       "      <td>11.111735</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11726.927761</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.472768</td>\n",
+       "      <td>3.060640</td>\n",
+       "      <td>2.982339e+03</td>\n",
+       "      <td>18.540146</td>\n",
+       "      <td>18.746210</td>\n",
+       "      <td>1.589255e+04</td>\n",
+       "      <td>3.155599e+03</td>\n",
+       "      <td>18.540146</td>\n",
+       "      <td>2.263483</td>\n",
+       "      <td>0.121280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>27.555586</td>\n",
+       "      <td>4.549896</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>36391.809051</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>21.807327</td>\n",
+       "      <td>3.386975</td>\n",
+       "      <td>5.605057e+04</td>\n",
+       "      <td>41.627173</td>\n",
+       "      <td>46.397969</td>\n",
+       "      <td>6.986193e+04</td>\n",
+       "      <td>5.605378e+04</td>\n",
+       "      <td>41.627173</td>\n",
+       "      <td>2.930853</td>\n",
+       "      <td>0.326544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</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>NaN</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>324.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1853.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>6.720000e+02</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>5.790000e+02</td>\n",
+       "      <td>7.350000e+02</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11323.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>2.328000e+03</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>9.806000e+03</td>\n",
+       "      <td>2.701000e+03</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>249.000000</td>\n",
+       "      <td>43.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>649263.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>708.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>2.362906e+06</td>\n",
+       "      <td>1198.000000</td>\n",
+       "      <td>1284.000000</td>\n",
+       "      <td>2.060012e+06</td>\n",
+       "      <td>2.362906e+06</td>\n",
+       "      <td>1198.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>11 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           URL   URL_LENGTH  NUMBER_SPECIAL_CHARACTERS CHARSET  SERVER  \\\n",
+       "count     1781  1781.000000                1781.000000    1774    1605   \n",
+       "unique    1781          NaN                        NaN       8     238   \n",
+       "top     M0_109          NaN                        NaN   UTF-8  Apache   \n",
+       "freq         1          NaN                        NaN     676     386   \n",
+       "mean       NaN    56.961258                  11.111735     NaN     NaN   \n",
+       "std        NaN    27.555586                   4.549896     NaN     NaN   \n",
+       "min        NaN    16.000000                   5.000000     NaN     NaN   \n",
+       "25%        NaN    39.000000                   8.000000     NaN     NaN   \n",
+       "50%        NaN    49.000000                  10.000000     NaN     NaN   \n",
+       "75%        NaN    68.000000                  13.000000     NaN     NaN   \n",
+       "max        NaN   249.000000                  43.000000     NaN     NaN   \n",
+       "\n",
+       "        CONTENT_LENGTH WHOIS_COUNTRY WHOIS_STATEPRO    WHOIS_REGDATE  \\\n",
+       "count       969.000000          1475           1419             1654   \n",
+       "unique             NaN            48            181              890   \n",
+       "top                NaN            US             CA  17/09/2008 0:00   \n",
+       "freq               NaN          1103            372               62   \n",
+       "mean      11726.927761           NaN            NaN              NaN   \n",
+       "std       36391.809051           NaN            NaN              NaN   \n",
+       "min           0.000000           NaN            NaN              NaN   \n",
+       "25%         324.000000           NaN            NaN              NaN   \n",
+       "50%        1853.000000           NaN            NaN              NaN   \n",
+       "75%       11323.000000           NaN            NaN              NaN   \n",
+       "max      649263.000000           NaN            NaN              NaN   \n",
+       "\n",
+       "       WHOIS_UPDATED_DATE  ...  DIST_REMOTE_TCP_PORT   REMOTE_IPS  \\\n",
+       "count                1642  ...           1781.000000  1781.000000   \n",
+       "unique                593  ...                   NaN          NaN   \n",
+       "top        2/09/2016 0:00  ...                   NaN          NaN   \n",
+       "freq                   64  ...                   NaN          NaN   \n",
+       "mean                  NaN  ...              5.472768     3.060640   \n",
+       "std                   NaN  ...             21.807327     3.386975   \n",
+       "min                   NaN  ...              0.000000     0.000000   \n",
+       "25%                   NaN  ...              0.000000     0.000000   \n",
+       "50%                   NaN  ...              0.000000     2.000000   \n",
+       "75%                   NaN  ...              5.000000     5.000000   \n",
+       "max                   NaN  ...            708.000000    17.000000   \n",
+       "\n",
+       "           APP_BYTES  SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  \\\n",
+       "count   1.781000e+03         1781.000000         1781.000000   \n",
+       "unique           NaN                 NaN                 NaN   \n",
+       "top              NaN                 NaN                 NaN   \n",
+       "freq             NaN                 NaN                 NaN   \n",
+       "mean    2.982339e+03           18.540146           18.746210   \n",
+       "std     5.605057e+04           41.627173           46.397969   \n",
+       "min     0.000000e+00            0.000000            0.000000   \n",
+       "25%     0.000000e+00            0.000000            0.000000   \n",
+       "50%     6.720000e+02            8.000000            9.000000   \n",
+       "75%     2.328000e+03           26.000000           25.000000   \n",
+       "max     2.362906e+06         1198.000000         1284.000000   \n",
+       "\n",
+       "        SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  DNS_QUERY_TIMES  \\\n",
+       "count       1.781000e+03      1.781000e+03  1781.000000      1780.000000   \n",
+       "unique               NaN               NaN          NaN              NaN   \n",
+       "top                  NaN               NaN          NaN              NaN   \n",
+       "freq                 NaN               NaN          NaN              NaN   \n",
+       "mean        1.589255e+04      3.155599e+03    18.540146         2.263483   \n",
+       "std         6.986193e+04      5.605378e+04    41.627173         2.930853   \n",
+       "min         0.000000e+00      0.000000e+00     0.000000         0.000000   \n",
+       "25%         0.000000e+00      0.000000e+00     0.000000         0.000000   \n",
+       "50%         5.790000e+02      7.350000e+02     8.000000         0.000000   \n",
+       "75%         9.806000e+03      2.701000e+03    26.000000         4.000000   \n",
+       "max         2.060012e+06      2.362906e+06  1198.000000        20.000000   \n",
+       "\n",
+       "               Type  \n",
+       "count   1781.000000  \n",
+       "unique          NaN  \n",
+       "top             NaN  \n",
+       "freq            NaN  \n",
+       "mean       0.121280  \n",
+       "std        0.326544  \n",
+       "min        0.000000  \n",
+       "25%        0.000000  \n",
+       "50%        0.000000  \n",
+       "75%        0.000000  \n",
+       "max        1.000000  \n",
+       "\n",
+       "[11 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your comment here\n",
+    "\n",
+    "#Si que hay que transformar alguna columna categorica a ordinal."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Next, evaluate if the columns in this dataset are strongly correlated.\n",
+    "\n",
+    "In class, we discussed that we are concerned if our dataset has strongly correlated columns because if this is the case we need to choose certain ML algorithms instead of others. We need to evaluate this for our dataset now.\n",
+    "\n",
+    "Luckily, most of the columns in this dataset are ordinal which makes things a lot easier for us. In the cells below, evaluate the level of collinearity of the data.\n",
+    "\n",
+    "We provide some general directions for you to consult in order to complete this step:\n",
+    "\n",
+    "1. You will create a correlation matrix using the numeric columns in the dataset.\n",
+    "\n",
+    "1. Create a heatmap using `seaborn` to visualize which columns have high collinearity.\n",
+    "\n",
+    "1. Comment on which columns you might need to remove due to high collinearity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# Identificar columnas numéricas\n",
+    "numeric_columns = websites.select_dtypes(include=[np.number]).columns\n",
+    "\n",
+    "# Crear la matriz de correlación usando solo las columnas numéricas\n",
+    "correlation_matrix = websites[numeric_columns].corr()\n",
+    "\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "\n",
+    "# Crear el heatmap\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
+    "\n",
+    "plt.title('Matriz de Correlación de las Columnas Numéricas del Dataset')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 2 - Remove Column Collinearity.\n",
+    "\n",
+    "From the heatmap you created, you should have seen at least 3 columns that can be removed due to high collinearity. Remove these columns from the dataset.\n",
+    "\n",
+    "Note that you should remove as few columns as you can. You don't have to remove all the columns at once. But instead, try removing one column, then produce the heatmap again to determine if additional columns should be removed. As long as the dataset no longer contains columns that are correlated for over 90%, you can stop. Also, keep in mind when two columns have high collinearity, you only need to remove one of them but not both.\n",
+    "\n",
+    "In the cells below, remove as few columns as you can to eliminate the high collinearity in the dataset. Make sure to comment on your way so that the instructional team can learn about your thinking process which allows them to give feedback. At the end, print the heatmap again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Columnas con alta colinealidad:\n",
+      "                           NUMBER_SPECIAL_CHARACTERS  SOURCE_APP_PACKETS  \\\n",
+      "URL_LENGTH                                  0.917986                 NaN   \n",
+      "TCP_CONVERSATION_EXCHANGE                        NaN            0.997796   \n",
+      "APP_BYTES                                        NaN                 NaN   \n",
+      "SOURCE_APP_PACKETS                               NaN                 NaN   \n",
+      "REMOTE_APP_PACKETS                               NaN                 NaN   \n",
+      "SOURCE_APP_BYTES                                 NaN                 NaN   \n",
+      "\n",
+      "                           REMOTE_APP_PACKETS  SOURCE_APP_BYTES  \\\n",
+      "URL_LENGTH                                NaN               NaN   \n",
+      "TCP_CONVERSATION_EXCHANGE            0.990848          0.865580   \n",
+      "APP_BYTES                                 NaN               NaN   \n",
+      "SOURCE_APP_PACKETS                   0.989285          0.857495   \n",
+      "REMOTE_APP_PACKETS                        NaN          0.880555   \n",
+      "SOURCE_APP_BYTES                          NaN               NaN   \n",
+      "\n",
+      "                           REMOTE_APP_BYTES  APP_PACKETS  \n",
+      "URL_LENGTH                              NaN          NaN  \n",
+      "TCP_CONVERSATION_EXCHANGE               NaN     0.997796  \n",
+      "APP_BYTES                          0.999992          NaN  \n",
+      "SOURCE_APP_PACKETS                      NaN     1.000000  \n",
+      "REMOTE_APP_PACKETS                      NaN     0.989285  \n",
+      "SOURCE_APP_BYTES                        NaN     0.857495  \n",
+      "\n",
+      "La columna 'NUMBER_SPECIAL_CHARACTERS' tiene alta colinealidad con: URL_LENGTH    0.917986\n",
+      "Name: NUMBER_SPECIAL_CHARACTERS, dtype: float64\n",
+      "\n",
+      "La columna 'SOURCE_APP_PACKETS' tiene alta colinealidad con: TCP_CONVERSATION_EXCHANGE    0.997796\n",
+      "Name: SOURCE_APP_PACKETS, dtype: float64\n",
+      "\n",
+      "La columna 'REMOTE_APP_PACKETS' tiene alta colinealidad con: TCP_CONVERSATION_EXCHANGE    0.990848\n",
+      "SOURCE_APP_PACKETS           0.989285\n",
+      "Name: REMOTE_APP_PACKETS, dtype: float64\n",
+      "\n",
+      "La columna 'SOURCE_APP_BYTES' tiene alta colinealidad con: TCP_CONVERSATION_EXCHANGE    0.865580\n",
+      "SOURCE_APP_PACKETS           0.857495\n",
+      "REMOTE_APP_PACKETS           0.880555\n",
+      "Name: SOURCE_APP_BYTES, dtype: float64\n",
+      "\n",
+      "La columna 'REMOTE_APP_BYTES' tiene alta colinealidad con: APP_BYTES    0.999992\n",
+      "Name: REMOTE_APP_BYTES, dtype: float64\n",
+      "\n",
+      "La columna 'APP_PACKETS' tiene alta colinealidad con: TCP_CONVERSATION_EXCHANGE    0.997796\n",
+      "SOURCE_APP_PACKETS           1.000000\n",
+      "REMOTE_APP_PACKETS           0.989285\n",
+      "SOURCE_APP_BYTES             0.857495\n",
+      "Name: APP_PACKETS, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# Identificar las columnas con alta colinealidad. Correlation_matrix: son las columnas numéricas\n",
+    "\n",
+    "high_correlation = correlation_matrix[(correlation_matrix > 0.8) | (correlation_matrix < -0.8)]\n",
+    "\n",
+    "# Mostrar las correlaciones altas (excepto la diagonal)\n",
+    "high_correlation_without_diagonal = high_correlation.where(np.triu(np.ones(high_correlation.shape), k=1).astype(bool))\n",
+    "print(\"Columnas con alta colinealidad:\")\n",
+    "print(high_correlation_without_diagonal.dropna(axis=0, how='all').dropna(axis=1, how='all'))\n",
+    "\n",
+    "# Comentarios sobre las columnas con alta colinealidad\n",
+    "for column in high_correlation_without_diagonal.columns:\n",
+    "    correlated_columns = high_correlation_without_diagonal[column].dropna()\n",
+    "    if not correlated_columns.empty:\n",
+    "        print(f\"\\nLa columna '{column}' tiene alta colinealidad con: {correlated_columns}\")\n",
+    "        correlated_columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Print heatmap again\n",
+    "\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
+    "\n",
+    "plt.title('Matriz de Correlación de las Columnas Numéricas del Dataset')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 3 - Handle Missing Values\n",
+    "\n",
+    "The next step would be handling missing values. **We start by examining the number of missing values in each column, which you will do in the next cell.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of missing values in each column:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "URL                            0\n",
+       "URL_LENGTH                     0\n",
+       "NUMBER_SPECIAL_CHARACTERS      0\n",
+       "CHARSET                        7\n",
+       "SERVER                       176\n",
+       "CONTENT_LENGTH               812\n",
+       "WHOIS_COUNTRY                306\n",
+       "WHOIS_STATEPRO               362\n",
+       "WHOIS_REGDATE                127\n",
+       "WHOIS_UPDATED_DATE           139\n",
+       "TCP_CONVERSATION_EXCHANGE      0\n",
+       "DIST_REMOTE_TCP_PORT           0\n",
+       "REMOTE_IPS                     0\n",
+       "APP_BYTES                      0\n",
+       "SOURCE_APP_PACKETS             0\n",
+       "REMOTE_APP_PACKETS             0\n",
+       "SOURCE_APP_BYTES               0\n",
+       "REMOTE_APP_BYTES               0\n",
+       "APP_PACKETS                    0\n",
+       "DNS_QUERY_TIMES                1\n",
+       "Type                           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# numero de missing values por columna\n",
+    "missing_values = websites.isnull().sum()\n",
+    "\n",
+    "print(\"Number of missing values in each column:\")\n",
+    "missing_values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you remember in the previous labs, we drop a column if the column contains a high proportion of missing values. After dropping those problematic columns, we drop the rows with missing values.\n",
+    "\n",
+    "#### In the cells below, handle the missing values from the dataset. Remember to comment the rationale of your decisions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape del dataset limpio: (636, 21)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# Identifico las columnas con una alta proporción de valores faltantes y los borro\n",
+    "missing_threshold = 0.5  # Define the threshold for proportion of missing values\n",
+    "columns_to_drop = websites.columns[websites.isnull().mean() > missing_threshold]  # Identify columns with proportion of missing values exceeding the threshold\n",
+    "websites_cleaned = websites.drop(columns=columns_to_drop)  # Drop columns with high proportion of missing values\n",
+    "\n",
+    "# Ahora lo mismo con las filas\n",
+    "websites_cleaned = websites_cleaned.dropna()  # Drop rows with missing values in the remaining columns\n",
+    "\n",
+    "\n",
+    "print(\"Shape del dataset limpio:\", websites_cleaned.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Again, examine the number of missing values in each column. \n",
+    "\n",
+    "If all cleaned, proceed. Otherwise, go back and do more cleaning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of missing values in each column of the cleaned dataset:\n",
+      "URL                          0\n",
+      "URL_LENGTH                   0\n",
+      "NUMBER_SPECIAL_CHARACTERS    0\n",
+      "CHARSET                      0\n",
+      "SERVER                       0\n",
+      "CONTENT_LENGTH               0\n",
+      "WHOIS_COUNTRY                0\n",
+      "WHOIS_STATEPRO               0\n",
+      "WHOIS_REGDATE                0\n",
+      "WHOIS_UPDATED_DATE           0\n",
+      "TCP_CONVERSATION_EXCHANGE    0\n",
+      "DIST_REMOTE_TCP_PORT         0\n",
+      "REMOTE_IPS                   0\n",
+      "APP_BYTES                    0\n",
+      "SOURCE_APP_PACKETS           0\n",
+      "REMOTE_APP_PACKETS           0\n",
+      "SOURCE_APP_BYTES             0\n",
+      "REMOTE_APP_BYTES             0\n",
+      "APP_PACKETS                  0\n",
+      "DNS_QUERY_TIMES              0\n",
+      "Type                         0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Examine missing values in each column\n",
+    "\n",
+    "# Contamos el numero de missing values en cada columna del dataset\n",
+    "missing_values_cleaned = websites_cleaned.isnull().sum()\n",
+    "\n",
+    "# missing values en cada columna\n",
+    "print(\"Number of missing values in each column of the cleaned dataset:\")\n",
+    "print(missing_values_cleaned)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 4 - Handle `WHOIS_*` Categorical Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are several categorical columns we need to handle. These columns are:\n",
+    "\n",
+    "* `URL`\n",
+    "* `CHARSET`\n",
+    "* `SERVER`\n",
+    "* `WHOIS_COUNTRY`\n",
+    "* `WHOIS_STATEPRO`\n",
+    "* `WHOIS_REGDATE`\n",
+    "* `WHOIS_UPDATED_DATE`\n",
+    "\n",
+    "How to handle string columns is always case by case. Let's start by working on `WHOIS_COUNTRY`. Your steps are:\n",
+    "\n",
+    "1. List out the unique values of `WHOIS_COUNTRY`.\n",
+    "1. Consolidate the country values with consistent country codes. For example, the following values refer to the same country and should use consistent country code:\n",
+    "    * `CY` and `Cyprus`\n",
+    "    * `US` and `us`\n",
+    "    * `SE` and `se`\n",
+    "    * `GB`, `United Kingdom`, and `[u'GB'; u'UK']`\n",
+    "\n",
+    "#### In the cells below, fix the country values as intructed above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valores unicos de WHOIS_COUNTRY antes de la consolidation:\n",
+      "['US' 'RU' 'AU' 'CA' 'PA' 'GB' \"[u'GB'; u'UK']\" 'IN' 'UG' 'JP' 'UK' 'SI'\n",
+      " 'AT' 'CN' 'KY' 'TR' 'SC' 'NL' 'UA' 'CH' 'HK' 'IL' 'DE' 'IT' 'BS' 'NO'\n",
+      " 'us' 'BE' 'BY' 'AE' 'IE' 'PH' 'UY']\n",
+      "\n",
+      "Valores unicos de WHOIS_COUNTRY despues de la consolidation:\n",
+      "['US' 'RU' 'AU' 'CA' 'PA' 'GB' 'IN' 'UG' 'JP' 'UK' 'SI' 'AT' 'CN' 'KY'\n",
+      " 'TR' 'SC' 'NL' 'UA' 'CH' 'HK' 'IL' 'DE' 'IT' 'BS' 'NO' 'BE' 'BY' 'AE'\n",
+      " 'IE' 'PH' 'UY']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# sacamos los valore únicos de WHOIS_COUNTRY\n",
+    "unique_countries = websites_cleaned['WHOIS_COUNTRY'].unique()\n",
+    "\n",
+    "print(\"Valores unicos de WHOIS_COUNTRY antes de la consolidation:\")\n",
+    "print(unique_countries)\n",
+    "\n",
+    "# Hacemos la consolidate\n",
+    "country_mapping = {\n",
+    "    'Cyprus': 'CY',\n",
+    "    'us': 'US',\n",
+    "    'se': 'SE',\n",
+    "    'United Kingdom': 'GB',\n",
+    "    \"[u'GB'; u'UK']\": 'GB' \n",
+    "}\n",
+    "\n",
+    "# Subimos los nuevos valores a WHOIS_COUNTRY\n",
+    "websites_cleaned['WHOIS_COUNTRY'] = websites_cleaned['WHOIS_COUNTRY'].replace(country_mapping)\n",
+    "\n",
+    "\n",
+    "print(\"\\nValores unicos de WHOIS_COUNTRY despues de la consolidation:\")\n",
+    "print(websites_cleaned['WHOIS_COUNTRY'].unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we have fixed the country values, can we convert this column to ordinal now?\n",
+    "\n",
+    "Not yet. If you reflect on the previous labs how we handle categorical columns, you probably remember we ended up dropping a lot of those columns because there are too many unique values. Too many unique values in a column is not desirable in machine learning because it makes prediction inaccurate. But there are workarounds under certain conditions. One of the fixable conditions is:\n",
+    "\n",
+    "#### If a limited number of values account for the majority of data, we can retain these top values and re-label all other rare values.\n",
+    "\n",
+    "The `WHOIS_COUNTRY` column happens to be this case. You can verify it by print a bar chart of the `value_counts` in the next cell to verify:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# Plot de WHOIS_COUNTRY\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "websites_cleaned['WHOIS_COUNTRY'].value_counts().plot(kind='bar', color='skyblue')\n",
+    "plt.title('Value Counts of WHOIS_COUNTRY')\n",
+    "plt.xlabel('Country Code')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.grid(axis='y')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### After verifying, now let's keep the top 10 values of the column and re-label other columns with `OTHER`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valores de WHOIS_COUNTRY: \n",
+      "['US' 'OTHER' 'AU' 'CA' 'PA' 'GB' 'IN' 'JP' 'AT' 'CN' 'CH']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# 10 valores mas frecuentes de WHOIS_COUNTRY\n",
+    "top_countries = websites_cleaned['WHOIS_COUNTRY'].value_counts().head(10).index.tolist()\n",
+    "\n",
+    "# Remplazo todos los valores que no esten el el top 10 por 'OTHER'\n",
+    "websites_cleaned['WHOIS_COUNTRY'] = websites_cleaned['WHOIS_COUNTRY'].apply(lambda x: x if x in top_countries else 'OTHER')\n",
+    "\n",
+    "print(\"Valores de WHOIS_COUNTRY: \")\n",
+    "print(websites_cleaned['WHOIS_COUNTRY'].unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now since `WHOIS_COUNTRY` has been re-labelled, we don't need `WHOIS_STATEPRO` any more because the values of the states or provinces may not be relevant any more. We'll drop this column.\n",
+    "\n",
+    "In addition, we will also drop `WHOIS_REGDATE` and `WHOIS_UPDATED_DATE`. These are the registration and update dates of the website domains. Not of our concerns.\n",
+    "\n",
+    "#### In the next cell, drop `['WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE']`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# Borro estas columnas 'WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE' del dataset.\n",
+    "columns_to_drop = ['WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE']\n",
+    "websites_cleaned = websites_cleaned.drop(columns=columns_to_drop)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COLUMNAS EXISTENTES TRAS BORRAR ESTAS WHOIS_STATEPRO, WHOIS_REGDATE y WHOIS_UPDATED_DATE:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Index(['URL', 'URL_LENGTH', 'NUMBER_SPECIAL_CHARACTERS', 'CHARSET', 'SERVER',\n",
+       "       'CONTENT_LENGTH', 'WHOIS_COUNTRY', 'TCP_CONVERSATION_EXCHANGE',\n",
+       "       'DIST_REMOTE_TCP_PORT', 'REMOTE_IPS', 'APP_BYTES', 'SOURCE_APP_PACKETS',\n",
+       "       'REMOTE_APP_PACKETS', 'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES',\n",
+       "       'APP_PACKETS', 'DNS_QUERY_TIMES', 'Type'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"COLUMNAS EXISTENTES TRAS BORRAR ESTAS WHOIS_STATEPRO, WHOIS_REGDATE y WHOIS_UPDATED_DATE:\")\n",
+    "\n",
+    "websites_cleaned.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 5 - Handle Remaining Categorical Data & Convert to Ordinal\n",
+    "\n",
+    "Now print the `dtypes` of the data again. Besides `WHOIS_COUNTRY` which we already fixed, there should be 3 categorical columns left: `URL`, `CHARSET`, and `SERVER`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tipos de datos en el dataset:\n",
+      "URL                           object\n",
+      "URL_LENGTH                     int64\n",
+      "NUMBER_SPECIAL_CHARACTERS      int64\n",
+      "CHARSET                       object\n",
+      "SERVER                        object\n",
+      "CONTENT_LENGTH               float64\n",
+      "WHOIS_COUNTRY                 object\n",
+      "TCP_CONVERSATION_EXCHANGE      int64\n",
+      "DIST_REMOTE_TCP_PORT           int64\n",
+      "REMOTE_IPS                     int64\n",
+      "APP_BYTES                      int64\n",
+      "SOURCE_APP_PACKETS             int64\n",
+      "REMOTE_APP_PACKETS             int64\n",
+      "SOURCE_APP_BYTES               int64\n",
+      "REMOTE_APP_BYTES               int64\n",
+      "APP_PACKETS                    int64\n",
+      "DNS_QUERY_TIMES              float64\n",
+      "Type                           int64\n",
+      "dtype: object\n",
+      "\n",
+      "Columnas categóricas:\n",
+      "Index(['URL', 'CHARSET', 'SERVER', 'WHOIS_COUNTRY'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "print(\"Tipos de datos en el dataset:\")\n",
+    "print(websites_cleaned.dtypes)\n",
+    "\n",
+    "# Columnas categóricas\n",
+    "categorical_columns = websites_cleaned.select_dtypes(include=['object']).columns\n",
+    "print(\"\\nColumnas categóricas:\")\n",
+    "print(categorical_columns)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `URL` is easy. We'll simply drop it because it has too many unique values that there's no way for us to consolidate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Despues de borrar columna URL:\n",
+      "Index(['URL_LENGTH', 'NUMBER_SPECIAL_CHARACTERS', 'CHARSET', 'SERVER',\n",
+      "       'CONTENT_LENGTH', 'WHOIS_COUNTRY', 'TCP_CONVERSATION_EXCHANGE',\n",
+      "       'DIST_REMOTE_TCP_PORT', 'REMOTE_IPS', 'APP_BYTES', 'SOURCE_APP_PACKETS',\n",
+      "       'REMOTE_APP_PACKETS', 'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES',\n",
+      "       'APP_PACKETS', 'DNS_QUERY_TIMES', 'Type'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# Borramos columna URL\n",
+    "websites_cleaned = websites_cleaned.drop(columns=['URL'])\n",
+    "\n",
+    "print(\"Despues de borrar columna URL:\")\n",
+    "print(websites_cleaned.columns)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Print the unique value counts of `CHARSET`. You see there are only a few unique values. So we can keep it as it is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CHARSET\n",
+       "UTF-8           200\n",
+       "ISO-8859-1      169\n",
+       "utf-8           108\n",
+       "us-ascii         92\n",
+       "iso-8859-1       66\n",
+       "windows-1251      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# Valores únicos de CHARSET\n",
+    "charset_value_counts = websites_cleaned['CHARSET'].value_counts()\n",
+    "\n",
+    "charset_value_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`SERVER` is a little more complicated. Print its unique values and think about how you can consolidate those values.\n",
+    "\n",
+    "#### Before you think of your own solution, don't read the instructions that come next."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['nginx', 'Apache/2', 'Microsoft-HTTPAPI/2.0',\n",
+       "       'Apache/2.4.7 (Ubuntu)', 'Apache',\n",
+       "       'Apache/2.4.12 (Unix) OpenSSL/1.0.1e-fips mod_bwlimited/1.4',\n",
+       "       'Apache/2.2.22', 'Microsoft-IIS/7.5', 'nginx/1.12.0',\n",
+       "       'Apache/2.4.23 (Unix) OpenSSL/1.0.1e-fips mod_bwlimited/1.4',\n",
+       "       'Apache/2.2.22 (Debian)', 'Apache/2.4.25 (Amazon) PHP/7.0.14',\n",
+       "       'GSE',\n",
+       "       'Apache/2.4.23 (Unix) OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4',\n",
+       "       'Apache/2.4.25 (Amazon) OpenSSL/1.0.1k-fips',\n",
+       "       'Apache/2.2.22 (Ubuntu)', 'Apache/2.4.25',\n",
+       "       'Apache/2.4.18 (Unix) OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4',\n",
+       "       'Apache/2.4.6 (CentOS) PHP/5.6.8', 'AmazonS3', 'ATS',\n",
+       "       'CherryPy/3.6.0', 'Apache/2.2.15 (CentOS)',\n",
+       "       'Apache/2.2.15 (Red Hat)',\n",
+       "       'Apache/2.4.6 (CentOS) OpenSSL/1.0.1e-fips mod_fcgid/2.3.9 PHP/5.4.16 mod_jk/1.2.40',\n",
+       "       'Apache/2.2.3 (CentOS)', 'Apache/2.4', 'Apache/2.4.10 (Debian)',\n",
+       "       'Apache/2.2.29 (Unix) mod_ssl/2.2.29 OpenSSL/1.0.1e-fips mod_bwlimited/1.4',\n",
+       "       'mw2232.codfw.wmnet', 'Sucuri/Cloudproxy', 'cloudflare-nginx',\n",
+       "       'Apache/2.0.52 (Red Hat)',\n",
+       "       'Apache/1.3.31 (Unix) PHP/4.3.9 mod_perl/1.29 rus/PL30.20',\n",
+       "       'Apache/2.2.13 (Unix) mod_ssl/2.2.13 OpenSSL/0.9.8e-fips-rhel5 mod_auth_passthrough/2.1 mod_bwlimited/1.4 PHP/5.2.10',\n",
+       "       'ATS/5.3.0', 'nginx/1.4.3',\n",
+       "       'Apache/2.2.29 (Unix) mod_ssl/2.2.29 OpenSSL/1.0.1e-fips mod_bwlimited/1.4 PHP/5.4.35',\n",
+       "       'Apache/2.2.14 (FreeBSD) mod_ssl/2.2.14 OpenSSL/0.9.8y DAV/2 PHP/5.2.12 with Suhosin-Patch',\n",
+       "       'Apache/2.2.14 (Unix) mod_ssl/2.2.14 OpenSSL/0.9.8e-fips-rhel5',\n",
+       "       'Apache/2.4.18 (Ubuntu)',\n",
+       "       'Apache/2.4.6 (CentOS) OpenSSL/1.0.1e-fips PHP/5.4.16 mod_apreq2-20090110/2.8.0 mod_perl/2.0.10 Perl/v5.24.1',\n",
+       "       'Apache/2.2.20 (Unix)', 'nginx/1.11.10', 'Yippee-Ki-Yay',\n",
+       "       'mw2165.codfw.wmnet', 'Apache/2.2.23 (Amazon)', 'LiteSpeed',\n",
+       "       'openresty/1.11.2.2', 'Apache-Coyote/1.1', 'mw2225.codfw.wmnet',\n",
+       "       'Varnish', 'Microsoft-IIS/8.5', 'nginx/1.6.2',\n",
+       "       'Apache/2.4.6 (CentOS)', 'barista/5.1.3', 'nginx/1.11.2',\n",
+       "       'Apache/2.4.25 (Debian)', 'ECD (fll/0790)', 'nginx/1.10.3',\n",
+       "       'mw2239.codfw.wmnet', 'mw2255.codfw.wmnet',\n",
+       "       'Apache/2.2.31 (Unix) mod_ssl/2.2.31 OpenSSL/1.0.1e-fips mod_bwlimited/1.4 mod_fcgid/2.3.9',\n",
+       "       'nginx/1.13.0',\n",
+       "       'Apache/2.2.31 (Unix) mod_ssl/2.2.31 OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4',\n",
+       "       'nginx/1.11.3', 'mw2230.codfw.wmnet',\n",
+       "       'Apache/2.4.6 (CentOS) OpenSSL/1.0.1e-fips', 'AkamaiGHost',\n",
+       "       'nginx/1.2.1', 'Play', 'Apache/2.2.11 (Unix) PHP/5.2.6',\n",
+       "       'nginx/1.4.6 (Ubuntu)', 'nginx/0.8.35', 'squid/3.3.8',\n",
+       "       'Apache/2.2.27 (CentOS)', 'Nginx (OpenBSD)',\n",
+       "       'Apache/2.2.31 (Amazon)',\n",
+       "       'Apache/2.2.21 (Unix) mod_ssl/2.2.21 OpenSSL/0.9.8e-fips-rhel5 PHP/5.3.10',\n",
+       "       'Apache/2.2.32',\n",
+       "       'Apache/2.4.25 (cPanel) OpenSSL/1.0.1e-fips mod_bwlimited/1.4',\n",
+       "       'Apache/2.4.6 (Unix) mod_jk/1.2.37 PHP/5.5.1 OpenSSL/1.0.1g mod_fcgid/2.3.9',\n",
+       "       'mw2106.codfw.wmnet', 'Aeria Games & Entertainment',\n",
+       "       'Apache/2.4.10 (Debian) PHP/5.6.30-0+deb8u1 mod_perl/2.0.9dev Perl/v5.20.2',\n",
+       "       'mw2173.codfw.wmnet',\n",
+       "       'Apache/2.2.15 (CentOS) DAV/2 mod_ssl/2.2.15 OpenSSL/1.0.1e-fips PHP/5.3.3',\n",
+       "       'Apache/2.4.6 (CentOS) OpenSSL/1.0.1e-fips PHP/7.0.14',\n",
+       "       'Microsoft-IIS/7.0',\n",
+       "       'Apache/2.4.6 (CentOS) OpenSSL/1.0.1e-fips PHP/5.4.16', 'Server',\n",
+       "       'nginx/1.8.0', 'Apache/2.4.6 (Unix) mod_jk/1.2.37', 'Cowboy',\n",
+       "       'Apache/2.2.14 (Unix) mod_ssl/2.2.14 OpenSSL/0.9.8a',\n",
+       "       'Apache/2.4.10 (Ubuntu)', 'mw2257.codfw.wmnet',\n",
+       "       'Application-Server', 'mw2177.codfw.wmnet', 'nginx/1.8.1',\n",
+       "       'mw2197.codfw.wmnet',\n",
+       "       'Apache/2.2.26 (Unix) mod_ssl/2.2.26 OpenSSL/1.0.1e-fips DAV/2 mod_bwlimited/1.4',\n",
+       "       'Microsoft-IIS/6.0',\n",
+       "       'Apache/2.2.26 (Unix) mod_ssl/2.2.26 OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4 PHP/5.4.26',\n",
+       "       'www.lexisnexis.com  9999', 'nginx/0.8.38', 'mw2238.codfw.wmnet',\n",
+       "       'Pizza/pepperoni', 'MI', 'Roxen/5.4.98-r2', 'nginx/1.10.1',\n",
+       "       'mw2180.codfw.wmnet', 'nginx/1.9.13', 'nginx/1.7.12',\n",
+       "       'Apache/2.0.63 (Unix) mod_ssl/2.0.63 OpenSSL/0.9.8e-fips-rhel5 mod_auth_passthrough/2.1 mod_bwlimited/1.4 PHP/5.3.6',\n",
+       "       'Boston.com Frontend', 'My Arse',\n",
+       "       'Apache/2.4.17 (Unix) OpenSSL/1.0.1e-fips PHP/5.6.19',\n",
+       "       'Microsoft-IIS/7.5; litigation_essentials.lexisnexis.com  9999',\n",
+       "       'Apache/2.2.16 (Debian)'], dtype=object)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# Valores únicos de SERVER\n",
+    "server_unique_values = websites_cleaned['SERVER'].unique()\n",
+    "\n",
+    "server_unique_values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Think Hard](../images/think-hard.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Although there are so many unique values in the `SERVER` column, there are actually only 3 main server types: `Microsoft`, `Apache`, and `nginx`. Just check if each `SERVER` value contains any of those server types and re-label them. For `SERVER` values that don't contain any of those substrings, label with `Other`.\n",
+    "\n",
+    "At the end, your `SERVER` column should only contain 4 unique values: `Microsoft`, `Apache`, `nginx`, and `Other`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique values of SERVER after consolidation:\n",
+      "['nginx' 'Apache' 'Microsoft' 'Other']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# Agrupamos los valores únicos de la columna SERVER porque hay demasiados.\n",
+    "def consolidate_server(server_value):\n",
+    "    if 'Microsoft' in server_value:\n",
+    "        return 'Microsoft'\n",
+    "    elif 'Apache' in server_value:\n",
+    "        return 'Apache'\n",
+    "    elif 'nginx' in server_value:\n",
+    "        return 'nginx'\n",
+    "    else:\n",
+    "        return 'Other'\n",
+    "\n",
+    "# Aplicamos en la columna SERVER\n",
+    "websites_cleaned['SERVER'] = websites_cleaned['SERVER'].apply(consolidate_server)\n",
+    "\n",
+    "# Valores únicos de SERVER ahora\n",
+    "print(\"Unique values of SERVER after consolidation:\")\n",
+    "print(websites_cleaned['SERVER'].unique())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Valores únicos de SERVER:\n",
+      "SERVER\n",
+      "Apache       298\n",
+      "Microsoft    127\n",
+      "Other        109\n",
+      "nginx        102\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Valores únicos de SERVER\n",
+    "\n",
+    "print(\"\\nValores únicos de SERVER:\")\n",
+    "print(websites_cleaned['SERVER'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "OK, all our categorical data are fixed now. **Let's convert them to ordinal data using Pandas' `get_dummies` function ([documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html)).** Make sure you drop the categorical columns by passing `drop_first=True` to `get_dummies` as we don't need them any more. **Also, assign the data with dummy values to a new variable `website_dummy`.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Columns after converting to dummy variables:\n",
+      "Index(['URL_LENGTH', 'NUMBER_SPECIAL_CHARACTERS', 'CONTENT_LENGTH',\n",
+      "       'TCP_CONVERSATION_EXCHANGE', 'DIST_REMOTE_TCP_PORT', 'REMOTE_IPS',\n",
+      "       'APP_BYTES', 'SOURCE_APP_PACKETS', 'REMOTE_APP_PACKETS',\n",
+      "       'SOURCE_APP_BYTES', 'REMOTE_APP_BYTES', 'APP_PACKETS',\n",
+      "       'DNS_QUERY_TIMES', 'Type', 'CHARSET_UTF-8', 'CHARSET_iso-8859-1',\n",
+      "       'CHARSET_us-ascii', 'CHARSET_utf-8', 'CHARSET_windows-1251',\n",
+      "       'SERVER_Microsoft', 'SERVER_Other', 'SERVER_nginx', 'WHOIS_COUNTRY_AU',\n",
+      "       'WHOIS_COUNTRY_CA', 'WHOIS_COUNTRY_CH', 'WHOIS_COUNTRY_CN',\n",
+      "       'WHOIS_COUNTRY_GB', 'WHOIS_COUNTRY_IN', 'WHOIS_COUNTRY_JP',\n",
+      "       'WHOIS_COUNTRY_OTHER', 'WHOIS_COUNTRY_PA', 'WHOIS_COUNTRY_US'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# Convertir columnas categóricas a dummy variables\n",
+    "website_dummy = pd.get_dummies(websites_cleaned, drop_first=True)\n",
+    "\n",
+    "\n",
+    "print(\"Columns after converting to dummy variables:\")\n",
+    "print(website_dummy.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, inspect `website_dummy` to make sure the data and types are intended - there shouldn't be any categorical columns at this point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tipos de datos en website_dummy:\n",
+      "URL_LENGTH                     int64\n",
+      "NUMBER_SPECIAL_CHARACTERS      int64\n",
+      "CONTENT_LENGTH               float64\n",
+      "TCP_CONVERSATION_EXCHANGE      int64\n",
+      "DIST_REMOTE_TCP_PORT           int64\n",
+      "REMOTE_IPS                     int64\n",
+      "APP_BYTES                      int64\n",
+      "SOURCE_APP_PACKETS             int64\n",
+      "REMOTE_APP_PACKETS             int64\n",
+      "SOURCE_APP_BYTES               int64\n",
+      "REMOTE_APP_BYTES               int64\n",
+      "APP_PACKETS                    int64\n",
+      "DNS_QUERY_TIMES              float64\n",
+      "Type                           int64\n",
+      "CHARSET_UTF-8                   bool\n",
+      "CHARSET_iso-8859-1              bool\n",
+      "CHARSET_us-ascii                bool\n",
+      "CHARSET_utf-8                   bool\n",
+      "CHARSET_windows-1251            bool\n",
+      "SERVER_Microsoft                bool\n",
+      "SERVER_Other                    bool\n",
+      "SERVER_nginx                    bool\n",
+      "WHOIS_COUNTRY_AU                bool\n",
+      "WHOIS_COUNTRY_CA                bool\n",
+      "WHOIS_COUNTRY_CH                bool\n",
+      "WHOIS_COUNTRY_CN                bool\n",
+      "WHOIS_COUNTRY_GB                bool\n",
+      "WHOIS_COUNTRY_IN                bool\n",
+      "WHOIS_COUNTRY_JP                bool\n",
+      "WHOIS_COUNTRY_OTHER             bool\n",
+      "WHOIS_COUNTRY_PA                bool\n",
+      "WHOIS_COUNTRY_US                bool\n",
+      "dtype: object\n",
+      "\n",
+      "Columnas categóricas en website_dummy (debería estar vacío):\n",
+      "Index([], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "# Revisar las columnas de website_dummy\n",
+    "print(\"Tipos de datos en website_dummy:\")\n",
+    "print(website_dummy.dtypes)\n",
+    "\n",
+    "categorical_columns_dummy = website_dummy.select_dtypes(include=['object']).columns\n",
+    "\n",
+    "print(\"\\nColumnas categóricas en website_dummy (debería estar vacío):\")\n",
+    "print(categorical_columns_dummy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 6 - Modeling, Prediction, and Evaluation\n",
+    "\n",
+    "We'll start off this section by splitting the data to train and test. **Name your 4 variables `X_train`, `X_test`, `y_train`, and `y_test`. Select 80% of the data for training and 20% for testing.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (508, 31)\n",
+      "X_test shape: (128, 31)\n",
+      "y_train shape: (508,)\n",
+      "y_test shape: (128,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "\n",
+    "# Assume website_dummy is already loaded and target_column_name is the name of your target column\n",
+    "\n",
+    "# Separate features and target\n",
+    "X = website_dummy.drop(columns=['Type']) \n",
+    "y = website_dummy['Type'] \n",
+    "\n",
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Print the shapes of the resulting datasets to verify the split\n",
+    "print(f\"X_train shape: {X_train.shape}\")\n",
+    "print(f\"X_test shape: {X_test.shape}\")\n",
+    "print(f\"y_train shape: {y_train.shape}\")\n",
+    "print(f\"y_test shape: {y_test.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "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>URL_LENGTH</th>\n",
+       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
+       "      <th>CONTENT_LENGTH</th>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>...</th>\n",
+       "      <th>WHOIS_COUNTRY_AU</th>\n",
+       "      <th>WHOIS_COUNTRY_CA</th>\n",
+       "      <th>WHOIS_COUNTRY_CH</th>\n",
+       "      <th>WHOIS_COUNTRY_CN</th>\n",
+       "      <th>WHOIS_COUNTRY_GB</th>\n",
+       "      <th>WHOIS_COUNTRY_IN</th>\n",
+       "      <th>WHOIS_COUNTRY_JP</th>\n",
+       "      <th>WHOIS_COUNTRY_OTHER</th>\n",
+       "      <th>WHOIS_COUNTRY_PA</th>\n",
+       "      <th>WHOIS_COUNTRY_US</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>22</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3812</td>\n",
+       "      <td>39</td>\n",
+       "      <td>37</td>\n",
+       "      <td>18784</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>18</td>\n",
+       "      <td>6</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1189</td>\n",
+       "      <td>14</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8559</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "      <td>324.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>20</td>\n",
+       "      <td>6</td>\n",
+       "      <td>324.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>21</td>\n",
+       "      <td>7</td>\n",
+       "      <td>686.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>603</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1580</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1754</th>\n",
+       "      <td>143</td>\n",
+       "      <td>22</td>\n",
+       "      <td>374.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>564</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>442</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1762</th>\n",
+       "      <td>146</td>\n",
+       "      <td>24</td>\n",
+       "      <td>11833.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>20</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4167</td>\n",
+       "      <td>33</td>\n",
+       "      <td>33</td>\n",
+       "      <td>3181</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1774</th>\n",
+       "      <td>183</td>\n",
+       "      <td>29</td>\n",
+       "      <td>4890.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2062</td>\n",
+       "      <td>30</td>\n",
+       "      <td>26</td>\n",
+       "      <td>8161</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1778</th>\n",
+       "      <td>201</td>\n",
+       "      <td>34</td>\n",
+       "      <td>8904.0</td>\n",
+       "      <td>83</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6631</td>\n",
+       "      <td>87</td>\n",
+       "      <td>89</td>\n",
+       "      <td>132181</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>249</td>\n",
+       "      <td>40</td>\n",
+       "      <td>24435.0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2314</td>\n",
+       "      <td>25</td>\n",
+       "      <td>28</td>\n",
+       "      <td>3039</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>636 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      URL_LENGTH  NUMBER_SPECIAL_CHARACTERS  CONTENT_LENGTH  \\\n",
+       "3             17                          6           162.0   \n",
+       "6             18                          6           345.0   \n",
+       "7             19                          6           324.0   \n",
+       "11            20                          6           324.0   \n",
+       "16            21                          7           686.0   \n",
+       "...          ...                        ...             ...   \n",
+       "1754         143                         22           374.0   \n",
+       "1762         146                         24         11833.0   \n",
+       "1774         183                         29          4890.0   \n",
+       "1778         201                         34          8904.0   \n",
+       "1780         249                         40         24435.0   \n",
+       "\n",
+       "      TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  REMOTE_IPS  APP_BYTES  \\\n",
+       "3                            31                    22           3       3812   \n",
+       "6                            12                     0           3       1189   \n",
+       "7                             0                     0           0          0   \n",
+       "11                            0                     0           0          0   \n",
+       "16                            6                     0           2        603   \n",
+       "...                         ...                   ...         ...        ...   \n",
+       "1754                          6                     5           2        564   \n",
+       "1762                         29                    20           8       4167   \n",
+       "1774                         22                     2           7       2062   \n",
+       "1778                         83                     2           6       6631   \n",
+       "1780                         19                     6          11       2314   \n",
+       "\n",
+       "      SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  SOURCE_APP_BYTES  ...  \\\n",
+       "3                     39                  37             18784  ...   \n",
+       "6                     14                  13              8559  ...   \n",
+       "7                      0                   0                 0  ...   \n",
+       "11                     0                   0                 0  ...   \n",
+       "16                     8                   9              1580  ...   \n",
+       "...                  ...                 ...               ...  ...   \n",
+       "1754                   6                   7               442  ...   \n",
+       "1762                  33                  33              3181  ...   \n",
+       "1774                  30                  26              8161  ...   \n",
+       "1778                  87                  89            132181  ...   \n",
+       "1780                  25                  28              3039  ...   \n",
+       "\n",
+       "      WHOIS_COUNTRY_AU  WHOIS_COUNTRY_CA  WHOIS_COUNTRY_CH  WHOIS_COUNTRY_CN  \\\n",
+       "3                False             False             False             False   \n",
+       "6                False             False             False             False   \n",
+       "7                False             False             False             False   \n",
+       "11               False             False             False             False   \n",
+       "16               False             False             False             False   \n",
+       "...                ...               ...               ...               ...   \n",
+       "1754             False             False             False             False   \n",
+       "1762             False             False             False             False   \n",
+       "1774             False             False             False             False   \n",
+       "1778             False             False             False             False   \n",
+       "1780             False             False             False             False   \n",
+       "\n",
+       "      WHOIS_COUNTRY_GB  WHOIS_COUNTRY_IN  WHOIS_COUNTRY_JP  \\\n",
+       "3                False             False             False   \n",
+       "6                False             False             False   \n",
+       "7                False             False             False   \n",
+       "11               False             False             False   \n",
+       "16               False             False             False   \n",
+       "...                ...               ...               ...   \n",
+       "1754             False             False             False   \n",
+       "1762             False             False             False   \n",
+       "1774             False             False             False   \n",
+       "1778             False             False             False   \n",
+       "1780             False             False             False   \n",
+       "\n",
+       "      WHOIS_COUNTRY_OTHER  WHOIS_COUNTRY_PA  WHOIS_COUNTRY_US  \n",
+       "3                   False             False              True  \n",
+       "6                   False             False              True  \n",
+       "7                   False             False              True  \n",
+       "11                  False             False              True  \n",
+       "16                   True             False             False  \n",
+       "...                   ...               ...               ...  \n",
+       "1754                False             False              True  \n",
+       "1762                False             False              True  \n",
+       "1774                False             False              True  \n",
+       "1778                False             False              True  \n",
+       "1780                False             False              True  \n",
+       "\n",
+       "[636 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "website_dummy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### In this lab, we will try two different models and compare our results.\n",
+    "\n",
+    "The first model we will use in this lab is logistic regression. We have previously learned about logistic regression as a classification algorithm. In the cell below, load `LogisticRegression` from scikit-learn and initialize the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LogisticRegression()\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "\n",
+    "log_reg_model = LogisticRegression()\n",
+    "\n",
+    "# Print the initialized model to verify\n",
+    "print(log_reg_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, fit the model to our training data. We have already separated our data into 4 parts. Use those in your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.9375\n",
+      "Confusion Matrix:\n",
+      " [[116   1]\n",
+      " [  7   4]]\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.94      0.99      0.97       117\n",
+      "           1       0.80      0.36      0.50        11\n",
+      "\n",
+      "    accuracy                           0.94       128\n",
+      "   macro avg       0.87      0.68      0.73       128\n",
+      "weighted avg       0.93      0.94      0.93       128\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mario\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "\n",
+    "# Fit the model to the training data\n",
+    "log_reg_model.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict the labels on the test set\n",
+    "y_pred = log_reg_model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model\n",
+    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
+    "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
+    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "finally, import `confusion_matrix` and `accuracy_score` from `sklearn.metrics` and fit our testing data. Assign the fitted data to `y_pred` and print the confusion matrix as well as the accuracy score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion Matrix:\n",
+      " [[116   1]\n",
+      " [  7   4]]\n",
+      "Accuracy Score: 0.9375\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "\n",
+    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
+    "print(\"Accuracy Score:\", accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What are your thoughts on the performance of the model? Write your conclusions below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your conclusions here:\n",
+    "\n",
+    "# Es más preciso este modelo. La matriz de confusión proporciona un resumen de los resultados de la predicción. \n",
+    "# Muestra el número de predicciones verdaderas positivas, verdaderas negativas, falsas positivas y falsas negativas.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Our second algorithm is is K-Nearest Neighbors. \n",
+    "\n",
+    "Though is it not required, we will fit a model using the training data and then test the performance of the model using the testing data. Start by loading `KNeighborsClassifier` from scikit-learn and then initializing and fitting the model. We'll start off with a model where k=3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Flags' object has no attribute 'c_contiguous'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[71], line 18\u001b[0m\n\u001b[0;32m     15\u001b[0m knn_model\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n\u001b[0;32m     17\u001b[0m \u001b[38;5;66;03m# Predict the labels on the test set\u001b[39;00m\n\u001b[1;32m---> 18\u001b[0m y_pred_knn \u001b[38;5;241m=\u001b[39m knn_model\u001b[38;5;241m.\u001b[39mpredict(X_test)\n\u001b[0;32m     20\u001b[0m \u001b[38;5;66;03m# Evaluate the KNN model\u001b[39;00m\n\u001b[0;32m     21\u001b[0m accuracy_knn \u001b[38;5;241m=\u001b[39m accuracy_score(y_test, y_pred_knn)\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\neighbors\\_classification.py:246\u001b[0m, in \u001b[0;36mKNeighborsClassifier.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    244\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_fit_method\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muniform\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 246\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbrute\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m ArgKminClassMode\u001b[38;5;241m.\u001b[39mis_usable_for(\n\u001b[0;32m    247\u001b[0m         X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetric\n\u001b[0;32m    248\u001b[0m     ):\n\u001b[0;32m    249\u001b[0m         probabilities \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict_proba(X)\n\u001b[0;32m    250\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutputs_2d_:\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_pairwise_distances_reduction\\_dispatcher.py:471\u001b[0m, in \u001b[0;36mArgKminClassMode.is_usable_for\u001b[1;34m(cls, X, Y, metric)\u001b[0m\n\u001b[0;32m    448\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[0;32m    449\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_usable_for\u001b[39m(\u001b[38;5;28mcls\u001b[39m, X, Y, metric) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n\u001b[0;32m    450\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return True if the dispatcher can be used for the given parameters.\u001b[39;00m\n\u001b[0;32m    451\u001b[0m \n\u001b[0;32m    452\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    468\u001b[0m \u001b[38;5;124;03m    True if the PairwiseDistancesReduction can be used, else False.\u001b[39;00m\n\u001b[0;32m    469\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m    470\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[1;32m--> 471\u001b[0m         ArgKmin\u001b[38;5;241m.\u001b[39mis_usable_for(X, Y, metric)\n\u001b[0;32m    472\u001b[0m         \u001b[38;5;66;03m# TODO: Support CSR matrices.\u001b[39;00m\n\u001b[0;32m    473\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m issparse(X)\n\u001b[0;32m    474\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m issparse(Y)\n\u001b[0;32m    475\u001b[0m         \u001b[38;5;66;03m# TODO: implement Euclidean specialization with GEMM.\u001b[39;00m\n\u001b[0;32m    476\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m metric \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meuclidean\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msqeuclidean\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    477\u001b[0m     )\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_pairwise_distances_reduction\\_dispatcher.py:115\u001b[0m, in \u001b[0;36mBaseDistancesReductionDispatcher.is_usable_for\u001b[1;34m(cls, X, Y, metric)\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_valid_sparse_matrix\u001b[39m(X):\n\u001b[0;32m    102\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m    103\u001b[0m         isspmatrix_csr(X)\n\u001b[0;32m    104\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    110\u001b[0m         X\u001b[38;5;241m.\u001b[39mindices\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m X\u001b[38;5;241m.\u001b[39mindptr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mint32\n\u001b[0;32m    111\u001b[0m     )\n\u001b[0;32m    113\u001b[0m is_usable \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    114\u001b[0m     get_config()\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menable_cython_pairwise_dist\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m--> 115\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (is_numpy_c_ordered(X) \u001b[38;5;129;01mor\u001b[39;00m is_valid_sparse_matrix(X))\n\u001b[0;32m    116\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (is_numpy_c_ordered(Y) \u001b[38;5;129;01mor\u001b[39;00m is_valid_sparse_matrix(Y))\n\u001b[0;32m    117\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m Y\u001b[38;5;241m.\u001b[39mdtype\n\u001b[0;32m    118\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01min\u001b[39;00m (np\u001b[38;5;241m.\u001b[39mfloat32, np\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[0;32m    119\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m metric \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mvalid_metrics()\n\u001b[0;32m    120\u001b[0m )\n\u001b[0;32m    122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m is_usable\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_pairwise_distances_reduction\\_dispatcher.py:99\u001b[0m, in \u001b[0;36mBaseDistancesReductionDispatcher.is_usable_for.<locals>.is_numpy_c_ordered\u001b[1;34m(X)\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_numpy_c_ordered\u001b[39m(X):\n\u001b[1;32m---> 99\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflags\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m X\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mc_contiguous\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'Flags' object has no attribute 'c_contiguous'"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "\n",
+    "\n",
+    "X = website_dummy.drop(columns=['Type'])\n",
+    "y = website_dummy['Type']\n",
+    "\n",
+    "# Separo los datos 80/20 para poder entrenar el modelo\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Inicializo el K-Nearest Neighbors\n",
+    "knn_model = KNeighborsClassifier(n_neighbors=3)\n",
+    "\n",
+    "# Entreno el modelo\n",
+    "knn_model.fit(X_train, y_train)\n",
+    "\n",
+    "# Hago la predicción\n",
+    "y_pred_knn = knn_model.predict(X_test)\n",
+    "\n",
+    "# Evaluo el KNN model\n",
+    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
+    "conf_matrix_knn = confusion_matrix(y_test, y_pred_knn)\n",
+    "\n",
+    "\n",
+    "print(\"Confusion Matrix for K-Nearest Neighbors:\\n\", conf_matrix_knn)\n",
+    "print(\"Accuracy Score for K-Nearest Neighbors:\", accuracy_knn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To test your model, compute the predicted values for the testing sample and print the confusion matrix as well as the accuracy score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Flags' object has no attribute 'c_contiguous'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[68], line 10\u001b[0m\n\u001b[0;32m      7\u001b[0m knn_model\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Predict the labels on the test set\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m y_pred_knn \u001b[38;5;241m=\u001b[39m knn_model\u001b[38;5;241m.\u001b[39mpredict(X_test)\n\u001b[0;32m     12\u001b[0m \u001b[38;5;66;03m# Evaluate the KNN model\u001b[39;00m\n\u001b[0;32m     13\u001b[0m accuracy_knn \u001b[38;5;241m=\u001b[39m accuracy_score(y_test, y_pred_knn)\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\neighbors\\_classification.py:246\u001b[0m, in \u001b[0;36mKNeighborsClassifier.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    244\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_fit_method\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muniform\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 246\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbrute\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m ArgKminClassMode\u001b[38;5;241m.\u001b[39mis_usable_for(\n\u001b[0;32m    247\u001b[0m         X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetric\n\u001b[0;32m    248\u001b[0m     ):\n\u001b[0;32m    249\u001b[0m         probabilities \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict_proba(X)\n\u001b[0;32m    250\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutputs_2d_:\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_pairwise_distances_reduction\\_dispatcher.py:471\u001b[0m, in \u001b[0;36mArgKminClassMode.is_usable_for\u001b[1;34m(cls, X, Y, metric)\u001b[0m\n\u001b[0;32m    448\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[0;32m    449\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_usable_for\u001b[39m(\u001b[38;5;28mcls\u001b[39m, X, Y, metric) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n\u001b[0;32m    450\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return True if the dispatcher can be used for the given parameters.\u001b[39;00m\n\u001b[0;32m    451\u001b[0m \n\u001b[0;32m    452\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    468\u001b[0m \u001b[38;5;124;03m    True if the PairwiseDistancesReduction can be used, else False.\u001b[39;00m\n\u001b[0;32m    469\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m    470\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[1;32m--> 471\u001b[0m         ArgKmin\u001b[38;5;241m.\u001b[39mis_usable_for(X, Y, metric)\n\u001b[0;32m    472\u001b[0m         \u001b[38;5;66;03m# TODO: Support CSR matrices.\u001b[39;00m\n\u001b[0;32m    473\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m issparse(X)\n\u001b[0;32m    474\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m issparse(Y)\n\u001b[0;32m    475\u001b[0m         \u001b[38;5;66;03m# TODO: implement Euclidean specialization with GEMM.\u001b[39;00m\n\u001b[0;32m    476\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m metric \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meuclidean\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msqeuclidean\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    477\u001b[0m     )\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_pairwise_distances_reduction\\_dispatcher.py:115\u001b[0m, in \u001b[0;36mBaseDistancesReductionDispatcher.is_usable_for\u001b[1;34m(cls, X, Y, metric)\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_valid_sparse_matrix\u001b[39m(X):\n\u001b[0;32m    102\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m    103\u001b[0m         isspmatrix_csr(X)\n\u001b[0;32m    104\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    110\u001b[0m         X\u001b[38;5;241m.\u001b[39mindices\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m X\u001b[38;5;241m.\u001b[39mindptr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mint32\n\u001b[0;32m    111\u001b[0m     )\n\u001b[0;32m    113\u001b[0m is_usable \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    114\u001b[0m     get_config()\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menable_cython_pairwise_dist\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m--> 115\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (is_numpy_c_ordered(X) \u001b[38;5;129;01mor\u001b[39;00m is_valid_sparse_matrix(X))\n\u001b[0;32m    116\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (is_numpy_c_ordered(Y) \u001b[38;5;129;01mor\u001b[39;00m is_valid_sparse_matrix(Y))\n\u001b[0;32m    117\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m Y\u001b[38;5;241m.\u001b[39mdtype\n\u001b[0;32m    118\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01min\u001b[39;00m (np\u001b[38;5;241m.\u001b[39mfloat32, np\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[0;32m    119\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m metric \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mvalid_metrics()\n\u001b[0;32m    120\u001b[0m )\n\u001b[0;32m    122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m is_usable\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_pairwise_distances_reduction\\_dispatcher.py:99\u001b[0m, in \u001b[0;36mBaseDistancesReductionDispatcher.is_usable_for.<locals>.is_numpy_c_ordered\u001b[1;34m(X)\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_numpy_c_ordered\u001b[39m(X):\n\u001b[1;32m---> 99\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflags\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m X\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mc_contiguous\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'Flags' object has no attribute 'c_contiguous'"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "\n",
+    "# Inicializo el K-Nearest Neighbors \n",
+    "knn_model = KNeighborsClassifier(n_neighbors=3)\n",
+    "\n",
+    "# Entreno el KNN model \n",
+    "knn_model.fit(X_train, y_train)\n",
+    "\n",
+    "# Hago la predicción\n",
+    "y_pred_knn = knn_model.predict(X_test)\n",
+    "\n",
+    "# Evaluo el KNN model\n",
+    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
+    "conf_matrix_knn = confusion_matrix(y_test, y_pred_knn)\n",
+    "\n",
+    "\n",
+    "print(\"Confusion Matrix for K-Nearest Neighbors:\\n\", conf_matrix_knn)\n",
+    "print(\"Accuracy Score for K-Nearest Neighbors:\", accuracy_knn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### We'll create another K-Nearest Neighbors model with k=5. \n",
+    "\n",
+    "Initialize and fit the model below and print the confusion matrix and the accuracy score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Flags' object has no attribute 'c_contiguous'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[70], line 17\u001b[0m\n\u001b[0;32m     14\u001b[0m knn_model_k5\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n\u001b[0;32m     16\u001b[0m \u001b[38;5;66;03m# Predict the labels on the test set\u001b[39;00m\n\u001b[1;32m---> 17\u001b[0m y_pred_knn_k5 \u001b[38;5;241m=\u001b[39m knn_model_k5\u001b[38;5;241m.\u001b[39mpredict(X_test)\n\u001b[0;32m     19\u001b[0m \u001b[38;5;66;03m# Evaluate the KNN model with k=5\u001b[39;00m\n\u001b[0;32m     20\u001b[0m accuracy_knn_k5 \u001b[38;5;241m=\u001b[39m accuracy_score(y_test, y_pred_knn_k5)\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\neighbors\\_classification.py:246\u001b[0m, in \u001b[0;36mKNeighborsClassifier.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    244\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_fit_method\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muniform\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 246\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbrute\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m ArgKminClassMode\u001b[38;5;241m.\u001b[39mis_usable_for(\n\u001b[0;32m    247\u001b[0m         X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetric\n\u001b[0;32m    248\u001b[0m     ):\n\u001b[0;32m    249\u001b[0m         probabilities \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict_proba(X)\n\u001b[0;32m    250\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutputs_2d_:\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_pairwise_distances_reduction\\_dispatcher.py:471\u001b[0m, in \u001b[0;36mArgKminClassMode.is_usable_for\u001b[1;34m(cls, X, Y, metric)\u001b[0m\n\u001b[0;32m    448\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[0;32m    449\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_usable_for\u001b[39m(\u001b[38;5;28mcls\u001b[39m, X, Y, metric) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n\u001b[0;32m    450\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return True if the dispatcher can be used for the given parameters.\u001b[39;00m\n\u001b[0;32m    451\u001b[0m \n\u001b[0;32m    452\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    468\u001b[0m \u001b[38;5;124;03m    True if the PairwiseDistancesReduction can be used, else False.\u001b[39;00m\n\u001b[0;32m    469\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m    470\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[1;32m--> 471\u001b[0m         ArgKmin\u001b[38;5;241m.\u001b[39mis_usable_for(X, Y, metric)\n\u001b[0;32m    472\u001b[0m         \u001b[38;5;66;03m# TODO: Support CSR matrices.\u001b[39;00m\n\u001b[0;32m    473\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m issparse(X)\n\u001b[0;32m    474\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m issparse(Y)\n\u001b[0;32m    475\u001b[0m         \u001b[38;5;66;03m# TODO: implement Euclidean specialization with GEMM.\u001b[39;00m\n\u001b[0;32m    476\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m metric \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meuclidean\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msqeuclidean\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    477\u001b[0m     )\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_pairwise_distances_reduction\\_dispatcher.py:115\u001b[0m, in \u001b[0;36mBaseDistancesReductionDispatcher.is_usable_for\u001b[1;34m(cls, X, Y, metric)\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_valid_sparse_matrix\u001b[39m(X):\n\u001b[0;32m    102\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m    103\u001b[0m         isspmatrix_csr(X)\n\u001b[0;32m    104\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    110\u001b[0m         X\u001b[38;5;241m.\u001b[39mindices\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m X\u001b[38;5;241m.\u001b[39mindptr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mint32\n\u001b[0;32m    111\u001b[0m     )\n\u001b[0;32m    113\u001b[0m is_usable \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    114\u001b[0m     get_config()\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menable_cython_pairwise_dist\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m--> 115\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (is_numpy_c_ordered(X) \u001b[38;5;129;01mor\u001b[39;00m is_valid_sparse_matrix(X))\n\u001b[0;32m    116\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (is_numpy_c_ordered(Y) \u001b[38;5;129;01mor\u001b[39;00m is_valid_sparse_matrix(Y))\n\u001b[0;32m    117\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m Y\u001b[38;5;241m.\u001b[39mdtype\n\u001b[0;32m    118\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m X\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01min\u001b[39;00m (np\u001b[38;5;241m.\u001b[39mfloat32, np\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[0;32m    119\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m metric \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mvalid_metrics()\n\u001b[0;32m    120\u001b[0m )\n\u001b[0;32m    122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m is_usable\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_pairwise_distances_reduction\\_dispatcher.py:99\u001b[0m, in \u001b[0;36mBaseDistancesReductionDispatcher.is_usable_for.<locals>.is_numpy_c_ordered\u001b[1;34m(X)\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_numpy_c_ordered\u001b[39m(X):\n\u001b[1;32m---> 99\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflags\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m X\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mc_contiguous\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'Flags' object has no attribute 'c_contiguous'"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "\n",
+    "X = website_dummy.drop(columns=['Type'])\n",
+    "y = website_dummy['Type']\n",
+    "\n",
+    "# Separo los datos 80/20 para poder entrenar el modelo\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Inicializo el K-Nearest Neighbors \n",
+    "knn_model_k5 = KNeighborsClassifier(n_neighbors=5)\n",
+    "\n",
+    "# Entreno al modelo\n",
+    "knn_model_k5.fit(X_train, y_train)\n",
+    "\n",
+    "# Hago la predicción\n",
+    "y_pred_knn_k5 = knn_model_k5.predict(X_test)\n",
+    "\n",
+    "# Evaluo el KNN model con k=5\n",
+    "accuracy_knn_k5 = accuracy_score(y_test, y_pred_knn_k5)\n",
+    "conf_matrix_knn_k5 = confusion_matrix(y_test, y_pred_knn_k5)\n",
+    "\n",
+    "\n",
+    "print(\"Confusion Matrix for K-Nearest Neighbors with k=5:\\n\", conf_matrix_knn_k5)\n",
+    "print(\"Accuracy Score for K-Nearest Neighbors with k=5:\", accuracy_knn_k5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Did you see an improvement in the confusion matrix when increasing k to 5? Did you see an improvement in the accuracy score? Write your conclusions below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your conclusions here:\n",
+    "\n",
+    "He sido incapaz de hacerlo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bonus Challenge - Feature Scaling\n",
+    "\n",
+    "Problem-solving in machine learning is iterative. You can improve your model prediction with various techniques (there is a sweetspot for the time you spend and the improvement you receive though). Now you've completed only one iteration of ML analysis. There are more iterations you can conduct to make improvements. In order to be able to do that, you will need deeper knowledge in statistics and master more data analysis techniques. In this bootcamp, we don't have time to achieve that advanced goal. But you will make constant efforts after the bootcamp to eventually get there.\n",
+    "\n",
+    "However, now we do want you to learn one of the advanced techniques which is called *feature scaling*. The idea of feature scaling is to standardize/normalize the range of independent variables or features of the data. This can make the outliers more apparent so that you can remove them. This step needs to happen during Challenge 6 after you split the training and test data because you don't want to split the data again which makes it impossible to compare your results with and without feature scaling. For general concepts about feature scaling, click [here](https://en.wikipedia.org/wiki/Feature_scaling). To read deeper, click [here](https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e).\n",
+    "\n",
+    "In the next cell, attempt to improve your model prediction accuracy by means of feature scaling. A library you can utilize is `sklearn.preprocessing.RobustScaler` ([documentation](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html)). You'll use the `RobustScaler` to fit and transform your `X_train`, then transform `X_test`. You will use logistic regression to fit and predict your transformed data and obtain the accuracy score in the same way. Compare the accuracy score with your normalized data with the previous accuracy data. Is there an improvement?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}