diff --git a/your-code/main.ipynb b/your-code/main.ipynb index a5caf8b..25e6206 100755 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -12,16 +12,25 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "# Import your libraries:\n", "\n", - "%matplotlib inline\n", - "\n", "import numpy as np\n", - "import pandas as pd" + "import pandas as pd\n", + "pd.set_option(\"display.max_columns\", None) #Muestra todas las columnas cuando llamas a un DataFrame\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# 🤖 Machine Learning\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression \n", + "from sklearn.metrics import roc_curve, confusion_matrix, ConfusionMatrixDisplay\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.neighbors import KNeighborsClassifier" ] }, { @@ -44,6 +53,416 @@ "websites = pd.read_csv('../data/website.csv')" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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count1781.0000001781.000000969.0000001781.0000001781.0000001781.0000001.781000e+031781.0000001781.0000001.781000e+031.781000e+031781.0000001780.0000001781.000000
mean56.96125811.11173511726.92776116.2610895.4727683.0606402.982339e+0318.54014618.7462101.589255e+043.155599e+0318.5401462.2634830.121280
std27.5555864.54989636391.80905140.50097521.8073273.3869755.605057e+0441.62717346.3979696.986193e+045.605378e+0441.6271732.9308530.326544
min16.0000005.0000000.0000000.0000000.0000000.0000000.000000e+000.0000000.0000000.000000e+000.000000e+000.0000000.0000000.000000
25%39.0000008.000000324.0000000.0000000.0000000.0000000.000000e+000.0000000.0000000.000000e+000.000000e+000.0000000.0000000.000000
50%49.00000010.0000001853.0000007.0000000.0000002.0000006.720000e+028.0000009.0000005.790000e+027.350000e+028.0000000.0000000.000000
75%68.00000013.00000011323.00000022.0000005.0000005.0000002.328000e+0326.00000025.0000009.806000e+032.701000e+0326.0000004.0000000.000000
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" + ], + "text/plain": [ + " URL_LENGTH NUMBER_SPECIAL_CHARACTERS CONTENT_LENGTH \\\n", + "count 1781.000000 1781.000000 969.000000 \n", + "mean 56.961258 11.111735 11726.927761 \n", + "std 27.555586 4.549896 36391.809051 \n", + "min 16.000000 5.000000 0.000000 \n", + "25% 39.000000 8.000000 324.000000 \n", + "50% 49.000000 10.000000 1853.000000 \n", + "75% 68.000000 13.000000 11323.000000 \n", + "max 249.000000 43.000000 649263.000000 \n", + "\n", + " TCP_CONVERSATION_EXCHANGE DIST_REMOTE_TCP_PORT REMOTE_IPS \\\n", + "count 1781.000000 1781.000000 1781.000000 \n", + "mean 16.261089 5.472768 3.060640 \n", + "std 40.500975 21.807327 3.386975 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 \n", + "50% 7.000000 0.000000 2.000000 \n", + "75% 22.000000 5.000000 5.000000 \n", + "max 1194.000000 708.000000 17.000000 \n", + "\n", + " APP_BYTES SOURCE_APP_PACKETS REMOTE_APP_PACKETS SOURCE_APP_BYTES \\\n", + "count 1.781000e+03 1781.000000 1781.000000 1.781000e+03 \n", + "mean 2.982339e+03 18.540146 18.746210 1.589255e+04 \n", + "std 5.605057e+04 41.627173 46.397969 6.986193e+04 \n", + "min 0.000000e+00 0.000000 0.000000 0.000000e+00 \n", + "25% 0.000000e+00 0.000000 0.000000 0.000000e+00 \n", + "50% 6.720000e+02 8.000000 9.000000 5.790000e+02 \n", + "75% 2.328000e+03 26.000000 25.000000 9.806000e+03 \n", + "max 2.362906e+06 1198.000000 1284.000000 2.060012e+06 \n", + "\n", + " REMOTE_APP_BYTES APP_PACKETS DNS_QUERY_TIMES Type \n", + "count 1.781000e+03 1781.000000 1780.000000 1781.000000 \n", + "mean 3.155599e+03 18.540146 2.263483 0.121280 \n", + "std 5.605378e+04 41.627173 2.930853 0.326544 \n", + "min 0.000000e+00 0.000000 0.000000 0.000000 \n", + "25% 0.000000e+00 0.000000 0.000000 0.000000 \n", + "50% 7.350000e+02 8.000000 0.000000 0.000000 \n", + "75% 2.701000e+03 26.000000 4.000000 0.000000 \n", + "max 2.362906e+06 1198.000000 20.000000 1.000000 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "websites.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "URL 1781\n", + "URL_LENGTH 142\n", + "NUMBER_SPECIAL_CHARACTERS 31\n", + "CHARSET 8\n", + "SERVER 238\n", + "CONTENT_LENGTH 637\n", + "WHOIS_COUNTRY 48\n", + "WHOIS_STATEPRO 181\n", + "WHOIS_REGDATE 890\n", + "WHOIS_UPDATED_DATE 593\n", + "TCP_CONVERSATION_EXCHANGE 103\n", + "DIST_REMOTE_TCP_PORT 66\n", + "REMOTE_IPS 18\n", + "APP_BYTES 825\n", + "SOURCE_APP_PACKETS 113\n", + "REMOTE_APP_PACKETS 116\n", + "SOURCE_APP_BYTES 885\n", + "REMOTE_APP_BYTES 822\n", + "APP_PACKETS 113\n", + "DNS_QUERY_TIMES 10\n", + "Type 2\n", + "dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "websites.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "URL ['M0_109' 'B0_2314' 'B0_911' ... 'B0_162' 'B0_1152' 'B0_676']\n", + "URL_LENGTH [ 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n", + " 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51\n", + " 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69\n", + " 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87\n", + " 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105\n", + " 106 107 108 109 110 111 112 113 114 115 116 117 118 120 122 123 124 125\n", + " 126 128 129 131 132 134 135 136 137 139 140 141 142 143 144 145 146 149\n", + " 150 151 154 156 160 161 169 170 173 178 183 194 198 201 234 249]\n", + "NUMBER_SPECIAL_CHARACTERS [ 7 6 5 8 9 11 10 13 12 14 15 16 17 18 21 19 20 22 23 28 24 25 36 26\n", + " 27 43 30 29 31 34 40]\n", + "CHARSET ['iso-8859-1' 'UTF-8' 'us-ascii' 'ISO-8859-1' 'utf-8' nan 'windows-1251'\n", + " 'ISO-8859' 'windows-1252']\n", + "SERVER ['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", + "CONTENT_LENGTH [2.63000e+02 1.50870e+04 3.24000e+02 1.62000e+02 1.24140e+05 nan\n", + " 3.45000e+02 1.37160e+04 3.69200e+03 1.30540e+04 9.57000e+02 6.86000e+02\n", + " 1.50250e+04 3.18000e+02 2.24000e+02 4.42100e+03 4.41000e+02 6.67100e+03\n", + " 4.34000e+02 1.30010e+04 3.25700e+03 6.74800e+03 2.40000e+02 3.98500e+03\n", + " 6.17300e+03 3.19000e+02 3.40000e+01 1.77800e+03 7.56000e+02 1.48390e+04\n", + " 6.58000e+02 1.95000e+02 4.16600e+03 9.62000e+02 2.68800e+03 1.99000e+02\n", + " 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'20/01/2017 1:26' '23/10/2013 0:00' '31/03/2016 0:00' '17/05/2013 0:00'\n", + " '11/01/2016 0:00' '22/09/2016 0:00' '1/11/2012 0:00' '2/05/2016 0:00'\n", + " '12/02/2016 0:00' '18/04/2017 17:38' '15/01/2015 0:00' '6/08/2016 0:00'\n", + " '30/09/2009 2:59' '26/04/2017 0:00' '12/03/2017 9:17' '30/04/2013 0:00'\n", + " '3/08/2014 0:00' '20/07/2011 0:00' '28/10/2015 0:00' '20/07/2014 0:00'\n", + " '3/06/2016 0:00' '31/07/2016 0:00' '8/02/2017 0:00' '30/08/2013 0:00'\n", + " '12/06/2015 0:00' '24/08/2015 0:00' '8/08/2016 0:00' '17/08/2016 0:00'\n", + " '10/08/2016 0:00' '14/06/2016 0:00' '27/12/2015 0:00' '26/12/2012 0:00'\n", + " '8/10/2014 0:00' '29/08/2016 20:58' '12/08/2016 0:00' '2/02/2016 0:00'\n", + " '24/04/2015 0:00' '13/04/2016 0:00' '21/04/2016 0:00' '3/03/2016 0:00'\n", + " '20/12/2013 0:00' '9/03/2015 0:00' '27/09/2012 0:00' '28/04/2016 0:00'\n", + " '13/02/2017 0:00' '25/02/2017 14:18' '11/02/2017 0:00' '30/05/2016 18:18'\n", + " '26/09/2013 0:00' '5/08/2013 0:00' '2/07/2015 17:44' '2/04/2017 1:35'\n", + " '7/04/2017 0:00' '13/08/2015 0:00' '15/02/2016 0:00' '11/12/2016 0:00'\n", + " '16/01/2017 17:49' '14/01/2017 16:52' '12/07/2016 0:00' '7/05/2016 0:00'\n", + " '25/11/2016 0:00' '17/02/2016 0:00' '13/11/2013 0:00' '29/11/2014 0:00'\n", + " '13/07/2014 0:00' '11/08/2015 20:35' '28/01/2017 0:00' '10/06/2016 13:57'\n", + " '12/11/2015 0:00' '14/01/2012 0:00' '20/05/2016 0:00' '27/05/2016 0:00'\n", + " '2/04/2016 0:00' '2/06/2009 0:00' '17/03/2016 4:34' '20/11/2015 0:00'\n", + " '15/07/2015 0:00' '9/12/2016 0:00']\n", + "TCP_CONVERSATION_EXCHANGE [ 7 17 0 31 57 11 12 16 25 6 13 9 39 8\n", + " 4 24 19 10 43 40 2 84 65 14 21 23 5 18\n", + " 36 26 46 38 3 66 28 33 34 709 15 32 62 22\n", + " 90 76 48 27 103 49 94 71 52 105 42 45 37 44\n", + " 41 208 61 85 1 68 35 58 20 288 30 64 63 69\n", + " 60 156 56 101 51 29 83 157 53 185 47 55 70 50\n", + " 127 74 125 197 113 80 226 1194 79 107 67 104 188 326\n", + " 59 73 75 54 95]\n", + "DIST_REMOTE_TCP_PORT [ 0 7 22 2 6 19 1 29 3 4 5 14 17 54 39 9 12 8\n", + " 13 26 708 21 52 23 47 53 45 10 42 11 98 27 25 20 15 40\n", + " 18 73 30 279 48 24 36 33 67 60 56 50 49 51 41 35 32 16\n", + " 43 59 28 46 31 38 58 37 317 34 44 89]\n", + "REMOTE_IPS [ 2 4 0 3 5 9 8 1 6 11 16 7 15 14 10 12 13 17]\n", + "APP_BYTES [ 700 1230 0 3812 4278 894 1189 1492 3946\n", + " 717 603 618 1099 850 3833 696 420 2259\n", + " 650 1696 630 1740 2404 1980 519 1186 5738\n", + " 723 3285 132 720 10490 6286 1907 528 564\n", + " 2131 984 1606 2279 1390 2485 474 1327 1232\n", + " 1586 4737 1052 878 2508 842 722 1378 2727\n", + " 6325 702 882 396 3252 3783 2373 4207 1838\n", + " 366 3781 631 592 556 552 2830 2094 1848\n", + " 2314 6311 2324 632 3590 2632 2934 593 2519\n", + " 900 2362906 486 1134 1249 2861 612 4561 3533\n", + " 3183 1515 2195 666 1918 8974 3914 1188 276\n", + " 1528 8646 4412 3605 9007 1902 3418 707 8568\n", + " 738 432 2773 2743 2262 2201 3981 7073 1355\n", + " 8136 2783 1457 1314 2000 7613 2176 11902 798\n", + " 816 10999 4214 3185 1888 734 4607 2065 1497\n", + " 2851 2147 264 1151 1045 3438 606 3694 408\n", + " 660 3375 2042 8692 2721 1632 3569 5235 2412\n", + " 498 1661 4895 3787 2292 4372 716 3240 2102\n", + " 1635 2367 1340 2223 2157 2864 1850 1527 2435\n", + " 2961 756 1446 3087 3507 3263 1258 6945 4769\n", + " 2965 2771 1275 20074 466 1770 1266 2359 2227\n", + " 372 1432 1096 1706 6100 7506 784 2680 1060\n", + " 1331 730 2319 3505 128 66 5009 1961 1525\n", + " 1674 834 540 648 268 2531 3355 3767 1607\n", + " 1283 1643 5207 2248 888 2813 2342 2594 1458\n", + " 1851 5509 1002 330 786 5306 3072 1967 1068\n", + " 2823 8165 3099 1927 1879 4047 1578 4268 20749\n", + " 5064 2329 2328 2976 809 3521 7271 1059 2241\n", + " 6413 1224 5528 492 1560 1524 1308 684 912\n", + " 714 3771 927 2679 6884 865 6028 5129 6419\n", + " 7582 1063 2750 1970 8450 7076 1313 6269 5951\n", + " 3234 6503 8252 1325 2383 2243 1285 2610 1016\n", + " 1092 23383 924 1784 1394 2272 1088 2021 1724\n", + " 8268 1166 4229 4551 2112 6057 1615 2948 1981\n", + " 2995 1142 1653 4034 2316 737 1089 576 1895\n", + " 1078 4891 1868 1202 3699 2657 3079 54 1198\n", + " 10813 2185 2124 4035 4101 3969 4601 985 636\n", + " 2706 3088 2780 950 1540 334 7856 2767 1102\n", + " 2432 2188 3970 925 883 1110 2022 4044 750\n", + " 2756 6008 7403 2267 803 2444 1511 2479 2715\n", + " 4178 6081 3444 1809 1242 640 2742 4203 2512\n", + " 5987 5805 11053 870 972 1556 2326 5647 1942\n", + " 672 7834 2401 1480 1197 2274 2097 531 1201\n", + " 780 541 13834 6464 3409 2595 10205 4590 5308\n", + " 198 1801 1816 5631 2683 5852 3891 805 2730\n", + " 1274 742 8062 6883 3416 1392 582 5894 680\n", + " 4509 3146 8942 18084 3089 2218 9463 3331 6306\n", + " 3516 4103 1351 2115 1716 4364 1244 3094 8528\n", + " 3244 13622 4376 3213 4948 810 2133 4167 1182\n", + " 6679 4084 1552 3312 3007 296 6240 1180 3538\n", + " 7049 2457 5283 729 3759 1320 2912 3448 4041\n", + " 3450 6235 2313 6925 3179 4576 3878 2873 2634\n", + " 7746 2037 811 3571 1205 851 1662 864 1122\n", + " 1476 2736 4739 1720 1567 4464 516 2270 5413\n", + " 5371 2681 3386 2330 746 7487 2382 6904 5203\n", + " 8291 2829 2949 1570 1960 995 926 6820 15162\n", + " 1849 3321 769 762 1278 1872 1651 3637 4677\n", + " 3227 2992 1609 1428 1628 4466 1257 5314 1682\n", + " 2221 1538 1364 4765 4049 1156 1668 960 1017\n", + " 1944 2232 1277 4558 2881 99843 5998 7178 4562\n", + " 2903 280 5325 3025 2036 5591 1404 3721 3229\n", + " 1715 6128 1074 4405 5666 3101 7621 4861 5665\n", + " 5137 752 8171 450 3928 1876 2910 1080 944\n", + " 3050 6351 3354 847 1256 356 1062 8280 5965\n", + " 1345 1293 5863 1764 2335 3944 4195 1627 8986\n", + " 1965 4725 871 949 3592 1692 1481 6256 620\n", + " 1794 2344 1146 6317 3076 2421 828 768 2638\n", + " 1863 2752 1797 3325 1684 3796 1170 1589 1300\n", + " 3142 3936 1438 3104 238 202 12231 4932 664\n", + " 622 2630 7002 4488 4192 3208 1930 1673 1344\n", + " 792 3798 743 4514 1040 1810 2772 1676 6011\n", + " 3840 7535 2506 2293 3414 3967 2108 5105 583\n", + " 3472 4825 14064 3181 1238 4631 3201 1910 2820\n", + " 5029 1194 1634 26631 2510 3999 1026 1012 1366\n", + " 3471 2135 7723 2381 2154 1766 1620 858 2317\n", + " 1900 5242 6020 4703 4896 903 1483 5347 1783\n", + " 2186 3997 1878 2708 1150 916 6221 1688 3802\n", + " 3523 1100 3684 7802 3317 2794 3428 4515 2877\n", + " 6391 1131 6078 3862 3042 2083 2490 1165 6525\n", + " 3149 14530 2425 2396 3932 5365 1260 801 3647\n", + " 2472 4544 3635 2032 1206 2445 2403 3591 1335\n", + " 1594 5949 2981 2781 2106 1763 1301 3333 7282\n", + " 3156 1739 3764 7388 2088 6257 2074 2362 1894\n", + " 2281 3718 488 3286 3565 2200 90 3705 2523\n", + " 2676 13331 3630 2296 804 2487 2202 4523 1482\n", + " 3124 936 1008 3207 1346 7350 1818 4155 6789\n", + " 2162 3432 3223 1591 1358 5321 1316 1948 5024\n", + " 1176 5171 2784 2038 2137 2010 3458 4721 3327\n", + " 7415 6027 4104 2790 3238 1212 3381 5078 4422\n", + " 822 728 1984 4284 2278 4317 970 2624 5466\n", + " 2126 3425 7556 1675 4871 1368 1020 3925 1649\n", + " 5398 2854 1354 2402 2062 6631]\n", + "SOURCE_APP_PACKETS [ 9 17 0 39 61 11 14 2 20 35 8 7 15 43\n", + " 4 16 10 18 24 23 47 46 96 69 6 25 22 21\n", + " 27 5 42 30 19 53 50 44 3 29 70 40 12 709\n", + " 38 37 36 76 28 92 88 52 77 32 33 105 102 73\n", + " 49 31 56 111 41 34 45 228 65 91 51 48 26 13\n", + " 54 66 294 71 72 79 81 74 78 162 84 60 1 110\n", + " 57 87 86 159 187 55 75 58 131 80 129 200 63 117\n", + " 98 59 62 1198 64 83 107 106 194 330 67 210 90 68\n", + " 99]\n", + "REMOTE_APP_PACKETS [ 10 19 0 37 62 13 3 1 20 29 9 17 42 6\n", + " 12 4 11 25 8 15 50 45 5 106 75 18 23 16\n", + " 22 28 7 35 14 30 47 27 44 24 64 33 48 36\n", + " 40 837 26 69 100 41 70 32 113 52 121 51 130 31\n", + " 21 61 46 39 216 79 96 53 88 77 431 68 73 80\n", + " 71 65 38 66 206 59 54 2 101 55 34 107 176 263\n", + " 56 76 144 145 255 43 134 84 103 93 284 49 63 58\n", + " 1284 57 67 74 83 148 124 217 442 60 82 102 278 110\n", + " 89 72 78 157]\n", + "SOURCE_APP_BYTES [ 1153 1265 0 18784 129889 838 8559 213 62\n", + " 2334 16408 1960 1580 562 15476 1354 22495 636\n", + " 372 5165 1417 13422 244 696 6179 5737 1138\n", + " 1900 56926 1837 12003 318 1269 106925 90508 5601\n", + " 508 9471 1839 1276 2562 3890 9332 250 442\n", + " 14050 9806 1735 10343 11935 2242 21098 2101 2000\n", + " 1913 6226 5765 32025 482 60076 5605 6199 1650\n", + " 39662 4524 306 34101 812 1062 1154 612 634\n", + " 364 6137 8019 2167 21419 63634 24662 593 30057\n", + " 27404 34995 1128 22379 568 83056 542 605 436\n", + " 15260 918 10429 46688 24455 1334 55576 1094 5546\n", + " 30726 9833 1451 564 5586 95659 27412 34388 23985\n", + " 2764 57377 1018 26049 766 955 6566 43878 16553\n", + " 7756 9867 179439 1145 25686 3824 3229 1881 9588\n", + " 24774 17335 310 426 173484 1124 1541 88707 9246\n", + " 50608 40314 7145 58676 7280 2116 3814 2848 388\n", + " 1211 1362 46498 304 474 11543 382 556 476\n", + " 18396 4760 179133 3896 2661 10750 1382 14571 10015\n", + " 39544 33677 16147 13095 1979 7939 35301 20976 2640\n", + " 3801 964 2868 2996 3773 2970 1928 39924 38255\n", + " 861 1788 19078 42286 52720 1862 26115 69898 37146\n", + " 36261 2306 270752 822 2579 966 3542 3612 703\n", + " 2113 1294 184 438 2165 123896 107416 16959 7746\n", + " 668 5214 4860 1284 14373 66214 407 404 40316\n", + " 2991 1363 882 535 328 739 557 31623 129181\n", + " 1690 2201 21144 247488 3605 316 3916 7249 7997\n", + " 1097 19623 15551 25776 1012 539 968 39325 9718\n", + " 6542 1006 15940 54909 7395 8052 8768 54041 20606\n", + " 40666 1058608 35677 960 4850 3285 7783 1321 33021\n", + " 60586 1506 6701 58285 7472 702 246 1030 762\n", + " 788 11756 2926 7080 15626 57005 19264 20061 59068\n", + " 29141 82537 1287 9290 2183 68036 63630 1612 54988\n", + " 56509 129083 58109 61807 2091 3786 3021 2462 7715\n", + " 295213 642 23886 12358 16035 2036 26799 38477 117625\n", + " 1626 32381 67881 2833 16019 15706 25358 5557 6759\n", + " 41717 1991 2798 12486 31378 1162 1977 124 522\n", + " 13792 1532 37001 18348 1335 27522 34427 420 38924\n", + " 2763 142735 2969 12884 12416 12614 12354 5911 45279\n", + " 1496 473 376 41737 12104 27390 1327 10465 25529\n", + " 412 82332 3550 1015 27169 3031 12548 1399 2806\n", + " 8893 3023 803 44173 448 366 502 41938 147266\n", + " 117282 9811 2190 1984 14430 6085 20139 44810 63457\n", + " 9819 26805 962 606 970 47931 7155 38230 40585\n", + " 284743 5876 15901 9023 3623 120385 15275 1381 22947\n", + " 19291 10665 1424 31811 9280 494 7650 4922 805\n", + " 579 488313 39038 82259 18392 15941 38894 84636 40052\n", + " 186 26643 12138 82281 11690 37285 37000 1087 9605\n", + " 3132 1263 82775 38056 17574 496 504 596 69918\n", + " 35953 4258 233106 113453 7088 5203 133235 62295 22528\n", + " 33488 16662 9692 17150 11285 22879 1616 7809 231799\n", + " 24696 486769 40918 16931 19634 35687 744 624 730\n", + " 17914 3181 670 59369 3258 36205 2790 36225 23628\n", + " 778 69305 1834 13191 22895 110342 68869 268442 2045\n", + " 11243 1967 3753 12420 66124 16697 1814 9531 17604\n", + " 11372 49426 8839 5727 4384 229040 2944 6187 14342\n", + " 6062 66449 16593 2135 799 1344 4846 1064 480\n", + " 490 18554 2880 6818 39561 10000 64860 16215 21979\n", + " 66317 3609 2189 75625 7775 197501 13097 135375 23089\n", + " 43296 6049 2807 1507 79053 383760 26589 66184 1298\n", + " 640 1323 1388 44294 14310 12120 65646 2655 1504\n", + " 14894 414 424 95122 1374 1919 51761 25579 3809\n", + " 1121 16978 17280 61312 2638 894 492 1024 1563\n", + " 2698 3283 2663 26259 39646 182 2060012 43787 20954\n", + " 49870 30772 86136 9200 7035 46553 1587 66445 16895\n", + " 22292 41385 35749 628 46271 69645 13795 58567 43523\n", + " 19433 67877 1197 26659 479 10186 2205 7648 2498\n", + " 3869 65986 22390 8544 1998 1546 378 23559 20145\n", + " 18597 1754 127368 2364 35231 23329 45691 2044 234205\n", + " 7616 97272 4257 2597 66100 8467 672 63202 626\n", + " 6649 416 38708 8668 7924 768 1036 890 15088\n", + " 8910 2308 29835 14589 37621 1324 2404 13818 7684\n", + " 29443 4800 8857 654 574 617 1186 23578 609\n", + " 9159 35655 172138 44959 62498 8206 8177 8029 2525\n", + " 971 18719 1520 26105 36967 2206 2519 32655 21002\n", + " 28050 52729 204971 2966 17664 47213 23969 3406 13437\n", + " 737 24847 37575 298694 16286 760 570 1909 12921\n", + " 20250 3124 3034 954 44203 1736 14294 947971 34105\n", + " 17322 7303 1806 23243 706 1697 3485 37516 5596\n", + " 29677 2293 9227 678 3635 15998 49446 37704 69613\n", + " 73318 1489 2073 1196 43924 18427 6913 16884 18769\n", + " 6195 30035 15499 2074 36834 742 57628 30933 1570\n", + " 892 47558 7448 105565 55460 8700 30923 28007 12970\n", + " 14140 83389 1747 40968 1144 498 11108 5914 17862\n", + " 18969 1673 126205 37964 466055 1268 913 33598 26621\n", + " 87340 78671 2399 9017 677 32399 6776 53238 23404\n", + " 2033 991 41408 27476 24630 13757 2109 1152 904\n", + " 32891 31493 18410 6415 552 42229 24220 4966 12620\n", + " 7356 65108 7459 30118 10646 5472 7062 29619 44519\n", + " 28351 58127 6009 1874 44807 3594 33205 67914 34113\n", + " 632 688 826 2587 51527 3304 26400 661 8797\n", + " 2112 9563 486 48750 17337 1725 143113 432 45079\n", + " 110553 28829 14374 64807 42120 1936 9432 354 24126\n", + " 8187 16475 630 384 12186 15154 36825 14114 17922\n", + " 13434 256 190 3254 798 55407 82395 10505 93298\n", + " 124751 131895 1646 23294 26870 682 14656 68512 44454\n", + " 1527 1603 1180 595 62932 28130 43123 2847 21620\n", + " 1484 30904 97306 1627 1272 12391 244958 1927 456\n", + " 81223 3046 21489 14395 137144 3490 2517 4491 752\n", + " 8161 132181 3039]\n", + "REMOTE_APP_BYTES [ 832 1230 0 4380 4586 894 1327 146 1784\n", + " 4746 1011 745 618 1243 994 4125 696 420\n", + " 2559 950 1844 630 1740 2684 2276 823 1476\n", + " 6046 871 3729 132 868 11482 6596 2171 528\n", + " 564 2441 1292 1914 2729 1540 2799 474 1615\n", + " 1530 1898 5173 1204 1018 2992 1016 1036 1856\n", + " 2991 7063 702 882 396 3564 4047 2637 1300\n", + " 4679 1992 366 4253 787 732 860 552 3400\n", + " 2094 2322 2740 6633 2482 776 4064 2916 3416\n", + " 751 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in list_1:\n", + " print(column,websites[column].unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Data format

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type0.1618520.281407-0.090727-0.040311-0.082698-0.079073-0.011248-0.034493-0.032976-0.044012-0.010989-0.0344930.0691841.000000
\n", + "
" + ], + "text/plain": [ + " url_length number_special_characters \\\n", + "url_length 1.000000 0.918005 \n", + "number_special_characters 0.918005 1.000000 \n", + "content_length 0.129748 0.214137 \n", + "tcp_conversation_exchange -0.038433 -0.037442 \n", + "dist_remote_tcp_port -0.039897 -0.042712 \n", + "remote_ips -0.046477 -0.047119 \n", + "app_bytes -0.026442 -0.023907 \n", + "source_app_packets -0.042315 -0.040106 \n", + "remote_app_packets -0.033809 -0.030587 \n", + "source_app_bytes -0.014824 -0.014305 \n", + "remote_app_bytes -0.026682 -0.024092 \n", + "app_packets -0.042315 -0.040106 \n", + "dns_query_times -0.068992 -0.050447 \n", + "type 0.161852 0.281407 \n", + "\n", + " content_length tcp_conversation_exchange \\\n", + "url_length 0.129748 -0.038433 \n", + "number_special_characters 0.214137 -0.037442 \n", + "content_length 1.000000 0.078609 \n", + "tcp_conversation_exchange 0.078609 1.000000 \n", + "dist_remote_tcp_port -0.000184 0.555181 \n", + "remote_ips 0.005147 0.330964 \n", + "app_bytes 0.051382 0.457330 \n", + "source_app_packets 0.074318 0.997799 \n", + "remote_app_packets 0.091242 0.990848 \n", + "source_app_bytes 0.100187 0.865591 \n", + "remote_app_bytes 0.047789 0.458711 \n", + "app_packets 0.074318 0.997799 \n", + "dns_query_times -0.045335 0.349843 \n", + "type -0.090727 -0.040311 \n", + "\n", + " dist_remote_tcp_port remote_ips app_bytes \\\n", + "url_length -0.039897 -0.046477 -0.026442 \n", + "number_special_characters -0.042712 -0.047119 -0.023907 \n", + "content_length -0.000184 0.005147 0.051382 \n", + "tcp_conversation_exchange 0.555181 0.330964 0.457330 \n", + "dist_remote_tcp_port 1.000000 0.210067 0.780288 \n", + "remote_ips 0.210067 1.000000 0.023105 \n", + "app_bytes 0.780288 0.023105 1.000000 \n", + "source_app_packets 0.558588 0.360967 0.445837 \n", + "remote_app_packets 0.591181 0.304555 0.469009 \n", + "source_app_bytes 0.313386 0.171602 0.074459 \n", + "remote_app_bytes 0.781261 0.025301 0.999992 \n", + "app_packets 0.558588 0.360967 0.445837 \n", + "dns_query_times 0.259724 0.548079 0.012206 \n", + "type -0.082698 -0.079073 -0.011248 \n", + "\n", + " source_app_packets remote_app_packets \\\n", + "url_length -0.042315 -0.033809 \n", + "number_special_characters -0.040106 -0.030587 \n", + "content_length 0.074318 0.091242 \n", + "tcp_conversation_exchange 0.997799 0.990848 \n", + "dist_remote_tcp_port 0.558588 0.591181 \n", + "remote_ips 0.360967 0.304555 \n", + "app_bytes 0.445837 0.469009 \n", + "source_app_packets 1.000000 0.989285 \n", + "remote_app_packets 0.989285 1.000000 \n", + "source_app_bytes 0.857520 0.880570 \n", + "remote_app_bytes 0.447462 0.470411 \n", + "app_packets 1.000000 0.989285 \n", + "dns_query_times 0.410820 0.355714 \n", + "type -0.034493 -0.032976 \n", + "\n", + " source_app_bytes remote_app_bytes app_packets \\\n", + "url_length -0.014824 -0.026682 -0.042315 \n", + "number_special_characters -0.014305 -0.024092 -0.040106 \n", + "content_length 0.100187 0.047789 0.074318 \n", + "tcp_conversation_exchange 0.865591 0.458711 0.997799 \n", + "dist_remote_tcp_port 0.313386 0.781261 0.558588 \n", + "remote_ips 0.171602 0.025301 0.360967 \n", + "app_bytes 0.074459 0.999992 0.445837 \n", + "source_app_packets 0.857520 0.447462 1.000000 \n", + "remote_app_packets 0.880570 0.470411 0.989285 \n", + "source_app_bytes 1.000000 0.075323 0.857520 \n", + "remote_app_bytes 0.075323 1.000000 0.447462 \n", + "app_packets 0.857520 0.447462 1.000000 \n", + "dns_query_times 0.215420 0.016197 0.410820 \n", + "type -0.044012 -0.010989 -0.034493 \n", + "\n", + " dns_query_times type \n", + "url_length -0.068992 0.161852 \n", + "number_special_characters -0.050447 0.281407 \n", + "content_length -0.045335 -0.090727 \n", + "tcp_conversation_exchange 0.349843 -0.040311 \n", + "dist_remote_tcp_port 0.259724 -0.082698 \n", + "remote_ips 0.548079 -0.079073 \n", + "app_bytes 0.012206 -0.011248 \n", + "source_app_packets 0.410820 -0.034493 \n", + "remote_app_packets 0.355714 -0.032976 \n", + "source_app_bytes 0.215420 -0.044012 \n", + "remote_app_bytes 0.016197 -0.010989 \n", + "app_packets 0.410820 -0.034493 \n", + "dns_query_times 1.000000 0.069184 \n", + "type 0.069184 1.000000 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Correlation Matrix\n", + "\n", + "num_corr = numerics.corr()\n", + "num_corr\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Correlation Matrix-Heatmap Plot\n", + "mask = np.zeros_like(num_corr)\n", + "mask[np.triu_indices_from(mask)] = True # optional, to hide repeat half of the matrix\n", + "\n", + "f, ax = plt.subplots(figsize=(25, 15))\n", + "sns.set(font_scale=1.5) # increase font size\n", + "\n", + "ax = sns.heatmap(num_corr, mask=mask, annot=True, annot_kws={\"size\": 12}, linewidths=.5, cmap=\"coolwarm\", fmt=\".2f\", ax=ax) # round to 2 decimal places\n", + "ax.set_title(\"Dealing with Multicollinearity\", fontsize=20) # add title\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['url', 'url_length', 'number_special_characters', 'charset', 'server',\n", + " 'content_length', 'whois_country', 'whois_statepro', 'whois_regdate',\n", + " 'whois_updated_date', 'tcp_conversation_exchange',\n", + " 'dist_remote_tcp_port', 'remote_ips', 'app_bytes', 'source_app_packets',\n", + " 'remote_app_packets', 'source_app_bytes', 'remote_app_bytes',\n", + " 'app_packets', 'dns_query_times', 'type'],\n", + " dtype='object')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "websites.columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Según el heatmap, vemos que hay una alta coolinearidad entre:\n", + "# tcp_conversation_exchange - app_packets\n", + "# - remote_app_packets\n", + "# - source_app_packets\n", + "# Analizando estas variables, veo que conviene eliminar las 3 que se relacionan con tcp_conversation_exchange, ya que esta tiene más correlación con type\n", + "\n", + "# app_bytes - remote_app_bytes\n", + "# Elimino \"remote_app_bytes\"\n", + "\n", + "# source_app_packets - app_packets\n", + "# - remote_app_packets\n", + "# Al hacer la primera eliminación de arriba, no queda ninguna de estas variables\n", + "\n", + "# remote_app_packets - app_packets\n", + "# Al hacer la eliminación de arriba, no queda ninguna de estas dos variables" + ] + }, + { + "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": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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url_lengthnumber_special_characterscontent_lengthtcp_conversation_exchangedist_remote_tcp_portremote_ipsapp_bytessource_app_packetsremote_app_packetssource_app_bytesremote_app_bytesdns_query_timestype
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4176124140.057254278616212988945864.00
..........................................
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1778 rows × 13 columns

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" + ], + "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", + " remote_app_bytes dns_query_times type \n", + "0 832 2.0 1 \n", + "1 1230 0.0 0 \n", + "2 0 0.0 0 \n", + "3 4380 8.0 0 \n", + "4 4586 4.0 0 \n", + "... ... ... ... \n", + "1776 0 0.0 1 \n", + "1777 0 0.0 1 \n", + "1778 6945 4.0 0 \n", + "1779 0 0.0 0 \n", + "1780 2776 6.0 0 \n", + "\n", + "[1778 rows x 13 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# PRIMERA ITERACIÓN\n", + "\n", + "# Elimino la columna \"app_packets\" de mi data frame de NUMERICAS (no confundir y eliminar del original)\n", + "\n", + "numerics = numerics.drop(columns = [\"app_packets\"])\n", + "numerics" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Correlation matrix sin columna elimindad\n", + "\n", + "num_corr = numerics.corr()\n", + "num_corr\n", + "\n", + "# Chequeo el heatmap de correlaciones de nuevo\n", + "\n", + "mask = np.zeros_like(num_corr)\n", + "mask[np.triu_indices_from(mask)] = True # optional, to hide repeat half of the matrix\n", + "\n", + "f, ax = plt.subplots(figsize=(25, 15))\n", + "sns.set(font_scale=1.5) # increase font size\n", + "\n", + "ax = sns.heatmap(num_corr, mask=mask, annot=True, annot_kws={\"size\": 12}, linewidths=.5, cmap=\"coolwarm\", fmt=\".2f\", ax=ax) # round to 2 decimal places\n", + "ax.set_title(\"Dealing with Multicollinearity\", fontsize=20) # add title\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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url_lengthnumber_special_characterscontent_lengthtcp_conversation_exchangedist_remote_tcp_portremote_ipsapp_bytessource_app_packetssource_app_bytesremote_app_bytesdns_query_timestype
0167263.0702700911538322.01
116615087.01774123017126512300.00
2166324.000000000.00
3176162.0312233812391878443808.00
4176124140.0572542786112988945864.00
.......................................
177619416NaN0000018600.01
177719817NaN0000012400.01
1778201348904.0832666318713218169454.00
177923434NaN00000000.00
17802494024435.019611231425303927766.00
\n", + "

1778 rows × 12 columns

\n", + "
" + ], + "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 source_app_bytes remote_app_bytes dns_query_times \\\n", + "0 9 1153 832 2.0 \n", + "1 17 1265 1230 0.0 \n", + "2 0 0 0 0.0 \n", + "3 39 18784 4380 8.0 \n", + "4 61 129889 4586 4.0 \n", + "... ... ... ... ... \n", + "1776 0 186 0 0.0 \n", + "1777 0 124 0 0.0 \n", + "1778 87 132181 6945 4.0 \n", + "1779 0 0 0 0.0 \n", + "1780 25 3039 2776 6.0 \n", + "\n", + " type \n", + "0 1 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "... ... \n", + "1776 1 \n", + "1777 1 \n", + "1778 0 \n", + "1779 0 \n", + "1780 0 \n", + "\n", + "[1778 rows x 12 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SEGUNDA ITERACIÓN\n", + "\n", + "# Elimino la columna \"remote_app_packets\" de mi data frame de NUMERICAS (no confundir y eliminar del original)\n", + "\n", + "numerics = numerics.drop(columns = [\"remote_app_packets\"])\n", + "numerics" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Correlation matrix sin columna elimindad\n", + "\n", + "num_corr = numerics.corr()\n", + "num_corr\n", + "\n", + "# Chequeo el heatmap de correlaciones de nuevo\n", + "\n", + "mask = np.zeros_like(num_corr)\n", + "mask[np.triu_indices_from(mask)] = True # optional, to hide repeat half of the matrix\n", + "\n", + "f, ax = plt.subplots(figsize=(25, 15))\n", + "sns.set(font_scale=1.5) # increase font size\n", + "\n", + "ax = sns.heatmap(num_corr, mask=mask, annot=True, annot_kws={\"size\": 12}, linewidths=.5, cmap=\"coolwarm\", fmt=\".2f\", ax=ax) # round to 2 decimal places\n", + "ax.set_title(\"Dealing with Multicollinearity\", fontsize=20) # add title\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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url_lengthnumber_special_characterscontent_lengthtcp_conversation_exchangedist_remote_tcp_portremote_ipsapp_bytessource_app_bytesremote_app_bytesdns_query_timestype
0167263.070270011538322.01
116615087.017741230126512300.00
2166324.00000000.00
3176162.03122338121878443808.00
4176124140.05725427812988945864.00
....................................
177619416NaN000018600.01
177719817NaN000012400.01
1778201348904.08326663113218169454.00
177923434NaN0000000.00
17802494024435.0196112314303927766.00
\n", + "

1778 rows × 11 columns

\n", + "
" + ], + "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_bytes remote_app_bytes dns_query_times type \n", + "0 1153 832 2.0 1 \n", + "1 1265 1230 0.0 0 \n", + "2 0 0 0.0 0 \n", + "3 18784 4380 8.0 0 \n", + "4 129889 4586 4.0 0 \n", + "... ... ... ... ... \n", + "1776 186 0 0.0 1 \n", + "1777 124 0 0.0 1 \n", + "1778 132181 6945 4.0 0 \n", + "1779 0 0 0.0 0 \n", + "1780 3039 2776 6.0 0 \n", + "\n", + "[1778 rows x 11 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# TERCERA ITERACIÓN\n", + "\n", + "# Elimino la columna \"source_app_packets\" de mi data frame de NUMERICAS (no confundir y eliminar del original)\n", + "\n", + "numerics = numerics.drop(columns = [\"source_app_packets\"])\n", + "numerics" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Correlation matrix sin columna elimindad\n", + "\n", + "num_corr = numerics.corr()\n", + "num_corr\n", + "\n", + "# Chequeo el heatmap de correlaciones de nuevo\n", + "\n", + "mask = np.zeros_like(num_corr)\n", + "mask[np.triu_indices_from(mask)] = True # optional, to hide repeat half of the matrix\n", + "\n", + "f, ax = plt.subplots(figsize=(25, 15))\n", + "sns.set(font_scale=1.5) # increase font size\n", + "\n", + "ax = sns.heatmap(num_corr, mask=mask, annot=True, annot_kws={\"size\": 12}, linewidths=.5, cmap=\"coolwarm\", fmt=\".2f\", ax=ax) # round to 2 decimal places\n", + "ax.set_title(\"Dealing with Multicollinearity\", fontsize=20) # add title\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0167263.070270011532.01
116615087.01774123012650.00
2166324.0000000.00
3176162.0312233812187848.00
4176124140.0572542781298894.00
.................................
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\n", + "

1778 rows × 10 columns

\n", + "
" + ], + "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_bytes dns_query_times type \n", + "0 1153 2.0 1 \n", + "1 1265 0.0 0 \n", + "2 0 0.0 0 \n", + "3 18784 8.0 0 \n", + "4 129889 4.0 0 \n", + "... ... ... ... \n", + "1776 186 0.0 1 \n", + "1777 124 0.0 1 \n", + "1778 132181 4.0 0 \n", + "1779 0 0.0 0 \n", + "1780 3039 6.0 0 \n", + "\n", + "[1778 rows x 10 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# CUARTA ITERACIÓN\n", + "\n", + "# Elimino la columna \"remote_app_bytes\" de mi data frame de NUMERICAS (no confundir y eliminar del original)\n", + "\n", + "numerics = numerics.drop(columns = [\"remote_app_bytes\"])\n", + "numerics" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Correlation matrix sin columna elimindad\n", + "\n", + "num_corr = numerics.corr()\n", + "num_corr\n", + "\n", + "# Chequeo el heatmap de correlaciones de nuevo\n", + "\n", + "mask = np.zeros_like(num_corr)\n", + "mask[np.triu_indices_from(mask)] = True # optional, to hide repeat half of the matrix\n", + "\n", + "f, ax = plt.subplots(figsize=(25, 15))\n", + "sns.set(font_scale=1.5) # increase font size\n", + "\n", + "ax = sns.heatmap(num_corr, mask=mask, annot=True, annot_kws={\"size\": 12}, linewidths=.5, cmap=\"coolwarm\", fmt=\".2f\", ax=ax) # round to 2 decimal places\n", + "ax.set_title(\"Dealing with Multicollinearity\", fontsize=20) # add title\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "#En esta última iteración vemos que quedan solo dos variables con una alta colinearidad, pero en el enunciado nos dicen que máximo podemos eliminar 3 variables y ya nos excedimos, por lo tanto dejamos de iterar." + ] + }, + { + "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": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "url 0\n", + "url_length 0\n", + "number_special_characters 0\n", + "charset 7\n", + "server 175\n", + "content_length 810\n", + "whois_country 303\n", + "whois_statepro 359\n", + "whois_regdate 127\n", + "whois_updated_date 138\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": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "websites.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1778, 21)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "websites.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "charset\n", + "UTF-8 674\n", + "ISO-8859-1 426\n", + "utf-8 379\n", + "us-ascii 155\n", + "iso-8859-1 134\n", + "windows-1251 1\n", + "ISO-8859 1\n", + "windows-1252 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "websites.charset.value_counts()" + ] + }, + { + "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": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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urlcharsetserverwhois_countrywhois_stateprowhois_regdatewhois_updated_date
0M0_109iso-8859-1nginxNaNNaN<bound method NDFrame.bfill of 0 2015-10...<bound method NDFrame.bfill of 0 ...
1B0_2314UTF-8Apache/2.4.10NaNNaN<bound method NDFrame.bfill of 0 2015-10...<bound method NDFrame.bfill of 0 ...
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3B0_113ISO-8859-1nginxUSAK<bound method NDFrame.bfill of 0 2015-10...<bound method NDFrame.bfill of 0 ...
4B0_403UTF-8NaNUSTX<bound method NDFrame.bfill of 0 2015-10...<bound method NDFrame.bfill of 0 ...
........................
1776M4_48UTF-8ApacheESBarcelona<bound method NDFrame.bfill of 0 2015-10...<bound method NDFrame.bfill of 0 ...
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1780B0_676utf-8Microsoft-IIS/8.5USWisconsin<bound method NDFrame.bfill of 0 2015-10...<bound method NDFrame.bfill of 0 ...
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whois_countrycount
0US1106
1CA84
2ES63
3UK35
4AU35
5PA21
6JP11
7CN10
8IN10
9FR9
10CZ9
11CH6
12NL6
13KR5
14PH4
15ru4
16AT4
17BS4
18SE4
19KY3
20SC3
21BE3
22TR3
23HK3
24DE3
25BR2
26CY2
27UA2
28UY2
29KG2
30RU2
31NO2
32IL2
33SI2
34LV1
35PK1
36IT1
37LU1
38BY1
39AE1
40IE1
41UG1
42TH1
\n", + "" + ], + "text/plain": [ + " whois_country count\n", + "0 US 1106\n", + "1 CA 84\n", + "2 ES 63\n", + "3 UK 35\n", + "4 AU 35\n", + "5 PA 21\n", + "6 JP 11\n", + "7 CN 10\n", + "8 IN 10\n", + "9 FR 9\n", + "10 CZ 9\n", + "11 CH 6\n", + "12 NL 6\n", + "13 KR 5\n", + "14 PH 4\n", + "15 ru 4\n", + "16 AT 4\n", + "17 BS 4\n", + "18 SE 4\n", + "19 KY 3\n", + "20 SC 3\n", + "21 BE 3\n", + "22 TR 3\n", + "23 HK 3\n", + "24 DE 3\n", + "25 BR 2\n", + "26 CY 2\n", + "27 UA 2\n", + "28 UY 2\n", + "29 KG 2\n", + "30 RU 2\n", + "31 NO 2\n", + "32 IL 2\n", + "33 SI 2\n", + "34 LV 1\n", + "35 PK 1\n", + "36 IT 1\n", + "37 LU 1\n", + "38 BY 1\n", + "39 AE 1\n", + "40 IE 1\n", + "41 UG 1\n", + "42 TH 1" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Tabla de frecuencias de \"whois_country\"\n", + "tabla_frecuencias = categorics.whois_country.value_counts().reset_index()\n", + "tabla_frecuencias" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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whois_countryabs_frequency
0US1106
1CA84
2ES63
3UK35
4AU35
5PA21
6JP11
7CN10
8IN10
9FR9
10CZ9
11CH6
12NL6
13KR5
14PH4
15ru4
16AT4
17BS4
18SE4
19KY3
20SC3
21BE3
22TR3
23HK3
24DE3
25BR2
26CY2
27UA2
28UY2
29KG2
30RU2
31NO2
32IL2
33SI2
34LV1
35PK1
36IT1
37LU1
38BY1
39AE1
40IE1
41UG1
42TH1
\n", + "
" + ], + "text/plain": [ + " whois_country abs_frequency\n", + "0 US 1106\n", + "1 CA 84\n", + "2 ES 63\n", + "3 UK 35\n", + "4 AU 35\n", + "5 PA 21\n", + "6 JP 11\n", + "7 CN 10\n", + "8 IN 10\n", + "9 FR 9\n", + "10 CZ 9\n", + "11 CH 6\n", + "12 NL 6\n", + "13 KR 5\n", + "14 PH 4\n", + "15 ru 4\n", + "16 AT 4\n", + "17 BS 4\n", + "18 SE 4\n", + "19 KY 3\n", + "20 SC 3\n", + "21 BE 3\n", + "22 TR 3\n", + "23 HK 3\n", + "24 DE 3\n", + "25 BR 2\n", + "26 CY 2\n", + "27 UA 2\n", + "28 UY 2\n", + "29 KG 2\n", + "30 RU 2\n", + "31 NO 2\n", + "32 IL 2\n", + "33 SI 2\n", + "34 LV 1\n", + "35 PK 1\n", + "36 IT 1\n", + "37 LU 1\n", + "38 BY 1\n", + "39 AE 1\n", + "40 IE 1\n", + "41 UG 1\n", + "42 TH 1" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Defino nombre de columnas (para poner lindo)\n", + "\n", + "tabla_frecuencias.columns = [\"whois_country\", \"abs_frequency\"]\n", + "tabla_frecuencias" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dalmi\\AppData\\Local\\Temp\\ipykernel_10612\\3883551541.py:3: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(data=tabla_frecuencias, x=\"whois_country\", y=\"abs_frequency\", palette=\"coolwarm\")\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# BarChart de \"tabla_frecuencias\"\n", + "\n", + "sns.barplot(data=tabla_frecuencias, x=\"whois_country\", y=\"abs_frequency\", palette=\"coolwarm\")\n", + "plt.xticks(rotation=45)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### After verifying, now let's keep the top 10 values of the column and re-label other columns with `OTHER`." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dalmi\\AppData\\Local\\Temp\\ipykernel_10612\\252653989.py:4: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(data=top_10_whois_country, x=\"whois_country\", y=\"abs_frequency\", palette=\"coolwarm\")\n" + ] + }, + { + "data": { + "image/png": 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whois_countryabs_frequency
10CZ9
11CH6
12NL6
13KR5
14PH4
15ru4
16AT4
17BS4
18SE4
19KY3
20SC3
21BE3
22TR3
23HK3
24DE3
25BR2
26CY2
27UA2
28UY2
29KG2
30RU2
31NO2
32IL2
33SI2
34LV1
35PK1
36IT1
37LU1
38BY1
39AE1
40IE1
41UG1
42TH1
\n", + "
" + ], + "text/plain": [ + " whois_country abs_frequency\n", + "10 CZ 9\n", + "11 CH 6\n", + "12 NL 6\n", + "13 KR 5\n", + "14 PH 4\n", + "15 ru 4\n", + "16 AT 4\n", + "17 BS 4\n", + "18 SE 4\n", + "19 KY 3\n", + "20 SC 3\n", + "21 BE 3\n", + "22 TR 3\n", + "23 HK 3\n", + "24 DE 3\n", + "25 BR 2\n", + "26 CY 2\n", + "27 UA 2\n", + "28 UY 2\n", + "29 KG 2\n", + "30 RU 2\n", + "31 NO 2\n", + "32 IL 2\n", + "33 SI 2\n", + "34 LV 1\n", + "35 PK 1\n", + "36 IT 1\n", + "37 LU 1\n", + "38 BY 1\n", + "39 AE 1\n", + "40 IE 1\n", + "41 UG 1\n", + "42 TH 1" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Guardo los demas valores en other\n", + "\n", + "other_whois_country = tabla_frecuencias[10: ]\n", + "other_whois_country" + ] + }, + { + "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": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " url charset server whois_country\n", + "0 M0_109 iso-8859-1 nginx NaN\n", + "1 B0_2314 UTF-8 Apache/2.4.10 NaN\n", + "2 B0_911 us-ascii Microsoft-HTTPAPI/2.0 NaN\n", + "3 B0_113 ISO-8859-1 nginx US\n", + "4 B0_403 UTF-8 NaN US\n", + "... ... ... ... ...\n", + "1776 M4_48 UTF-8 Apache ES\n", + "1777 M4_41 UTF-8 Apache ES\n", + "1778 B0_162 utf-8 Apache/2.2.16 (Debian) US\n", + "1779 B0_1152 ISO-8859-1 cloudflare-nginx US\n", + "1780 B0_676 utf-8 Microsoft-IIS/8.5 US\n", + "\n", + "[1778 rows x 4 columns]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Elimino las columnas \"whois_statepro\", \"whois_regdate\", \"whois_updated_date\"\n", + "\n", + "categorics = categorics.drop(columns = [\"whois_statepro\", \"whois_regdate\", \"whois_updated_date\"])\n", + "categorics" + ] + }, + { + "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": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "url object\n", + "charset object\n", + "server object\n", + "whois_country object\n", + "dtype: object" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "categorics.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1778" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "categorics.url.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['nginx', 'Apache/2.4.10', 'Microsoft-HTTPAPI/2.0', nan, 'Apache/2',\n", + " 'nginx/1.10.1', 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'Apache/2.2.31 (CentOS)',\n", + " 'Apache/2.4.12 (Ubuntu)', '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',\n", + " 'Sucuri/Cloudproxy', 'Apache/2.4.23 (Unix)', 'nginx/0.7.65',\n", + " 'mw2260.codfw.wmnet', 'Apache/2.2.32', 'mw2239.codfw.wmnet',\n", + " 'DPS/1.1.8', '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)',\n", + " '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',\n", + " 'nginx/1.11.2', 'nginx/1.10.0 (Ubuntu)', 'nginx/1.8.1',\n", + " 'nginx/1.11.10', '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)',\n", + " 'nginx/1.4.6 (Ubuntu)', 'nginx + Phusion Passenger',\n", + " 'Proxy Pandeiro UOL', 'mw2231.codfw.wmnet', 'openresty/1.11.2.2',\n", + " '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',\n", + " 'Resin/3.1.8', 'mw2164.codfw.wmnet', 'Microsoft-IIS/8.5',\n", + " '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',\n", + " 'mw2199.codfw.wmnet', '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',\n", + " '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',\n", + " 'Apache/2.4.10 (Unix) OpenSSL/1.0.1k',\n", + " 'Apache/Not telling (Unix) AuthTDS/1.1',\n", + " 'Apache/2.2.11 (Unix) PHP/5.2.6', 'Scratch Web Server',\n", + " 'marrakesh 1.12.2', 'nginx/0.8.35', 'mw2182.codfw.wmnet',\n", + " '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',\n", + " 'mw2171.codfw.wmnet', 'mw2257.codfw.wmnet', 'mw2226.codfw.wmnet',\n", + " 'DMS/1.0.42', 'nginx/1.6.3', 'Application-Server',\n", + " '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',\n", + " 'nginx/0.8.38', 'mw2238.codfw.wmnet', 'Pizza/pepperoni',\n", + " 'XXXXXXXXXXXXXXXXXXXXXX', 'MI', '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)',\n", + " '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)'], dtype=object)" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "categorics.server.unique()" + ] + }, + { + "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": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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............
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" + ], + "text/plain": [ + " charset server whois_country\n", + "0 iso-8859-1 nginx NaN\n", + "1 UTF-8 Apache/2.4.10 NaN\n", + "2 us-ascii Microsoft-HTTPAPI/2.0 NaN\n", + "3 ISO-8859-1 nginx US\n", + "4 UTF-8 NaN US\n", + "... ... ... ...\n", + "1776 UTF-8 Apache ES\n", + "1777 UTF-8 Apache ES\n", + "1778 utf-8 Apache/2.2.16 (Debian) US\n", + "1779 ISO-8859-1 cloudflare-nginx US\n", + "1780 utf-8 Microsoft-IIS/8.5 US\n", + "\n", + "[1778 rows x 3 columns]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Elimino columna url\n", + "\n", + "categorics = categorics.drop(columns=[\"url\"])\n", + "categorics\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Print the unique value counts of `CHARSET`. You see there are only a few unique values. So we can keep it as it is." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "categorics.charset.nunique()" + ] + }, + { + "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": "markdown", + "metadata": {}, + "source": [ + "![Think Hard](../images/think-hard.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although there are so many unique values in the `SERVER` column, there are actually only 3 main server types: `Microsoft`, `Apache`, and `nginx`. Just check if each `SERVER` value contains any of those server types and re-label them. For `SERVER` values that don't contain any of those substrings, label with `Other`.\n", + "\n", + "At the end, your `SERVER` column should only contain 4 unique values: `Microsoft`, `Apache`, `nginx`, and `Other`." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "import re\n", + "\n", + "regex = r\"^[^/]+\"\n", + "categorics['server'] = categorics['server'].apply(lambda x: re.match(regex, str(x)).group(0) if isinstance(x, str) and re.match(regex, str(x)) else x)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 54, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array(['nginx', 'Apache', 'Microsoft-HTTPAPI', nan, 'openresty',\n", + " 'Oracle-iPlanet-Web-Server', 'cloudflare-nginx',\n", + " 'Heptu web server', 'Pepyaka', 'Microsoft-IIS', 'LiteSpeed',\n", + " 'tsa_c', 'GSE', 'Tengine', 'Sun-ONE-Web-Server', 'AmazonS3', 'ATS',\n", + " 'CherryPy', 'Server', 'KHL', 'mw2114.codfw.wmnet',\n", + " 'Jetty(9.0.z-SNAPSHOT)', 'HTTPDaemon', 'MediaFire', 'DOSarrest',\n", + " 'mw2232.codfw.wmnet', 'Sucuri', 'mw2260.codfw.wmnet',\n", + " 'mw2239.codfw.wmnet', 'DPS', 'SSWS', 'YouTubeFrontEnd', 'Squeegit',\n", + " 'Virtuoso', 'Apache-Coyote', 'Yippee-Ki-Yay', 'mw2165.codfw.wmnet',\n", + " 'mw2192.codfw.wmnet', 'nginx + Phusion Passenger',\n", + " 'Proxy Pandeiro UOL', 'mw2231.codfw.wmnet', 'mw2109.codfw.wmnet',\n", + " 'mw2225.codfw.wmnet', 'mw2236.codfw.wmnet', 'mw2101.codfw.wmnet',\n", + " 'Varnish', 'Resin', 'mw2164.codfw.wmnet', 'mw2242.codfw.wmnet',\n", + " 'mw2175.codfw.wmnet', 'mw2107.codfw.wmnet', 'mw2190.codfw.wmnet',\n", + " 'barista', 'mw2103.codfw.wmnet', 'ECD (fll', 'Pagely Gateway',\n", + " 'mw2097.codfw.wmnet', 'mw2233.codfw.wmnet', 'fbs',\n", + " 'mw2199.codfw.wmnet', 'mw2255.codfw.wmnet', 'mw2228.codfw.wmnet',\n", + " 'gunicorn', 'mw2241.codfw.wmnet', 'lighttpd', 'mw2230.codfw.wmnet',\n", + " 'AkamaiGHost', 'mw2240.codfw.wmnet', 'PWS', 'nxfps', 'Play',\n", + " 'mw2185.codfw.wmnet', 'Scratch Web Server', 'marrakesh 1.12.2',\n", + " 'mw2182.codfw.wmnet', 'squid', 'Nginx (OpenBSD)', 'Zope',\n", + " 'mw2104.codfw.wmnet', '.V01 Apache', 'mw2110.codfw.wmnet',\n", + " 'mw2176.codfw.wmnet', 'mw2187.codfw.wmnet', 'mw2106.codfw.wmnet',\n", + " 'Aeria Games & Entertainment', 'mw2173.codfw.wmnet',\n", + " 'mw2198.codfw.wmnet', 'mw2172.codfw.wmnet', 'Cowboy',\n", + " 'mw2113.codfw.wmnet', 'mw2224.codfw.wmnet', 'mw2171.codfw.wmnet',\n", + " 'mw2257.codfw.wmnet', 'mw2226.codfw.wmnet', 'DMS',\n", + " 'Application-Server', 'mw2177.codfw.wmnet', 'mw2197.codfw.wmnet',\n", + " 'mw2178.codfw.wmnet', '294', 'www.lexisnexis.com 9999',\n", + " 'mw2238.codfw.wmnet', 'Pizza', 'XXXXXXXXXXXXXXXXXXXXXX', 'MI',\n", + " 'Roxen', 'mw2180.codfw.wmnet', 'ebay server',\n", + " 'Boston.com Frontend', 'My Arse', 'IdeaWebServer'], dtype=object)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your comment here" + "categorics.server.unique()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 55, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "server\n", + "Apache 621\n", + "nginx 337\n", + "Microsoft-HTTPAPI 113\n", + "cloudflare-nginx 94\n", + "Microsoft-IIS 85\n", + " ... \n", + "mw2103.codfw.wmnet 1\n", + "barista 1\n", + "mw2190.codfw.wmnet 1\n", + "mw2107.codfw.wmnet 1\n", + "IdeaWebServer 1\n", + "Name: count, Length: 110, dtype: int64" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### Next, evaluate if the columns in this dataset are strongly correlated.\n", - "\n", - "If our dataset has strongly correlated columns, 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." + "categorics.server.value_counts()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "top_3_values = categorics[\"server\"].value_counts().head(3).index\n", + "categorics[\"server\"] = categorics[\"server\"].apply(lambda x: x if x in top_3_values else \"other\")" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 57, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "server\n", + "other 707\n", + "Apache 621\n", + "nginx 337\n", + "Microsoft-HTTPAPI 113\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your comment here" + "categorics.server.value_counts()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 58, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['nginx', 'Apache', 'Microsoft-HTTPAPI', 'other'], dtype=object)" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], "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", + "#Cheque los nuevos valores únicos de server \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." + "categorics.server.unique()" ] }, { - "cell_type": "code", - "execution_count": 7, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Your code here\n" + "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)). Also, assign the data with dummy values to a new variable `website_dummy`.**" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "charset 8\n", + "server 4\n", + "whois_country 43\n", + "dtype: int64" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your comment here" + "categorics.nunique()" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ - "# Print heatmap again\n" + "websites_dummy = pd.get_dummies(categorics, columns=[\"charset\",\"whois_country\",\"whois_country\"])" ] }, { "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.**" + "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": 10, + "execution_count": 61, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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After dropping those problematic columns, we drop the rows with missing values.\n", + "#Agrego type a website_dummy\n", "\n", - "#### In the cells below, handle the missing values from the dataset. Remember to comment the rationale of your decisions." + "websites_dummy[\"type\"] = websites.type\n", + "websites_dummy" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 63, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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comment here" + "websites_dummy.reset_index(inplace=True)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 65, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " url_length number_special_characters tcp_conversation_exchange \\\n", + "0 16 7 7 \n", + "1 16 6 17 \n", + "2 16 6 0 \n", + "3 17 6 31 \n", + "4 17 6 57 \n", + "... ... ... ... \n", + "1776 194 16 0 \n", + "1777 198 17 0 \n", + "1778 201 34 83 \n", + "1779 234 34 0 \n", + "1780 249 40 19 \n", + "\n", + " dist_remote_tcp_port remote_ips app_bytes source_app_bytes \\\n", + "0 0 2 700 1153 \n", + "1 7 4 1230 1265 \n", + "2 0 0 0 0 \n", + "3 22 3 3812 18784 \n", + "4 2 5 4278 129889 \n", + "... ... ... ... ... \n", + "1776 0 0 0 186 \n", + "1777 0 0 0 124 \n", + "1778 2 6 6631 132181 \n", + "1779 0 0 0 0 \n", + "1780 6 11 2314 3039 \n", + "\n", + " dns_query_times \n", + "0 2.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 8.0 \n", + "4 4.0 \n", + "... ... \n", + "1776 0.0 \n", + "1777 0.0 \n", + "1778 4.0 \n", + "1779 0.0 \n", + "1780 6.0 \n", + "\n", + "[1778 rows x 8 columns]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### Again, examine the number of missing values in each column. \n", + "#Elimino type del df numerics\n", "\n", - "If all cleaned, proceed. Otherwise, go back and do more cleaning." + "numerics = numerics.drop(columns=[\"type\"])\n", + "numerics" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ - "# Examine missing values in each column\n" + "numerics.reset_index(inplace=True)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 67, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1778, 96)" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Challenge 4 - Handle `WHOIS_*` Categorical Data" + "websites_dummy.shape" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 69, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1778, 9)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], "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." + "numerics.shape" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "#Juntamos los df websites_dummy y numerics\n", + "\n", + "websites_final = numerics.merge(websites_dummy, on=\"index\", how=\"inner\")" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 71, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1778 rows × 103 columns

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... ... ... ... \n", + "1773 0 0 0 0 \n", + "1774 0 0 0 0 \n", + "1775 0 0 0 0 \n", + "1776 0 0 0 0 \n", + "1777 0 0 0 0 \n", + "\n", + " whois_country_PK whois_country_RU whois_country_SC whois_country_SE \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "3 0 0 0 0 \n", + "4 0 0 0 0 \n", + "... ... ... ... ... \n", + "1773 0 0 0 0 \n", + "1774 0 0 0 0 \n", + "1775 0 0 0 0 \n", + "1776 0 0 0 0 \n", + "1777 0 0 0 0 \n", + "\n", + " whois_country_SI whois_country_TH whois_country_TR whois_country_UA \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "3 0 0 0 0 \n", + "4 0 0 0 0 \n", + "... ... ... ... ... \n", + "1773 0 0 0 0 \n", + "1774 0 0 0 0 \n", + "1775 0 0 0 0 \n", + "1776 0 0 0 0 \n", + "1777 0 0 0 0 \n", + "\n", + " whois_country_UG whois_country_UK whois_country_US whois_country_UY \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "3 0 0 1 0 \n", + "4 0 0 1 0 \n", + "... ... ... ... ... \n", + "1773 0 0 0 0 \n", + "1774 0 0 0 0 \n", + "1775 0 0 1 0 \n", + "1776 0 0 1 0 \n", + "1777 0 0 1 0 \n", + "\n", + " whois_country_ru type \n", + "0 0 1 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "... ... ... \n", + "1773 0 1 \n", + "1774 0 1 \n", + "1775 0 0 \n", + "1776 0 0 \n", + "1777 0 0 \n", + "\n", + "[1778 rows x 103 columns]" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], "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", + "#Elimino la columna de indice que no aporta nada para el modelado\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:" + "websites_final = websites_final.drop(columns=[\"index\"])\n", + "websites_final" ] }, { - "cell_type": "code", - "execution_count": 15, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Your code here\n" + "# 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": "markdown", "metadata": {}, "source": [ - "#### After verifying, now let's keep the top 10 values of the column and re-label other columns with `OTHER`." + "

X-y Split

" ] }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "X = websites_final.drop(\"type\", axis=1)\n", + "y = websites_final[\"type\"]" ] }, { "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']`." + "

Train-Test Split

" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 75, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100% of our data: 1778.\n", + "70% for training data: 1422.\n", + "30% for test data: 356.\n" + ] + } + ], "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`." + "print(f'100% of our data: {len(websites_final)}.')\n", + "print(f'70% for training data: {len(X_train)}.')\n", + "print(f'30% for test data: {len(X_test)}.')" ] }, { - "cell_type": "code", - "execution_count": 18, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Your code here\n" + "#### 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": "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." + "

Model Selection: Logistic Regression

" ] }, { - "cell_type": "code", - "execution_count": 19, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Your code here\n" + "Logistic regression is one of the most popular and used algorithms for classification problems. Since it is also relatively uncomplicated and easy to implement, it is often used as a starting model, although it can also produce very high-performance results used in production. Here we are going to talk about Binomial Logistic Regression, which is used for binary results. Multinomial Logistic Regression exists and can be used for multiclass classification problems, but it is used less frequently. We will not cover it in this lesson.\n", + "\n", + "Logistic regression is actually a transformed linear regression function. We can see in the image below that if we tried to fit a linear regression to some data with a binary result, we would fit a line that does not predict very well for any value that is not in the extreme values: in the middle there is a lot of area where the line is very far from the points. To make our function closer to the data, we have to transform the function we are using. In this case, it is useful to use a sigmoid function, which estimates an \"S\" shape. Now we can see that our line fits the data much better." ] }, { "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." + "![regresiónlogística](http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1534281070/linear_vs_logistic_regression_edxw03.png)" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 77, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\dalmi\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: 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/html": [ + "
LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LogisticRegression()" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here" + "model = LogisticRegression()\n", + "model.fit(X_train, y_train)" ] }, { "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." + "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": 21, + "execution_count": 78, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20% for test prediction data: 356.\n", + " precision recall f1-score support\n", + "\n", + " 0 0.91 0.98 0.95 316\n", + " 1 0.62 0.25 0.36 40\n", + "\n", + " accuracy 0.90 356\n", + " macro avg 0.77 0.62 0.65 356\n", + "weighted avg 0.88 0.90 0.88 356\n", + "\n" + ] + } + ], "source": [ - "# Your code here\n" + "predictions = model.predict(X_test)\n", + "print(f'20% for test prediction data: {len(predictions)}.')\n", + "print(classification_report(y_test, predictions))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 79, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data accuracy: 0.898876404494382\n", + "Train data accuracy: 0.89943741209564\n" + ] + } + ], "source": [ - "![Think Hard](../images/think-hard.jpg)" + "print(\"Test data accuracy: \",model.score(X_test,y_test))\n", + "print(\"Train data accuracy: \", model.score(X_train, y_train))" ] }, { - "cell_type": "code", - "execution_count": 24, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Your comment here\n" + "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": "markdown", + "cell_type": "code", + "execution_count": 80, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "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`." + "cm = confusion_matrix(y_test, predictions)\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n", + "plt.figure(figsize=(8, 6))\n", + "disp.plot(cmap='Oranges') \n", + "plt.grid(True)\n", + "plt.show()" ] }, { - "cell_type": "code", - "execution_count": 25, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Your code here\n" + "What are your thoughts on the performance of the model? Write your conclusions below." ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ - "# Count `SERVER` value counts here\n" + "# Podemos ver que tiene una precisión de 0.91 para predecir las páginas que no son fraudulentas (0), mientras que únicamente tiene\n", + "# una precisión del 0.62 para las páginas que si lo son (1)" ] }, { "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)). Also, assign the data with dummy values to a new variable `website_dummy`.**" + "#### 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": 27, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "model = KNeighborsClassifier(n_neighbors=3)\n", + "model = model.fit(X_train, y_train)" ] }, { "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." + "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": 28, + "execution_count": 83, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.95 0.98 0.97 316\n", + " 1 0.81 0.62 0.70 40\n", + "\n", + " accuracy 0.94 356\n", + " macro avg 0.88 0.80 0.84 356\n", + "weighted avg 0.94 0.94 0.94 356\n", + "\n" + ] + } + ], "source": [ - "# Your code here\n" + "predictions = model.predict(X_test)\n", + "print(classification_report(y_test, predictions))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 84, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data accuracy: 0.9410112359550562\n", + "Train data accuracy: 0.9571026722925458\n" + ] + } + ], "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.**" + "print(\"Test data accuracy: \",model.score(X_test,y_test))\n", + "print(\"Train data accuracy: \", model.score(X_train, y_train))" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 85, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "# Your code here:\n" + "cm = confusion_matrix(y_test, predictions)\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n", + "plt.figure(figsize=(8, 6))\n", + "disp.plot(cmap='Oranges') \n", + "plt.grid(True)\n", + "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### In this lab, we will try two different models and compare our results.\n", + "#### We'll create another K-Nearest Neighbors model with k=5. \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." + "Initialize and fit the model below and print the confusion matrix and the accuracy score." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 86, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.95 0.97 0.96 316\n", + " 1 0.74 0.62 0.68 40\n", + "\n", + " accuracy 0.93 356\n", + " macro avg 0.84 0.80 0.82 356\n", + "weighted avg 0.93 0.93 0.93 356\n", + "\n" + ] + } + ], "source": [ - "# Your code here:\n", - "\n" + "model = KNeighborsClassifier(n_neighbors=5)\n", + "model = model.fit(X_train, y_train)\n", + "predictions = model.predict(X_test)\n", + "print(classification_report(y_test, predictions))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 87, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data accuracy: 0.9325842696629213\n", + "Train data accuracy: 0.9430379746835443\n" + ] + } + ], "source": [ - "Next, fit the model to our training data. We have already separated our data into 4 parts. Use those in your model." + "print(\"Test data accuracy: \",model.score(X_test,y_test))\n", + "print(\"Train data accuracy: \", model.score(X_train, y_train))" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 88, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Your code here:\n", - "\n" + "cm = confusion_matrix(y_test, predictions)\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n", + "plt.figure(figsize=(8, 6))\n", + "disp.plot(cmap='Oranges') \n", + "plt.grid(True)\n", + "plt.show()" ] }, { "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" + "Did you see an improvement in the confusion matrix when increasing k to 5? Did you see an improvement in the accuracy score? Write your conclusions below." ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n", - "\n" + "# Train-data accuracy con k=3: 0.957\n", + "# Train-data accuracy con k=5: 0.943\n", + "# La confusión matrix empeora un poco al incrementar la k a 5\n", + "# La accuracy score también empeora un poco al incrementar esta misma k" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "What are your thoughts on the performance of the model? Write your conclusions below." + "# 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": 33, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ - "# Your conclusions here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Our second algorithm is is K-Nearest Neighbors. \n", - "\n", - "Though is it not required, we will fit a model using the training data and then test the performance of the model using the testing data. Start by loading `KNeighborsClassifier` from scikit-learn and then initializing and fitting the model. We'll start off with a model where k=3." + "from sklearn.preprocessing import RobustScaler" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n", - "\n" + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 91, "metadata": {}, + "outputs": [], "source": [ - "To test your model, compute the predicted values for the testing sample and print the confusion matrix as well as the accuracy score." + "scaler = RobustScaler()" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n", - "\n" + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 93, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LogisticRegression()
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" + ], + "text/plain": [ + "LogisticRegression()" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], "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." + "model = LogisticRegression()\n", + "model.fit(X_train_scaled, y_train)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n", - "\n" + "predictions = model.predict(X_test_scaled)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 95, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20% for test prediction data: 356.\n" + ] + } + ], "source": [ - "Did you see an improvement in the confusion matrix when increasing k to 5? Did you see an improvement in the accuracy score? Write your conclusions below." + "print(f'20% for test prediction data: {len(predictions)}.')" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 96, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.95 1.00 0.97 316\n", + " 1 1.00 0.55 0.71 40\n", + "\n", + " accuracy 0.95 356\n", + " macro avg 0.97 0.78 0.84 356\n", + "weighted avg 0.95 0.95 0.94 356\n", + "\n" + ] + } + ], "source": [ - "# Your conclusions here:\n", - "\n" + "print(classification_report(y_test, predictions))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 97, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data accuracy: 0.949438202247191\n", + "Train data accuracy: 0.9388185654008439\n" + ] + } + ], "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?" + "print(\"Test data accuracy: \",model.score(X_test_scaled,y_test))\n", + "print(\"Train data accuracy: \", model.score(X_train_scaled, y_train))" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 98, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Your code here" + "cm = confusion_matrix(y_test, predictions)\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n", + "plt.figure(figsize=(8, 6))\n", + "disp.plot(cmap='Oranges') \n", + "plt.grid(True)\n", + "plt.show()" ] } ], "metadata": { "kernelspec": { - "display_name": "ironhack-3.7", + "display_name": "base", "language": "python", - "name": "ironhack-3.7" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -669,7 +15762,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.12.4" } }, "nbformat": 4,