diff --git a/your-code/main.ipynb b/your-code/main.ipynb index a5caf8b..9e79014 100755 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 132, "metadata": {}, "outputs": [], "source": [ @@ -21,7 +21,76 @@ "%matplotlib inline\n", "\n", "import numpy as np\n", - "import pandas as pd" + "import pandas as pd\n", + "import warnings # nobody likes warnings\n", + "\n", + "# 📊 Visualizations\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.neighbors import KNeighborsClassifier\n", + "from sklearn.metrics import roc_curve, confusion_matrix, ConfusionMatrixDisplay\n", + "from sklearn.metrics import classification_report \n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.preprocessing import RobustScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [], + "source": [ + "# ⚙️ Settings\n", + "pd.set_option('display.max_columns', None) # display all columns\n", + "warnings.filterwarnings('ignore') # ignore warnings" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [], + "source": [ + "# 🔧 Basic functions\n", + "def snake_columns(data): \n", + " \"\"\"\n", + " returns the columns in snake case\n", + " \"\"\"\n", + " data.columns = [column.lower().replace(' ', '_') for column in data.columns]\n", + " \n", + "def open_data(data): # returns shape, data types & shows a small sample\n", + " print(f\"Data shape is {data.shape}.\")\n", + " print()\n", + " print(data.dtypes)\n", + " print()\n", + " print(\"Data row sample and full columns:\")\n", + " return data.sample(5)\n", + "\n", + "# 🎯 Specific functions\n", + "def explore_data(data): # sum & returns duplicates, NaN & empty spaces\n", + " duplicate_rows = data.duplicated().sum()\n", + " nan_values = data.isna().sum()\n", + " empty_spaces = data.eq(' ').sum()\n", + " import pandas as pd\n", + " exploration = pd.DataFrame({\"NaN\": nan_values, \"EmptySpaces\": empty_spaces}) # New dataframe with the results\n", + " print(f\"There are {data.duplicated().sum()} duplicate rows. Also;\")\n", + " return exploration\n", + "\n", + "## La siguiente USAR CON MODERACIÓN\n", + "\n", + "def outlier_slayer(data): # automatically removes outliers based on Q1, Q3\n", + " for column in data.select_dtypes(include=[np.number]):\n", + " Q1 = data[column].quantile(0.25)\n", + " Q3 = data[column].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " lower_bound = Q1 - 1.5 * IQR\n", + " upper_bound = Q3 + 1.5 * IQR\n", + " data = data[(data[column] >= lower_bound) & (data[column] <= upper_bound)]\n", + " return data" ] }, { @@ -37,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 135, "metadata": {}, "outputs": [], "source": [ @@ -65,20 +134,251 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 136, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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URLURL_LENGTHNUMBER_SPECIAL_CHARACTERSCHARSETSERVERCONTENT_LENGTHWHOIS_COUNTRYWHOIS_STATEPROWHOIS_REGDATEWHOIS_UPDATED_DATETCP_CONVERSATION_EXCHANGEDIST_REMOTE_TCP_PORTREMOTE_IPSAPP_BYTESSOURCE_APP_PACKETSREMOTE_APP_PACKETSSOURCE_APP_BYTESREMOTE_APP_BYTESAPP_PACKETSDNS_QUERY_TIMESType
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" + ], + "text/plain": [ + " URL URL_LENGTH NUMBER_SPECIAL_CHARACTERS CHARSET \\\n", + "0 M0_109 16 7 iso-8859-1 \n", + "1 B0_2314 16 6 UTF-8 \n", + "2 B0_911 16 6 us-ascii \n", + "3 B0_113 17 6 ISO-8859-1 \n", + "4 B0_403 17 6 UTF-8 \n", + "\n", + " SERVER CONTENT_LENGTH WHOIS_COUNTRY WHOIS_STATEPRO \\\n", + "0 nginx 263.0 NaN NaN \n", + "1 Apache/2.4.10 15087.0 NaN NaN \n", + "2 Microsoft-HTTPAPI/2.0 324.0 NaN NaN \n", + "3 nginx 162.0 US AK \n", + "4 NaN 124140.0 US TX \n", + "\n", + " WHOIS_REGDATE WHOIS_UPDATED_DATE TCP_CONVERSATION_EXCHANGE \\\n", + "0 10/10/2015 18:21 NaN 7 \n", + "1 NaN NaN 17 \n", + "2 NaN NaN 0 \n", + "3 7/10/1997 4:00 12/09/2013 0:45 31 \n", + "4 12/05/1996 0:00 11/04/2017 0:00 57 \n", + "\n", + " DIST_REMOTE_TCP_PORT REMOTE_IPS APP_BYTES SOURCE_APP_PACKETS \\\n", + "0 0 2 700 9 \n", + "1 7 4 1230 17 \n", + "2 0 0 0 0 \n", + "3 22 3 3812 39 \n", + "4 2 5 4278 61 \n", + "\n", + " REMOTE_APP_PACKETS SOURCE_APP_BYTES REMOTE_APP_BYTES APP_PACKETS \\\n", + "0 10 1153 832 9 \n", + "1 19 1265 1230 17 \n", + "2 0 0 0 0 \n", + "3 37 18784 4380 39 \n", + "4 62 129889 4586 61 \n", + "\n", + " DNS_QUERY_TIMES Type \n", + "0 2.0 1 \n", + "1 0.0 0 \n", + "2 0.0 0 \n", + "3 8.0 0 \n", + "4 4.0 0 " + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "df=websites.copy()\n", + "df.head()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 137, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(1781, 21)" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your comment here" + "# Your code here\n", + "df.shape" ] }, { @@ -102,562 +402,5016 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 138, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(1781, 21)" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "df.shape" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 139, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1781 entries, 0 to 1780\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 URL 1781 non-null object \n", + " 1 URL_LENGTH 1781 non-null int64 \n", + " 2 NUMBER_SPECIAL_CHARACTERS 1781 non-null int64 \n", + " 3 CHARSET 1774 non-null object \n", + " 4 SERVER 1605 non-null object \n", + " 5 CONTENT_LENGTH 969 non-null float64\n", + " 6 WHOIS_COUNTRY 1475 non-null object \n", + " 7 WHOIS_STATEPRO 1419 non-null object \n", + " 8 WHOIS_REGDATE 1654 non-null object \n", + " 9 WHOIS_UPDATED_DATE 1642 non-null object \n", + " 10 TCP_CONVERSATION_EXCHANGE 1781 non-null int64 \n", + " 11 DIST_REMOTE_TCP_PORT 1781 non-null int64 \n", + " 12 REMOTE_IPS 1781 non-null int64 \n", + " 13 APP_BYTES 1781 non-null int64 \n", + " 14 SOURCE_APP_PACKETS 1781 non-null int64 \n", + " 15 REMOTE_APP_PACKETS 1781 non-null int64 \n", + " 16 SOURCE_APP_BYTES 1781 non-null int64 \n", + " 17 REMOTE_APP_BYTES 1781 non-null int64 \n", + " 18 APP_PACKETS 1781 non-null int64 \n", + " 19 DNS_QUERY_TIMES 1780 non-null float64\n", + " 20 Type 1781 non-null int64 \n", + "dtypes: float64(2), int64(12), object(7)\n", + "memory usage: 292.3+ KB\n" + ] + } + ], "source": [ - "# Your comment here" + "# Your comment here\n", + "df.info()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 140, "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": 140, + "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", - "\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." + "df.nunique()" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 141, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['URL',\n", + " 'URL_LENGTH',\n", + " 'NUMBER_SPECIAL_CHARACTERS',\n", + " 'CHARSET',\n", + " 'SERVER',\n", + " 'CONTENT_LENGTH',\n", + " 'WHOIS_COUNTRY',\n", + " 'WHOIS_STATEPRO',\n", + " 'WHOIS_REGDATE',\n", + " 'WHOIS_UPDATED_DATE',\n", + " 'TCP_CONVERSATION_EXCHANGE',\n", + " 'DIST_REMOTE_TCP_PORT',\n", + " 'REMOTE_IPS',\n", + " 'APP_BYTES',\n", + " 'SOURCE_APP_PACKETS',\n", + " 'REMOTE_APP_PACKETS',\n", + " 'SOURCE_APP_BYTES',\n", + " 'REMOTE_APP_BYTES',\n", + " 'APP_PACKETS',\n", + " 'DNS_QUERY_TIMES',\n", + " 'Type']" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "df.columns.to_list()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 142, "metadata": {}, - "outputs": [], + "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 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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 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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 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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 2851 900 2362906 486 1134 1249 3229 772\n", + " 4997 3859 3777 1675 2515 826 2182 10006 4350\n", + " 1188 276 1860 8808 4848 4183 9891 2194 3706\n", + " 1031 9452 738 432 929 3037 3181 2736 2543\n", + " 4417 7217 1519 9020 3367 1881 1452 2498 8497\n", + " 2456 12492 798 958 11147 4650 3465 2168 1060\n", + " 4945 2349 1787 3431 2577 264 1445 1333 3718\n", + " 606 4286 408 660 816 3703 2622 9172 3313\n", + " 2068 4037 990 5835 2412 498 2167 5241 4365\n", + " 2450 4808 1030 2520 3722 2252 2073 2957 1622\n", + " 2661 2595 3454 2302 1699 2715 3261 756 1446\n", + " 3535 3857 3697 1542 7849 5119 3279 3051 1423\n", + " 21646 760 2210 1554 2951 2819 520 1572 1246\n", + " 1994 6388 7962 3172 640 1234 1631 870 2615\n", + " 3657 206 5511 2245 1667 1674 834 540 648\n", + " 444 3005 3781 4079 2055 1425 1797 5345 2890\n", + " 888 3409 2806 3058 880 1616 2143 6115 1002\n", + " 330 1106 5906 3506 2417 1216 3153 8787 3439\n", + " 2219 2307 4497 1890 4998 21187 5586 1548 2759\n", + " 2772 3440 1101 3947 8017 1361 2683 7005 1224\n", + " 5528 492 1560 1524 1308 684 912 714 4385\n", + " 1241 3121 2879 7630 1045 6502 5721 7171 8282\n", + " 1213 3190 2262 9350 7822 1605 6861 6543 3384\n", + " 7095 9152 1749 2983 2691 1741 3074 1324 1092\n", + " 23877 924 1950 1546 2576 2165 1886 8898 1538\n", + " 4517 4843 1637 2404 6657 1931 3248 2497 3531\n", + " 1430 2103 4498 2640 1047 1601 576 1390 5499\n", + " 2202 1500 4153 3359 54 11423 2270 4499 4565\n", + " 4433 2334 5103 1433 636 2982 3720 3064 1110\n", + " 3168 8384 3073 1248 2716 4434 1079 1209 1037\n", + " 1305 1988 2022 4420 750 3032 6312 7549 2701\n", + " 1097 2874 1663 2743 3187 4646 6463 3908 2125\n", + " 1242 2742 6595 6413 11211 1020 1118 2626 6295\n", + " 2222 8472 2827 1776 1503 2554 2381 681 1440\n", + " 1495 780 689 13992 7072 4204 3565 2881 11305\n", + " 5046 5902 198 2097 2104 6091 3307 6460 4567\n", + " 1099 3046 1537 1562 890 8950 7491 3858 1392\n", + " 582 6478 5267 3442 9242 18384 3391 2546 10217\n", + " 3739 6780 3808 4405 1641 2479 2012 1384 3414\n", + " 8856 4114 13794 4832 3821 5516 3886 810 2133\n", + " 1182 7271 2609 4684 2000 3612 3335 442 6824\n", + " 1484 2530 3994 7819 2617 5451 4193 1474 3494\n", + " 3912 4505 3602 6835 1696 2621 7525 3613 5282\n", + " 4198 3453 2932 8062 2123 2355 1935 961 3723\n", + " 1383 1003 1662 864 1122 5195 2138 1859 5064\n", + " 516 2562 5841 5845 3159 3696 2912 904 8219\n", + " 2682 7208 5803 8889 3131 3263 1842 2420 1287\n", + " 1266 6966 15314 2279 3473 909 762 1278 1872\n", + " 4127 5093 3507 3144 2119 1712 1944 4602 1555\n", + " 5742 1986 2513 1852 1502 5381 4361 1316 1668\n", + " 960 2416 2532 1559 4724 3219 1166 100151 6438\n", + " 7644 4884 3525 422 5785 3479 2320 6231 1700\n", + " 3873 3837 1867 6736 1458 1074 4833 6250 3405\n", + " 8357 5301 6401 5597 916 9059 450 4368 2354\n", + " 3402 1260 1252 3492 6989 3810 1435 1576 356\n", + " 1062 9168 6439 1617 6173 2120 2493 4404 4623\n", + " 1933 9294 2249 5023 1021 1309 3744 2004 1773\n", + " 6896 770 2102 2668 1146 6925 2861 672 828\n", + " 768 3208 2033 3230 2105 3635 4118 1584 1909\n", + " 1618 3746 1734 394 202 12231 5392 808 812\n", + " 3102 7422 4944 4834 3883 2610 1837 1344 792\n", + " 4444 1033 5168 3540 1374 3258 1968 6453 4156\n", + " 7683 2814 2459 4429 2388 5105 735 3944 5567\n", + " 14522 3605 1580 5353 3517 2552 3428 2232 5469\n", + " 1498 1922 26931 2810 4619 2175 1026 1850 3967\n", + " 2443 8331 2675 2310 2196 1804 858 2633 2204\n", + " 5670 6628 5033 5200 1205 1775 953 5787 2095\n", + " 2486 4425 2358 3036 2437 1304 6963 1688 3802\n", + " 4041 1380 922 4164 8302 4174 3747 3110 3736\n", + " 5084 3317 6819 1473 6520 972 4290 3520 2371\n", + " 2802 1673 6843 3647 14688 2856 4252 5793 3177\n", + " 951 4247 2796 5134 2464 1206 2601 2741 1835\n", + " 1594 6557 3449 3085 2410 2233 1455 3715 8042\n", + " 3156 2059 4412 7734 6891 2540 3008 2198 2581\n", + " 632 3776 3903 90 1750 4319 3000 2588 13771\n", + " 2296 804 2979 6619 2490 4979 1781 936 1008\n", + " 3527 1670 7830 1818 786 4533 7245 2480 2634\n", + " 3754 3597 1798 5935 3476 1468 2280 5500 1176\n", + " 5811 3312 2425 66 3622 5009 3769 7975 6601\n", + " 3104 3550 1212 3851 5512 4982 1178 7895 2484\n", + " 4772 2616 2758 4637 1298 2904 5774 2126 3591\n", + " 7848 1652 1823 5159 4535 1789 5740 3146 2010\n", + " 2900 6945 2776]\n", + "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", + "DNS_QUERY_TIMES [ 2. 0. 8. 4. 10. 6. 12. 14. 20. 9. nan]\n", + "Type [1 0]\n" + ] + } + ], "source": [ - "# Your comment here" + "list_1=df.columns.to_list()\n", + "\n", + "for column in list_1:\n", + " print(column, df[column].unique())" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 143, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "URL 0\n", + "URL_LENGTH 0\n", + "NUMBER_SPECIAL_CHARACTERS 0\n", + "CHARSET 7\n", + "SERVER 176\n", + "CONTENT_LENGTH 812\n", + "WHOIS_COUNTRY 306\n", + "WHOIS_STATEPRO 362\n", + "WHOIS_REGDATE 127\n", + "WHOIS_UPDATED_DATE 139\n", + "TCP_CONVERSATION_EXCHANGE 0\n", + "DIST_REMOTE_TCP_PORT 0\n", + "REMOTE_IPS 0\n", + "APP_BYTES 0\n", + "SOURCE_APP_PACKETS 0\n", + "REMOTE_APP_PACKETS 0\n", + "SOURCE_APP_BYTES 0\n", + "REMOTE_APP_BYTES 0\n", + "APP_PACKETS 0\n", + "DNS_QUERY_TIMES 1\n", + "Type 0\n", + "dtype: int64" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Print heatmap again\n" + "df.isna().sum()" ] }, { "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.**" + "Primero nos quitaremos la columna URL ya que es como el \"id\" que no necesitamos" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 144, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "df=df.drop(columns=[\"URL\"])" ] }, { "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." + "Charset quizá habría que rellenar nulos y codificar one hot encoding, pero miraremos luego. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Formatting date columns

" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 145, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "snake_columns(df)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 146, "metadata": {}, "outputs": [], "source": [ - "# Your comment here" + "df1=df.copy()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 147, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "127" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### Again, examine the number of missing values in each column. \n", - "\n", - "If all cleaned, proceed. Otherwise, go back and do more cleaning." + "df1.whois_regdate.isna().sum()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 148, "metadata": {}, "outputs": [], "source": [ - "# Examine missing values in each column\n" + "df1.whois_regdate=df1.whois_regdate.fillna(method=\"ffill\")" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 149, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "139" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Challenge 4 - Handle `WHOIS_*` Categorical Data" + "df1.whois_updated_date.isna().sum()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 150, "metadata": {}, + "outputs": [], "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." + "df1.whois_updated_date=df1.whois_updated_date.fillna(method=\"ffill\")" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 151, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "df1.whois_updated_date=df1.whois_updated_date.fillna(method=\"bfill\")" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 152, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1781" + ] + }, + "execution_count": 152, + "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", - "\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:" + "len(df1)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 153, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "639" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "len(df1.dropna(how=\"any\"))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 154, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "url_length 0\n", + "number_special_characters 0\n", + "charset 7\n", + "server 176\n", + "content_length 812\n", + "whois_country 306\n", + "whois_statepro 362\n", + "whois_regdate 0\n", + "whois_updated_date 0\n", + "tcp_conversation_exchange 0\n", + "dist_remote_tcp_port 0\n", + "remote_ips 0\n", + "app_bytes 0\n", + "source_app_packets 0\n", + "remote_app_packets 0\n", + "source_app_bytes 0\n", + "remote_app_bytes 0\n", + "app_packets 0\n", + "dns_query_times 1\n", + "type 0\n", + "dtype: int64" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### After verifying, now let's keep the top 10 values of the column and re-label other columns with `OTHER`." + "df1.isna().sum()" ] }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, + "execution_count": 155, + "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "df2=df1.copy()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 156, "metadata": {}, + "outputs": [], "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']`." + "# df2.whois_regdate=pd.to_datetime(df2.whois_regdate,format='mixed')" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 157, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "df3=df2.copy()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 158, "metadata": {}, + "outputs": [], "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`." + "df3=df3.drop(index=1067)\n" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-1" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df3)-len(df2)" + ] + }, + { + "cell_type": "code", + "execution_count": 160, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "# df3.whois_regdate=pd.to_datetime(df3.whois_regdate,format='mixed')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 161, "metadata": {}, + "outputs": [], "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." + "df3=df3.drop(index=1359)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 162, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "# df3.whois_regdate=pd.to_datetime(df3.whois_regdate,format='mixed')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 163, "metadata": {}, + "outputs": [], "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." + "df3=df3.drop(index=1358)" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 164, "metadata": {}, "outputs": [], "source": [ - "# Your code here" + "# df3.whois_regdate=pd.to_datetime(df3.whois_regdate,format='mixed')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 165, "metadata": {}, + "outputs": [], "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." + "df3=df3[df3[\"whois_regdate\"]!=\"0\"]" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 166, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "df3=df3[df3[\"whois_regdate\"]!=\"b\"]" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 167, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1776" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "![Think Hard](../images/think-hard.jpg)" + "len(df3)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 168, "metadata": {}, "outputs": [], "source": [ - "# Your comment here\n" + "df3.whois_regdate=pd.to_datetime(df3.whois_regdate,format='mixed')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 169, "metadata": {}, + "outputs": [], "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`." + "df3.whois_regdate=df3.whois_regdate.apply(lambda x:x.toordinal())" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 170, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "df3.whois_updated_date=pd.to_datetime(df3.whois_updated_date,format='mixed')" ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": false - }, + "execution_count": 171, + "metadata": {}, "outputs": [], "source": [ - "# Count `SERVER` value counts here\n" + "df3.whois_updated_date=df3.whois_updated_date.apply(lambda x:x.toordinal())" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 1776 entries, 0 to 1780\n", + "Data columns (total 20 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 url_length 1776 non-null int64 \n", + " 1 number_special_characters 1776 non-null int64 \n", + " 2 charset 1769 non-null object \n", + " 3 server 1601 non-null object \n", + " 4 content_length 967 non-null float64\n", + " 5 whois_country 1473 non-null object \n", + " 6 whois_statepro 1417 non-null object \n", + " 7 whois_regdate 1776 non-null int64 \n", + " 8 whois_updated_date 1776 non-null int64 \n", + " 9 tcp_conversation_exchange 1776 non-null int64 \n", + " 10 dist_remote_tcp_port 1776 non-null int64 \n", + " 11 remote_ips 1776 non-null int64 \n", + " 12 app_bytes 1776 non-null int64 \n", + " 13 source_app_packets 1776 non-null int64 \n", + " 14 remote_app_packets 1776 non-null int64 \n", + " 15 source_app_bytes 1776 non-null int64 \n", + " 16 remote_app_bytes 1776 non-null int64 \n", + " 17 app_packets 1776 non-null int64 \n", + " 18 dns_query_times 1775 non-null float64\n", + " 19 type 1776 non-null int64 \n", + "dtypes: float64(2), int64(14), object(4)\n", + "memory usage: 291.4+ KB\n" + ] + } + ], + "source": [ + "df3.info()" ] }, { "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`.**" + "

Dropping null values

" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "url_length 0\n", + "number_special_characters 0\n", + "charset 7\n", + "server 175\n", + "content_length 809\n", + "whois_country 303\n", + "whois_statepro 359\n", + "whois_regdate 0\n", + "whois_updated_date 0\n", + "tcp_conversation_exchange 0\n", + "dist_remote_tcp_port 0\n", + "remote_ips 0\n", + "app_bytes 0\n", + "source_app_packets 0\n", + "remote_app_packets 0\n", + "source_app_bytes 0\n", + "remote_app_bytes 0\n", + "app_packets 0\n", + "dns_query_times 1\n", + "type 0\n", + "dtype: int64" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3.isna().sum()" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 174, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "df3.dns_query_times=df3.dns_query_times.fillna(method=\"ffill\")" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 175, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "charset\n", + "UTF-8 672\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": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "Now, inspect `website_dummy` to make sure the data and types are intended - there shouldn't be any categorical columns at this point." + "df3.charset.value_counts()" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 176, "metadata": {}, "outputs": [], "source": [ - "# Your code here\n" + "df3.charset=df3.charset.fillna(\"UTF-8\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Challenge 6 - Modeling, Prediction, and Evaluation\n", - "\n", - "We'll start off this section by splitting the data to train and test. **Name your 4 variables `X_train`, `X_test`, `y_train`, and `y_test`. Select 80% of the data for training and 20% for testing.**" + "

Dropping Cero values

" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 177, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "url_length 0\n", + "number_special_characters 0\n", + "charset 0\n", + "server 0\n", + "content_length 5\n", + "whois_country 0\n", + "whois_statepro 0\n", + "whois_regdate 0\n", + "whois_updated_date 0\n", + "tcp_conversation_exchange 655\n", + "dist_remote_tcp_port 913\n", + "remote_ips 655\n", + "app_bytes 655\n", + "source_app_packets 653\n", + "remote_app_packets 588\n", + "source_app_bytes 588\n", + "remote_app_bytes 653\n", + "app_packets 653\n", + "dns_query_times 974\n", + "type 1561\n", + "dtype: int64" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "# Your code here:\n" + "(df3 == 0).sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "content_length\n", + "324.0 138\n", + "1819.0 20\n", + "2516.0 13\n", + "162.0 12\n", + "345.0 11\n", + " ... \n", + "44447.0 1\n", + "226.0 1\n", + "217.0 1\n", + "4695.0 1\n", + "24435.0 1\n", + "Name: count, Length: 635, dtype: int64" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3.content_length.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### In this lab, we will try two different models and compare our results.\n", - "\n", - "The first model we will use in this lab is logistic regression. We have previously learned about logistic regression as a classification algorithm. In the cell below, load `LogisticRegression` from scikit-learn and initialize the model." + "#### We decide not to drop any 0 values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Checking Distributions

" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 179, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n", - "\n" + "num=df3.select_dtypes(include=\"number\")" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 180, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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BA4ZhXPpNff/9940ePXoYVqvV+Ne//lVm/a52q3///ka3bt2MN9980zh37pxhGIZx5swZ409/+pNZh6VLl5Yp/+233xq9evUyrFarMW3aNOP77783Ll68aBiGYWRlZRkzZswwrFar0bNnT+O7774rVXbLli3muvv162fExcUZ33//vZl++bGsittuu82wWq1GTEyMsX//fnN5QUGBsX//fmPevHnGRx99VG59+vfvb0ycONGsj9PpNNatW2e2TePGjTP308XhcBjjxo0zrFarMXbsWGPTpk1m+37hwgVj9erVxvXXX29YrVZj1qxZZer8j3/8w9z+448/buzZs8dMO3v2rPHVV18ZTz75pHl+XCr6jj355JPG+++/bxw5csRwOBxmPTdv3mzcddddZl3dqWxbWxnnzp0zbr/9dvPf0YoVK4y8vDzDMC59lw8dOmS89957xuLFi0uVc30/Bw4caPTq1ctYvHixuf/Z2dnG888/b+7/5s2by2z3ww8/NF566SVjx44dRm5urmEYhlFcXGxkZGQYf/7zn41u3boZPXr0MHbv3l2mrOtap1+/fobVajX++te/mtc6DofDsNlsZt7qHGdPzntmZqZZJjMzs9zjXt3vZGWuX+12u3HdddcZVqvVmDp1qnHkyBGz/IULF4ydO3caf/nLX4xNmzaVW08A9WvTpk1Gt27dDKvVakyYMMHYunWr2Tc6d+6csXXrVuOFF14wDh48aJaJj483rFar0atXL2PZsmXmb8vJkyeN5557zvyN+vDDD8tsrzrXVYZRuX65YRjGn//8Z8NqtRo33HCD8Y9//MM4e/asYRiX2p0tW7YYY8aMMX8fL+8LVrf9qck2tKSK7hfk5uaax+bBBx80tm7darZJeXl5xuLFi802bcmSJWXKV6fvXFhYaIwePdqwWq3G4MGDjc8++8y8Tvr++++NBx980LyGcnfNUvIarCLVvQ8BAE3BZ599Zv5eX/57/NBDDxlWq9WIiooyrFZrqb6FYRjGm2++WeZea8nf4IEDBxqPP/642Q92OBzGunXrzOv9p59+ukx9jh49agwYMMCwWq3Gr371K2Pbtm1m2tatW832Y9CgQeb935LKaxv+8Ic/mO3O5s2bzXbl4sWLhs1mMz788EPjlVdeqeLRK6uy/b7MzExzP+Pj441du3YZTqfTMAzDOH36tPHGG28YPXv2NKxWq/Gf//yn1Daq206eO3fOuPnmmw2r1WrcfPPNxldffWUUFxcbhmEYaWlpxqhRo8y6uesjHjt2zJg0aZKxbt064/jx4+b35vz588aqVauMG2+80bBarcZLL71U5vjUVd8TVwcC2UAdqIlAdnk3S13qKpB95swZ49prrzW6devmtuNtGJdu2o8dO9awWq1lbiy7GvmKOudPPPGEYbVajYcffrjU8uLiYvNm9iOPPGI2vCW5jkNNBbKHDBlinD59ukz6vn37zDwlL7QAoCTXb47VajW+/vrrMukOh8O88F+wYEGptJoKZN9www1GdnZ2mfQ33njDzDNq1CjzhmVJTz/9tGG1Wo2HHnqoTJqr3bJarcYbb7zhtn5Tp041O54FBQWl0lwdlfLKGoZhvPjii4bVajUmTZpUannJDvNdd91VJjDsqdOnT5vrPXHiRKXLlazP7bffbt5ULyk5OdnM8+mnn5ZK++CDD8zzcHmw2WXnzp1Gt27djF69epVql3JycsybA//3f//ntm0sT2W+Y+6cP3/euOGGGwyr1Wps3bq1THpN3oR//fXXDavVavTu3bvC66DLlfx+lnf95bpWef7556tcr5kzZxpWq9X4wx/+UCat5LXOyy+/XOV1u1R0nD0975W9mVCd76RhVO769dtvvzVv/Lhu5ABoPJxOp3HrrbcaVqvVuPfee91eR1zO9e/earUay5cvd5vHFegePHhwmWuH6lxXGUbl+uX79+83unXrZlx77bXGvn373OY5d+6ccdNNN7m94Vzd9qc+AtmzZ882g9jl/R67gh+DBw8uk6c6feePP/7YsFqtRrdu3dxeUxQUFBgjR46ssUC2J/chAKCpyMnJMbp3725YrVZj165d5nKHw2H07dvXuPbaa8024b333itV9oEHHjCsVqsxb948c1nJ3+AHHnjA7Ys+y5YtM6xWq9G3b98y7ccf//hHMwjuekmqpGPHjpmB9ZkzZ5ZJL69t+OUvf2lYrVbj3//+d+UOjIcq2+9zXdu4C+a7uF4a+3//7/+VWl7ddvKdd94xrNZLDxCWfPjf5eTJk6UC4RX1Ed357rvvzD7d5ddtddX3xNWh7IRwABqkn//85xUO011X/v3vfys/P1+9e/cudwgWX19fjR49WpL01Vdfuc1jsVgUExPjNs01b8bl85Tt3bvXHOrssccecztM7dixY9WhQ4dK7Utl3HPPPWrVqlWZ5d26dTOHhnU3nxoAlBQVFaUhQ4aUWW6xWHTjjTdKqr3fkrvvvlthYWFllru2K0kPP/yw2+GZKlO3gIAA/frXv3abNmXKFEmXhhdOTk42l+/bt087d+6Un59fuW2BJHNI8a+//lpFRUVu8/z617+useEfmzVrZg4BdurUKY/WERsb63a40htuuMEcgu3TTz8tleYauvTee+8tdxjM3r17q2vXrnI6nUpJSTGXb9iwQRcuXJCfn5+effbZOpkzqlmzZho4cKAkafv27bW6rVWrVkm69D325DroJz/5SblD0996662SPPu394tf/EKSlJqaWm4eb29vxcXFVXndLhUd59o+79X5TpZU0fVr8+bNJV2aby8nJ6f6lQZQp1JSUmSz2SRdmnO6MsM8rlu3TpLUvn173X333W7z/Pa3v5UknT17ttS1Q0m1eV310UcfyTAM/eIXvygz57JLcHCwhg8fLkn63//+5zZPbbU/Nc0wDCUkJEiSYmJi5OvrfgbC4cOHKzg4WGfPntXu3bvd5vGk75yYmChJGjhwoNthY/39/cu9zvSEJ/chAKCpCA0NVffu3SWVHkp8x44dKigoUFRUlG666aYy6YWFhdqxY4ckuZ1/WZIef/zxMsNpSz/+thYUFOjIkSPmcsMwzDZgwoQJatOmTZmy7du3N+ftdl1DVIZrOi1P+/RVVVG/LycnR//5z38kSY8++mi567jzzjslXbpX4hp+XKp+O+m69zBy5Ej97Gc/K5Pepk0b8xh7ok+fPmrVqpXsdrv27t3r0Tpqqu+Jpo05soFGIioqqr6rIOnHG7YHDx7U0KFDy81XUFAgSeZ8JJfr2rWrmjVr5jatbdu2kmTO3eHi6jD7+fm5nY9FujSX18CBA7VmzZoK9qLyrr322nLT2rZtK5vNVqaeAHC5K/2WSGV/82pK37593S4vOY9gnz59Kszjmq/Knd69e5fb2ejcubPat2+v48ePa9euXeaNW1dbUlxcrJEjR5a7blfw2m63Kycnx+3N0ZpsHwMCAnT99dcrOTlZsbGxmjBhgm6++Wb16NGj0vMwubuxXjItLS1Nu3btMpedP3/evGE6d+5cLViwoNzyru+Ia05lSUpLS5Mk9erVy/wu1ZT//ve/WrNmjXbu3KkzZ84oPz+/TJ7jx4/X6DZLysrKMucBu+WWWzxaR58+fdzeUJFkztFW3r+9zMxMffjhh0pJSVFGRoYuXLhgzkXtcuLEiXK3HRER4fY7ezlPjnNtnvfqfidLqujfZ0REhK655hodOnRI99xzjyZMmKCf//znslqtzE0KNAKu36E2bdqUex1xOVf7N3jw4HJ/m3/2s5+pXbt2OnHiRKlrh5Jq87rKdY3y5ZdfVtjftdvtksrv71an/alL33//vfkw0bPPPltunaUf9zkrK8vtOfCk77xnzx5JMh/ccqe8oIknPLkPAQBNyZAhQ7Rnzx5t2bLFDIC6gtZDhgzRddddJ4vFoq1bt+rixYvy9fXV9u3b5XA4FBAQUO5vfXn3HUr2VUo+vGqz2cy/K5qreujQoXr33XeVk5OjzMxMderU6Yr7ePPNNystLU2vvfaaDh06pNtuu01RUVHl3reoror6fTt27DD7kA899FCl1nf06FHzXkx12snCwkIdOHBA0pXvU7z99tvlphcWFmrVqlX6z3/+owMHDig3N1eFhYVl8nlyb6Am+55o2ghkA41Ey5Yt67sKkmTeUC4oKDCD1RUpL095nUdJ5s3Lixcvllp+9uxZSVKLFi0qDCi4bgzUhIrq6Xpa/fJ6AsDl6vO3pLxtlwwUXSlPRXW70m9uu3btdPz4cZ05c8Zc5mpLioqKSj1tXBF3wT1JlQoUVsWf//xnTZo0Sfv27dOCBQu0YMEC+fn5qU+fPho2bJjuuusutWjRotzyFR0PV1rJY3H69GmzY1vZt1JLtq2up8xrcjSS4uJiPf300/rkk0/MZb6+vgoNDZWfn58k6dy5c3I4HOWel5pQ8rvh6f55cr0hSf/5z3/01FNPleqgBwcHy9/fX15eXnI6ncrNzTVv6rtzpe9mdY5zbZx3l+p+J0uq6PrVx8dHr7/+uqZMmSKbzabXXntNr732mgIDA3Xdddfptttu09ixYxUYGFjlfQBQ+zz5HXK1f1e6dmjfvr1OnDhRqr0sqTavq1zXKHa7vcLfeJea7O/WB9f+SlJ2dnalyniyz+WdF9c2K3ooq6769w3pvABAbRk8eLDee+89bdu2zQxUu95wHTJkiAICAtSvXz9988032rVrl/r162emu4Lc7pQXJC450kfJ39eSbXxl+tDSpTajMoHsX//619q3b5/Wr1+vf/3rX/rXv/4lLy8vde3aVTfeeKPuuece/fSnP73ieiqron5fyXbWk3sf1Wknc3NzzWNe2WN8uTNnzujhhx82A+LSpbfAw8LCzHYzOztbxcXFHt0bqMm+J5o2AtlAI9FQ3kxxNS4TJkzQzJkz63TbhmHUaD4AQPV5MqSx603ra665RuvXr6/W9mu6fezQoYNWr16t5ORkffHFF9q+fbv279+v7du3a/v27XrnnXc0Z86cCp8ar4qSQ6b/61//qvBtporU5NDSH330kT755BP5+Pjo8ccf15133qlOnTqVekvr6aef1tq1a+usza2LIdNdzp49q2effVaFhYUaMmSIpkyZor59+5YaMv7rr7/Www8/XOF6rvTdrInjXBvHpaa+k9KVj0H37t21fv16bdq0SV999ZXS0tJ08OBBbd68WZs3b9Y777yjt99+u9zhfQHUP09+hypbpi5/+11c/d3f/e53FQ4B2lSU/M1PTk4uNWJPXaqPcw0AV6MBAwbI19dXdrtd3333nXr06KFvv/1WzZs3V69evSRdCnZ/88032rJli/r161fqje3aUNPXBX5+fnrjjTf0+OOP67PPPlNqaqq+++47HThwQAcOHNDSpUs1derUCqc5q4qK+jyudjYgIEDffvutx9uobjvpafmXXnpJBw4cUIsWLfT73/9eN910U5lh4H/xi1/o+PHjHt0bqMm+J5o25sgG6oCrQXM4HOXmOXfuXF1Vp1pcHduST2LVFddbPTk5OW6HMHEp+bQbADRWjaXtuNLwUa6hl0s+pexqSzIzMyv1tlNd8/b21s9//nO98MILSkhIUEpKil599VV16NBBubm5mjp1arntUEVDTVd0LCTP2lZXJ7Imh9dyzT9211136Te/+Y0iIyPLDDVa2afJq6NkB7kuhw/74osvdP78eYWGhuqtt97SoEGDysx7XhPzrVXnONfGeXep7neyqiwWi26//Xb96U9/0r///W99/fXXmjlzplq0aKFjx47p2WefrfU6AKg61++Qa57synC1f8eOHaswn+vaoj5GJavP/m59KNnW1mcfv6I+fEXXVgCAqgkODjYD1lu2bFFqaqqcTqcGDBhg3oNwBay3bNkiu92unTt3SqrZqR5K9okruqdQsg0ICwur0ja6d++u3/zmN1q6dKm2bt2qJUuWaODAgSoqKtJf//pX7du3r+oVryJXO3v5HOGVVZ12MjQ01DynFR3j8tbtdDrN+b3/+Mc/aty4cWWC2EVFReYIqp6o674nGi8C2UAdCAkJkVR+o1FcXFxqvsyGzDXX4bffflvnc1K4LrScTqc5J9vlDMPQtm3byl2H6wk03toG0NBdqe04f/68fvjhh7qsklu7du3S+fPn3aYdOXLErH/v3r3N5a62pGTHqCELDg7WHXfcoVmzZkm6FFwsr5PlelrdHdeQbCWPRWhoqLp06SLpx8BmVbiO5a5du2rsQS7XOevZs6fb9AsXLlTrafLK6tChg9q3by/p0jzSdcW1/z/96U/LHdb666+/rrHteHKcPT3vJQPl5V0LVfc7WV1hYWGaMGGCpk6dKunSvHDVuTkCoHa4fodOnz5t3uC+Elf7l5KSYr75fLkffvjBvCFb2bm3K6syfUHXfn3xxRe6cOFCjW6/Muq6v9q1a1dzONj6+M13tYHffPNNuXlc10/uVKZdAwCU5gpIb9mypdSw4i7XXnutAgMDlZaWpq+//lpOp1NBQUE12i6Hh4ebU3ZV1LfavHmzpEvTTFZmWPHy+Pr66vrrr9fbb78ti8UiwzDMddem6667zmzbPWlnq9NOWiwWc2SritrS8u5hZGdnmy9W9OjRw22e1NTUcl++aAx9TzQeBLKBOtC9e3dJl+ZbdPfDvXr16iu+0dZQ3HnnnQoICFBRUZH+9Kc/lRoC5HLFxcXKy8ursW336NFDkZGRkqR33nnH7bFcs2ZNhQF2Vye9JusFALXB1XZs2LDBbfp7771X4egUdaWgoECLFy92m/b3v/9d0qVO59ChQ83lffr0MTtkb7zxxhXnZKzsXEnVdaXj6e/vb34ub/iw9957z21HbsuWLdq+fbsk6Ze//GWptHvuuUfSpQ78lTpvlx+LkSNHKjg4WBcvXtTLL79cIzdxXW1leU+oL1iwoM5u7o8bN06StHLlSu3Zs6dOttm8eXNJUnp6uttzuXfvXv373/+u9naqc5w9Pe8l566raESH6nwnK6sm/r0BqD+DBw82byi//PLLlbomGTVqlKRLbw6tXLnSbZ65c+dKuvRQyw033FBDtb3E9Rt4pd8/Ly8v5eXl6a9//WuF63M6nTXeHtZ1f9XX19dsaz/++OMKHwqXav6abMSIEZKkrVu3KjU1tUx6YWGh3nvvvXLLl2zX6OMDQOW4gtY7duzQl19+WWqZdGlo7qioKBUUFOjtt9+WJPXv37/UfNfV5eXlZfaLV6xY4XbEqxMnTmjFihWSpNGjR1d63RVdk1gsFrNvcfloWLWhVatWGjZsmCRp0aJFOnz4cIX5L29nq9tOuo5xYmKiDh06VCb9zJkzWr58uduywcHBZhDeXZ/14sWLev3118vddkPqe6LxI5AN1AFXY/vDDz9o2rRp5lsl58+f15IlSzR9+nTzKbSGrk2bNvrd734nSdq0aZMeeeQRpaammgFtwzD0ww8/aPHixRo9enSNvkHl5eWl+Ph4SdJXX32lZ555xnxa3+FwaOXKlZo+fbpCQ0PLXUfXrl0lSV9++SVDlAFo0Fw3e7/66ivNnTvXfOs5Oztbf/vb3/T3v//dfGu7PjVv3lwLFizQ22+/XaqOf/7zn7V69WpJ0qRJk0oFpby8vDRz5kxZLBYdPXpUd999txITE5Wfn2/mOXHihNasWaNHHnlEr776ap3sS1pamu644w4tWbJEP/zwg/m2mGEY2r59u2bMmCFJat++vaxWq9t1nDp1So8++qjZSbx48aISExP129/+VtKl0UVuv/32UmXuvfdecy6o3//+93r99ddLDbuan5+vlJQU/elPf9Jtt91Wqmzz5s319NNPS5I+/fRTTZkyRXv37jXTc3NztWnTJk2aNKncN+cv9/Of/1zSpeDxihUrzBsBp06d0ksvvaR33323zq5bYmJi1LlzZxUWFurhhx/Wv/71L3M/nE6nDh8+rPnz52vRokU1ts2hQ4fK29tbOTk5mjp1qnm9UFhYqE8//VQxMTFq1qxZtbdTnePs6XkPCQlRu3btJEkJCQm6ePGi2/VX5ztZWevWrdOECRO0fPlyZWZmmsuLior0v//9T6+99pqkS28xNITfOgCl+fj4aNq0afLy8lJqaqoefvhhbdu2zWw7z58/r5SUFE2dOlXff/+9JKlv377mDdkXX3xRH3zwgdn2nzp1Si+88IISExMlSb/97W9LXTvUBFdfMDExUbm5uW7z9OjRQw899JAkafny5frNb36jvXv3mg8MFRUVad++fXrzzTd12223lfrtrck61mV/dfLkyYqIiNDFixcVFxenxYsXl3rI8Ny5c/ryyy/1zDPP6P7776/Rbf/qV79S165dZRiG4uPjtXHjRvPewqFDh/TYY49VOJ1J586d5efnJ+lSe8pb2QBwZVFRUfLz85PD4dC+ffsUFhZmvrnr4gpsu0aIqslhxV0ef/xxhYSEKCcnR4888oj54Ld06U3fRx55RHl5eWrRooUeffTRSq/3lltu0WuvvaYdO3aUCmofOXJEU6dOVX5+vjmdWF149tln1aJFC50/f1733XefPvroo1KB3ezsbH322Wd64oknzHvuLtVtJ++77z61b99ehYWFio2N1ddff222ld99950efvjhckfJadasmTlSzezZs/X111+beQ8cOKBHH31Uu3btUlBQkNvyDanvicav5h6jAVCu66+/XmPGjNHHH3+slStXauXKlQoJCdH58+dVXFysiRMn6sKFC+YN/4buwQcfVGFhof72t78pJSVF9913n/z8/NSsWTNduHBBTqfTzOt6cqum3HHHHdq5c6eWLl2qNWvWaO3atQoJCZHdbpfT6dSQIUN07bXXmkPFXG7s2LFavHixjhw5optvvlktW7Y0b5B8+OGH5hCmAFDfoqOj9e9//1spKSl68803tWDBAoWEhJhvmzz99NPatGlThUNM1YXhw4fL4XDob3/7m+bMmaPg4GDl5eWZnaMxY8bowQcfLFOub9++euutt/TUU0/JZrPpt7/9rXx8fNS8eXM5HI5SQe277767zvbnwIEDevnll/Xyyy+bbdv58+fNTldwcLBee+21ct8QnT17tp588kn98pe/NPfF1Xnu0KGD5syZU+ZJdovForfeekv/93//py1btuitt97SW2+9peDgYHl7e+vcuXPm8XT3FPyECROUm5urN954Q59//rk+//xzBQQEyMfHp9SbYuV1UC8XExOjDRs26NChQ/rjH/+oGTNmKDg42KzH+PHjVVhYWCfXLcHBwXr33Xf1+OOP6/vvv9e0adM0ffp0NW/eXOfPnzc78e6+Y57q3Lmzfv3rX2vhwoX67LPP9Nlnn6l58+YqKCiQ0+lUeHi4nnzySXPoa09V9zh7et4nTJigOXPm6P3339eKFSvUqlUreXt769prrzWfqK/ud7IyDMNQWlqaOV2MxWJRUFCQ8vLyzDq3bdvWHNIfQMPzi1/8QrNnz9a0adOUmpqq+++/XxaLRQEBAaXejv31r39tfp41a5bOnj2rb775Ri+++KJefvllNWvWrNS1Q0xMjO69994ar+/48eP1ySefKC0tTddff71atmxp9hmTkpLMfL///e9lGIaWLl2qDRs2aMOGDfL391dgYGCpawKp5vu79dFfbdGihd577z098cQT2rdvn2bPnq3Zs2crJCRExcXFpR6Ico2QVlMsFovmzJmjhx56SKdOndKUKVNksVjk7++vc+fOyWKxaO7cuXr88cclqczDDYGBgbrzzjv10Ucf6ZVXXtH8+fMVFhYmLy8vjRgxQs8880yN1hcAmoLAwEBde+215igcgwcPLtOeXR64ro1Advv27fXmm29q8uTJOnjwoO69914zKGq32yVdCoa++eabZkC0Mk6fPq133nlH77zzjry9vc2+nGu0LS8vLz3zzDP62c9+VuP75E6nTp20ePFiPfHEE8rKytLzzz+vF154QSEhIXI6nea+SiozGk1128ng4GDNnz9fMTExysrK0sMPP6zAwEB5eXnJbre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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "Next, fit the model to our training data. We have already separated our data into 4 parts. Use those in your model." + "color = '#FF8C00'\n", + "\n", + "# grid size\n", + "nrows, ncols = 5, 4 # adjust for your number of features\n", + "\n", + "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(20, 16))\n", + "\n", + "axes = axes.flatten()\n", + "\n", + "# Plot each numerical feature\n", + "for i, ax in enumerate(axes):\n", + " if i >= len(num.columns):\n", + " ax.set_visible(False) # hide unesed plots\n", + " continue\n", + " ax.hist(num.iloc[:, i], bins=30, color=color, edgecolor='black')\n", + " ax.set_title(num.columns[i])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 181, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Your code here:\n", - "\n" + "color = '#FF8C00'\n", + "\n", + "# grid size\n", + "nrows, ncols = 5, 4 \n", + "\n", + "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(20, 16))\n", + "\n", + "axes = axes.flatten()\n", + "\n", + "for i, ax in enumerate(axes):\n", + " if i >= len(num.columns):\n", + " ax.set_visible(False)\n", + " continue\n", + " ax.boxplot(num.iloc[:, i].dropna(), vert=False, patch_artist=True, \n", + " boxprops=dict(facecolor=color, color='black'), \n", + " medianprops=dict(color='yellow'), whiskerprops=dict(color='black'), \n", + " capprops=dict(color='black'), flierprops=dict(marker='o', color='red', markersize=5))\n", + " ax.set_title(num.columns[i], fontsize=10)\n", + " ax.tick_params(axis='x', labelsize=8)\n", + "\n", + "plt.tight_layout()\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" + "# 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": 32, + "execution_count": 182, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n", - "\n" + "num_corr = num.corr().round(2)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 183, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "What are your thoughts on the performance of the model? Write your conclusions below." + "# 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()" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 184, "metadata": {}, "outputs": [], "source": [ - "# Your conclusions here:\n", - "\n" + "# Print heatmap again\n", + "df4=df3.copy()\n", + "\n", + "df4=df4.drop(columns=[\"tcp_conversation_exchange\",\"app_packets\"])" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 185, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#### Our second algorithm is is K-Nearest Neighbors. \n", + "num_2=df4.select_dtypes(include=\"number\")\n", + "num_corr = num_2.corr().round(2)\n", + "# 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", - "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." + "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": 34, + "execution_count": 186, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n", - "\n" + "df4=df4.drop(columns=[\"source_app_packets\",\"remote_app_bytes\"])" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 187, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "To test your model, compute the predicted values for the testing sample and print the confusion matrix as well as the accuracy score." + "num_2=df4.select_dtypes(include=\"number\")\n", + "num_corr = num_2.corr().round(2)\n", + "# 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()" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 188, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['url_length',\n", + " 'number_special_characters',\n", + " 'charset',\n", + " 'server',\n", + " 'content_length',\n", + " 'whois_country',\n", + " 'whois_statepro',\n", + " 'whois_regdate',\n", + " 'whois_updated_date',\n", + " 'dist_remote_tcp_port',\n", + " 'remote_ips',\n", + " 'app_bytes',\n", + " 'remote_app_packets',\n", + " 'source_app_bytes',\n", + " 'dns_query_times',\n", + " 'type']" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n", - "\n" + "df4.columns.to_list()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### We'll create another K-Nearest Neighbors model with k=5. \n", + "# Challenge 3 - Handle Missing Values\n", "\n", - "Initialize and fit the model below and print the confusion matrix and the accuracy score." + "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": "markdown", + "metadata": {}, + "source": [ + "#### Ya hemos procesado los nulos donde consideraba pero volvemos a hacerlo siguiendo los pasos. " ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 189, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "url_length 0\n", + "number_special_characters 0\n", + "charset 0\n", + "server 175\n", + "content_length 809\n", + "whois_country 303\n", + "whois_statepro 359\n", + "whois_regdate 0\n", + "whois_updated_date 0\n", + "dist_remote_tcp_port 0\n", + "remote_ips 0\n", + "app_bytes 0\n", + "remote_app_packets 0\n", + "source_app_bytes 0\n", + "dns_query_times 0\n", + "type 0\n", + "dtype: int64" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n", - "\n" + "# Your code here\n", + "df4.isna().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Did you see an improvement in the confusion matrix when increasing k to 5? Did you see an improvement in the accuracy score? Write your conclusions below." + "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": 37, + "execution_count": 190, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.455518018018018" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your conclusions here:\n", - "\n" + "# Your code here\n", + "df4.content_length.isna().sum()/len(df4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Bonus Challenge - Feature Scaling\n", - "\n", - "Problem-solving in machine learning is iterative. You can improve your model prediction with various techniques (there is a sweetspot for the time you spend and the improvement you receive though). Now you've completed only one iteration of ML analysis. There are more iterations you can conduct to make improvements. In order to be able to do that, you will need deeper knowledge in statistics and master more data analysis techniques. In this bootcamp, we don't have time to achieve that advanced goal. But you will make constant efforts after the bootcamp to eventually get there.\n", - "\n", - "However, now we do want you to learn one of the advanced techniques which is called *feature scaling*. The idea of feature scaling is to standardize/normalize the range of independent variables or features of the data. This can make the outliers more apparent so that you can remove them. This step needs to happen during Challenge 6 after you split the training and test data because you don't want to split the data again which makes it impossible to compare your results with and without feature scaling. For general concepts about feature scaling, click [here](https://en.wikipedia.org/wiki/Feature_scaling). To read deeper, click [here](https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e).\n", - "\n", - "In the next cell, attempt to improve your model prediction accuracy by means of feature scaling. A library you can utilize is `sklearn.preprocessing.RobustScaler` ([documentation](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html)). You'll use the `RobustScaler` to fit and transform your `X_train`, then transform `X_test`. You will use logistic regression to fit and predict your transformed data and obtain the accuracy score in the same way. Compare the accuracy score with your normalized data with the previous accuracy data. Is there an improvement?" + "Vemos que en content_lenght, casi la mitad de los datos son nulos, por lo que la eliminamos" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 191, "metadata": {}, "outputs": [], "source": [ - "# Your code here" + "# Your comment here\n", + "df5=df4.copy()\n", + "\n", + "df5=df5.drop(columns=[\"content_length\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "url_length 0\n", + "number_special_characters 0\n", + "charset 0\n", + "server 175\n", + "whois_country 303\n", + "whois_statepro 359\n", + "whois_regdate 0\n", + "whois_updated_date 0\n", + "dist_remote_tcp_port 0\n", + "remote_ips 0\n", + "app_bytes 0\n", + "remote_app_packets 0\n", + "source_app_bytes 0\n", + "dns_query_times 0\n", + "type 0\n", + "dtype: int64" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df5.isna().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Again, examine the number of missing values in each column. \n", + "\n", + "If all cleaned, proceed. Otherwise, go back and do more cleaning." + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [], + "source": [ + "# Examine missing values in each column\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 4 - Handle `WHOIS_*` Categorical Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are several categorical columns we need to handle. These columns are:\n", + "\n", + "* `URL`\n", + "* `CHARSET`\n", + "* `SERVER`\n", + "* `WHOIS_COUNTRY`\n", + "* `WHOIS_STATEPRO`\n", + "* `WHOIS_REGDATE`\n", + "* `WHOIS_UPDATED_DATE`\n", + "\n", + "How to handle string columns is always case by case. Let's start by working on `WHOIS_COUNTRY`. Your steps are:\n", + "\n", + "1. List out the unique values of `WHOIS_COUNTRY`.\n", + "1. Consolidate the country values with consistent country codes. For example, the following values refer to the same country and should use consistent country code:\n", + " * `CY` and `Cyprus`\n", + " * `US` and `us`\n", + " * `SE` and `se`\n", + " * `GB`, `United Kingdom`, and `[u'GB'; u'UK']`\n", + "\n", + "#### In the cells below, fix the country values as intructed above." + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([nan, 'US', 'SC', 'GB', 'UK', 'RU', 'AU', 'CA', 'PA', 'se', 'IN',\n", + " 'LU', 'TH', \"[u'GB'; u'UK']\", 'FR', 'NL', 'UG', 'JP', 'CN', 'SE',\n", + " 'SI', 'IL', 'ru', 'KY', 'AT', 'CZ', 'PH', 'BE', 'NO', 'TR', 'LV',\n", + " 'DE', 'ES', 'BR', 'us', 'KR', 'HK', 'UA', 'CH', 'United Kingdom',\n", + " 'BS', 'PK', 'IT', 'Cyprus', 'BY', 'AE', 'IE', 'UY', 'KG'],\n", + " dtype=object)" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "df5.whois_country.unique()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [], + "source": [ + "df5['whois_country'] = df5['whois_country'].str.replace(r'.*Cyprus.*', 'CY', regex=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [], + "source": [ + "df5['whois_country'] = df5['whois_country'].str.replace(r'.*us.*', 'US', regex=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [], + "source": [ + "df5['whois_country'] = df5[\"whois_country\"].str.replace(r'.*se.*', 'SE', regex=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [], + "source": [ + "df5['whois_country'] = df5['whois_country'].str.replace(r'.*GB.*', 'GB', regex=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [], + "source": [ + "df5['whois_country'] = df5['whois_country'].str.replace(r'.*UK.*', 'GB', regex=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [], + "source": [ + "df5['whois_country'] = df5['whois_country'].str.replace(r'.*United Kingdom.*', 'GB', regex=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [], + "source": [ + "df5['whois_country'] = df5['whois_country'].str.replace(r'.*ru.*', 'RU', regex=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we have fixed the country values, can we convert this column to ordinal now?\n", + "\n", + "Not yet. If you reflect on the previous labs how we handle categorical columns, you probably remember we ended up dropping a lot of those columns because there are too many unique values. Too many unique values in a column is not desirable in machine learning because it makes prediction inaccurate. But there are workarounds under certain conditions. One of the fixable conditions is:\n", + "\n", + "#### If a limited number of values account for the majority of data, we can retain these top values and re-label all other rare values.\n", + "\n", + "The `WHOIS_COUNTRY` column happens to be this case. You can verify it by print a bar chart of the `value_counts` in the next cell to verify:" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['US', 'CA', 'ES', 'GB', 'AU', 'PA', 'JP', 'CN', 'IN', 'FR'], dtype='object', name='whois_country')" + ] + }, + "execution_count": 202, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "country_tab=df5.whois_country.value_counts().head(10)\n", + "country_tab.index\n" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "sns.barplot(country_tab)" + ] + }, + { + "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": 204, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Your code here\n", + "df6=df5.copy()\n", + "df6['whois_country'] = df6['whois_country'].apply(lambda x: x if x in country_tab.index else \"OTHER\")" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "whois_country\n", + "US 1104\n", + "OTHER 394\n", + "CA 84\n", + "ES 63\n", + "GB 35\n", + "AU 35\n", + "PA 21\n", + "JP 11\n", + "IN 10\n", + "CN 10\n", + "FR 9\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 205, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df6.whois_country.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now since `WHOIS_COUNTRY` has been re-labelled, we don't need `WHOIS_STATEPRO` any more because the values of the states or provinces may not be relevant any more. We'll drop this column.\n", + "\n", + "In addition, we will also drop `WHOIS_REGDATE` and `WHOIS_UPDATED_DATE`. These are the registration and update dates of the website domains. Not of our concerns.\n", + "\n", + "#### In the next cell, drop `['WHOIS_STATEPRO', 'WHOIS_REGDATE', 'WHOIS_UPDATED_DATE']`." + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here\n", + "df6=df6.drop(columns=[\"whois_regdate\",\"whois_updated_date\",\"whois_statepro\"])" + ] + }, + { + "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": 207, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 1776 entries, 0 to 1780\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 url_length 1776 non-null int64 \n", + " 1 number_special_characters 1776 non-null int64 \n", + " 2 charset 1776 non-null object \n", + " 3 server 1601 non-null object \n", + " 4 whois_country 1776 non-null object \n", + " 5 dist_remote_tcp_port 1776 non-null int64 \n", + " 6 remote_ips 1776 non-null int64 \n", + " 7 app_bytes 1776 non-null int64 \n", + " 8 remote_app_packets 1776 non-null int64 \n", + " 9 source_app_bytes 1776 non-null int64 \n", + " 10 dns_query_times 1776 non-null float64\n", + " 11 type 1776 non-null int64 \n", + "dtypes: float64(1), int64(8), object(3)\n", + "memory usage: 180.4+ KB\n" + ] + } + ], + "source": [ + "# Your code here\n", + "df6.info()" + ] + }, + { + "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": "markdown", + "metadata": {}, + "source": [ + "#### Ya la eliminé anteriormente" + ] + }, + { + "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": 208, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['iso-8859-1', 'UTF-8', 'us-ascii', 'ISO-8859-1', 'utf-8',\n", + " 'windows-1251', 'ISO-8859', 'windows-1252'], dtype=object)" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "df6.charset.unique()" + ] + }, + { + "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": [ + "Mediante Regex intentaremos eliminar todo lo que haya después de \"/\" y \"-\" para consolidar el nombre de los servidores. " + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['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',\n", + " 'Apache/2.2.29 (Amazon)', 'Microsoft-IIS/7.5', 'LiteSpeed',\n", + " 'Apache/2.4.25 (cPanel) OpenSSL/1.0.1e-fips mod_bwlimited/1.4',\n", + " 'tsa_c', 'Apache/2.2.0 (Fedora)', 'Apache/2.2.22 (Debian)',\n", + " 'Apache/2.2.15 (CentOS)', 'Apache/2.4.25',\n", + " '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',\n", + " 'Apache/2.2.22 (Ubuntu)', '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',\n", + " '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)',\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": 209, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df6.server.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [], + "source": [ + "df7=df6.copy()\n", + "df7['server'] = df7['server'].str.replace(r'/.*', '', regex=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "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": 211, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df7.server.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [], + "source": [ + "df7['server'] = df7['server'].str.replace(r'-.*', '', regex=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "server\n", + "Apache 0.400375\n", + "nginx 0.210493\n", + "Microsoft 0.123673\n", + "cloudflare 0.058713\n", + "GSE 0.029981\n", + " ... \n", + "mw2103.codfw.wmnet 0.000625\n", + "barista 0.000625\n", + "mw2190.codfw.wmnet 0.000625\n", + "mw2107.codfw.wmnet 0.000625\n", + "IdeaWebServer 0.000625\n", + "Name: proportion, Length: 108, dtype: float64" + ] + }, + "execution_count": 213, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df7.server.value_counts(10)" + ] + }, + { + "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": 214, + "metadata": {}, + "outputs": [], + "source": [ + "top_server=df7.server.value_counts().head(3).index" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here\n", + "df7.server=df7.server.apply(lambda x:x if x in top_server else \"OTHER\")" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "server\n", + "Apache 641\n", + "OTHER 600\n", + "nginx 337\n", + "Microsoft 198\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Count `SERVER` value counts here\n", + "df7.server.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 1776 entries, 0 to 1780\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 url_length 1776 non-null int64 \n", + " 1 number_special_characters 1776 non-null int64 \n", + " 2 charset 1776 non-null object \n", + " 3 server 1776 non-null object \n", + " 4 whois_country 1776 non-null object \n", + " 5 dist_remote_tcp_port 1776 non-null int64 \n", + " 6 remote_ips 1776 non-null int64 \n", + " 7 app_bytes 1776 non-null int64 \n", + " 8 remote_app_packets 1776 non-null int64 \n", + " 9 source_app_bytes 1776 non-null int64 \n", + " 10 dns_query_times 1776 non-null float64\n", + " 11 type 1776 non-null int64 \n", + "dtypes: float64(1), int64(8), object(3)\n", + "memory usage: 180.4+ KB\n" + ] + } + ], + "source": [ + "df7.info()" + ] + }, + { + "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`.**" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "url_length 142\n", + "number_special_characters 31\n", + "charset 8\n", + "server 4\n", + "whois_country 11\n", + "dist_remote_tcp_port 66\n", + "remote_ips 18\n", + "app_bytes 823\n", + "remote_app_packets 116\n", + "source_app_bytes 883\n", + "dns_query_times 10\n", + "type 2\n", + "dtype: int64" + ] + }, + "execution_count": 218, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here\n", + "df7.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [], + "source": [ + "df8=df7.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [], + "source": [ + "columns_to_encode=[\"charset\",\"server\",\"whois_country\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [], + "source": [ + "df_ready=pd.get_dummies(df8, columns=columns_to_encode)" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " url_length number_special_characters dist_remote_tcp_port remote_ips \\\n", + "0 16 7 0 2 \n", + "1 16 6 7 4 \n", + "2 16 6 0 0 \n", + "3 17 6 22 3 \n", + "4 17 6 2 5 \n", + "... ... ... ... ... \n", + "1776 194 16 0 0 \n", + "1777 198 17 0 0 \n", + "1778 201 34 2 6 \n", + "1779 234 34 0 0 \n", + "1780 249 40 6 11 \n", + "\n", + " app_bytes remote_app_packets source_app_bytes dns_query_times type \\\n", + "0 700 10 1153 2 1 \n", + "1 1230 19 1265 0 0 \n", + "2 0 0 0 0 0 \n", + "3 3812 37 18784 8 0 \n", + "4 4278 62 129889 4 0 \n", + "... ... ... ... ... ... \n", + "1776 0 3 186 0 1 \n", + "1777 0 2 124 0 1 \n", + "1778 6631 89 132181 4 0 \n", + "1779 0 0 0 0 0 \n", + "1780 2314 28 3039 6 0 \n", + "\n", + " charset_ISO-8859 charset_ISO-8859-1 charset_UTF-8 charset_iso-8859-1 \\\n", + "0 0 0 0 1 \n", + "1 0 0 1 0 \n", + "2 0 0 0 0 \n", + "3 0 1 0 0 \n", + "4 0 0 1 0 \n", + "... ... ... ... ... \n", + "1776 0 0 1 0 \n", + "1777 0 0 1 0 \n", + "1778 0 0 0 0 \n", + "1779 0 1 0 0 \n", + "1780 0 0 0 0 \n", + "\n", + " charset_us-ascii charset_utf-8 charset_windows-1251 \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 1 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "... ... ... ... \n", + "1776 0 0 0 \n", + "1777 0 0 0 \n", + "1778 0 1 0 \n", + "1779 0 0 0 \n", + "1780 0 1 0 \n", + "\n", + " charset_windows-1252 server_Apache server_Microsoft server_OTHER \\\n", + "0 0 0 0 0 \n", + "1 0 1 0 0 \n", + "2 0 0 1 0 \n", + "3 0 0 0 0 \n", + "4 0 0 0 1 \n", + "... ... ... ... ... \n", + "1776 0 1 0 0 \n", + "1777 0 1 0 0 \n", + "1778 0 1 0 0 \n", + "1779 0 0 0 1 \n", + "1780 0 0 1 0 \n", + "\n", + " server_nginx whois_country_AU whois_country_CA whois_country_CN \\\n", + "0 1 0 0 0 \n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "3 1 0 0 0 \n", + "4 0 0 0 0 \n", + "... ... ... ... ... \n", + "1776 0 0 0 0 \n", + "1777 0 0 0 0 \n", + "1778 0 0 0 0 \n", + "1779 0 0 0 0 \n", + "1780 0 0 0 0 \n", + "\n", + " whois_country_ES whois_country_FR whois_country_GB whois_country_IN \\\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", + "1776 1 0 0 0 \n", + "1777 1 0 0 0 \n", + "1778 0 0 0 0 \n", + "1779 0 0 0 0 \n", + "1780 0 0 0 0 \n", + "\n", + " whois_country_JP whois_country_OTHER whois_country_PA \\\n", + "0 0 1 0 \n", + "1 0 1 0 \n", + "2 0 1 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "... ... ... ... \n", + "1776 0 0 0 \n", + "1777 0 0 0 \n", + "1778 0 0 0 \n", + "1779 0 0 0 \n", + "1780 0 0 0 \n", + "\n", + " whois_country_US \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 1 \n", + "4 1 \n", + "... ... \n", + "1776 0 \n", + "1777 0 \n", + "1778 1 \n", + "1779 1 \n", + "1780 1 \n", + "\n", + "[1776 rows x 32 columns]" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ready=df_ready.astype(int)\n", + "df_ready" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['url_length',\n", + " 'number_special_characters',\n", + " 'dist_remote_tcp_port',\n", + " 'remote_ips',\n", + " 'app_bytes',\n", + " 'remote_app_packets',\n", + " 'source_app_bytes',\n", + " 'dns_query_times',\n", + " 'type',\n", + " 'charset_ISO-8859',\n", + " 'charset_ISO-8859-1',\n", + " 'charset_UTF-8',\n", + " 'charset_iso-8859-1',\n", + " 'charset_us-ascii',\n", + " 'charset_utf-8',\n", + " 'charset_windows-1251',\n", + " 'charset_windows-1252',\n", + " 'server_Apache',\n", + " 'server_Microsoft',\n", + " 'server_OTHER',\n", + " 'server_nginx',\n", + " 'whois_country_AU',\n", + " 'whois_country_CA',\n", + " 'whois_country_CN',\n", + " 'whois_country_ES',\n", + " 'whois_country_FR',\n", + " 'whois_country_GB',\n", + " 'whois_country_IN',\n", + " 'whois_country_JP',\n", + " 'whois_country_OTHER',\n", + " 'whois_country_PA',\n", + " 'whois_country_US']" + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ready.columns.to_list()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, inspect `website_dummy` to make sure the data and types are intended - there shouldn't be any categorical columns at this point." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 6 - Modeling, Prediction, and Evaluation\n", + "\n", + "We'll start off this section by splitting the data to train and test. **Name your 4 variables `X_train`, `X_test`, `y_train`, and `y_test`. Select 80% of the data for training and 20% for testing.**" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Your code here:\n", + "X=df_ready.drop(columns=[\"type\"])\n", + "y=df_ready.type\n", + "\n", + "X_train, X_test, y_train, y_test= train_test_split(X,y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(356, 31)" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### In this lab, we will try two different models and compare our results.\n", + "\n", + "The first model we will use in this lab is logistic regression. We have previously learned about logistic regression as a classification algorithm. In the cell below, load `LogisticRegression` from scikit-learn and initialize the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, fit the model to our training data. We have already separated our data into 4 parts. Use those in your model." + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "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": 226, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "model=LogisticRegression()\n", + "model.fit(X_train,y_train)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "finally, import `confusion_matrix` and `accuracy_score` from `sklearn.metrics` and fit our testing data. Assign the fitted data to `y_pred` and print the confusion matrix as well as the accuracy score" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "predictions=model.predict(X_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20% for test prediction data: 356.\n" + ] + } + ], + "source": [ + "print(f'20% for test prediction data: {len(predictions)}.')" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.90 0.97 0.94 308\n", + " 1 0.67 0.33 0.44 48\n", + "\n", + " accuracy 0.89 356\n", + " macro avg 0.79 0.65 0.69 356\n", + "weighted avg 0.87 0.89 0.87 356\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data accuracy: 0.8876404494382022\n", + "Train data accuracy: 0.8873239436619719\n" + ] + } + ], + "source": [ + "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": 231, + "metadata": {}, + "outputs": [], + "source": [ + "cm = confusion_matrix(y_test, predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n", + "plt.figure(figsize=(8, 6))\n", + "disp.pldisp = 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": [ + "What are your thoughts on the performance of the model? Write your conclusions below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is quite accurate. However, the data is not ideal for training, as the target variable is imbalanced. Consequently, the model predicts negative cases (non-malware sites) well, but struggles to accurately predict positive cases (malware sites)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Our second algorithm is is K-Nearest Neighbors. \n", + "\n", + "Though is it not required, we will fit a model using the training data and then test the performance of the model using the testing data. Start by loading `KNeighborsClassifier` from scikit-learn and then initializing and fitting the model. We'll start off with a model where k=3." + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n", + "model=KNeighborsClassifier(n_neighbors=11)\n", + "model=model.fit(X_train,y_train)\n", + "predictions=model.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To test your model, compute the predicted values for the testing sample and print the confusion matrix as well as the accuracy score." + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.92 0.97 0.95 308\n", + " 1 0.72 0.48 0.57 48\n", + "\n", + " accuracy 0.90 356\n", + " macro avg 0.82 0.72 0.76 356\n", + "weighted avg 0.90 0.90 0.90 356\n", + "\n" + ] + } + ], + "source": [ + "# Your code here:\n", + "print(classification_report(y_test, predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data accuracy: 0.9044943820224719\n", + "Train data accuracy: 0.9316901408450704\n" + ] + } + ], + "source": [ + "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": 236, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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8POju3KqQulH5UNn4InNV4mdy4cIF6PV6aLVatGnTxmKZ9u3bAwBOnTpVkVUjFDQ/P//xErh41kdebk6x2QIxfx0xTfV9/D//tXiO7v953dSyodZoYGfvYNr395H9pveP/+eNEo7/97z2tVwsliEqL94t28C5bj0AwJHVS2HMyytWJmrL2n8HbgPIvZtVYfUj+QmCbS8yVyXCSGxsLADA29sbGo3lmRJ+fn5mZaniBE/7P7R4/GkAwK557yPt7/Nm+7Mz7iBi/UoAQNPOT2DQx0tRt7AlI9+IRwePQpcRr5nWYgAK1iEpdP3qJZzZsw0A0H7Qy+jz1hzUbuALlZ0d6jRsjP7TP0PQUwNMxxc9lqgiqNRq9BhfsEbOzdi/sf71EUg+fwp5Bj3u3kzD0R++xt4v5kBVZKaXoKoS//klqhBVYgXWjIwMAICbm1uJZQr3FZa1hspOgwaBj1h9fE3Uafh/8HC/gmm34Wu/Qerflyz+DC8c2gsv/yD4d3sSbZ55wTRVFzl30G7gMKSnJCL+5DG06fs8DLk5qNc8yOz4vzavRW2fRmjYuh06vzwenV8eb7Y/9fJ5ZKQlo0X33sjT5/LfsSRC+S8IVVN1eHEM0lOScGTVYlyN+ANXI/4w21/LwxMdBr+CP775HADg4FaH/x5VGNcKkVeVCCO5uQWzNEpqFQEArVZrVtYarvV98erWv6w+vsbR3wPy/llpVeOELq9OR5dXH7Cgk1EP5OUC+XkAREBQAWotajd5GLUbNgPycqBxqGX530EUC4435gL5xiLH26P+w91RPzcTyDfA3a8Z/x1JEb3fXYCA4CGI2vwdkk4fQ05WBmrV8USzbk+j0+j/4sSm7wAADm7uqOXdnO31VRj/5eRVJcKIvX3B0smGf6ZuWqLX683KWiMzNREbJr1g9fE1SafhY/Fwv8EAgIgfv8OpXVskn6NukxZ44fO12PL2SNyMuYQh//sa7j5+OPfbdhwpYT2RkqjUdhi1/EfYO9VCxA/LrapPTfDqutKf/0O28/X3h+/0/xV8ENSAgyuQkwmIRlyLPAAAaNi6PYScdOUqWV3Zu1bIcvACYPMzhhhmzFWJMFKWLpiydOU8SH6eASnno60+vqZ4+o2ZpiDy25cfIXzNcpvOdzPmEuw0dnD3KRj3E77mK6ScPynpHK37DoK9Uy0YDQaErV6KrBupNtWp2hJLnnZK5Ug0Ij35Gq5GFnTdPNz/Rf5bEBVRJcJI48aNAQDJyckwGAwWu2vi4+PNylL5ePqNmegyagIAYO/C2Tj6wzc2n1Pj4Ihnps0BAFw5eghJ505KOt65bj089XpB91D09h8ZRKjSMRoM+OXjaRCNRtRrFoCWPfsqXSWyhRwzYtg0YqZKDOcODAyERqOBXq/H6dOnLZaJiooCALRt27YCa1az9H59uimI7FkwS1IQ8Wn1CLqPfR2eD/lDZVeQgVV2doBRj+dmLUD9Fq1w9+b1Ep/a27xbLzw2PATuvo1MsxA0Do5o1WcgQkJ3wdXLGzdi/8ZvX35k410SWed24jXs/2oeki+chiE3B0DBM2vi/voToa8OwtWjh6B1qoXnP1ps9vRrqqI4t1dWVaJlpFatWujWrRsOHjyITZs2mdYUKRQXF4eIiAgAQHBwsBJVrPbc6vug2+jJAAr+A9vtlUno9sqkEsuHr12O8LVfmz671PVCr8nvo9fk95FvNCL3bibsnV2B3Ex4NGyMm7FX8OObryAzLdni+Tz8miD47Y/Qd9r/wWgwQK+7CwcXN1MwSTwThR/fHG16gB5RRcu9l4XD3y0qeHKvIMDBxQ163V3k/7PmiItnfQz5bAUaBLRWuKZElU+VCCMAMHHiRBw6dAjbt29Hu3btMGTIEAiCgOvXr2Pq1KnIz89H7969ERBgeYVPsk3RwVoqtdq0wFNJtE61zD4nXziNI6uXolG7TnD39oOjW23k3M2Ek3s9HP7uCxxa/hmMeSUPUL4a8Qci1q+E3yMd4VbfBw7OLrh76zpSLp7BmT0/4+yebRBF0babJLJBbe+GeGLcVMRFheN2Qhx06bdh7+wKj8b+CHjiKTz64iuwv+//F1R1sXFDXoJYhf4Lvnr1asybNw+iKKJBgwZwd3fHlStXoNfr8dBDD2H9+vWoU6eO1ee/kxCDRU+1kLHGVJoGgY/g1a1/4ZtBHTlwuILMPpGidBVqFkENOLoD2Xc4YLUiOLhXyGwaQ2oMEsbb9rui4beXoKnfRKYaVX1VpmUEAEaPHo0WLVrg+++/x+nTp3Hr1i14e3sjODgY48ePR61a/KuDiIioqqlSYQQAOnfujM6dOytdDSIiqsFsXWeEzFW5MEJERKS4KjEXtergj5OIiIgUxZYRIiIiSQQZumnYzVMUwwgREZEUXIFVdgwjREREEvBBefLjmBEiIiJSFFtGiIiIpGLThqwYRoiIiCTiOiPyYjcNERERKYotI0RERBKxYUReDCNEREQSsZtGXuymISIiIkWxZYSIiEgqtozIimGEiIhICq7AKjt20xAREZGi2DJCREQkEQewyothhIiISCKls4goioiOjsaBAwcQFRWFmJgY3L17Fy4uLggMDMTAgQMxYMCAEkOTwWBAaGgoduzYgfj4eGi1WgQEBGDEiBF4+umnS712QkICli1bhrCwMNy+fRseHh7o2rUrJkyYgIYNG1p1PwwjREREUimcRiIiIjB69GjT54YNG8LHxwdJSUkICwtDWFgYdu3ahSVLlkCr1Zodm5ubizFjxiAqKgpqtRrNmjVDdnY2IiMjERkZiXHjxuHtt9+2eN3o6GiMHTsWOp0Obm5u8Pf3R0JCArZs2YI9e/Zg9erVaNOmjeT74ZgRIiKiKkYURfj6+mL69OkIDw/Hvn37sHXrVkRGRuJ///sftFotDh06hMWLFxc7dv78+YiKioKvry927tyJHTt24Pfff8eyZcug1WqxYsUKHDhwoNhx2dnZmDJlCnQ6HV544QUcPnwYW7duxZEjRzBo0CDcu3cPU6ZMQU5OjuT7YRghIiKSSBBse9mqTZs22LNnD0aNGgUPDw+zfQMHDsSkSZMAAJs3b0Z+fr5p382bN7FhwwYAwCeffIImTZqY9vXq1QshISEAgKVLlxa75saNG3Hjxg00atQIs2fPhr29PQDA3t4ec+bMgZ+fH1JTU7F582bJ98MwQkREJIGAggGsNr1srIOzszM0Gk2J+x9//HEAQHp6Om7fvm3afuDAARgMBjRq1AidOnUqdtywYcMAAOfOnUN8fLzZvj179gAAnn/++WJdP1qtFoMGDQIA7N69W/L9MIwQERFVM7m5uab3Dg4OpvcnT54EALRv397icV5eXvD19TUrCwBGoxFnz54FAHTo0MHisYXbz5w5A6PRKKm+DCNERESS2NgqIggo71XPdu3aBQAICAiAs7OzaXtcXBwAoFGjRiUe6+fnBwCIjY01bUtKSoLBYDDbX9Jxer0eycnJkurL2TREREQSyTHuIzk5GSNHjixx//79+60677lz50zjQsaPH2+2LyMjAwDg5uZW4vGF+zIzM03b0tPTTe9r165d6nGF15EyzZctI0RERNXEzZs3MXnyZBgMBjz11FPo16+f2f7C7pvSxpsUjgcpOitGr9eb3pd0bNFxJFJn1JSpZWTUqFGSTloSQRAQGhoqy7mIiIgUUTCC1eZzeHt7W936YUlWVhbGjRuH5ORkBAUFYd68ecXKFM6AKexysaQweBQda1I0aBgMBtN5LB13/7FlUaYw8tdff0k6aUm4fC4REVUHle3X2b179xASEoLz58+jefPm+O6778zGihRydXUF8G93jSWF+wrLAuZdMOnp6fDy8irxuPvLl0WZwsjkyZMlnZSIiIgqRnZ2Nl599VWcPHkSjRs3xqpVq+Du7m6xbOPGjXHixAlcu3atxPMVTult3LixaZuPjw80Gg0MBgPi4+MthpHC47RaLby9vSXdA8MIERGRRJWlpT83NxcTJ07EsWPH4OPjg9DQUHh6epZYvm3btti6dStOnDhhcX9aWhoSExNNZQvZ2dmhVatWiI6OxvHjx/Hoo48WO/b48eMAgNatW0OtVku6Dw5gJSIikkjpFViBgrEbU6ZMQXh4OOrXr4/Q0FDUr1+/1GN69eoFjUaDuLg4REREFNtfOAsnMDCw2PTfPn36AAC2bdtWbMyJXq/H1q1bAQDBwcGS74VhhIiISCqF04jRaMTbb7+NP/74A56enggNDS3TVNq6deti6NChAIDp06cjJibGtO/AgQNYuXIlAJiWky9q6NCh8PT0xLVr1zBr1izTzJzc3FzMmjUL8fHxqFevHl588UXJ9yPLOiP79+/HkSNHkJycjJycHLMZMzqdDhcvXoQgCHjkkUfkuBwREVGNtnv3btPy7FqtFu+//36JZWfMmIHAwEDT52nTpuHcuXOIjo5G//790bx5c+h0OtOYj7Fjx6J3797FzuPk5IRFixYhJCQEW7Zswb59++Dr64vExERkZGTAyckJS5YsgaOjo+T7sSmMpKSkYPLkyTh//jyAgqcI3t+PptVq8dZbbyE1NRU///wzWrRoYcsliYiIFKf0mJGi02iTkpKQlJRUYtmsrCyzzw4ODlizZg1CQ0OxY8cOxMXFQaPRoGPHjhgxYoSpO8aS9u3bY/v27Vi2bBnCwsJw+fJluLu7Y9CgQZg4caKkhc6KsjqMZGdnY+zYsYiNjUX9+vXRu3dvbNmypdhCJ3Z2dhg8eDCWLFmC/fv3M4wQEVGVJtMyIzYZNGiQ6cF01tBqtRg3bhzGjRsn+Vg/Pz+La5jYwuoxI+vWrUNsbCwCAwPx66+/4sMPP0StWrUslu3VqxcAICwszNrLERERUTVldcvI3r17IQgC3n//fTg5OZVa1t/fH3Z2dqYH9BAREVVZggzdNJVjZnClYXUYiY2NhVqtRrt27R5YVqVSwdnZudQV34iIiKoMhglZWd1No9frYW9vX+aFTbKzs83WticiIiICbAgjHh4e0Ol0Zo8YLsnFixeRm5v7wMVYiIiIKj8Bgkpl04tNK+asDiOFa4bs3r37gWWXL18OQRAsLh9LRERU5VSGJVirEavDyLBhwyCKIpYsWYIrV65YLJOdnY05c+Zg7969AIDhw4dbezkiIiKqpqwewNqxY0cMHjwYP/30E1588UX06NEDOp0OALBy5UpcvnwZf/zxh6kb55VXXkFAQIA8tSYiIlISWzdkZdMKrHPmzIGjoyN++OEHU3eNIAhYsGABgH9XZB0zZgzeeecd22tLRERUCQgCH+0mJ5vCiFqtxvTp0zFkyBBs3rwZJ06cwPXr15Gfn4+6deuiXbt2GDJkCFtEiIioemHLiKxkeVBe8+bN8cEHH8hxKiIiIqphZAkjRERENUZleDhNNSNrGElKSsKtW7cAFKxD4uPjI+fpiYiIKgWln9pb3dgcRtLS0vDtt9/i119/RXp6utk+Nzc39OvXD+PGjeOCZ0RERGSRTcOBjxw5gv79+2P9+vW4c+cORFE0e6Wnp2P9+vXo378//vzzT7nqTEREpCABEFS2vdhPY8bqlpGYmBhMmjQJubm5cHNzw7Bhw9CpUyd4eXkBKGgxiYyMxMaNG3Hnzh1MmTIF27ZtQ5MmTWSrPBERkRIEFcOEnKwOI8uWLUNubi5atGiBVatWoU6dOmb7mzRpgs6dO2PUqFEYM2YMLl++jOXLl2P+/Pk2V5qIiIiqD6u7aSIiIiAIAj7++ONiQaSoOnXq4P/+7/8giiKOHj1q7eWIiIgqDz6bRlZWt4xkZmbCyckJrVu3fmDZNm3awMnJqUxP+CUiIqr0uAKrrKz+aXp6eiI/P7/M5UVRhKenp7WXIyIiomrK6jDyxBNPICcnp0xdL0ePHkV2djZ69uxp7eWIiIgqDUEQbHqROavDyMSJE+Hh4YHp06cjNja2xHJxcXH48MMP4enpiQkTJlh7OSIiosrB1vEiHDdSTJnGjBw7dszi9qlTp2Lu3Ll47rnnEBwcbJraKwgCUlNTERkZiT179sDe3h7vvfceYmJi4OHhIesNEBERVTiGCVmVKYyMHDnygc1Kv/zyC3755ReL+/R6PaZPnw5BEHD+/HnptSQiIqJqq8yzaURRtPlicpyDiIhIaQJn08iqTGHk4sWL5V0PIiKiqoPdNLJitCMiIiJF2fzUXiIioppEgO3PpmG7ijmGESIiIqk4ZkRWsoSR1NRUnDhxAmlpadDpdKUOVJ08ebIclyQiIqJqwqYwcvv2bcyePRv79u174EwZURQhCALDCBERVW1yLFrGAbBmrA4jOp0Oo0aNwtWrV6HRaBAQEIDTp09Do9GgTZs2uHnzJq5duwYAcHNzg7+/v2yVJiIiUhKXdJeX1Z1e69atw5UrV/DQQw9h37592LRpE4CC4LFu3Trs3bsX+/fvR9++fZGVlYXu3btj7dq1slWciIiIqgerw8i+ffsgCAKmTp2KevXqWSzj4+ODL774An379sUXX3xRpofqERERVXp8Lo2srA4jMTExAIDHH3/cbHteXl6xsm+88QZEUWTLCBERVQ+CyrYXmbF6zEhubi5cXV2h1WpN2+zt7aHT6YqVbdiwIVxcXHD69GlrL0dERFRpcMyIvKyOZ3Xr1sXdu3eRn59v2lanTh0YDAakpqaalTUajcjOzkZ6errVFSUiIqLqyeow4u3tjfz8fFy/ft20LSAgAADw+++/m5U9cOAA8vLy4OHhYe3liIiIKg+VYNuLzFjdTdOpUydERUUhIiICAwcOBAA888wzOHjwIBYuXIjc3Fy0bNkSFy9exPLlyyEIQrHxJURERFWPIMNTexlIirL6p/nkk09CFEX88ssvpm39+/dHx44dkZ2djQULFiAkJASff/457t69Cw8PDy54RkRERMVY3TISGBiIixcvmm0TBAHffvstli9fjl9//RUpKSlwcXFB9+7d8cYbb8DLy8vmChMRESlKgAwrsMpSk2pD9gflOTg44M0338Sbb74p96mJiIgqB86mkRUnOxMREZGiZG8ZISIiqu64zoi8yhRGkpOTZbugt7e3bOciIiJSBFdRlVWZwkivXr1kuZggCDh//rws5yIiIqLqoUxhRBRFWS4m13mIiIgUVQm6aW7cuIHw8HCcOXMGZ8+exYULF5CTk4OgoCBs3bq1xOOefPJJJCUllXru06dPw97e3uK+hIQELFu2DGFhYbh9+zY8PDzQtWtXTJgwAQ0bNrTqXsoURvbv32/VyYmIiKofQYYxI7aHmV27dmHu3LlWH+/v7w9nZ2eL+0q6v+joaIwdOxY6nQ5ubm7w9/dHQkICtmzZgj179mD16tVo06aN5LqUKYz4+PhIPnFVVNu7IWYdvax0NWoOdcFDFsev3gYY9QpXpmYQ9feUrkLNotZCACDm5fA7XhHs3SBAXf7XEQCobBwzIkPDirOzM7p06YJWrVqhVatWiIuLw8KFC8t8/IcffojHHnuszOWzs7MxZcoU6HQ6vPDCC5g1axbs7e2Rm5uL2bNnY+vWrZgyZQr27t0LBwcHSffC2TRERERV0ODBgzF48GDT59K6ZuSwceNG3LhxA40aNcLs2bOh1Rb8QWlvb485c+bg+PHjiI+Px+bNmzFy5EhJ5+ZwYCIiIqkEwbZXFbRnzx4AwPPPP28KIoW0Wi0GDRoEANi9e7fkc7NlhIiISKpqMLV3w4YN+P7775GTk4O6deuiQ4cOGDBggMVxJEajEWfPngUAdOjQweL5CrefOXMGRqMRanXZu8wYRoiIiGqgX3/91ezzzp07sWjRIixYsABdu3Y125eUlASDwQAA8PPzs3i+wu16vR7JycmSZtYwjBAREUklQ1dLcnJyqWMrymsm6yOPPILXXnsN7du3h7e3NwwGA6KiorB48WKcP38eEyZMwI8//oigoCDTMenp6ab3tWvXtnheNzc30/uMjAyGESIiovIjyNBNIwBQZu2tBQsWmH12dHREz5490blzZ7z00ks4d+4cPv/8c6xatcpURq//dzaYRqOxeN6i40hycnIk1YlhhIiISAHe3t6Vah0vBwcHvPHGGxg3bhwiIiKQmZkJV1dXAOZBw2AwWFwQrWhgkTq1t+qPwCEiIqpo1XQ2Tbt27QAA+fn5iI+PN20v2gVTtMumqIyMDIvly4JhhIiISCpBZdurkiraBWM0Gk3vfXx8TPuKhpSiCrdrtVrJD8WV7SciiiJu374t6xN+iYiIqOJcvvzvKuReXl6m93Z2dmjVqhUA4Pjx4xaPLdzeunVrSdN6ARnCyLlz5zB58mS0b98eXbt2Re/evc32Z2RkYObMmZg5c6ZZfxIREVGVVU27aVauXAkAaNasGerXr2+2r0+fPgCAbdu2mab5FtLr9aYVYIODgyVf16Yw8vPPP2Po0KHYt28fdDodRFEs9mReNzc3JCYmYvPmzQgPD7flckRERMoTYHs3jUJ55LvvvsPatWtx584ds+137tzBzJkzTausTpkypdixQ4cOhaenJ65du4ZZs2YhNzcXAJCbm4tZs2YhPj4e9erVw4svvii5XlbPprl69SpmzJiBvLw8jBw5EgMHDkRISIjFgS3PPfccwsPDsX//fvTo0cPaSxIREVUCcrRu2J5GUlJSMHDgQNPnwt6HS5cumT0ALyQkBOPGjQMApKamYs2aNfjkk0/g4+ODOnXqICcnBzExMcjLy4NKpcLUqVMttm44OTlh0aJFCAkJwZYtW7Bv3z74+voiMTERGRkZcHJywpIlS+Do6Cj5XqwOI6tWrYLBYMDLL7+M6dOnA0CJfUSdOnUCAJw8edLayxEREVERRqPRYgNAXl6e2faia37069cPoijizJkzSE5OxsWLF6FWq+Hr64uOHTvipZdeQsuWLUu8Zvv27bF9+3YsW7YMYWFhuHz5Mtzd3TFo0CBMnDhR0kJnRVkdRiIiIiAIgiltlcbLywuOjo4c3EpERNVDJRj34evri0uXLkk6pm3btmjbtq1N1/Xz88O8efNsOsf9rA4j169fh6OjY7EBLiWxt7fH3bt3rb0cERFR5VGJp+dWRVb/NLVaLQwGQ7EBq5bk5OQgKyvL4pMAiYiIqGazOoz4+PggLy8PcXFxDyz7xx9/wGg0olmzZtZejoiIqPKoplN7lWJ1GOnevTtEUcSaNWtKLXfnzh3Mnz8fgiDgiSeesPZyRERElUc1XYFVKVb/REaPHg0nJyds2LABS5cuLTYeJCcnB7/88gteeOEFJCYmonbt2hg+fLjNFSYiIqLqRRDLMuijBAcPHsTrr7+OvLw82NnZQRRFGI1GNGnSBAkJCaYxJVqtFt988w06d+4sZ91lJxoNQGai0tWoOdRaCK4+EDOTACNX560Q/IusYqm1EFwaQMxK4Xe8Irh4Q1Bbfry9nMTMVIibJ9p0DuHFZRBcyzYBpCaw6b9MPXv2xLp16xAUFASDwYC8vDyIooirV69Cr9dDFEUEBgbihx9+qPRBhIiIqEwEQYYVWDlupCirp/YWatOmDX766SdcvHgRUVFRuH79OvLz81G3bl20a9cOrVu3lqOeREREVE3ZHEYKBQQEICAgQK7TERERVV5s2ZCVbGGEiIioxuD4K1kxjBAREUnFlhFZWR1GRo0aJfkYQRAQGhpq7SWJiIioGrI6jPz1119lKif8kx5FUTS9JyIiqroEGbpp+PuwKKvDyOTJk0vdn5WVhVOnTuHkyZOmBc/UarW1lyMiIqo8+Me1rMotjBQ6evQopkyZgqtXr2Lx4sXWXo6IiIiqqXIfDty5c2dMnz4dv//+OzZv3lzelyMiIip/fDaNrCrkJ/LMM89ArVYzjBARUfXAp/bKqkLCiL29PRwdHXH16tWKuBwRERFVIRWyzkhaWhqysrLg5ORUEZcjIiIqPwJs72ph44iZcg8jOTk5mD17NgDA39+/vC9HRERU/tjVIiurw8jSpUtL3a/X65GSkoIjR44gPT0dgiDg5ZdftvZyREREVE3ZFEbKsoiZKIpQqVR47bXXMGDAAGsvR0REVElw0TO5WR1GHn300dJPbGcHV1dXBAQEoG/fvmjcuLG1lyIiIqpc2E0jK6vDyNq1a+WsBxERUdXBtUJkxZ8mERERKcrqMBIQEIDAwEBcu3ZNzvoQERFVfirBtheZsbqbxsHBAXZ2dmjUqJGc9SEiIqrcBNg+ZoR5xIzVLSNeXl7Iy8uTsy5ERERUA1kdRnr06IHc3Fz89ddfctaHiIiokhNkeFAem0aKsjqMvPrqq6hTpw5mz56N69evy1knIiKiyo0PypOV1WNGrl69ijfeeANz585Fv3798Nxzz6Fdu3aoU6cO1Gp1icc9aH0SIiIiqlnKHEZ+/vln2Nvbo2/fvgCAkSNHmq3Aum7dOqxbt67UcwiCgPPnz1tZVSIiokqC64zIqsxh5L333oOnp6cpjAAFS71LIbU8ERFRpcQwIitJ3TRFw8TFixdlrwwRERHVPFaPGSEiIqqZ+KA8uTGMEBERScUZMbJiGCEiIpKKY0ZkxZ8mERERKUpSy8itW7fQsmVLqy/Gqb1ERFTlCbC9ZYS9PGYkd9Nwei4REdV4HDMiK0lhxNHREWPHji2vuhAREVENJCmMODk5YfLkyeVVFyIioiqAU3vlxtk0REREUnE2jaz40yQiIiJFsWWEiIhIKraMyIphhIiISCrOppEVwwgREVEVdOPGDYSHh+PMmTM4e/YsLly4gJycHAQFBWHr1q2lHmswGBAaGoodO3YgPj4eWq0WAQEBGDFiBJ5++ulSj01ISMCyZcsQFhaG27dvw8PDA127dsWECRPQsGFDq+6lzGGET+klIiICKstsml27dmHu3LmSj8vNzcWYMWMQFRUFtVqNZs2aITs7G5GRkYiMjMS4cePw9ttvWzw2OjoaY8eOhU6ng5ubG/z9/ZGQkIAtW7Zgz549WL16Ndq0aSO5Tuz0IiIikkpQ2faSgbOzM7p06YLx48dj8eLFmDp1apmOmz9/PqKiouDr64udO3dix44d+P3337Fs2TJotVqsWLECBw4cKHZcdnY2pkyZAp1OhxdeeAGHDx/G1q1bceTIEQwaNAj37t3DlClTkJOTI/leGEaIiIikEACoVLa9ZBhyMnjwYKxatQpvvfUW+vTpA09Pzwcec/PmTWzYsAEA8Mknn6BJkyamfb169UJISAgAYOnSpcWO3bhxI27cuIFGjRph9uzZsLe3BwDY29tjzpw58PPzQ2pqKjZv3iz5XhhGiIiIaogDBw7AYDCgUaNG6NSpU7H9w4YNAwCcO3cO8fHxZvv27NkDAHj++eeh1WrN9mm1WgwaNAgAsHv3bsn1YhghIiKSShBseynk5MmTAID27dtb3O/l5QVfX1+zsgBgNBpx9uxZAECHDh0sHlu4/cyZMzAajZLqxdk0REREUskw7iM5ORkjR44scf/+/fttvsb94uLiAACNGjUqsYyfnx8SExMRGxtr2paUlASDwWDaX9JxAKDX65GcnCxpZg1bRoiIiGqIjIwMAICbm1uJZQr3ZWZmmralp6eb3teuXbvU44pep6zYMkJERCSJPFN7vb29y6X1ozS5ubkAAI1GU2KZwvEgRWfF6PV60/uSji06jkTqjBqGESIiIqmq6AqshTNgCrtcLCkMHg4ODqZtRYOGwWAwncfScfcfWxbspiEiIqohXF1dAZTejVK4r7AsYN4FU7TLxtJx95cvC4YRIiIiqSrBomfWaNy4MQDg2rVrJZYpnNJbWBYAfHx8TN0z90/5vf84rVYLb29vSfViGCEiIpJCgO1hRKFenrZt2wIATpw4YXF/WloaEhMTzcoCgJ2dHVq1agUAOH78uMVjC7e3bt0aarVaUr0YRoiIiGqIXr16QaPRIC4uDhEREcX2F67OGhgYWGz6b58+fQAA27ZtKzbmRK/Xmx7OFxwcLLleDCNERESSCDJ00yjTNFK3bl0MHToUADB9+nTExMSY9h04cAArV64EAEyaNKnYsUOHDoWnpyeuXbuGWbNmmWbm5ObmYtasWYiPj0e9evXw4osvSq6XIIqiaM0NVUei0QBkJipdjZpDrYXg6gMxMwkw6h9cnmynYF91jaTWQnBpADErhd/xiuDiDUFd8pRVuYg5GRBPrLbpHEK70RAcpA3yvF9KSgoGDhxo+qzX66HT6WBnZwdnZ2fT9pCQEIwbN870OScnB6NHj0Z0dDTUajWaN28OnU5nGvMxduxYvPvuuxavGRUVhZCQENNTe319fZGYmIiMjAw4OTlh1apVZt07ZcWpvURERFJVgmBvNBotzmzJy8sz237/mh8ODg5Ys2YNQkNDsWPHDsTFxUGj0aBjx44YMWKEqTvGkvbt22P79u1YtmwZwsLCcPnyZbi7u2PQoEGYOHGipFVXi2LLSBFsGalgbBmpeJXgP6A1CltGKlZFtoxEr7HpHMIjo2xuGalO2DJCREQkFYO9rBhGiIiIpKqiK7BWVgwjJBtdxh1cOnwAscePIuXSOWSkJiPfmAen2nXg3bIVHu77PFr2eNrisYdWLsYf3y194DWmbPoddRqW/LRJovJU8B3fj9hj4Ra+463x8DODSvyOx52IxNXII0i5eAZ3khKgy7gDvU4HB1dX1HuoOQKeeBrtnhsKjcRltImqA4YRks2Cfl2Rb8wzfbbT2kNlp0HWjTRcupGGS3/uR7POj2PIp0ugcXC0eA6VnQaOriX3o6rspC2kQySnBc90Nv+O29//Hd+HZp2fwJC5S4t9x8PXrcTfYQdNnzWOTlBrtdDduY24O5GIOxGJiI2rMeLL7+Hh91CF3RNZQ54H5dG/GEZINvnGPPgEtsHD/Qah2WPd4O7jBwBIT0nEn6uWIfqXn3Dl6J/Y+b8ZeH7W5xbP0bD1Ixi97IeKrDZRmRV8xx8u+I536v7vdzw5EX+u+grRv2zGlaN/YOe8D/H87AVmxzZ5tAuaPtYdfg+3Rx3fRrCvVTD1UpdxB2f2/oJ9X32G9OQEbHx3Aias+xWCimMSKjWOGZFVlQkjN27cQHh4OM6cOYOzZ8/iwoULyMnJQVBQkGnVN1LWqKVr8FD7TsW2127gi2c/+BQqtR2ift6A03t24MnX3oKbN7tbqGoZ9dVaPNS+c7Httb198ez0uVDZ2SFq2484vWc7npzwFty8G5vKdBo2xuI5ndzc8diQUbDTarFz3oe4EXsFCWei4fdw+/K6DaJKp8pEu127duGdd97B2rVrER0dXWzeNCnPUhAp6pEBg03vky+cKe/qEMnOUhAp6pEB/648mXzhrKRz+wa1Nb3PvJ4q6ViqYFX42TSVVZVpGXF2dkaXLl3QqlUrtGrVCnFxcVi4cKHS1SIJ7LT2pvdifr6CNSEqH+bfcaOkY6+dOmZ6X8fXT7Y6UTlRMU3IqcqEkcGDB2Pw4H//smbXTNUTFx1pel+vqb/FMjdi/8ayl/vhTmI8BLUarp5e8GvbAY8OehkNWgRWVFWJrBJ3ouh3vMUDyxtycpB5PRXnD/yKP74vmE3W6JFH4d2ydbnVkagyqjJhhKq2nKxMHFnzLQDAr20H1G3UxGI5XfodZGdmwMHZFbn37uJWfCxuxcci+pef0P2V1/Dkq29WZLWJyqzgO/41AMCv7aMlfsfv3rqBBf0sd/f4d3sSA2d8Vm51JLlwNo3cGEao3In5+dg2Zxru3rwOtVaLvlNnFCtTp2Fj9J70DgIe74Xa3r5Q22lgNOgRd+Iv7P96IVIunsXh1cvh4OKKLi/9R4G7ICqZmJ+PbbPf+vc7/tbMEssKKhVq1akLAMi9l4W8f558GtirL3qOewOObrUrospkK86mkRXDCJW73V98jMv/rK/Q7+3ZqN+8ZbEybfo8W2ybWqNF08e6oVHbR7FqwktIvnAGf3y3FO2eHQIHZ5dyrzdRWe1e+H//fsenzbH4HS9Uy90Db/8aAQAQRRFZN1JxfOuPOPrjd7j4xz488/YstB84rELqTTZgGJEVw0hRggCotUrXolr5bdEnOPZTwbohfd6cgUcGvvTvTpXG/H9LYOekRa9J72Lt5BHQ6+4h9sQxtOwZXF5Vrt74H1DZ/fblxzj201oAQJ83Z+KRgS//u1NlZ/6/9xEAuDZohCcnvYcGLR/Gpndfxa7PZsKndQfU9+cYKcm4RHuVxTBSlKCG4OqjdC2qjd/mv4ej61YAAJ6a9j90/s9Ui+UE53oPPFfDLv1N7+/czOC/E1UKBd/xgrFQpX7Ha3k+8FyBz42F26JPkZF8DdG7f8Ez7XvJWleSGYOPrBhGihKNELPSlK5FtfDb4k9x9IeC/0j3nvI+urw4FGJmknkhlQaCcz2Id68D+YZSzydm6/79kJtR/FxUNmwZkc1viz7B0R++AQD0fv0DdBkyHGJWinkhlR2EWp4Q790A8vMsnMWci0ddZCRfw+2Y88XPRQ9WyxNCCa1Q8mMYkRPDSFGiCBj1Steiyvtt8Twc/fF7AEDvSe+g60tjSv+55hse+HNPPPWX6X3t+g3472QthhFZ/LZ4Lo6u/w4A0Hvyu+j60tgHfMfzHvidFUUR6cnxAAB7R0d+x60hikrXgKzEMEKyKhpEnprybplmvoiiWOrfGHl6PQ588wWAgoeLNelQ+iqYROWpaBB5asp76PJyyAOPyc/Le+AaWSd3/oS7t24AABq1e8zmelI5EmSY2stuHjP8M4lk8/tX801B5OnX3y/zFNxr0ZFYM+UVnN6z3WwZbGOeATHHwrHqteFIOncKAPDE2ElwcHGVv/JEZfD70s9MQeTp/35QpiACAPGnjmHVa8Nxavc2ZF437365FR+HfV99hp3zCqa8u/v6oW2/F+StOMlPEGx7kRm2jJAsMlKTEf5DwWBVQaVC2A8rEPbPZ0u6vPQfdBk1oeCDKCL2+FHEHj8KALCzd4DW0RE5d+8iP89gOmfXkePRdcS48r0RohIUfMcLxkEJKhXC1n6LsLXflli+y8sh6DJqoulz/MljiD9ZsOS7nb09tI5O0GdnIy/33+dseTVviWH/Ww6Ng0M53QVR5cQwQrIQxX+fNSPm5+Pe7Zulltdn3zO9r9c0AE9NeReJZ0/i+tXL0KXfQU5WFjQODqj9UFP4PdwB7Z8bCq9mD15em6i8FH2eUpm+47p/v+MNAlpj4Kz5iDsRiZSL53D31g1kZ6RDrdXC3dcPDVoEoWWPYAQ+GQyVWl1u90ByYseCnARRrBojflJSUjBw4EDTZ71eD51OBzs7Ozg7O5u2h4SEYNw46/56Fo0GIDPR1qpSWam1EFx9CmbGcLBexeAA1oql1kJwaVAwM4bf8fLn4g1BXfq6RXIQ9Xch/r3bpnMIzftC0Do/uGANUWVaRoxGI9LT04ttz8vLM9uek5NTrAwRERFVXlUmjPj6+uLSpUtKV4OIiIiDUGVWZcIIERFR5SDA9jEjDDNFMYwQERFJxZYRWXE0GxERESmKLSNERERSsWVEVgwjREREkrFjQU78aRIREZGi2DJCREQkFbtpZMUwQkREJIkMT+3l1F4z7KYhIiIiRbFlhIiISDK2bMiJYYSIiEgKAbaPGWGWMcNuGiIiIlIUW0aIiIiksnkAKxXFMEJERCSRwKm9smIYISIikowtI3LiT5OIiIgUxZYRIiIiSQQZVmBlN09RDCNERERSccyIrNhNQ0RERIpiywgREZFk/FteTgwjREREUrGbRlaMdkRERKQotowQERFJVQlaRpYsWYKlS5eWWmb27NkYPnx4se0GgwGhoaHYsWMH4uPjodVqERAQgBEjRuDpp58uryqXiGGEiIhIEgG2dyzIF2Y8PDzQqFEji/s8PT2LbcvNzcWYMWMQFRUFtVqNZs2aITs7G5GRkYiMjMS4cePw9ttvy1a/smAYISIiqsIef/xxzJs3r8zl58+fj6ioKPj6+mLFihVo0qQJAGD//v144403sGLFCrRr1w5PPvlkeVW5GI4ZISIikkJAQTeNTS9lqn7z5k1s2LABAPDJJ5+YgggA9OrVCyEhIQDwwO4fuTGMEBERSSWobHsp5MCBAzAYDGjUqBE6depUbP+wYcMAAOfOnUN8fHyF1YvdNERERJIpP4C10MWLF/HWW2/hxo0bqFWrFlq0aIF+/fqhefPmxcqePHkSANC+fXuL5/Ly8oKvry8SExNx8uRJ+Pn5lWfVTRhGiIiIqrALFy7gwoULps8HDhzA119/jVGjRuHdd9+FWq027YuLiwOAEge8AoCfnx8SExMRGxtbbnW+H8MIERGRVDJM7U1OTsbIkSNL3L9///5Sj69bty5CQkLw9NNPo2HDhnB2dkZsbCzWr1+PDRs2IDQ0FBqNBtOmTTMdk5GRAQBwc3Mr8byF+zIzM6Xcjk0YRoiIiKRScNxHIUvrh7Ro0QJz5syBr68vPv/8c6xevRrDhw+Hr68vgIJpvQCg0WhKPK9WqwUA5OTklEOtLWMYISIiUoC3t/cDWz+sNXbsWKxZswbXr1/HwYMHTS0w9vb2AAoWPSuJXq8HADg4OJRL3SxRPtoRERFVKbZO6xVQ3gNg1Wo1Hn74YQD/jhMBAFdXVwD/dtdYUrivsGxFYBghIiKSTLDxVf4Ku2Ly8vJM2xo3bgwAuHbtWonHFU7pLSxbERhGiIiIqqG///4bAFC/fn3TtrZt2wIATpw4YfGYtLQ0JCYmmpWtCAwjREREUlXyRc8OHTpkCiNdu3Y1be/Vqxc0Gg3i4uIQERFR7LjC1VkDAwNLnf4rN4YRIiIiyZTtpvn7778xc+ZMXLx40Wx7fn4+du7cibfeegsA0KNHD7Rp08a0v27duhg6dCgAYPr06YiJiTHtO3DgAFauXAkAmDRpks11lIKzaYiIiKSSYZ0RW+Tl5WHjxo3YuHEjateuDW9vb6jVasTHx5sGoHbo0AHz588vduy0adNw7tw5REdHo3///mjevDl0Op1prMjYsWPRu3fvCr0fQRRFsUKvWImJRgOQmah0NWoOtRaCqw/EzCTAqFe6NjVDJVgboUZRayG4NICYlcLveEVw8YagLnn9DLmIebnAzYsPLliaugEQ7OytPjwzMxPr1q3DyZMncfXqVdy+fRt6vR5ubm4IDAxE//790b9/f7PVV4vS6/UIDQ3Fjh07EB8fD41Gg5YtW2LEiBHo06eP1fWyFsNIEQwjFYxhpOIxjFQshpGKVaFh5JJtJ6nbwqYwUt2wm4aIiEgSQYZumsrzoL3KgH8mERERkaIYRoiIiEhR7KYhIiKSQoDt3TTspTHDlhEiIiJSFFtGiIiIJGPThpwYRoiIiKRSeNGz6obdNERERKQotowQERFJxpYROTGMEBERScVuGlkxjBAREUnGMCInjhkhIiIiRbFlhIiISCp208iKYYSIiEgSAbZ30zDMFMVuGiIiIlIUW0aIiIikYjeNrBhGiIiIJGMYkRO7aYiIiEhRDCNERESkKHbTEBERSSAAHDMiM7aMEBERkaLYMkJERCQZW0bkxDBCREQkFbtpZMUwQkREJBnDiJw4ZoSIiIgUxZYRIiIiKQTB9m4advOYYRghIiKSjGFCTuymISIiIkWxZYSIiEgqdrPIimGEiIhIMoYRObGbhoiIiBTFlhEiIiKp2E0jK4YRIiIiyRhG5MRuGiIiIlIUW0aIiIikYsOIrBhGiIiIJGMakRPDCBERkVQcwCorjhkhIiIiRbFlhIiISBIBtnfTsGWlKIYRIiIiqdhNIyt20xAREZGiBFEURaUrUVmIogjk5yldjZpDECCo7CDm5wH8GlJ1xO94xVLZQaiIFgtRBMR8284hqNi6UgTDCBERESmK3TRERESkKIYRIiIiUhTDCBERESmKYYSIiIgUxTBCREREimIYISIiIkUxjBAREZGiGEaIiIhIUQwjREREpCiGESIiIlIUwwgREREpimGEiIiIFMUwQkRERIpiGCEiIiJF2SldAap5IiIisGrVKpw6dQo6nQ7e3t4IDg7G+PHj4eTkpHT1iKx248YNhIeH48yZMzh79iwuXLiAnJwcBAUFYevWrUpXj6jSYhihCrV27Vp88sknEEUR9evXR4MGDXDlyhUsX74cv/32G9avX4/atWsrXU0iq+zatQtz585VuhpEVQ7DCFWYs2fP4tNPPwUAfPTRRxgyZAgEQUBaWhomTJiAc+fOYcaMGViyZInCNSWyjrOzM7p06YJWrVqhVatWiIuLw8KFC5WuFlGlxzBCFWbZsmXIz8/HwIEDMXToUNN2Ly8vLFy4EH379sVvv/2GixcvIiAgQMGaElln8ODBGDx4sOkzu2aIyoYDWKlC3Lt3D4cPHwYADBkypNj+xo0bo1OnTgCAPXv2VGjdiIhIWQwjVCEuXLgAvV4PrVaLNm3aWCzTvn17AMCpU6cqsmpERKQwhhGqELGxsQAAb29vaDQai2X8/PzMyhIRUc3AMEIVIiMjAwDg5uZWYpnCfYVliYioZmAYoQqRm5sLACW2igCAVqs1K0tERDUDwwhVCHt7ewCAwWAosYxerzcrS0RENQPDCFWIsnTBlKUrh4iIqh+GEaoQjRs3BgAkJyeX2DoSHx9vVpaIiGoGhhGqEIGBgdBoNNDr9Th9+rTFMlFRUQCAtm3bVmDNiIhIaQwjVCFq1aqFbt26AQA2bdpUbH9cXBwiIiIAAMHBwRVaNyIiUhbDCFWYiRMnQhAEbN++HRs3boQoigCA69evY+rUqcjPz0fv3r25FDwRUQ0jiIW/EYgqwOrVqzFv3jyIoogGDRrA3d0dV65cgV6vx0MPPYT169ejTp06SleTyCopKSkYOHCg6bNer4dOp4OdnR2cnZ1N20NCQjBu3DgFakhUOfFBeVShRo8ejRYtWuD777/H6dOncevWLXh7eyM4OBjjx49HrVq1lK4ikdWMRiPS09OLbc/LyzPbnpOTU3GVIqoC2DJCREREiuKYESIiIlIUwwgREREpimGEiIiIFMUwQkRERIpiGCEiIiJFMYwQERGRohhGiIiISFEMI0RERKQohhEiIiJSFMMIkQzee+89tGjRAu+9916xfSNHjkSLFi2wZMkSBWpWvmy5tyeffBItWrTA1q1bK02dbBUZGYkWLVqgRYsWFX5toqqMz6YhxS1ZsgRLly4ttl2r1cLd3R2BgYF49tln0bdvXwiCoEANK5fMzEyEhoYCAF555RW4uroqXCMiItswjFClUrduXdP7rKwspKWlIS0tDQcPHsS2bdvw1VdfQavVKlhD6Ro0aICHHnoI7u7uspwvMzPTFN6ef/55hhEiqvIYRqhSCQsLM73Pz8/H1atXMXfuXISFheHPP//EF198gXfffVfBGkr32WefKV0FIqJKjWNGqNJSqVRo3rw5li9fjkaNGgEANm7ciLy8PIVrRkREcmLLCFV69vb2CA4OxjfffIN79+4hJiYG/v7+SExMRK9evQAA+/fvR35+PlasWIGwsDBcv34d9erVw4EDB8zOtW/fPmzduhWnT59Geno6HB0d4e/vj/79+2Pw4MHQaDQl1mPHjh1Yv349Ll26BJVKhSZNmmDw4MEYMmRIqfUfOXIk/vrrL0yePBlTpkyxWObq1av44YcfEBkZiZSUFABAvXr1EBgYiODgYDz11FNQqVSmcxUqvP9CHTt2xNq1a822GY1GbN++Hbt27cKFCxeQmZkJZ2dnBAUFYdCgQXjmmWdKHItjNBrx448/YsuWLYiNjYVWq0WLFi3w8ssvIzg4uNT7tkVycjIOHjyIP/74A9euXUNaWhoEQUCDBg3QtWtXjBkzBt7e3g88j16vx+rVq/HLL78gISEBGo0GrVq1wujRo/HEE0+Uemx8fDxCQ0MRHh6O1NRU5Ofnw9vbG926dSvz9YmobBhGqErw8vIyvb97926x/dHR0Zg5cyZ0Oh0cHR2LhYp79+7hrbfewsGDB03bnJ2dkZWVhePHj+P48ePYvn07vvnmG7i5uZkdK4oiPvjgA9OsD0EQ4OrqirNnz+L06dOIjIy0aRzLt99+iy+++AL5+fkACsKXnZ0d4uLiEBcXh19//RXHjh2Dq6sr3Nzc4O7ujjt37gAA3N3doVarTee6v+43b97ExIkTcerUKdM2FxcX3LlzB0eOHMGRI0ewc+dOLFq0qNg96PV6TJgwAUeOHAFQ0FKl0Whw7Ngx/PXXXxg3bpzV9/wg7777rlnocnFxwb1793D16lVcvXoV27Ztw9dff40OHTqUeA6DwYAxY8bg+PHjsLOzg5OTEzIzMxEeHo7w8PBSw+GmTZvw0UcfwWAwACgYTK1SqRATE4OYmBhs3boVixcvRteuXeW9caIaimGEqoSkpCTT+/t/4QLAzJkz0bx5c8yYMQOtW7cGAMTGxpr2v/POOzh48CAaNWqE119/HT169ICzszNyc3Nx5MgRzJ07F9HR0fjggw/w1VdfmZ177dq1piAyYsQITJo0CXXq1EFWVhZCQ0OxdOlSuLi4WHVf69evx4IFCwAUTHV9/fXX0bJlSwBAeno6zp07h59++gkqVUGP6tKlS81ahH766Sf4+vpaPLder8drr72GM2fOICgoCP/973/RsWNHODo6QqfT4bfffsNnn32GAwcO4PPPP8cHH3xgdvyCBQtw5MgRCIKA//73vxg5ciScnZ1x69YtLFmyBCtWrLD6vh+kefPm6N69O3r16gUfHx84ODggLy8P58+fx+LFi3H48GG8+eab+P333+Hg4GDxHOvXr0dubi7mzJmD559/Hvb29khJScHcuXOxd+9eLF26FIGBgcVal/bt24cZM2ZAo9Fg/PjxGDZsmKkVJDY2FosWLcKePXvw+uuv45dffmELCZEcRCKFLV68WPT39xf9/f0t7s/KyhK7desm+vv7ix07dhSNRqMoiqKYkJBgOq5nz57i3bt3LR5/8OBB0d/fX+zatauYmppqsUxKSorYtm1b0d/fXzx//rxpe05OjtixY0fR399fnDZtmsVjP//8c1M93n333WL7R4wYIfr7+4uLFy82256eni4+8sgjor+/v/jmm2+K+fn5Fs9/v6L3nZCQUGK5H374QfT39xf79esnZmVlWSxz5swZsUWLFmJQUJB48+ZN0/bU1FQxMDBQ9Pf3F7/88kuLx06dOtVUj/vvrSx69uwp+vv7i1u2bJF0XF5enjhgwADR399f/Pnnn4vtL/x5+/v7i5s3by6232g0ii+//LLo7+8vPvPMM2b7cnNzxe7du5d4bKHXXntN9Pf3Fz/++GOz7REREaV+l4nIMg5gpUorMzMTR48exahRo3D9+nUABeMvClsJinr55ZdRq1Yti+fZvHkzAODZZ5816+4pqn79+njssccAAIcPHzZtP3LkCNLT0wEAkyZNsnjs+PHjYW9vX7abKmLv3r24d+8eNBoN3nvvPdnXUCm87+HDh8PZ2dlimVatWqF58+YwGAyIjIw0q1teXh4cHBzwn//8x+KxkydPlrW+ZaVWq9G9e3cAQFRUVInlGjRogBdeeKHYdpVKhQkTJgAArly5gkuXLpn2/fnnn0hLS0PdunUtHlto4MCBAGDqwiIi27CbhiqV0laufPbZZ02/RO7Xrl27Eo8r/IW1adMmbN++vcRyWVlZAAoGTxY6e/YsgIJfbIUzeu7n4uKCoKAgnDhxosRzWxIdHQ0ACAoKQr169SQd+yB37941/ZJdvHgxli1bVmLZjIwMAOZdYYX33apVqxKDzEMPPQQvLy+kpaXJVW0zx48fx08//YSTJ08iLS0NOp2uWJnSrt2xY8cSA96jjz4KOzs75OXl4ezZs6bvXeF3JSMjA926dSvx3IVjSYp+V4jIegwjVKkUXfSscAXWli1bYsCAAejUqVOJx3l4eFjcbjAYTIM9s7KyTIGjNDk5Oab3t27dAoASW1QK1a9f/4Hnvd+NGzcAoFzGHNy8edM0ILawZedBrL3v8ggj8+fPx8qVK02f1Wo13NzcTAOTdTqd6VWS0uqu1WpRu3Zt3Lx503SvAEwtcAaDATdv3nxgPYv+zIjIegwjVKkUXfRMCktdNwBMv5AB4IsvvsAzzzxj1fnLcxn68ji30Wg0vd+0aRMefvhhq86jxPL7YWFhpiDy0ksvYfjw4WjatKnZrKEvv/wSy5cvL/U81tS98PvSvXt3szBEROWLY0aoWrO3tzfN+Cg6NqCsCltcUlNTSy1nTeuAp6cnAPPuEbkUbWG6fPmy5OPL874fZNeuXQCAbt26YdasWfD39zcLIgDK1GpRWt31er2pxahoq1rhz82anxkRWY9hhKq9wvEke/bsMWspKYtWrVoBAFJSUhAfH2+xzN27d3Hu3Dmr63X27FlT90BZFG0FEkXRYhk3Nzc0a9YMwL+/3KUovO+zZ89aXNcFAOLi4h4YVqxReM7AwECL+0VRRERExAPPc+zYsRJ/PsePHzet5Ft4r8C//yZpaWk4fvy4pHoTkfUYRqjaK1whNS4u7oFN7zqdDnq93vS5a9eupnVNShoEumLFCqvGDgQHB8PZ2Rl5eXmYO3duib8471d0QGlpY2AK7/vo0aMPDCT3jyvp06cP7OzskJOTg1WrVlk85v71WORSeH8XL160uP/HH39EQkLCA8+TnJyMbdu2Fduen5+Pr7/+GgDQtGlTs0HTTz75pKnF6tNPP0V2dnap1yjreBwiKh3DCFV7vXv3xlNPPQWgYCGvWbNmmS2IptfrcerUKcyfPx89e/bE7du3TfscHBwwceJEAMC2bdvwySefmAbE3r17F1999RW++eYbq56c6+LigmnTpgEAfv31V0yaNAkXLlww7c/IyMChQ4cwYcIEs9YJV1dX0+DMrVu3lvisnuHDh5vGirzzzjv44osvTEvNA0B2djYiIyPx0UcfmX4+hby8vDB8+HAABSHsm2++MdXh9u3b+Oijj7Bjx45yWfSscNrun3/+ia+++so0SDUzMxNff/01Pv74Y9SuXfuB53FxccHs2bOxadMm5ObmAiho4Zo6dappGvObb75pdoy9vT1mz54NQRBw7tw5DB8+HIcPHzYLqAkJCdiwYQMGDx6M9evXy3HLRDUeB7BSjTB//nxMnz4du3btwoYNG7BhwwY4OTlBo9EgKyvLrPvm/oGPo0aNwvnz57F9+3asWbMGP/zwA1xcXHD37l0YjUb069cPWq3W4l/hDzJs2DBkZGTgyy+/xP79+7F//344ODhArVbj3r17pnL3dy8NGzYMixYtwtq1a7Fx40Z4eHhApVLh4YcfxhdffAGgYMbI119/jTfffBMRERH4+uuv8fXXX8PZ2RkqlQpZWVmm1hg7u+L/KZg2bRquXr2K8PBwLFy4EIsWLYKzszMyMzMhiiLGjRuHU6dOmS3bLoeBAwfi559/xvHjx7F48WIsWbIErq6upn+nHj16oGXLlg8cwPrSSy/h+PHjmDFjBj766CM4OTmZpjEDwIQJE4qFMKAgvH722WeYOXMmLly4gJCQENjZ2cHZ2blYy9n9q7cSkXUYRqhGcHR0xMKFCzF06FBs2bIFJ06cwI0bN6DT6eDh4YGmTZuie/fueOqpp4pNCVWpVPjss8/QpUsX/Pjjj7h8+TLy8vIQGBiIwYMHY+jQoXj//fetrturr76Knj17Yu3atYiMjERaWhpEUcRDDz2EoKAgU3dOUa+99hqcnZ2xfft2xMTEIDU1FaIowsfHx6xcnTp1sHr1ahw4cADbt2/H6dOnzabt+vv7o0ePHujdu3exetnb22PFihVYv349tm7ditjYWIiiiA4dOuDll19G3759MXLkSKvvuyQajQbff/89vv32W+zcuRNJSUkQRRFt2rTBwIEDMXTo0DJ1EWk0GqxevRqrVq3Czp07kZCQABcXF7Rq1Qpjxowp9UF5zz77LDp16oT169fj8OHDiI+PR1ZWFpycnNC0aVO0b98evXv3xqOPPirnrRPVWIJY1o5qIiIionLAMSNERESkKIYRIiIiUhTDCBERESmKYYSIiIgUxTBCREREimIYISIiIkUxjBAREZGiGEaIiIhIUQwjREREpCiGESIiIlIUwwgREREpimGEiIiIFMUwQkRERIpiGCEiIiJF/T/eYaTWAAnvBwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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": [ + "#### We'll create another K-Nearest Neighbors model with k=5. \n", + "\n", + "Initialize and fit the model below and print the confusion matrix and the accuracy score." + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [], + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.95 0.96 0.95 308\n", + " 1 0.74 0.65 0.69 48\n", + "\n", + " accuracy 0.92 356\n", + " macro avg 0.84 0.81 0.82 356\n", + "weighted avg 0.92 0.92 0.92 356\n", + "\n" + ] + } + ], + "source": [ + "# Your code here:\n", + "print(classification_report(y_test, predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data accuracy: 0.9213483146067416\n", + "Train data accuracy: 0.9478873239436619\n" + ] + } + ], + "source": [ + "print(\"Test data accuracy: \",model.score(X_test,y_test))\n", + "print(\"Train data accuracy: \", model.score(X_train, y_train))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Did you see an improvement in the confusion matrix when increasing k to 5? Did you see an improvement in the accuracy score? Write your conclusions below." + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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": [ + "With 𝑘=5, the model seems to improve the accuracy for positive cases, even with the unbalanced data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bonus Challenge - Feature Scaling\n", + "\n", + "Problem-solving in machine learning is iterative. You can improve your model prediction with various techniques (there is a sweetspot for the time you spend and the improvement you receive though). Now you've completed only one iteration of ML analysis. There are more iterations you can conduct to make improvements. In order to be able to do that, you will need deeper knowledge in statistics and master more data analysis techniques. In this bootcamp, we don't have time to achieve that advanced goal. But you will make constant efforts after the bootcamp to eventually get there.\n", + "\n", + "However, now we do want you to learn one of the advanced techniques which is called *feature scaling*. The idea of feature scaling is to standardize/normalize the range of independent variables or features of the data. This can make the outliers more apparent so that you can remove them. This step needs to happen during Challenge 6 after you split the training and test data because you don't want to split the data again which makes it impossible to compare your results with and without feature scaling. For general concepts about feature scaling, click [here](https://en.wikipedia.org/wiki/Feature_scaling). To read deeper, click [here](https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e).\n", + "\n", + "In the next cell, attempt to improve your model prediction accuracy by means of feature scaling. A library you can utilize is `sklearn.preprocessing.RobustScaler` ([documentation](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html)). You'll use the `RobustScaler` to fit and transform your `X_train`, then transform `X_test`. You will use logistic regression to fit and predict your transformed data and obtain the accuracy score in the same way. Compare the accuracy score with your normalized data with the previous accuracy data. Is there an improvement?" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [], + "source": [ + "scaler=RobustScaler()\n", + "X_train_scaled=scaler.fit_transform(X_train)\n", + "X_test_scaled=scaler.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": {}, + "outputs": [ + { + "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": 244, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model=LogisticRegression()\n", + "model.fit(X_train_scaled, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": {}, + "outputs": [], + "source": [ + "predictions=model.predict(X_test_scaled)" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.92 0.99 0.95 308\n", + " 1 0.88 0.46 0.60 48\n", + "\n", + " accuracy 0.92 356\n", + " macro avg 0.90 0.72 0.78 356\n", + "weighted avg 0.92 0.92 0.91 356\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data scaled accuracy: 0.9185393258426966\n", + "Train data accuracy: 0.9338028169014084\n" + ] + } + ], + "source": [ + "print(\"Test data scaled 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": 248, + "metadata": {}, + "outputs": [], + "source": [ + "cm = confusion_matrix(y_test, predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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": [ + "After scaling with Robust Scaler, the model shows a slight improvement in accuracy for predicting positive cases. However, it still does not reach half of the total predicted positives. Therefore, we cannot consider this to be a satisfactory outcome." ] } ], "metadata": { "kernelspec": { - "display_name": "ironhack-3.7", + "display_name": "base", "language": "python", - "name": "ironhack-3.7" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -669,7 +5423,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.12.2" } }, "nbformat": 4,