diff --git a/your-code/lab_supervised_ejs_hechos.ipynb b/your-code/lab_supervised_ejs_hechos.ipynb
new file mode 100644
index 0000000..52c9d92
--- /dev/null
+++ b/your-code/lab_supervised_ejs_hechos.ipynb
@@ -0,0 +1,3881 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Before your 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": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import your libraries:\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "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('../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 your explorations. Make sure to comment what you find out."
+   ]
+  },
+  {
+   "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",
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+       "\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",
+       "  <tbody>\n",
+       "    <tr>\n",
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+       "      <td>16</td>\n",
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+       "      <td>263.0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>10/10/2015 18:21</td>\n",
+       "      <td>None</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>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</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>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</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>None</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          None           None   \n",
+       "1          Apache/2.4.10         15087.0          None           None   \n",
+       "2  Microsoft-HTTPAPI/2.0           324.0          None           None   \n",
+       "3                  nginx           162.0            US             AK   \n",
+       "4                   None        124140.0            US             TX   \n",
+       "\n",
+       "      WHOIS_REGDATE WHOIS_UPDATED_DATE  ...  DIST_REMOTE_TCP_PORT  REMOTE_IPS  \\\n",
+       "0  10/10/2015 18:21               None  ...                     0           2   \n",
+       "1              None               None  ...                     7           4   \n",
+       "2              None               None  ...                     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",
+    "websites.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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",
+       "WHOIS_STATEPRO                object\n",
+       "WHOIS_REGDATE                 object\n",
+       "WHOIS_UPDATED_DATE            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"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your comment here\n",
+    "websites.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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                    1781 non-null   object \n",
+      " 4   SERVER                     1780 non-null   object \n",
+      " 5   CONTENT_LENGTH             969 non-null    float64\n",
+      " 6   WHOIS_COUNTRY              1781 non-null   object \n",
+      " 7   WHOIS_STATEPRO             1781 non-null   object \n",
+      " 8   WHOIS_REGDATE              1781 non-null   object \n",
+      " 9   WHOIS_UPDATED_DATE         1781 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": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                            0\n",
+       "URL_LENGTH                     0\n",
+       "NUMBER_SPECIAL_CHARACTERS      0\n",
+       "CHARSET                        0\n",
+       "SERVER                         1\n",
+       "CONTENT_LENGTH               812\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                1\n",
+       "Type                           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</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>WHOIS_COUNTRY</th>\n",
+       "      <th>WHOIS_STATEPRO</th>\n",
+       "      <th>WHOIS_REGDATE</th>\n",
+       "      <th>WHOIS_UPDATED_DATE</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>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>M0_109</td>\n",
+       "      <td>16</td>\n",
+       "      <td>7</td>\n",
+       "      <td>iso-8859-1</td>\n",
+       "      <td>nginx</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>10/10/2015 18:21</td>\n",
+       "      <td>None</td>\n",
+       "      <td>7</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>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>17</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>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</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</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>US</td>\n",
+       "      <td>AK</td>\n",
+       "      <td>7/10/1997 4:00</td>\n",
+       "      <td>12/09/2013 0:45</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>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>None</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>57</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",
+       "    <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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1776</th>\n",
+       "      <td>M4_48</td>\n",
+       "      <td>194</td>\n",
+       "      <td>16</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>Apache</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>Barcelona</td>\n",
+       "      <td>17/09/2008 0:00</td>\n",
+       "      <td>2/09/2016 0:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>186</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1777</th>\n",
+       "      <td>M4_41</td>\n",
+       "      <td>198</td>\n",
+       "      <td>17</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>Apache</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>Barcelona</td>\n",
+       "      <td>17/09/2008 0:00</td>\n",
+       "      <td>2/09/2016 0:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>124</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1778</th>\n",
+       "      <td>B0_162</td>\n",
+       "      <td>201</td>\n",
+       "      <td>34</td>\n",
+       "      <td>utf-8</td>\n",
+       "      <td>Apache/2.2.16 (Debian)</td>\n",
+       "      <td>US</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>15/02/1999 0:00</td>\n",
+       "      <td>15/07/2015 0:00</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>6945</td>\n",
+       "      <td>87</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1779</th>\n",
+       "      <td>B0_1152</td>\n",
+       "      <td>234</td>\n",
+       "      <td>34</td>\n",
+       "      <td>ISO-8859-1</td>\n",
+       "      <td>cloudflare-nginx</td>\n",
+       "      <td>US</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>1/04/1998 0:00</td>\n",
+       "      <td>9/12/2016 0:00</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</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>B0_676</td>\n",
+       "      <td>249</td>\n",
+       "      <td>40</td>\n",
+       "      <td>utf-8</td>\n",
+       "      <td>Microsoft-IIS/8.5</td>\n",
+       "      <td>US</td>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>14/11/2008 0:00</td>\n",
+       "      <td>20/11/2013 0:00</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>2776</td>\n",
+       "      <td>25</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1781 rows × 20 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",
+       "1776    M4_48         194                         16       UTF-8   \n",
+       "1777    M4_41         198                         17       UTF-8   \n",
+       "1778   B0_162         201                         34       utf-8   \n",
+       "1779  B0_1152         234                         34  ISO-8859-1   \n",
+       "1780   B0_676         249                         40       utf-8   \n",
+       "\n",
+       "                      SERVER WHOIS_COUNTRY WHOIS_STATEPRO     WHOIS_REGDATE  \\\n",
+       "0                      nginx          None           None  10/10/2015 18:21   \n",
+       "1              Apache/2.4.10          None           None              None   \n",
+       "2      Microsoft-HTTPAPI/2.0          None           None              None   \n",
+       "3                      nginx            US             AK    7/10/1997 4:00   \n",
+       "4                       None            US             TX   12/05/1996 0:00   \n",
+       "...                      ...           ...            ...               ...   \n",
+       "1776                  Apache            ES      Barcelona   17/09/2008 0:00   \n",
+       "1777                  Apache            ES      Barcelona   17/09/2008 0:00   \n",
+       "1778  Apache/2.2.16 (Debian)            US             FL   15/02/1999 0:00   \n",
+       "1779        cloudflare-nginx            US             CA    1/04/1998 0:00   \n",
+       "1780       Microsoft-IIS/8.5            US      Wisconsin   14/11/2008 0:00   \n",
+       "\n",
+       "     WHOIS_UPDATED_DATE  TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  \\\n",
+       "0                  None                          7                     0   \n",
+       "1                  None                         17                     7   \n",
+       "2                  None                          0                     0   \n",
+       "3       12/09/2013 0:45                         31                    22   \n",
+       "4       11/04/2017 0:00                         57                     2   \n",
+       "...                 ...                        ...                   ...   \n",
+       "1776     2/09/2016 0:00                          0                     0   \n",
+       "1777     2/09/2016 0:00                          0                     0   \n",
+       "1778    15/07/2015 0:00                         83                     2   \n",
+       "1779     9/12/2016 0:00                          0                     0   \n",
+       "1780    20/11/2013 0:00                         19                     6   \n",
+       "\n",
+       "      REMOTE_IPS  APP_BYTES  SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  \\\n",
+       "0              2        700                   9                  10   \n",
+       "1              4       1230                  17                  19   \n",
+       "2              0          0                   0                   0   \n",
+       "3              3       3812                  39                  37   \n",
+       "4              5       4278                  61                  62   \n",
+       "...          ...        ...                 ...                 ...   \n",
+       "1776           0          0                   0                   3   \n",
+       "1777           0          0                   0                   2   \n",
+       "1778           6       6631                  87                  89   \n",
+       "1779           0          0                   0                   0   \n",
+       "1780          11       2314                  25                  28   \n",
+       "\n",
+       "      SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  DNS_QUERY_TIMES  Type  \n",
+       "0                 1153               832            9              2.0     1  \n",
+       "1                 1265              1230           17              0.0     0  \n",
+       "2                    0                 0            0              0.0     0  \n",
+       "3                18784              4380           39              8.0     0  \n",
+       "4               129889              4586           61              4.0     0  \n",
+       "...                ...               ...          ...              ...   ...  \n",
+       "1776               186                 0            0              0.0     1  \n",
+       "1777               124                 0            0              0.0     1  \n",
+       "1778            132181              6945           87              4.0     0  \n",
+       "1779                 0                 0            0              0.0     0  \n",
+       "1780              3039              2776           25              6.0     0  \n",
+       "\n",
+       "[1781 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites.dropna()\n",
+    "#dropping content length since it has c.50% data missing\n",
+    "websites.drop(['CONTENT_LENGTH'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Next, evaluate if the columns in this dataset are strongly correlated.\n",
+    "\n",
+    "In the Mushroom supervised learning lab we did recently, we mentioned we are concerned if our dataset has strongly correlated columns because if it 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 next 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": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>URL_LENGTH</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.917986</td>\n",
+       "      <td>0.129745</td>\n",
+       "      <td>-0.038407</td>\n",
+       "      <td>-0.039839</td>\n",
+       "      <td>-0.046367</td>\n",
+       "      <td>-0.026446</td>\n",
+       "      <td>-0.042264</td>\n",
+       "      <td>-0.033779</td>\n",
+       "      <td>-0.014857</td>\n",
+       "      <td>-0.026686</td>\n",
+       "      <td>-0.042264</td>\n",
+       "      <td>-0.068582</td>\n",
+       "      <td>0.162104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
+       "      <td>0.917986</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.214225</td>\n",
+       "      <td>-0.037459</td>\n",
+       "      <td>-0.042619</td>\n",
+       "      <td>-0.047103</td>\n",
+       "      <td>-0.023914</td>\n",
+       "      <td>-0.040096</td>\n",
+       "      <td>-0.030597</td>\n",
+       "      <td>-0.014376</td>\n",
+       "      <td>-0.024098</td>\n",
+       "      <td>-0.040096</td>\n",
+       "      <td>-0.050048</td>\n",
+       "      <td>0.280897</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CONTENT_LENGTH</th>\n",
+       "      <td>0.129745</td>\n",
+       "      <td>0.214225</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.078451</td>\n",
+       "      <td>-0.000381</td>\n",
+       "      <td>0.004774</td>\n",
+       "      <td>0.051202</td>\n",
+       "      <td>0.074142</td>\n",
+       "      <td>0.091077</td>\n",
+       "      <td>0.100094</td>\n",
+       "      <td>0.047595</td>\n",
+       "      <td>0.074142</td>\n",
+       "      <td>-0.045644</td>\n",
+       "      <td>-0.090852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <td>-0.038407</td>\n",
+       "      <td>-0.037459</td>\n",
+       "      <td>0.078451</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.555188</td>\n",
+       "      <td>0.331080</td>\n",
+       "      <td>0.457320</td>\n",
+       "      <td>0.997796</td>\n",
+       "      <td>0.990848</td>\n",
+       "      <td>0.865580</td>\n",
+       "      <td>0.458702</td>\n",
+       "      <td>0.997796</td>\n",
+       "      <td>0.349832</td>\n",
+       "      <td>-0.040202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <td>-0.039839</td>\n",
+       "      <td>-0.042619</td>\n",
+       "      <td>-0.000381</td>\n",
+       "      <td>0.555188</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.210188</td>\n",
+       "      <td>0.780238</td>\n",
+       "      <td>0.558612</td>\n",
+       "      <td>0.591188</td>\n",
+       "      <td>0.313359</td>\n",
+       "      <td>0.781212</td>\n",
+       "      <td>0.558612</td>\n",
+       "      <td>0.259942</td>\n",
+       "      <td>-0.082925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <td>-0.046367</td>\n",
+       "      <td>-0.047103</td>\n",
+       "      <td>0.004774</td>\n",
+       "      <td>0.331080</td>\n",
+       "      <td>0.210188</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.023126</td>\n",
+       "      <td>0.361104</td>\n",
+       "      <td>0.304683</td>\n",
+       "      <td>0.171651</td>\n",
+       "      <td>0.025324</td>\n",
+       "      <td>0.361104</td>\n",
+       "      <td>0.548189</td>\n",
+       "      <td>-0.078783</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <td>-0.026446</td>\n",
+       "      <td>-0.023914</td>\n",
+       "      <td>0.051202</td>\n",
+       "      <td>0.457320</td>\n",
+       "      <td>0.780238</td>\n",
+       "      <td>0.023126</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.445822</td>\n",
+       "      <td>0.468999</td>\n",
+       "      <td>0.074464</td>\n",
+       "      <td>0.999992</td>\n",
+       "      <td>0.445822</td>\n",
+       "      <td>0.012221</td>\n",
+       "      <td>-0.011262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <td>-0.042264</td>\n",
+       "      <td>-0.040096</td>\n",
+       "      <td>0.074142</td>\n",
+       "      <td>0.997796</td>\n",
+       "      <td>0.558612</td>\n",
+       "      <td>0.361104</td>\n",
+       "      <td>0.445822</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.989285</td>\n",
+       "      <td>0.857495</td>\n",
+       "      <td>0.447448</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.410843</td>\n",
+       "      <td>-0.034414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <td>-0.033779</td>\n",
+       "      <td>-0.030597</td>\n",
+       "      <td>0.091077</td>\n",
+       "      <td>0.990848</td>\n",
+       "      <td>0.591188</td>\n",
+       "      <td>0.304683</td>\n",
+       "      <td>0.468999</td>\n",
+       "      <td>0.989285</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.880555</td>\n",
+       "      <td>0.470401</td>\n",
+       "      <td>0.989285</td>\n",
+       "      <td>0.355716</td>\n",
+       "      <td>-0.032897</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <td>-0.014857</td>\n",
+       "      <td>-0.014376</td>\n",
+       "      <td>0.100094</td>\n",
+       "      <td>0.865580</td>\n",
+       "      <td>0.313359</td>\n",
+       "      <td>0.171651</td>\n",
+       "      <td>0.074464</td>\n",
+       "      <td>0.857495</td>\n",
+       "      <td>0.880555</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.075328</td>\n",
+       "      <td>0.857495</td>\n",
+       "      <td>0.215285</td>\n",
+       "      <td>-0.043852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <td>-0.026686</td>\n",
+       "      <td>-0.024098</td>\n",
+       "      <td>0.047595</td>\n",
+       "      <td>0.458702</td>\n",
+       "      <td>0.781212</td>\n",
+       "      <td>0.025324</td>\n",
+       "      <td>0.999992</td>\n",
+       "      <td>0.447448</td>\n",
+       "      <td>0.470401</td>\n",
+       "      <td>0.075328</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.447448</td>\n",
+       "      <td>0.016215</td>\n",
+       "      <td>-0.011004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <td>-0.042264</td>\n",
+       "      <td>-0.040096</td>\n",
+       "      <td>0.074142</td>\n",
+       "      <td>0.997796</td>\n",
+       "      <td>0.558612</td>\n",
+       "      <td>0.361104</td>\n",
+       "      <td>0.445822</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.989285</td>\n",
+       "      <td>0.857495</td>\n",
+       "      <td>0.447448</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.410843</td>\n",
+       "      <td>-0.034414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <td>-0.068582</td>\n",
+       "      <td>-0.050048</td>\n",
+       "      <td>-0.045644</td>\n",
+       "      <td>0.349832</td>\n",
+       "      <td>0.259942</td>\n",
+       "      <td>0.548189</td>\n",
+       "      <td>0.012221</td>\n",
+       "      <td>0.410843</td>\n",
+       "      <td>0.355716</td>\n",
+       "      <td>0.215285</td>\n",
+       "      <td>0.016215</td>\n",
+       "      <td>0.410843</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.068753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Type</th>\n",
+       "      <td>0.162104</td>\n",
+       "      <td>0.280897</td>\n",
+       "      <td>-0.090852</td>\n",
+       "      <td>-0.040202</td>\n",
+       "      <td>-0.082925</td>\n",
+       "      <td>-0.078783</td>\n",
+       "      <td>-0.011262</td>\n",
+       "      <td>-0.034414</td>\n",
+       "      <td>-0.032897</td>\n",
+       "      <td>-0.043852</td>\n",
+       "      <td>-0.011004</td>\n",
+       "      <td>-0.034414</td>\n",
+       "      <td>0.068753</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           URL_LENGTH  NUMBER_SPECIAL_CHARACTERS  \\\n",
+       "URL_LENGTH                   1.000000                   0.917986   \n",
+       "NUMBER_SPECIAL_CHARACTERS    0.917986                   1.000000   \n",
+       "CONTENT_LENGTH               0.129745                   0.214225   \n",
+       "TCP_CONVERSATION_EXCHANGE   -0.038407                  -0.037459   \n",
+       "DIST_REMOTE_TCP_PORT        -0.039839                  -0.042619   \n",
+       "REMOTE_IPS                  -0.046367                  -0.047103   \n",
+       "APP_BYTES                   -0.026446                  -0.023914   \n",
+       "SOURCE_APP_PACKETS          -0.042264                  -0.040096   \n",
+       "REMOTE_APP_PACKETS          -0.033779                  -0.030597   \n",
+       "SOURCE_APP_BYTES            -0.014857                  -0.014376   \n",
+       "REMOTE_APP_BYTES            -0.026686                  -0.024098   \n",
+       "APP_PACKETS                 -0.042264                  -0.040096   \n",
+       "DNS_QUERY_TIMES             -0.068582                  -0.050048   \n",
+       "Type                         0.162104                   0.280897   \n",
+       "\n",
+       "                           CONTENT_LENGTH  TCP_CONVERSATION_EXCHANGE  \\\n",
+       "URL_LENGTH                       0.129745                  -0.038407   \n",
+       "NUMBER_SPECIAL_CHARACTERS        0.214225                  -0.037459   \n",
+       "CONTENT_LENGTH                   1.000000                   0.078451   \n",
+       "TCP_CONVERSATION_EXCHANGE        0.078451                   1.000000   \n",
+       "DIST_REMOTE_TCP_PORT            -0.000381                   0.555188   \n",
+       "REMOTE_IPS                       0.004774                   0.331080   \n",
+       "APP_BYTES                        0.051202                   0.457320   \n",
+       "SOURCE_APP_PACKETS               0.074142                   0.997796   \n",
+       "REMOTE_APP_PACKETS               0.091077                   0.990848   \n",
+       "SOURCE_APP_BYTES                 0.100094                   0.865580   \n",
+       "REMOTE_APP_BYTES                 0.047595                   0.458702   \n",
+       "APP_PACKETS                      0.074142                   0.997796   \n",
+       "DNS_QUERY_TIMES                 -0.045644                   0.349832   \n",
+       "Type                            -0.090852                  -0.040202   \n",
+       "\n",
+       "                           DIST_REMOTE_TCP_PORT  REMOTE_IPS  APP_BYTES  \\\n",
+       "URL_LENGTH                            -0.039839   -0.046367  -0.026446   \n",
+       "NUMBER_SPECIAL_CHARACTERS             -0.042619   -0.047103  -0.023914   \n",
+       "CONTENT_LENGTH                        -0.000381    0.004774   0.051202   \n",
+       "TCP_CONVERSATION_EXCHANGE              0.555188    0.331080   0.457320   \n",
+       "DIST_REMOTE_TCP_PORT                   1.000000    0.210188   0.780238   \n",
+       "REMOTE_IPS                             0.210188    1.000000   0.023126   \n",
+       "APP_BYTES                              0.780238    0.023126   1.000000   \n",
+       "SOURCE_APP_PACKETS                     0.558612    0.361104   0.445822   \n",
+       "REMOTE_APP_PACKETS                     0.591188    0.304683   0.468999   \n",
+       "SOURCE_APP_BYTES                       0.313359    0.171651   0.074464   \n",
+       "REMOTE_APP_BYTES                       0.781212    0.025324   0.999992   \n",
+       "APP_PACKETS                            0.558612    0.361104   0.445822   \n",
+       "DNS_QUERY_TIMES                        0.259942    0.548189   0.012221   \n",
+       "Type                                  -0.082925   -0.078783  -0.011262   \n",
+       "\n",
+       "                           SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  \\\n",
+       "URL_LENGTH                          -0.042264           -0.033779   \n",
+       "NUMBER_SPECIAL_CHARACTERS           -0.040096           -0.030597   \n",
+       "CONTENT_LENGTH                       0.074142            0.091077   \n",
+       "TCP_CONVERSATION_EXCHANGE            0.997796            0.990848   \n",
+       "DIST_REMOTE_TCP_PORT                 0.558612            0.591188   \n",
+       "REMOTE_IPS                           0.361104            0.304683   \n",
+       "APP_BYTES                            0.445822            0.468999   \n",
+       "SOURCE_APP_PACKETS                   1.000000            0.989285   \n",
+       "REMOTE_APP_PACKETS                   0.989285            1.000000   \n",
+       "SOURCE_APP_BYTES                     0.857495            0.880555   \n",
+       "REMOTE_APP_BYTES                     0.447448            0.470401   \n",
+       "APP_PACKETS                          1.000000            0.989285   \n",
+       "DNS_QUERY_TIMES                      0.410843            0.355716   \n",
+       "Type                                -0.034414           -0.032897   \n",
+       "\n",
+       "                           SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  \\\n",
+       "URL_LENGTH                        -0.014857         -0.026686    -0.042264   \n",
+       "NUMBER_SPECIAL_CHARACTERS         -0.014376         -0.024098    -0.040096   \n",
+       "CONTENT_LENGTH                     0.100094          0.047595     0.074142   \n",
+       "TCP_CONVERSATION_EXCHANGE          0.865580          0.458702     0.997796   \n",
+       "DIST_REMOTE_TCP_PORT               0.313359          0.781212     0.558612   \n",
+       "REMOTE_IPS                         0.171651          0.025324     0.361104   \n",
+       "APP_BYTES                          0.074464          0.999992     0.445822   \n",
+       "SOURCE_APP_PACKETS                 0.857495          0.447448     1.000000   \n",
+       "REMOTE_APP_PACKETS                 0.880555          0.470401     0.989285   \n",
+       "SOURCE_APP_BYTES                   1.000000          0.075328     0.857495   \n",
+       "REMOTE_APP_BYTES                   0.075328          1.000000     0.447448   \n",
+       "APP_PACKETS                        0.857495          0.447448     1.000000   \n",
+       "DNS_QUERY_TIMES                    0.215285          0.016215     0.410843   \n",
+       "Type                              -0.043852         -0.011004    -0.034414   \n",
+       "\n",
+       "                           DNS_QUERY_TIMES      Type  \n",
+       "URL_LENGTH                       -0.068582  0.162104  \n",
+       "NUMBER_SPECIAL_CHARACTERS        -0.050048  0.280897  \n",
+       "CONTENT_LENGTH                   -0.045644 -0.090852  \n",
+       "TCP_CONVERSATION_EXCHANGE         0.349832 -0.040202  \n",
+       "DIST_REMOTE_TCP_PORT              0.259942 -0.082925  \n",
+       "REMOTE_IPS                        0.548189 -0.078783  \n",
+       "APP_BYTES                         0.012221 -0.011262  \n",
+       "SOURCE_APP_PACKETS                0.410843 -0.034414  \n",
+       "REMOTE_APP_PACKETS                0.355716 -0.032897  \n",
+       "SOURCE_APP_BYTES                  0.215285 -0.043852  \n",
+       "REMOTE_APP_BYTES                  0.016215 -0.011004  \n",
+       "APP_PACKETS                       0.410843 -0.034414  \n",
+       "DNS_QUERY_TIMES                   1.000000  0.068753  \n",
+       "Type                              0.068753  1.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "corrMatrix = websites.corr()\n",
+    "corrMatrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sn\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your comment here\n",
+    "fig, ax = plt.subplots(figsize=(10,10))   \n",
+    "sn.heatmap(corrMatrix, annot=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-11-c963d9e02053>:2: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['NUMBER_SPECIAL_CHARACTERS',\n",
+       " 'SOURCE_APP_PACKETS',\n",
+       " 'REMOTE_APP_PACKETS',\n",
+       " 'REMOTE_APP_BYTES',\n",
+       " 'APP_PACKETS']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_matrix = websites.corr().abs()\n",
+    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
+    "to_drop = [column for column in upper.columns if any(upper[column] > 0.90)]\n",
+    "to_drop"
+   ]
+  },
+  {
+   "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": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "selected_to_drop = ['NUMBER_SPECIAL_CHARACTERS', 'SOURCE_APP_PACKETS', 'REMOTE_APP_PACKETS']\n",
+    "websites_new = websites.drop(websites[selected_to_drop], axis=1)\n",
+    "corr_matrix = websites_new.corr().abs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Your comment here\n",
+    "I  selected those columns which have a high collinearity. \n",
+    "After producing the correlation matrix, I  selected the upper triangle of the correlation matrix and then, created a list (to drop) which allowed me to find the index of feature columns with correlation greater than 90%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10,10))   \n",
+    "sn.heatmap(corr_matrix, annot=True)\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": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                            0\n",
+       "URL_LENGTH                     0\n",
+       "CHARSET                        0\n",
+       "SERVER                         1\n",
+       "CONTENT_LENGTH               812\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_BYTES               0\n",
+       "REMOTE_APP_BYTES               0\n",
+       "APP_PACKETS                    0\n",
+       "DNS_QUERY_TIMES                1\n",
+       "Type                           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "websites_new.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "websites_new2 = websites_new.dropna()"
+   ]
+  },
+  {
+   "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": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "websites_clean = websites_new2.drop(['CONTENT_LENGTH'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Your comment here\n",
+    "I have decided to drop every column that has a missing value or na."
+   ]
+  },
+  {
+   "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": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 967 entries, 0 to 1780\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                     Non-Null Count  Dtype  \n",
+      "---  ------                     --------------  -----  \n",
+      " 0   URL                        967 non-null    object \n",
+      " 1   URL_LENGTH                 967 non-null    int64  \n",
+      " 2   CHARSET                    967 non-null    object \n",
+      " 3   SERVER                     967 non-null    object \n",
+      " 4   WHOIS_COUNTRY              967 non-null    object \n",
+      " 5   WHOIS_STATEPRO             967 non-null    object \n",
+      " 6   WHOIS_REGDATE              967 non-null    object \n",
+      " 7   WHOIS_UPDATED_DATE         967 non-null    object \n",
+      " 8   TCP_CONVERSATION_EXCHANGE  967 non-null    int64  \n",
+      " 9   DIST_REMOTE_TCP_PORT       967 non-null    int64  \n",
+      " 10  REMOTE_IPS                 967 non-null    int64  \n",
+      " 11  APP_BYTES                  967 non-null    int64  \n",
+      " 12  SOURCE_APP_BYTES           967 non-null    int64  \n",
+      " 13  REMOTE_APP_BYTES           967 non-null    int64  \n",
+      " 14  APP_PACKETS                967 non-null    int64  \n",
+      " 15  DNS_QUERY_TIMES            967 non-null    float64\n",
+      " 16  Type                       967 non-null    int64  \n",
+      "dtypes: float64(1), int64(9), object(7)\n",
+      "memory usage: 136.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Examine missing values in each column\n",
+    "websites_clean.info()"
+   ]
+  },
+  {
+   "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": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: country_converter in /home/bribas/anaconda3/envs/clase/lib/python3.8/site-packages (0.7.3)\n",
+      "Requirement already satisfied: pandas>=1.0 in /home/bribas/anaconda3/envs/clase/lib/python3.8/site-packages (from country_converter) (1.2.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/bribas/.local/lib/python3.8/site-packages (from pandas>=1.0->country_converter) (2.8.1)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /home/bribas/anaconda3/envs/clase/lib/python3.8/site-packages (from pandas>=1.0->country_converter) (2021.1)\n",
+      "Requirement already satisfied: numpy>=1.16.5 in /home/bribas/anaconda3/envs/clase/lib/python3.8/site-packages (from pandas>=1.0->country_converter) (1.20.2)\n",
+      "Requirement already satisfied: six>=1.5 in /home/bribas/anaconda3/envs/clase/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas>=1.0->country_converter) (1.15.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install country_converter "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import country_converter as coco"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['None', 'US', 'GB', 'UK', 'RU', 'AU', 'CA', 'PA', 'se', 'IN',\n",
+       "       \"[u'GB'; u'UK']\", 'UG', 'JP', 'SI', 'IL', 'AT', 'CN', 'BE', 'NO',\n",
+       "       'TR', 'KY', 'BR', 'SC', 'NL', 'FR', 'CZ', 'KR', 'UA', 'CH', 'HK',\n",
+       "       'United Kingdom', 'DE', 'IT', 'BS', 'SE', 'Cyprus', 'us', 'BY',\n",
+       "       'AE', 'IE', 'PH', 'UY'], dtype=object)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "websites_clean.WHOIS_COUNTRY.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "websites_clean[\"WHOIS_COUNTRY\"].replace({\"us\": \"US\", \"Cyprus\": \"CY\", \"ru\": \"RU\", \"[u'GB'; u'UK']\": \"UK\", \"United Kingdom\": \"UK\", \"se\": \"SE\"}, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "UK not found in ISO2\n",
+      "None not found in regex\n",
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+      "UK not found in ISO2\n",
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+      "UK not found in ISO2\n",
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+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "UK not found in ISO2\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
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+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "UK not found in ISO2\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n",
+      "None not found in regex\n"
+     ]
+    }
+   ],
+   "source": [
+    "cc = coco.CountryConverter()\n",
+    "standard_names = coco.convert(names=websites.WHOIS_COUNTRY, to='name_short')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['None', 'US', 'GB', 'UK', 'RU', 'AU', 'CA', 'PA', 'SE', 'IN', 'UG',\n",
+       "       'JP', 'SI', 'IL', 'AT', 'CN', 'BE', 'NO', 'TR', 'KY', 'BR', 'SC',\n",
+       "       'NL', 'FR', 'CZ', 'KR', 'UA', 'CH', 'HK', 'DE', 'IT', 'BS', 'CY',\n",
+       "       'BY', 'AE', 'IE', 'PH', 'UY'], dtype=object)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_clean.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": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "US      591\n",
+       "None    208\n",
+       "CA       45\n",
+       "AU       16\n",
+       "GB       15\n",
+       "UK       12\n",
+       "PA       10\n",
+       "IN        6\n",
+       "JP        6\n",
+       "CH        6\n",
+       "AT        4\n",
+       "CN        3\n",
+       "TR        3\n",
+       "FR        3\n",
+       "KR        3\n",
+       "BR        2\n",
+       "BS        2\n",
+       "NL        2\n",
+       "HK        2\n",
+       "NO        2\n",
+       "UY        2\n",
+       "IL        2\n",
+       "BE        2\n",
+       "CY        2\n",
+       "SE        2\n",
+       "DE        2\n",
+       "UA        2\n",
+       "SC        2\n",
+       "PH        1\n",
+       "BY        1\n",
+       "IE        1\n",
+       "IT        1\n",
+       "UG        1\n",
+       "CZ        1\n",
+       "AE        1\n",
+       "KY        1\n",
+       "SI        1\n",
+       "RU        1\n",
+       "Name: WHOIS_COUNTRY, dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_clean[\"WHOIS_COUNTRY\"].value_counts()"
+   ]
+  },
+  {
+   "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": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "list_others = [\"FR\", \"CZ\", \"NL\", \"RU\", \"CH\", \"KR\", \"PH\", \"BS\", \"SE\", \"AT\", \"DE\", \"SC\", \"TR\", \"KY\", \"HK\", \"BE\", \"IL\", \"UY\", \"SI\", \"NO\", \"UA\", \"CY\", \"KG\", \"BR\", \"IT\", \"LV\", \"BY\", \"AE\", \"TH\", \n",
+    "\"LU\", \"UG\", \"IE\", \"PK\"]\n",
+    "websites_clean[\"WHOIS_COUNTRY\"].replace(list_others, \"OTHER\", inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "US       591\n",
+       "None     208\n",
+       "OTHER     55\n",
+       "CA        45\n",
+       "AU        16\n",
+       "GB        15\n",
+       "UK        12\n",
+       "PA        10\n",
+       "JP         6\n",
+       "IN         6\n",
+       "CN         3\n",
+       "Name: WHOIS_COUNTRY, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_clean[\"WHOIS_COUNTRY\"].value_counts()"
+   ]
+  },
+  {
+   "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": 27,
+   "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>CHARSET</th>\n",
+       "      <th>SERVER</th>\n",
+       "      <th>WHOIS_COUNTRY</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_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",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>M0_109</td>\n",
+       "      <td>16</td>\n",
+       "      <td>iso-8859-1</td>\n",
+       "      <td>nginx</td>\n",
+       "      <td>None</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>700</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>UTF-8</td>\n",
+       "      <td>Apache/2.4.10</td>\n",
+       "      <td>None</td>\n",
+       "      <td>17</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1230</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>us-ascii</td>\n",
+       "      <td>Microsoft-HTTPAPI/2.0</td>\n",
+       "      <td>None</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>ISO-8859-1</td>\n",
+       "      <td>nginx</td>\n",
+       "      <td>US</td>\n",
+       "      <td>31</td>\n",
+       "      <td>22</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3812</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>UTF-8</td>\n",
+       "      <td>None</td>\n",
+       "      <td>US</td>\n",
+       "      <td>57</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4278</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",
+       "    <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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1768</th>\n",
+       "      <td>B0_62</td>\n",
+       "      <td>160</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>None</td>\n",
+       "      <td>US</td>\n",
+       "      <td>19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2402</td>\n",
+       "      <td>4491</td>\n",
+       "      <td>2900</td>\n",
+       "      <td>25</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1769</th>\n",
+       "      <td>B0_2237</td>\n",
+       "      <td>161</td>\n",
+       "      <td>iso-8859-1</td>\n",
+       "      <td>Apache/2.4.18 (Ubuntu)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>582</td>\n",
+       "      <td>752</td>\n",
+       "      <td>582</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1774</th>\n",
+       "      <td>B0_156</td>\n",
+       "      <td>183</td>\n",
+       "      <td>ISO-8859-1</td>\n",
+       "      <td>Microsoft-IIS/7.5; litigation_essentials.lexis...</td>\n",
+       "      <td>US</td>\n",
+       "      <td>22</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2062</td>\n",
+       "      <td>8161</td>\n",
+       "      <td>2742</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1778</th>\n",
+       "      <td>B0_162</td>\n",
+       "      <td>201</td>\n",
+       "      <td>utf-8</td>\n",
+       "      <td>Apache/2.2.16 (Debian)</td>\n",
+       "      <td>US</td>\n",
+       "      <td>83</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6631</td>\n",
+       "      <td>132181</td>\n",
+       "      <td>6945</td>\n",
+       "      <td>87</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>B0_676</td>\n",
+       "      <td>249</td>\n",
+       "      <td>utf-8</td>\n",
+       "      <td>Microsoft-IIS/8.5</td>\n",
+       "      <td>US</td>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2314</td>\n",
+       "      <td>3039</td>\n",
+       "      <td>2776</td>\n",
+       "      <td>25</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>967 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          URL  URL_LENGTH     CHARSET  \\\n",
+       "0      M0_109          16  iso-8859-1   \n",
+       "1     B0_2314          16       UTF-8   \n",
+       "2      B0_911          16    us-ascii   \n",
+       "3      B0_113          17  ISO-8859-1   \n",
+       "4      B0_403          17       UTF-8   \n",
+       "...       ...         ...         ...   \n",
+       "1768    B0_62         160       UTF-8   \n",
+       "1769  B0_2237         161  iso-8859-1   \n",
+       "1774   B0_156         183  ISO-8859-1   \n",
+       "1778   B0_162         201       utf-8   \n",
+       "1780   B0_676         249       utf-8   \n",
+       "\n",
+       "                                                 SERVER WHOIS_COUNTRY  \\\n",
+       "0                                                 nginx          None   \n",
+       "1                                         Apache/2.4.10          None   \n",
+       "2                                 Microsoft-HTTPAPI/2.0          None   \n",
+       "3                                                 nginx            US   \n",
+       "4                                                  None            US   \n",
+       "...                                                 ...           ...   \n",
+       "1768                                               None            US   \n",
+       "1769                             Apache/2.4.18 (Ubuntu)          None   \n",
+       "1774  Microsoft-IIS/7.5; litigation_essentials.lexis...            US   \n",
+       "1778                             Apache/2.2.16 (Debian)            US   \n",
+       "1780                                  Microsoft-IIS/8.5            US   \n",
+       "\n",
+       "      TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  REMOTE_IPS  APP_BYTES  \\\n",
+       "0                             7                     0           2        700   \n",
+       "1                            17                     7           4       1230   \n",
+       "2                             0                     0           0          0   \n",
+       "3                            31                    22           3       3812   \n",
+       "4                            57                     2           5       4278   \n",
+       "...                         ...                   ...         ...        ...   \n",
+       "1768                         19                     3           7       2402   \n",
+       "1769                          7                     7           2        582   \n",
+       "1774                         22                     2           7       2062   \n",
+       "1778                         83                     2           6       6631   \n",
+       "1780                         19                     6          11       2314   \n",
+       "\n",
+       "      SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  DNS_QUERY_TIMES  Type  \n",
+       "0                 1153               832            9              2.0     1  \n",
+       "1                 1265              1230           17              0.0     0  \n",
+       "2                    0                 0            0              0.0     0  \n",
+       "3                18784              4380           39              8.0     0  \n",
+       "4               129889              4586           61              4.0     0  \n",
+       "...                ...               ...          ...              ...   ...  \n",
+       "1768              4491              2900           25              6.0     0  \n",
+       "1769               752               582            7              0.0     0  \n",
+       "1774              8161              2742           30              8.0     0  \n",
+       "1778            132181              6945           87              4.0     0  \n",
+       "1780              3039              2776           25              6.0     0  \n",
+       "\n",
+       "[967 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "websites_clean2 = websites_clean.drop(['WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE'], axis = 1)\n",
+    "websites_clean2"
+   ]
+  },
+  {
+   "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": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL                           object\n",
+       "URL_LENGTH                     int64\n",
+       "CHARSET                       object\n",
+       "SERVER                        object\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_BYTES               int64\n",
+       "REMOTE_APP_BYTES               int64\n",
+       "APP_PACKETS                    int64\n",
+       "DNS_QUERY_TIMES              float64\n",
+       "Type                           int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "websites_clean2.dtypes"
+   ]
+  },
+  {
+   "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": 29,
+   "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>CHARSET</th>\n",
+       "      <th>SERVER</th>\n",
+       "      <th>WHOIS_COUNTRY</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_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",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16</td>\n",
+       "      <td>iso-8859-1</td>\n",
+       "      <td>nginx</td>\n",
+       "      <td>None</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>700</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>16</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>Apache/2.4.10</td>\n",
+       "      <td>None</td>\n",
+       "      <td>17</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1230</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>16</td>\n",
+       "      <td>us-ascii</td>\n",
+       "      <td>Microsoft-HTTPAPI/2.0</td>\n",
+       "      <td>None</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>17</td>\n",
+       "      <td>ISO-8859-1</td>\n",
+       "      <td>nginx</td>\n",
+       "      <td>US</td>\n",
+       "      <td>31</td>\n",
+       "      <td>22</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3812</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>17</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>None</td>\n",
+       "      <td>US</td>\n",
+       "      <td>57</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4278</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",
+       "    <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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1768</th>\n",
+       "      <td>160</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>None</td>\n",
+       "      <td>US</td>\n",
+       "      <td>19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2402</td>\n",
+       "      <td>4491</td>\n",
+       "      <td>2900</td>\n",
+       "      <td>25</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1769</th>\n",
+       "      <td>161</td>\n",
+       "      <td>iso-8859-1</td>\n",
+       "      <td>Apache/2.4.18 (Ubuntu)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>582</td>\n",
+       "      <td>752</td>\n",
+       "      <td>582</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1774</th>\n",
+       "      <td>183</td>\n",
+       "      <td>ISO-8859-1</td>\n",
+       "      <td>Microsoft-IIS/7.5; litigation_essentials.lexis...</td>\n",
+       "      <td>US</td>\n",
+       "      <td>22</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2062</td>\n",
+       "      <td>8161</td>\n",
+       "      <td>2742</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1778</th>\n",
+       "      <td>201</td>\n",
+       "      <td>utf-8</td>\n",
+       "      <td>Apache/2.2.16 (Debian)</td>\n",
+       "      <td>US</td>\n",
+       "      <td>83</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6631</td>\n",
+       "      <td>132181</td>\n",
+       "      <td>6945</td>\n",
+       "      <td>87</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>249</td>\n",
+       "      <td>utf-8</td>\n",
+       "      <td>Microsoft-IIS/8.5</td>\n",
+       "      <td>US</td>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2314</td>\n",
+       "      <td>3039</td>\n",
+       "      <td>2776</td>\n",
+       "      <td>25</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>967 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      URL_LENGTH     CHARSET  \\\n",
+       "0             16  iso-8859-1   \n",
+       "1             16       UTF-8   \n",
+       "2             16    us-ascii   \n",
+       "3             17  ISO-8859-1   \n",
+       "4             17       UTF-8   \n",
+       "...          ...         ...   \n",
+       "1768         160       UTF-8   \n",
+       "1769         161  iso-8859-1   \n",
+       "1774         183  ISO-8859-1   \n",
+       "1778         201       utf-8   \n",
+       "1780         249       utf-8   \n",
+       "\n",
+       "                                                 SERVER WHOIS_COUNTRY  \\\n",
+       "0                                                 nginx          None   \n",
+       "1                                         Apache/2.4.10          None   \n",
+       "2                                 Microsoft-HTTPAPI/2.0          None   \n",
+       "3                                                 nginx            US   \n",
+       "4                                                  None            US   \n",
+       "...                                                 ...           ...   \n",
+       "1768                                               None            US   \n",
+       "1769                             Apache/2.4.18 (Ubuntu)          None   \n",
+       "1774  Microsoft-IIS/7.5; litigation_essentials.lexis...            US   \n",
+       "1778                             Apache/2.2.16 (Debian)            US   \n",
+       "1780                                  Microsoft-IIS/8.5            US   \n",
+       "\n",
+       "      TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  REMOTE_IPS  APP_BYTES  \\\n",
+       "0                             7                     0           2        700   \n",
+       "1                            17                     7           4       1230   \n",
+       "2                             0                     0           0          0   \n",
+       "3                            31                    22           3       3812   \n",
+       "4                            57                     2           5       4278   \n",
+       "...                         ...                   ...         ...        ...   \n",
+       "1768                         19                     3           7       2402   \n",
+       "1769                          7                     7           2        582   \n",
+       "1774                         22                     2           7       2062   \n",
+       "1778                         83                     2           6       6631   \n",
+       "1780                         19                     6          11       2314   \n",
+       "\n",
+       "      SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  DNS_QUERY_TIMES  Type  \n",
+       "0                 1153               832            9              2.0     1  \n",
+       "1                 1265              1230           17              0.0     0  \n",
+       "2                    0                 0            0              0.0     0  \n",
+       "3                18784              4380           39              8.0     0  \n",
+       "4               129889              4586           61              4.0     0  \n",
+       "...                ...               ...          ...              ...   ...  \n",
+       "1768              4491              2900           25              6.0     0  \n",
+       "1769               752               582            7              0.0     0  \n",
+       "1774              8161              2742           30              8.0     0  \n",
+       "1778            132181              6945           87              4.0     0  \n",
+       "1780              3039              2776           25              6.0     0  \n",
+       "\n",
+       "[967 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "websites_clean2.drop(['URL'], axis=1)"
+   ]
+  },
+  {
+   "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": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "UTF-8           287\n",
+       "ISO-8859-1      257\n",
+       "utf-8           161\n",
+       "us-ascii        146\n",
+       "iso-8859-1      110\n",
+       "None              4\n",
+       "ISO-8859          1\n",
+       "windows-1251      1\n",
+       "Name: CHARSET, dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "websites_clean2.CHARSET.unique()\n",
+    "websites_clean2.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": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Apache                            214\n",
+       "Microsoft-HTTPAPI/2.0             113\n",
+       "nginx                             112\n",
+       "None                               93\n",
+       "Microsoft-IIS/7.5                  47\n",
+       "                                 ... \n",
+       "mw2255.codfw.wmnet                  1\n",
+       "nginx/1.10.1                        1\n",
+       "marrakesh 1.12.2                    1\n",
+       "nginx/1.4.6 (Ubuntu)                1\n",
+       "Apache/2.2.11 (Unix) PHP/5.2.6      1\n",
+       "Name: SERVER, Length: 146, dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "websites_clean2[\"SERVER\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Think Hard](../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": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "for i in websites_clean2['SERVER']:\n",
+    "    if \"Microsoft\" in i: \n",
+    "        websites_clean2['SERVER'].replace(i, 'Microsoft', inplace = True)\n",
+    "    elif 'Apache' in i: \n",
+    "        websites_clean2['SERVER'].replace(i, 'Apache', inplace = True)\n",
+    "    elif 'nginx' in i: \n",
+    "        websites_clean2['SERVER'].replace(i, 'nginx', inplace = True)\n",
+    "    else: \n",
+    "        websites_clean2['SERVER'].replace(i, 'Other', inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Apache       401\n",
+       "Other        221\n",
+       "Microsoft    180\n",
+       "nginx        165\n",
+       "Name: SERVER, dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Count `SERVER` value counts here\n",
+    "websites_clean2[\"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": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "websites_clean2['SERVER'].replace('None', 'none', inplace = True)\n",
+    "websites_clean2['CHARSET'].replace('None', 'none', inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s_dummies = pd.get_dummies(websites['SERVER'], drop_first = True)\n",
+    "countries_dummies = pd.get_dummies(websites_clean2['WHOIS_COUNTRY'], drop_first = True)\n",
+    "c_dummies = pd.get_dummies(websites_clean2['CHARSET'], drop_first = True)"
+   ]
+  },
+  {
+   "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": [
+    {
+     "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>Microsoft</th>\n",
+       "      <th>Other</th>\n",
+       "      <th>nginx</th>\n",
+       "      <th>CA</th>\n",
+       "      <th>CN</th>\n",
+       "      <th>GB</th>\n",
+       "      <th>IN</th>\n",
+       "      <th>JP</th>\n",
+       "      <th>None</th>\n",
+       "      <th>OTHER</th>\n",
+       "      <th>PA</th>\n",
+       "      <th>UK</th>\n",
+       "      <th>US</th>\n",
+       "      <th>ISO-8859-1</th>\n",
+       "      <th>UTF-8</th>\n",
+       "      <th>iso-8859-1</th>\n",
+       "      <th>none</th>\n",
+       "      <th>us-ascii</th>\n",
+       "      <th>utf-8</th>\n",
+       "      <th>windows-1251</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</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>1</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1768</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1769</th>\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>1</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1774</th>\n",
+       "      <td>1</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</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1778</th>\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</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>1</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</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>967 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Microsoft  Other  nginx  CA  CN  GB  IN  JP  None  OTHER  PA  UK  US  \\\n",
+       "0             0      0      1   0   0   0   0   0     1      0   0   0   0   \n",
+       "1             0      0      0   0   0   0   0   0     1      0   0   0   0   \n",
+       "2             1      0      0   0   0   0   0   0     1      0   0   0   0   \n",
+       "3             0      0      1   0   0   0   0   0     0      0   0   0   1   \n",
+       "4             0      1      0   0   0   0   0   0     0      0   0   0   1   \n",
+       "...         ...    ...    ...  ..  ..  ..  ..  ..   ...    ...  ..  ..  ..   \n",
+       "1768          0      1      0   0   0   0   0   0     0      0   0   0   1   \n",
+       "1769          0      0      0   0   0   0   0   0     1      0   0   0   0   \n",
+       "1774          1      0      0   0   0   0   0   0     0      0   0   0   1   \n",
+       "1778          0      0      0   0   0   0   0   0     0      0   0   0   1   \n",
+       "1780          1      0      0   0   0   0   0   0     0      0   0   0   1   \n",
+       "\n",
+       "      ISO-8859-1  UTF-8  iso-8859-1  none  us-ascii  utf-8  windows-1251  \n",
+       "0              0      0           1     0         0      0             0  \n",
+       "1              0      1           0     0         0      0             0  \n",
+       "2              0      0           0     0         1      0             0  \n",
+       "3              1      0           0     0         0      0             0  \n",
+       "4              0      1           0     0         0      0             0  \n",
+       "...          ...    ...         ...   ...       ...    ...           ...  \n",
+       "1768           0      1           0     0         0      0             0  \n",
+       "1769           0      0           1     0         0      0             0  \n",
+       "1774           1      0           0     0         0      0             0  \n",
+       "1778           0      0           0     0         0      1             0  \n",
+       "1780           0      0           0     0         0      1             0  \n",
+       "\n",
+       "[967 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "join_s_countries = s_dummies.join(countries_dummies)\n",
+    "website_dummy = join_s_countries.join(c_dummies)\n",
+    "website_dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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>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_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Microsoft</th>\n",
+       "      <th>Other</th>\n",
+       "      <th>nginx</th>\n",
+       "      <th>ISO-8859-1</th>\n",
+       "      <th>UTF-8</th>\n",
+       "      <th>iso-8859-1</th>\n",
+       "      <th>none</th>\n",
+       "      <th>us-ascii</th>\n",
+       "      <th>utf-8</th>\n",
+       "      <th>windows-1251</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>700</td>\n",
+       "      <td>1153</td>\n",
+       "      <td>832</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>16</td>\n",
+       "      <td>17</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1230</td>\n",
+       "      <td>1265</td>\n",
+       "      <td>1230</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16</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",
+       "      <td>1</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17</td>\n",
+       "      <td>31</td>\n",
+       "      <td>22</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3812</td>\n",
+       "      <td>18784</td>\n",
+       "      <td>4380</td>\n",
+       "      <td>39</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17</td>\n",
+       "      <td>57</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4278</td>\n",
+       "      <td>129889</td>\n",
+       "      <td>4586</td>\n",
+       "      <td>61</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1768</th>\n",
+       "      <td>160</td>\n",
+       "      <td>19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2402</td>\n",
+       "      <td>4491</td>\n",
+       "      <td>2900</td>\n",
+       "      <td>25</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1769</th>\n",
+       "      <td>161</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>582</td>\n",
+       "      <td>752</td>\n",
+       "      <td>582</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.0</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1774</th>\n",
+       "      <td>183</td>\n",
+       "      <td>22</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2062</td>\n",
+       "      <td>8161</td>\n",
+       "      <td>2742</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1778</th>\n",
+       "      <td>201</td>\n",
+       "      <td>83</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6631</td>\n",
+       "      <td>132181</td>\n",
+       "      <td>6945</td>\n",
+       "      <td>87</td>\n",
+       "      <td>4.0</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</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>249</td>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2314</td>\n",
+       "      <td>3039</td>\n",
+       "      <td>2776</td>\n",
+       "      <td>25</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>967 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      URL_LENGTH  TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  REMOTE_IPS  \\\n",
+       "0             16                          7                     0           2   \n",
+       "1             16                         17                     7           4   \n",
+       "2             16                          0                     0           0   \n",
+       "3             17                         31                    22           3   \n",
+       "4             17                         57                     2           5   \n",
+       "...          ...                        ...                   ...         ...   \n",
+       "1768         160                         19                     3           7   \n",
+       "1769         161                          7                     7           2   \n",
+       "1774         183                         22                     2           7   \n",
+       "1778         201                         83                     2           6   \n",
+       "1780         249                         19                     6          11   \n",
+       "\n",
+       "      APP_BYTES  SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  \\\n",
+       "0           700              1153               832            9   \n",
+       "1          1230              1265              1230           17   \n",
+       "2             0                 0                 0            0   \n",
+       "3          3812             18784              4380           39   \n",
+       "4          4278            129889              4586           61   \n",
+       "...         ...               ...               ...          ...   \n",
+       "1768       2402              4491              2900           25   \n",
+       "1769        582               752               582            7   \n",
+       "1774       2062              8161              2742           30   \n",
+       "1778       6631            132181              6945           87   \n",
+       "1780       2314              3039              2776           25   \n",
+       "\n",
+       "      DNS_QUERY_TIMES  Type  Microsoft  Other  nginx  ISO-8859-1  UTF-8  \\\n",
+       "0                 2.0     1          0      0      1           0      0   \n",
+       "1                 0.0     0          0      0      0           0      1   \n",
+       "2                 0.0     0          1      0      0           0      0   \n",
+       "3                 8.0     0          0      0      1           1      0   \n",
+       "4                 4.0     0          0      1      0           0      1   \n",
+       "...               ...   ...        ...    ...    ...         ...    ...   \n",
+       "1768              6.0     0          0      1      0           0      1   \n",
+       "1769              0.0     0          0      0      0           0      0   \n",
+       "1774              8.0     0          1      0      0           1      0   \n",
+       "1778              4.0     0          0      0      0           0      0   \n",
+       "1780              6.0     0          1      0      0           0      0   \n",
+       "\n",
+       "      iso-8859-1  none  us-ascii  utf-8  windows-1251  \n",
+       "0              1     0         0      0             0  \n",
+       "1              0     0         0      0             0  \n",
+       "2              0     0         1      0             0  \n",
+       "3              0     0         0      0             0  \n",
+       "4              0     0         0      0             0  \n",
+       "...          ...   ...       ...    ...           ...  \n",
+       "1768           0     0         0      0             0  \n",
+       "1769           1     0         0      0             0  \n",
+       "1774           0     0         0      0             0  \n",
+       "1778           0     0         0      1             0  \n",
+       "1780           0     0         0      1             0  \n",
+       "\n",
+       "[967 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "websites_clean3 = websites_clean2.drop(['SERVER', 'WHOIS_COUNTRY','CHARSET', 'URL'],  axis = 1)\n",
+    "websites_clean4 = websites_clean3.join(s_dummies)\n",
+    "websites_clean5 = websites_clean4.join(countries_dummies)\n",
+    "websites_CLEAN = websites_clean4.join(c_dummies)\n",
+    "websites_CLEAN"
+   ]
+  },
+  {
+   "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": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Your code here:\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Your code here:\n",
+    "X = websites_CLEAN.drop('Type', axis = 1)\n",
+    "y = websites_CLEAN['Type']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
+    "                                                    y, \n",
+    "                                                    test_size=0.20, \n",
+    "                                                    random_state=123)"
+   ]
+  },
+  {
+   "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": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here:\n",
+    "\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "lr = LogisticRegression()"
+   ]
+  },
+  {
+   "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": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/bribas/anaconda3/envs/clase/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: 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"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "lr.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "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": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[839,  22],\n",
+       "       [ 56,  50]])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, fbeta_score\n",
+    "\n",
+    "y_true = websites_CLEAN['Type']\n",
+    "y_pred = lr.predict(X)\n",
+    "\n",
+    "confusion_matrix(y_true, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9193381592554292"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_true, y_pred)"
+   ]
+  },
+  {
+   "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": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here:\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "neigh = KNeighborsClassifier(n_neighbors=3)"
+   ]
+  },
+  {
+   "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": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "neigh.fit(X,y)"
+   ]
+  },
+  {
+   "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": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "neigh = KNeighborsClassifier(n_neighbors=5)\n",
+    "neigh.fit(X, y)"
+   ]
+  },
+  {
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "clase",
+   "language": "python",
+   "name": "clase"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}