diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index e18b75d..042a15b 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,8 @@
     "%matplotlib inline\n",
     "\n",
     "import numpy as np\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import seaborn as sns"
    ]
   },
   {
@@ -37,11 +38,400 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>URL</th>\n",
+       "      <th>URL_LENGTH</th>\n",
+       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
+       "      <th>CHARSET</th>\n",
+       "      <th>SERVER</th>\n",
+       "      <th>CONTENT_LENGTH</th>\n",
+       "      <th>WHOIS_COUNTRY</th>\n",
+       "      <th>WHOIS_STATEPRO</th>\n",
+       "      <th>WHOIS_REGDATE</th>\n",
+       "      <th>WHOIS_UPDATED_DATE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>REMOTE_APP_BYTES</th>\n",
+       "      <th>APP_PACKETS</th>\n",
+       "      <th>DNS_QUERY_TIMES</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <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>263.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/10/2015 18:21</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>700</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1153</td>\n",
+       "      <td>832</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B0_2314</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>Apache/2.4.10</td>\n",
+       "      <td>15087.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1230</td>\n",
+       "      <td>17</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1265</td>\n",
+       "      <td>1230</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>B0_911</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>us-ascii</td>\n",
+       "      <td>Microsoft-HTTPAPI/2.0</td>\n",
+       "      <td>324.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B0_113</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "      <td>ISO-8859-1</td>\n",
+       "      <td>nginx</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>AK</td>\n",
+       "      <td>7/10/1997 4:00</td>\n",
+       "      <td>12/09/2013 0:45</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3812</td>\n",
+       "      <td>39</td>\n",
+       "      <td>37</td>\n",
+       "      <td>18784</td>\n",
+       "      <td>4380</td>\n",
+       "      <td>39</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B0_403</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "      <td>UTF-8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>124140.0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>TX</td>\n",
+       "      <td>12/05/1996 0:00</td>\n",
+       "      <td>11/04/2017 0:00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4278</td>\n",
+       "      <td>61</td>\n",
+       "      <td>62</td>\n",
+       "      <td>129889</td>\n",
+       "      <td>4586</td>\n",
+       "      <td>61</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>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>NaN</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>...</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>NaN</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>...</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>8904.0</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>...</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>NaN</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>...</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>24435.0</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>...</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 × 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",
+       "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  CONTENT_LENGTH WHOIS_COUNTRY WHOIS_STATEPRO  \\\n",
+       "0                      nginx           263.0           NaN            NaN   \n",
+       "1              Apache/2.4.10         15087.0           NaN            NaN   \n",
+       "2      Microsoft-HTTPAPI/2.0           324.0           NaN            NaN   \n",
+       "3                      nginx           162.0            US             AK   \n",
+       "4                        NaN        124140.0            US             TX   \n",
+       "...                      ...             ...           ...            ...   \n",
+       "1776                  Apache             NaN            ES      Barcelona   \n",
+       "1777                  Apache             NaN            ES      Barcelona   \n",
+       "1778  Apache/2.2.16 (Debian)          8904.0            US             FL   \n",
+       "1779        cloudflare-nginx             NaN            US             CA   \n",
+       "1780       Microsoft-IIS/8.5         24435.0            US      Wisconsin   \n",
+       "\n",
+       "         WHOIS_REGDATE WHOIS_UPDATED_DATE  ...  DIST_REMOTE_TCP_PORT  \\\n",
+       "0     10/10/2015 18:21                NaN  ...                     0   \n",
+       "1                  NaN                NaN  ...                     7   \n",
+       "2                  NaN                NaN  ...                     0   \n",
+       "3       7/10/1997 4:00    12/09/2013 0:45  ...                    22   \n",
+       "4      12/05/1996 0:00    11/04/2017 0:00  ...                     2   \n",
+       "...                ...                ...  ...                   ...   \n",
+       "1776   17/09/2008 0:00     2/09/2016 0:00  ...                     0   \n",
+       "1777   17/09/2008 0:00     2/09/2016 0:00  ...                     0   \n",
+       "1778   15/02/1999 0:00    15/07/2015 0:00  ...                     2   \n",
+       "1779    1/04/1998 0:00     9/12/2016 0:00  ...                     0   \n",
+       "1780   14/11/2008 0:00    20/11/2013 0:00  ...                     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 21 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "websites = pd.read_csv('../website.csv')"
+    "websites = pd.read_csv('../website.csv')\n",
+    "websites"
    ]
   },
   {
@@ -65,11 +455,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "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": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "websites.dtypes"
    ]
   },
   {
@@ -102,11 +525,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['URL_LENGTH',\n",
+       " 'NUMBER_SPECIAL_CHARACTERS',\n",
+       " 'CONTENT_LENGTH',\n",
+       " 'TCP_CONVERSATION_EXCHANGE',\n",
+       " 'DIST_REMOTE_TCP_PORT',\n",
+       " 'REMOTE_IPS',\n",
+       " 'APP_BYTES',\n",
+       " 'SOURCE_APP_PACKETS',\n",
+       " 'REMOTE_APP_PACKETS',\n",
+       " 'SOURCE_APP_BYTES',\n",
+       " 'REMOTE_APP_BYTES',\n",
+       " 'APP_PACKETS',\n",
+       " 'DNS_QUERY_TIMES',\n",
+       " 'Type']"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "numeric_col = list(websites.dtypes[[websites.dtypes == \"int64\"][0] | [websites.dtypes == \"float64\"][0]].index)\n",
+    "df = websites.loc[:,numeric_col]\n",
+    "\n",
+    "list((websites.dtypes[websites.dtypes != \"object\"]).index)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "sns.heatmap(df.corr());"
    ]
   },
   {
@@ -115,7 +586,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your comment here"
+    "# Your comment here\n",
+    "\"\"\"\n",
+    "source_app_packets,source_app_packets, app_packets and tcp_conversation_exchange have \n",
+    "high correlations with more than on variable.\n",
+    "\n",
+    "\"\"\""
    ]
   },
   {
@@ -133,31 +609,231 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 240,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['URL_LENGTH',\n",
+       " 'NUMBER_SPECIAL_CHARACTERS',\n",
+       " 'TCP_CONVERSATION_EXCHANGE',\n",
+       " 'APP_BYTES',\n",
+       " 'SOURCE_APP_PACKETS',\n",
+       " 'REMOTE_APP_PACKETS',\n",
+       " 'REMOTE_APP_BYTES',\n",
+       " 'APP_PACKETS']"
+      ]
+     },
+     "execution_count": 240,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "to_drop = list(df.columns[(df.corr() > 0.90).sum() > 1])\n",
+    "to_drop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 465,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(df.drop(to_drop[1], axis=1).corr());"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 457,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(False, 0),\n",
+       " (False, 1),\n",
+       " (False, 2),\n",
+       " (False, 3),\n",
+       " (False, 4),\n",
+       " (False, 5),\n",
+       " (False, 6),\n",
+       " (False, 7),\n",
+       " (False, 1, 1),\n",
+       " (False, 2, 1),\n",
+       " (False, 3, 1),\n",
+       " (False, 4, 1),\n",
+       " (False, 5, 1),\n",
+       " (False, 6, 1),\n",
+       " (False, 7, 1),\n",
+       " (False, 2, 2),\n",
+       " (False, 3, 2),\n",
+       " (False, 4, 2),\n",
+       " (False, 5, 2),\n",
+       " (False, 6, 2),\n",
+       " (False, 7, 2),\n",
+       " (False, 3, 3),\n",
+       " (False, 4, 3),\n",
+       " (False, 5, 3),\n",
+       " (False, 6, 3),\n",
+       " (False, 7, 3),\n",
+       " (False, 4, 4),\n",
+       " (True, 5, 4),\n",
+       " (False, 6, 4),\n",
+       " (False, 7, 4),\n",
+       " (True, 5, 5),\n",
+       " (True, 6, 5),\n",
+       " (False, 7, 5),\n",
+       " (True, 6, 6),\n",
+       " (True, 7, 6),\n",
+       " (True, 7, 7)]"
+      ]
+     },
+     "execution_count": 457,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Lets check which columns to drop in order to get a correlation matrix without collineallity\n",
+    "# The idea is to get the combinations of columns to drop and try to drop the least amount of columns\n",
+    "\n",
+    "lst = []\n",
+    "\n",
+    "for i in range(len(to_drop)):\n",
+    "    lst.append((((df.drop(to_drop[i], axis=1).corr() > 0.90).sum() != 1).sum() == 0, i))\n",
+    "    \n",
+    "    \n",
+    "for i in range(1,len(to_drop)):\n",
+    "    lst.append((((df.drop(to_drop[(i-1):(i+1)], axis=1).corr() > 0.90).sum() != 1).sum() == 0, i,1))\n",
+    "\n",
+    "    \n",
+    "for i in range(2,len(to_drop)):\n",
+    "    lst.append((((df.drop(to_drop[(i-2):(i+1)], axis=1).corr() > 0.90).sum() != 1).sum() == 0, i,2))\n",
+    "   \n",
+    "   \n",
+    "for i in range(3,len(to_drop)):\n",
+    "    lst.append((((df.drop(to_drop[(i-3):(i+1)], axis=1).corr() > 0.90).sum() != 1).sum() == 0, i,3))\n",
+    "    \n",
+    "\n",
+    "for i in range(4,len(to_drop)):\n",
+    "    lst.append((((df.drop(to_drop[(i-4):(i+1)], axis=1).corr() > 0.90).sum() != 1).sum() == 0, i,4))\n",
+    "    \n",
+    "\n",
+    "for i in range(5,len(to_drop)):\n",
+    "    lst.append((((df.drop(to_drop[(i-5):(i+1)], axis=1).corr() > 0.90).sum() != 1).sum() == 0, i,5))\n",
+    "    \n",
+    "\n",
+    "for i in range(6,len(to_drop)):\n",
+    "    lst.append((((df.drop(to_drop[(i-6):(i+1)], axis=1).corr() > 0.90).sum() != 1).sum() == 0, i,6))\n",
+    " \n",
+    "for i in range(7,len(to_drop)):\n",
+    "    lst.append((((df.drop(to_drop[(i-7):(i+1)], axis=1).corr() > 0.90).sum() != 1).sum() == 0, i,7))\n",
+    "    \n",
+    "    \n",
+    "lst"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 477,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(5, 4), (5, 5), (6, 5), (6, 6), (7, 6), (7, 7)]"
+      ]
+     },
+     "execution_count": 477,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drop_lst = []    \n",
+    "for i in range(len(lst)):\n",
+    "    if lst[i][0]:\n",
+    "        drop_lst.append(lst[i][1:])\n",
+    "    else:\n",
+    "        pass\n",
+    "drop_lst"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 478,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "cols_to_drop = []\n",
+    "for i in range(len(drop_lst)):\n",
+    "    cols_to_drop.append(to_drop[(drop_lst[i][0]-drop_lst[i][1]):(drop_lst[i][0]+1)])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 479,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your comment here"
+    "col_to_drop = cols_to_drop[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 486,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Print heatmap again\n"
+    "# Your comment here\n",
+    "# With the algorithm from above we can get the least amount of columns to drop \n",
+    "# so we can get a correlation matrix without collineality"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 498,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Print heatmap again\n",
+    "sns.heatmap(df.drop(col_to_drop, axis=1).corr());"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -169,11 +845,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 493,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL_LENGTH                0\n",
+       "CONTENT_LENGTH          812\n",
+       "DIST_REMOTE_TCP_PORT      0\n",
+       "REMOTE_IPS                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": 493,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "df1 = df.drop(col_to_drop, axis=1)\n",
+    "df1.isna().sum()"
    ]
   },
   {
@@ -187,20 +885,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 494,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "na_columns = list(df1.columns[df1.isna().sum() > 0])\n",
+    "df1.drop(na_columns, axis=1, inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 495,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your comment here"
+    "# Your comment here\n",
+    "# using .drop() to the columns that has at least 1 NA we can solve the problem"
    ]
   },
   {
@@ -214,11 +915,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 496,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "URL_LENGTH              0\n",
+       "DIST_REMOTE_TCP_PORT    0\n",
+       "REMOTE_IPS              0\n",
+       "SOURCE_APP_BYTES        0\n",
+       "REMOTE_APP_BYTES        0\n",
+       "APP_PACKETS             0\n",
+       "Type                    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 496,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Examine missing values in each column\n"
+    "# Examine missing values in each column\n",
+    "df1.isna().sum()"
    ]
   },
   {
@@ -256,11 +976,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 497,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "def change_code(x):\n",
+    "    if x == 'Cyprus':\n",
+    "        return \"CY\"\n",
+    "    elif x == 'us':\n",
+    "        return \"US\"\n",
+    "    elif x == 'se':\n",
+    "        return \"SE\"\n",
+    "    elif x == \"[u'GB'; u'UK']\":\n",
+    "        return \"UK\"\n",
+    "    elif x == \"United Kingdom\":\n",
+    "        return \"UK\"\n",
+    "    else:\n",
+    "        return x\n",
+    "    \n",
+    "\n",
+    "websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].apply(change_code)"
    ]
   },
   {
@@ -278,11 +1014,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 386,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['US', 'OTHER', 'CA', 'ES', 'AU', 'PA', 'GB', 'UK', 'JP', 'IN']"
+      ]
+     },
+     "execution_count": 386,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "country_count = websites.WHOIS_COUNTRY.value_counts()\n",
+    "list(country_count.head(10).index)"
    ]
   },
   {
@@ -294,13 +1043,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 332,
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "top_countries = list(country_count.head(10).index)\n",
+    " \n",
+    "def country_to_other(x):\n",
+    "    if x in top_countries:\n",
+    "        return x\n",
+    "    else:\n",
+    "        return \"OTHER\"\n",
+    "    \n",
+    "websites[\"WHOIS_COUNTRY\"] = websites[\"WHOIS_COUNTRY\"].apply(country_to_other)"
    ]
   },
   {
@@ -316,11 +1074,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 335,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "websites.drop(['WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE'],axis=1, inplace=True)"
    ]
   },
   {
@@ -334,11 +1093,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 336,
+   "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",
+       "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": 336,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "websites.dtypes"
    ]
   },
   {
@@ -350,11 +1139,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 338,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "websites.drop(\"URL\", axis=1, inplace=True)"
    ]
   },
   {
@@ -366,11 +1156,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 341,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['iso-8859-1' 'UTF-8' 'us-ascii' 'ISO-8859-1' 'utf-8' nan 'windows-1251'\n",
+      " 'ISO-8859' 'windows-1252']\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "print(websites.CHARSET.unique())"
    ]
   },
   {
@@ -384,11 +1184,139 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 350,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['nginx' 'Apache/2.4.10' 'Microsoft-HTTPAPI/2.0' nan 'Apache/2'\n",
+      " 'nginx/1.10.1' 'Apache' 'Apache/2.2.15 (Red Hat)'\n",
+      " 'Apache/2.4.23 (Unix) OpenSSL/1.0.1e-fips mod_bwlimited/1.4'\n",
+      " 'openresty/1.11.2.1' 'Apache/2.2.22' 'Apache/2.4.7 (Ubuntu)'\n",
+      " 'nginx/1.12.0'\n",
+      " 'Apache/2.4.12 (Unix) OpenSSL/1.0.1e-fips mod_bwlimited/1.4'\n",
+      " 'Oracle-iPlanet-Web-Server/7.0' 'cloudflare-nginx' 'nginx/1.6.2'\n",
+      " 'openresty' 'Heptu web server' 'Pepyaka/1.11.3' 'nginx/1.8.0'\n",
+      " 'nginx/1.10.1 + Phusion Passenger 5.0.30' 'Apache/2.2.29 (Amazon)'\n",
+      " 'Microsoft-IIS/7.5' 'LiteSpeed'\n",
+      " 'Apache/2.4.25 (cPanel) OpenSSL/1.0.1e-fips mod_bwlimited/1.4' 'tsa_c'\n",
+      " 'Apache/2.2.0 (Fedora)' 'Apache/2.2.22 (Debian)' 'Apache/2.2.15 (CentOS)'\n",
+      " 'Apache/2.4.25' 'Apache/2.4.25 (Amazon) PHP/7.0.14' 'GSE'\n",
+      " 'Apache/2.4.23 (Unix) OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4'\n",
+      " 'Apache/2.4.25 (Amazon) OpenSSL/1.0.1k-fips' 'Apache/2.2.22 (Ubuntu)'\n",
+      " 'Tengine'\n",
+      " 'Apache/2.4.18 (Unix) OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4'\n",
+      " 'Apache/2.4.10 (Debian)' 'Apache/2.4.6 (CentOS) PHP/5.6.8'\n",
+      " 'Sun-ONE-Web-Server/6.1'\n",
+      " 'Apache/2.4.18 (Unix) OpenSSL/1.0.2e Communique/4.1.10' 'AmazonS3'\n",
+      " 'Apache/1.3.37 (Unix) mod_perl/1.29 mod_ssl/2.8.28 OpenSSL/0.9.7e-p1'\n",
+      " 'ATS' 'Apache/2.2.27 (CentOS)'\n",
+      " 'Apache/2.2.29 (Unix) mod_ssl/2.2.29 OpenSSL/1.0.1e-fips DAV/2 mod_bwlimited/1.4'\n",
+      " 'CherryPy/3.6.0' 'Server' 'KHL'\n",
+      " 'Apache/2.4.6 (CentOS) OpenSSL/1.0.1e-fips mod_fcgid/2.3.9 PHP/5.4.16 mod_jk/1.2.40'\n",
+      " 'Apache/2.2.3 (CentOS)' 'Apache/2.4'\n",
+      " 'Apache/1.3.27 (Unix)  (Red-Hat/Linux) mod_perl/1.26 PHP/4.3.3 FrontPage/5.0.2 mod_ssl/2.8.12 OpenSSL/0.9.6b'\n",
+      " 'mw2114.codfw.wmnet'\n",
+      " 'Apache/2.2.31 (Unix) mod_ssl/2.2.31 OpenSSL/1.0.1e-fips mod_bwlimited/1.4 mod_perl/2.0.8 Perl/v5.10.1'\n",
+      " 'Apache/1.3.34 (Unix) PHP/4.4.4' 'Apache/2.2.31 (Amazon)'\n",
+      " 'Jetty(9.0.z-SNAPSHOT)' 'Apache/2.2.31 (CentOS)' 'Apache/2.4.12 (Ubuntu)'\n",
+      " 'HTTPDaemon'\n",
+      " 'Apache/2.2.29 (Unix) mod_ssl/2.2.29 OpenSSL/1.0.1e-fips mod_bwlimited/1.4'\n",
+      " 'MediaFire' 'DOSarrest' 'mw2232.codfw.wmnet' 'Sucuri/Cloudproxy'\n",
+      " 'Apache/2.4.23 (Unix)' 'nginx/0.7.65' 'mw2260.codfw.wmnet'\n",
+      " 'Apache/2.2.32' 'mw2239.codfw.wmnet' 'DPS/1.1.8'\n",
+      " 'Apache/2.0.52 (Red Hat)'\n",
+      " 'Apache/2.2.25 (Unix) mod_ssl/2.2.25 OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4'\n",
+      " 'Apache/1.3.31 (Unix) PHP/4.3.9 mod_perl/1.29 rus/PL30.20'\n",
+      " 'Apache/2.2.13 (Unix) mod_ssl/2.2.13 OpenSSL/0.9.8e-fips-rhel5 mod_auth_passthrough/2.1 mod_bwlimited/1.4 PHP/5.2.10'\n",
+      " 'nginx/1.1.19' 'ATS/5.3.0' 'Apache/2.2.3 (Red Hat)' 'nginx/1.4.3'\n",
+      " 'Apache/2.2.29 (Unix) mod_ssl/2.2.29 OpenSSL/1.0.1e-fips mod_bwlimited/1.4 PHP/5.4.35'\n",
+      " 'Apache/2.2.14 (FreeBSD) mod_ssl/2.2.14 OpenSSL/0.9.8y DAV/2 PHP/5.2.12 with Suhosin-Patch'\n",
+      " 'Apache/2.2.14 (Unix) mod_ssl/2.2.14 OpenSSL/0.9.8e-fips-rhel5'\n",
+      " 'Apache/1.3.39 (Unix) PHP/5.2.5 mod_auth_passthrough/1.8 mod_bwlimited/1.4 mod_log_bytes/1.2 mod_gzip/1.3.26.1a FrontPage/5.0.2.2635 DAV/1.0.3 mod_ssl/2.8.30 OpenSSL/0.9.7a'\n",
+      " 'SSWS' 'Microsoft-IIS/8.0' 'Apache/2.4.18 (Ubuntu)'\n",
+      " 'Apache/2.4.6 (CentOS) OpenSSL/1.0.1e-fips PHP/5.4.16 mod_apreq2-20090110/2.8.0 mod_perl/2.0.10 Perl/v5.24.1'\n",
+      " 'Apache/2.2.20 (Unix)' 'YouTubeFrontEnd' 'nginx/1.11.3' 'nginx/1.11.2'\n",
+      " 'nginx/1.10.0 (Ubuntu)' 'nginx/1.8.1' 'nginx/1.11.10'\n",
+      " 'Squeegit/1.2.5 (3_sir)'\n",
+      " 'Virtuoso/07.20.3217 (Linux) i686-generic-linux-glibc212-64  VDB'\n",
+      " 'Apache-Coyote/1.1' 'Yippee-Ki-Yay' 'mw2165.codfw.wmnet'\n",
+      " 'mw2192.codfw.wmnet' 'Apache/2.2.23 (Amazon)' 'nginx/1.4.6 (Ubuntu)'\n",
+      " 'nginx + Phusion Passenger' 'Proxy Pandeiro UOL' 'mw2231.codfw.wmnet'\n",
+      " 'openresty/1.11.2.2' 'mw2109.codfw.wmnet' 'nginx/0.8.54' 'Apache/2.4.6'\n",
+      " 'mw2225.codfw.wmnet' 'Apache/1.3.27 (Unix) PHP/4.4.1'\n",
+      " 'mw2236.codfw.wmnet' 'mw2101.codfw.wmnet' 'Varnish' 'Resin/3.1.8'\n",
+      " 'mw2164.codfw.wmnet' 'Microsoft-IIS/8.5' 'mw2242.codfw.wmnet'\n",
+      " 'Apache/2.4.6 (CentOS) OpenSSL/1.0.1e-fips PHP/5.5.38'\n",
+      " 'mw2175.codfw.wmnet' 'mw2107.codfw.wmnet' 'mw2190.codfw.wmnet'\n",
+      " 'Apache/2.4.6 (CentOS)' 'nginx/1.13.0' 'barista/5.1.3'\n",
+      " 'mw2103.codfw.wmnet' 'Apache/2.4.25 (Debian)' 'ECD (fll/0790)'\n",
+      " 'Pagely Gateway/1.5.1' 'nginx/1.10.3'\n",
+      " 'Apache/2.4.25 (FreeBSD) OpenSSL/1.0.1s-freebsd PHP/5.6.30'\n",
+      " 'mw2097.codfw.wmnet' 'mw2233.codfw.wmnet' 'fbs' 'mw2199.codfw.wmnet'\n",
+      " 'mw2255.codfw.wmnet' 'mw2228.codfw.wmnet'\n",
+      " 'Apache/2.2.31 (Unix) mod_ssl/2.2.31 OpenSSL/1.0.1e-fips mod_bwlimited/1.4 mod_fcgid/2.3.9'\n",
+      " 'gunicorn/19.7.1'\n",
+      " 'Apache/2.2.31 (Unix) mod_ssl/2.2.31 OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4'\n",
+      " 'Apache/2.4.6 (CentOS) OpenSSL/1.0.1e-fips PHP/5.4.16'\n",
+      " 'mw2241.codfw.wmnet'\n",
+      " 'Apache/1.3.33 (Unix) mod_ssl/2.8.24 OpenSSL/0.9.7e-p1 PHP/4.4.8'\n",
+      " 'lighttpd' 'mw2230.codfw.wmnet'\n",
+      " 'Apache/2.4.6 (CentOS) OpenSSL/1.0.1e-fips' 'AkamaiGHost'\n",
+      " 'mw2240.codfw.wmnet' 'nginx/1.10.2' 'PWS/8.2.0.7' 'nginx/1.2.1' 'nxfps'\n",
+      " 'Apache/2.2.16 (Unix) mod_ssl/2.2.16 OpenSSL/0.9.8e-fips-rhel5 mod_auth_passthrough/2.1 mod_bwlimited/1.4'\n",
+      " 'Play' 'mw2185.codfw.wmnet' 'Apache/2.4.10 (Unix) OpenSSL/1.0.1k'\n",
+      " 'Apache/Not telling (Unix) AuthTDS/1.1' 'Apache/2.2.11 (Unix) PHP/5.2.6'\n",
+      " 'Scratch Web Server' 'marrakesh 1.12.2' 'nginx/0.8.35'\n",
+      " 'mw2182.codfw.wmnet' 'squid/3.3.8' 'nginx/1.10.0' 'Nginx (OpenBSD)'\n",
+      " 'Zope/(2.13.16; python 2.6.8; linux2) ZServer/1.1'\n",
+      " 'Apache/2.2.26 (Unix) mod_ssl/2.2.26 OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4 PHP/5.4.26'\n",
+      " 'Apache/2.2.21 (Unix) mod_ssl/2.2.21 OpenSSL/0.9.8e-fips-rhel5 PHP/5.3.10'\n",
+      " 'Apache/2.2.27 (Unix) OpenAM Web Agent/4.0.1-1 mod_ssl/2.2.27 OpenSSL/1.0.1p PHP/5.3.28'\n",
+      " 'mw2104.codfw.wmnet' '.V01 Apache' 'mw2110.codfw.wmnet'\n",
+      " 'Apache/2.4.6 (Unix) mod_jk/1.2.37 PHP/5.5.1 OpenSSL/1.0.1g mod_fcgid/2.3.9'\n",
+      " 'mw2176.codfw.wmnet' 'mw2187.codfw.wmnet' 'mw2106.codfw.wmnet'\n",
+      " 'Microsoft-IIS/7.0'\n",
+      " 'Apache/1.3.42 Ben-SSL/1.60 (Unix) mod_gzip/1.3.26.1a mod_fastcgi/2.4.6 mod_throttle/3.1.2 Chili!Soft-ASP/3.6.2 FrontPage/5.0.2.2635 mod_perl/1.31 PHP/4.4.9'\n",
+      " 'Aeria Games & Entertainment' 'nginx/1.6.3 + Phusion Passenger'\n",
+      " 'Apache/2.4.10 (Debian) PHP/5.6.30-0+deb8u1 mod_perl/2.0.9dev Perl/v5.20.2'\n",
+      " 'mw2173.codfw.wmnet'\n",
+      " 'Apache/2.4.6 (Red Hat Enterprise Linux) OpenSSL/1.0.1e-fips mod_fcgid/2.3.9 Communique/4.2.0'\n",
+      " 'Apache/2.2.15 (CentOS) DAV/2 mod_ssl/2.2.15 OpenSSL/1.0.1e-fips PHP/5.3.3'\n",
+      " 'Apache/2.4.6 (CentOS) OpenSSL/1.0.1e-fips PHP/7.0.14'\n",
+      " 'mw2198.codfw.wmnet' 'mw2172.codfw.wmnet' 'nginx/1.2.6'\n",
+      " 'Apache/2.4.6 (Unix) mod_jk/1.2.37'\n",
+      " 'Apache/2.4.25 (Unix) OpenSSL/1.0.1e-fips mod_bwlimited/1.4'\n",
+      " 'nginx/1.4.4' 'Cowboy' 'mw2113.codfw.wmnet'\n",
+      " 'Apache/2.2.14 (Unix) mod_ssl/2.2.14 OpenSSL/0.9.8a'\n",
+      " 'Apache/2.4.10 (Ubuntu)' 'mw2224.codfw.wmnet' 'mw2171.codfw.wmnet'\n",
+      " 'mw2257.codfw.wmnet' 'mw2226.codfw.wmnet' 'DMS/1.0.42' 'nginx/1.6.3'\n",
+      " 'Application-Server' 'Apache/2.4.6 (CentOS) mod_fcgid/2.3.9 PHP/5.6.30'\n",
+      " 'mw2177.codfw.wmnet' 'lighttpd/1.4.28' 'mw2197.codfw.wmnet'\n",
+      " 'Apache/2.2.31 (FreeBSD) PHP/5.4.15 mod_ssl/2.2.31 OpenSSL/1.0.2d DAV/2'\n",
+      " 'Apache/2.2.26 (Unix) mod_ssl/2.2.26 OpenSSL/1.0.1e-fips DAV/2 mod_bwlimited/1.4'\n",
+      " 'Apache/2.2.24 (Unix) DAV/2 PHP/5.3.26 mod_ssl/2.2.24 OpenSSL/0.9.8y'\n",
+      " 'mw2178.codfw.wmnet' '294' 'Microsoft-IIS/6.0' 'nginx/1.7.4'\n",
+      " 'Apache/2.2.22 (Debian) mod_python/3.3.1 Python/2.7.3 mod_ssl/2.2.22 OpenSSL/1.0.1t'\n",
+      " 'Apache/2.4.16 (Ubuntu)' 'www.lexisnexis.com  9999' 'nginx/0.8.38'\n",
+      " 'mw2238.codfw.wmnet' 'Pizza/pepperoni' 'XXXXXXXXXXXXXXXXXXXXXX' 'MI'\n",
+      " 'Roxen/5.4.98-r2'\n",
+      " 'Apache/2.2.31 (Unix) mod_ssl/2.2.31 OpenSSL/1.0.1e-fips mod_bwlimited/1.4'\n",
+      " 'nginx/1.9.13' 'mw2180.codfw.wmnet' 'Apache/2.2.14 (Ubuntu)'\n",
+      " 'ebay server' 'nginx/0.8.55' 'Apache/2.2.10 (Linux/SUSE)' 'nginx/1.7.12'\n",
+      " 'Apache/2.0.63 (Unix) mod_ssl/2.0.63 OpenSSL/0.9.8e-fips-rhel5 mod_auth_passthrough/2.1 mod_bwlimited/1.4 PHP/5.3.6'\n",
+      " 'Boston.com Frontend' 'My Arse' 'IdeaWebServer/v0.80'\n",
+      " 'Apache/2.4.17 (Unix) OpenSSL/1.0.1e-fips PHP/5.6.19'\n",
+      " 'Microsoft-IIS/7.5; litigation_essentials.lexisnexis.com  9999'\n",
+      " 'Apache/2.2.16 (Debian)']\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "print(websites.SERVER.unique())"
    ]
   },
   {
@@ -404,7 +1332,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your comment here\n"
+    "# Your comment here\n",
+    "# Most of the values starts with Microsoft, apache or ngix"
    ]
   },
   {
@@ -418,22 +1347,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 475,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['nginx', 'Apache', 'Microsoft', 'OTHER'], dtype=object)"
+      ]
+     },
+     "execution_count": 475,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "def server_string(x):\n",
+    "    if \"Apache\" in str(x):\n",
+    "        return \"Apache\"\n",
+    "    elif \"Microsoft\" in str(x):\n",
+    "        return \"Microsoft\"\n",
+    "    elif \"nginx\" in str(x):\n",
+    "        return \"nginx\"\n",
+    "    else:\n",
+    "        return \"OTHER\"\n",
+    "    \n",
+    "\n",
+    "websites[\"SERVER\"] = websites[\"SERVER\"].apply(server_string)\n",
+    "websites[\"SERVER\"].unique()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 474,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SERVER\n",
+       "Apache       643\n",
+       "OTHER        505\n",
+       "nginx        435\n",
+       "Microsoft    198\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 474,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Count `SERVER` value counts here\n"
+    "# Count `SERVER` value counts here\n",
+    "websites[\"SERVER\"].value_counts()"
    ]
   },
   {
@@ -445,11 +1415,415 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 499,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>URL_LENGTH</th>\n",
+       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
+       "      <th>CONTENT_LENGTH</th>\n",
+       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
+       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
+       "      <th>REMOTE_IPS</th>\n",
+       "      <th>APP_BYTES</th>\n",
+       "      <th>SOURCE_APP_PACKETS</th>\n",
+       "      <th>REMOTE_APP_PACKETS</th>\n",
+       "      <th>SOURCE_APP_BYTES</th>\n",
+       "      <th>...</th>\n",
+       "      <th>WHOIS_COUNTRY_CA</th>\n",
+       "      <th>WHOIS_COUNTRY_CN</th>\n",
+       "      <th>WHOIS_COUNTRY_ES</th>\n",
+       "      <th>WHOIS_COUNTRY_GB</th>\n",
+       "      <th>WHOIS_COUNTRY_IN</th>\n",
+       "      <th>WHOIS_COUNTRY_JP</th>\n",
+       "      <th>WHOIS_COUNTRY_OTHER</th>\n",
+       "      <th>WHOIS_COUNTRY_PA</th>\n",
+       "      <th>WHOIS_COUNTRY_UK</th>\n",
+       "      <th>WHOIS_COUNTRY_US</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16</td>\n",
+       "      <td>7</td>\n",
+       "      <td>263.0</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>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>15087.0</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>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>324.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>22</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3812</td>\n",
+       "      <td>39</td>\n",
+       "      <td>37</td>\n",
+       "      <td>18784</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "      <td>124140.0</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>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1776</th>\n",
+       "      <td>194</td>\n",
+       "      <td>16</td>\n",
+       "      <td>NaN</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>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1777</th>\n",
+       "      <td>198</td>\n",
+       "      <td>17</td>\n",
+       "      <td>NaN</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>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1778</th>\n",
+       "      <td>201</td>\n",
+       "      <td>34</td>\n",
+       "      <td>8904.0</td>\n",
+       "      <td>83</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6631</td>\n",
+       "      <td>87</td>\n",
+       "      <td>89</td>\n",
+       "      <td>132181</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1779</th>\n",
+       "      <td>234</td>\n",
+       "      <td>34</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>249</td>\n",
+       "      <td>40</td>\n",
+       "      <td>24435.0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2314</td>\n",
+       "      <td>25</td>\n",
+       "      <td>28</td>\n",
+       "      <td>3039</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1781 rows × 34 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      URL_LENGTH  NUMBER_SPECIAL_CHARACTERS  CONTENT_LENGTH  \\\n",
+       "0             16                          7           263.0   \n",
+       "1             16                          6         15087.0   \n",
+       "2             16                          6           324.0   \n",
+       "3             17                          6           162.0   \n",
+       "4             17                          6        124140.0   \n",
+       "...          ...                        ...             ...   \n",
+       "1776         194                         16             NaN   \n",
+       "1777         198                         17             NaN   \n",
+       "1778         201                         34          8904.0   \n",
+       "1779         234                         34             NaN   \n",
+       "1780         249                         40         24435.0   \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",
+       "1776                          0                     0           0          0   \n",
+       "1777                          0                     0           0          0   \n",
+       "1778                         83                     2           6       6631   \n",
+       "1779                          0                     0           0          0   \n",
+       "1780                         19                     6          11       2314   \n",
+       "\n",
+       "      SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  SOURCE_APP_BYTES  ...  \\\n",
+       "0                      9                  10              1153  ...   \n",
+       "1                     17                  19              1265  ...   \n",
+       "2                      0                   0                 0  ...   \n",
+       "3                     39                  37             18784  ...   \n",
+       "4                     61                  62            129889  ...   \n",
+       "...                  ...                 ...               ...  ...   \n",
+       "1776                   0                   3               186  ...   \n",
+       "1777                   0                   2               124  ...   \n",
+       "1778                  87                  89            132181  ...   \n",
+       "1779                   0                   0                 0  ...   \n",
+       "1780                  25                  28              3039  ...   \n",
+       "\n",
+       "      WHOIS_COUNTRY_CA  WHOIS_COUNTRY_CN  WHOIS_COUNTRY_ES  WHOIS_COUNTRY_GB  \\\n",
+       "0                False             False             False             False   \n",
+       "1                False             False             False             False   \n",
+       "2                False             False             False             False   \n",
+       "3                False             False             False             False   \n",
+       "4                False             False             False             False   \n",
+       "...                ...               ...               ...               ...   \n",
+       "1776             False             False              True             False   \n",
+       "1777             False             False              True             False   \n",
+       "1778             False             False             False             False   \n",
+       "1779             False             False             False             False   \n",
+       "1780             False             False             False             False   \n",
+       "\n",
+       "      WHOIS_COUNTRY_IN  WHOIS_COUNTRY_JP  WHOIS_COUNTRY_OTHER  \\\n",
+       "0                False             False                 True   \n",
+       "1                False             False                 True   \n",
+       "2                False             False                 True   \n",
+       "3                False             False                False   \n",
+       "4                False             False                False   \n",
+       "...                ...               ...                  ...   \n",
+       "1776             False             False                False   \n",
+       "1777             False             False                False   \n",
+       "1778             False             False                False   \n",
+       "1779             False             False                False   \n",
+       "1780             False             False                False   \n",
+       "\n",
+       "      WHOIS_COUNTRY_PA  WHOIS_COUNTRY_UK  WHOIS_COUNTRY_US  \n",
+       "0                False             False             False  \n",
+       "1                False             False             False  \n",
+       "2                False             False             False  \n",
+       "3                False             False              True  \n",
+       "4                False             False              True  \n",
+       "...                ...               ...               ...  \n",
+       "1776             False             False             False  \n",
+       "1777             False             False             False  \n",
+       "1778             False             False              True  \n",
+       "1779             False             False              True  \n",
+       "1780             False             False              True  \n",
+       "\n",
+       "[1781 rows x 34 columns]"
+      ]
+     },
+     "execution_count": 499,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "\n",
+    "website_dummy = pd.get_dummies(websites,drop_first=True)\n",
+    "website_dummy"
    ]
   },
   {
@@ -461,11 +1835,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 415,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "# Your code here\n",
+    "website_dummy.dtypes\n",
+    "website_dummy.dropna(inplace=True)"
    ]
   },
   {
@@ -479,13 +1855,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 416,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "\n",
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "X_train, X_test, y_train, y_test = train_test_split(website_dummy.iloc[:,:-1], website_dummy.iloc[:,-1], test_size=0.2)"
    ]
   },
   {
@@ -499,11 +1876,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 417,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Your code here:\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "lr = LogisticRegression()\n",
     "\n"
    ]
   },
@@ -516,12 +1896,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 418,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 418,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "lr.fit(X_train,y_train)"
    ]
   },
   {
@@ -533,12 +1927,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 423,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 17  56]\n",
+      " [ 15 106]]\n",
+      "0.634020618556701\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import precision_score\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.metrics import recall_score\n",
+    "from sklearn.metrics import f1_score\n",
+    "\n",
+    "y_pred = lr.predict(X_test)\n",
+    "\n",
+    "print(confusion_matrix(y_test, y_pred))\n",
+    "print(accuracy_score(y_test, y_pred))\n"
    ]
   },
   {
@@ -555,6 +1968,7 @@
    "outputs": [],
    "source": [
     "# Your conclusions here:\n",
+    "# The model has predictive power\n",
     "\n"
    ]
   },
@@ -569,12 +1983,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 427,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeClassifier(max_depth=3)"
+      ]
+     },
+     "execution_count": 427,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "dt = DecisionTreeClassifier(max_depth=3)\n",
+    "dt.fit(X_train, y_train)\n"
    ]
   },
   {
@@ -586,12 +2016,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 429,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 58  15]\n",
+      " [  0 121]]\n",
+      "0.9226804123711341\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "y_pred_dt = dt.predict(X_test)\n",
+    "\n",
+    "print(confusion_matrix(y_test, y_pred_dt))\n",
+    "print(accuracy_score(y_test, y_pred_dt))"
    ]
   },
   {
@@ -604,12 +2047,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 430,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 67   6]\n",
+      " [  0 121]]\n",
+      "0.9690721649484536\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "dt1 = DecisionTreeClassifier(max_depth=5)\n",
+    "dt1.fit(X_train, y_train)\n",
+    "y_pred_dt1 = dt1.predict(X_test)\n",
+    "\n",
+    "print(confusion_matrix(y_test, y_pred_dt1))\n",
+    "print(accuracy_score(y_test, y_pred_dt1))\n"
    ]
   },
   {
@@ -626,7 +2084,8 @@
    "outputs": [],
    "source": [
     "# Your conclusions here:\n",
-    "\n"
+    "# The decisiontreeclassifier improves highly the accuracy of the prediction, and \n",
+    "# as the max depth increases to 5, the overall score improves a little bit\n"
    ]
   },
   {
@@ -654,7 +2113,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -668,7 +2127,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.11.4"
   },
   "varInspector": {
    "cols": {