diff --git a/README.md b/README.md index 295a252..dc8fbad 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ +[![Review Assignment Due Date](https://classroom.github.com/assets/deadline-readme-button-24ddc0f5d75046c5622901739e7c5dd533143b0c8e959d652212380cedb1ea36.svg)](https://classroom.github.com/a/yYhm1UCY) # Introduction to Data Processing ## Introduction diff --git a/titanic.csv b/titanic.csv new file mode 100644 index 0000000..00dd924 --- /dev/null +++ b/titanic.csv @@ -0,0 +1,419 @@ +PassengerId,Survived +892,0.0 +893,0.0 +894,0.0 +895,0.0 +896,0.0 +897,1.0 +898,0.0 +899,0.0 +900,1.0 +901,0.0 +902,0.0 +903,0.0 +904,0.0 +905,0.0 +906,1.0 +907,0.0 +908,1.0 +909,0.0 +910,0.0 +911,0.0 +912,0.0 +913,1.0 +914,0.0 +915,1.0 +916,1.0 +917,1.0 +918,1.0 +919,0.0 +920,1.0 +921,0.0 +922,0.0 +923,0.0 +924,0.0 +925,0.0 +926,0.0 +927,0.0 +928,0.0 +929,1.0 +930,0.0 +931,0.0 +932,0.0 +933,0.0 +934,0.0 +935,1.0 +936,0.0 +937,1.0 +938,0.0 +939,0.0 +940,1.0 +941,1.0 +942,0.0 +943,1.0 +944,1.0 +945,0.0 +946,0.0 +947,0.0 +948,0.0 +949,0.0 +950,0.0 +951,0.0 +952,0.0 +953,0.0 +954,0.0 +955,0.0 +956,1.0 +957,0.0 +958,1.0 +959,0.0 +960,0.0 +961,0.0 +962,0.0 +963,0.0 +964,1.0 +965,0.0 +966,0.0 +967,0.0 +968,0.0 +969,1.0 +970,0.0 +971,0.0 +972,0.0 +973,1.0 +974,0.0 +975,1.0 +976,1.0 +977,0.0 +978,0.0 +979,0.0 +980,0.0 +981,1.0 +982,0.0 +983,1.0 +984,0.0 +985,0.0 +986,1.0 +987,1.0 +988,0.0 +989,0.0 +990,0.0 +991,0.0 +992,0.0 +993,0.0 +994,0.0 +995,0.0 +996,0.0 +997,1.0 +998,1.0 +999,0.0 +1000,0.0 +1001,0.0 +1002,0.0 +1003,0.0 +1004,1.0 +1005,1.0 +1006,0.0 +1007,0.0 +1008,0.0 +1009,1.0 +1010,0.0 +1011,0.0 +1012,0.0 +1013,0.0 +1014,0.0 +1015,0.0 +1016,0.0 +1017,0.0 +1018,0.0 +1019,0.0 +1020,0.0 +1021,0.0 +1022,0.0 +1023,0.0 +1024,1.0 +1025,1.0 +1026,0.0 +1027,0.0 +1028,0.0 +1029,0.0 +1030,1.0 +1031,0.0 +1032,0.0 +1033,1.0 +1034,1.0 +1035,0.0 +1036,0.0 +1037,0.0 +1038,0.0 +1039,1.0 +1040,0.0 +1041,1.0 +1042,1.0 +1043,0.0 +1044,0.0 +1045,0.0 +1046,0.0 +1047,0.0 +1048,0.0 +1049,0.0 +1050,0.0 +1051,0.0 +1052,0.0 +1053,0.0 +1054,1.0 +1055,0.0 +1056,0.0 +1057,0.0 +1058,0.0 +1059,0.0 +1060,0.0 +1061,0.0 +1062,0.0 +1063,0.0 +1064,1.0 +1065,0.0 +1066,0.0 +1067,0.0 +1068,1.0 +1069,1.0 +1070,0.0 +1071,0.0 +1072,0.0 +1073,0.0 +1074,0.0 +1075,0.0 +1076,0.0 +1077,1.0 +1078,0.0 +1079,1.0 +1080,0.0 +1081,1.0 +1082,0.0 +1083,0.0 +1084,0.0 +1085,1.0 +1086,0.0 +1087,1.0 +1088,1.0 +1089,1.0 +1090,1.0 +1091,0.0 +1092,1.0 +1093,1.0 +1094,1.0 +1095,0.0 +1096,0.0 +1097,0.0 +1098,0.0 +1099,1.0 +1100,0.0 +1101,1.0 +1102,0.0 +1103,1.0 +1104,0.0 +1105,0.0 +1106,0.0 +1107,0.0 +1108,0.0 +1109,1.0 +1110,0.0 +1111,0.0 +1112,1.0 +1113,1.0 +1114,0.0 +1115,1.0 +1116,0.0 +1117,0.0 +1118,1.0 +1119,0.0 +1120,0.0 +1121,0.0 +1122,0.0 +1123,0.0 +1124,0.0 +1125,0.0 +1126,1.0 +1127,0.0 +1128,0.0 +1129,0.0 +1130,0.0 +1131,0.0 +1132,1.0 +1133,1.0 +1134,1.0 +1135,0.0 +1136,1.0 +1137,0.0 +1138,1.0 +1139,0.0 +1140,0.0 +1141,0.0 +1142,0.0 +1143,0.0 +1144,1.0 +1145,0.0 +1146,0.0 +1147,0.0 +1148,0.0 +1149,0.0 +1150,0.0 +1151,1.0 +1152,0.0 +1153,0.0 +1154,0.0 +1155,1.0 +1156,0.0 +1157,0.0 +1158,0.0 +1159,0.0 +1160,1.0 +1161,0.0 +1162,1.0 +1163,0.0 +1164,0.0 +1165,0.0 +1166,0.0 +1167,1.0 +1168,0.0 +1169,0.0 +1170,0.0 +1171,0.0 +1172,0.0 +1173,0.0 +1174,0.0 +1175,0.0 +1176,0.0 +1177,0.0 +1178,0.0 +1179,0.0 +1180,0.0 +1181,1.0 +1182,0.0 +1183,0.0 +1184,1.0 +1185,1.0 +1186,1.0 +1187,0.0 +1188,0.0 +1189, +1190, +1191, +1192, +1193, +1194, +1195, +1196, +1197, +1198, +1199, +1200, +1201, +1202, +1203, +1204, +1205, +1206, +1207, +1208, +1209, +1210, +1211, +1212, +1213, +1214, +1215, +1216, +1217, +1218, +1219, +1220, +1221, +1222, +1223, +1224, +1225, +1226, +1227, +1228, +1229, +1230, +1231, +1232, +1233, +1234, +1235, +1236, +1237, +1238, +1239, +1240, +1241, +1242, +1243, +1244, +1245, +1246, +1247, +1248, +1249, +1250, +1251, +1252, +1253, +1254, +1255, +1256, +1257, +1258, +1259, +1260, +1261, +1262, +1263, +1264, +1265, +1266, +1267, +1268, +1269, +1270, +1271, +1272, +1273, +1274, +1275, +1276, +1277, +1278, +1279, +1280, +1281, +1282, +1283, +1284, +1285, +1286, +1287, +1288, +1289, +1290, +1291, +1292, +1293, +1294, +1295, +1296, +1297, +1298, +1299, +1300, +1301, +1302, +1303, +1304, +1305, +1306, +1307, +1308, +1309, diff --git a/titanic.ipynb b/titanic.ipynb index cde5079..d90c3a9 100644 --- a/titanic.ipynb +++ b/titanic.ipynb @@ -1 +1,4113 @@ -{"cells":[{"cell_type":"markdown","metadata":{},"source":["
\n","# Ignore this"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","import matplotlib.pyplot as plt\n","plt.style.use(\"seaborn-v0_8-whitegrid\")\n","\n","import seaborn as sns\n","\n","from collections import Counter\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"markdown","metadata":{},"source":["
\n","# Load and Check Data"]},{"cell_type":"markdown","metadata":{},"source":["DataFrames hold the dataset in a tabular format for easy manipulation and analysis.
\n","CSV data is read into 'df' using Pandas' read_csv() function."]},{"cell_type":"code","execution_count":null,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2024-04-01T06:45:27.416192Z","iopub.status.busy":"2024-04-01T06:45:27.415763Z","iopub.status.idle":"2024-04-01T06:45:27.433162Z","shell.execute_reply":"2024-04-01T06:45:27.431944Z","shell.execute_reply.started":"2024-04-01T06:45:27.416105Z"},"trusted":true},"outputs":[],"source":["train_df = pd.read_csv(\"./data/train.csv\")"]},{"cell_type":"markdown","metadata":{},"source":["### 1. Try to read the test .csv file into test_df"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.436611Z","iopub.status.busy":"2024-04-01T06:45:27.435916Z","iopub.status.idle":"2024-04-01T06:45:27.449974Z","shell.execute_reply":"2024-04-01T06:45:27.448230Z","shell.execute_reply.started":"2024-04-01T06:45:27.436517Z"},"trusted":true},"outputs":[],"source":["test_df = pd.read_csv(\"./data/test.csv\")\n","test_PassengerId = test_df[\"PassengerId\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","execution":{"iopub.execute_input":"2024-04-01T06:45:27.452397Z","iopub.status.busy":"2024-04-01T06:45:27.451949Z","iopub.status.idle":"2024-04-01T06:45:27.462622Z","shell.execute_reply":"2024-04-01T06:45:27.461859Z","shell.execute_reply.started":"2024-04-01T06:45:27.452348Z"},"trusted":true},"outputs":[],"source":["print(\"The Columns of train_df are: \")\n","train_df.columns"]},{"cell_type":"markdown","metadata":{},"source":["### We can use head() to see the first few rows in the dataframe"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.464289Z","iopub.status.busy":"2024-04-01T06:45:27.463866Z","iopub.status.idle":"2024-04-01T06:45:27.491984Z","shell.execute_reply":"2024-04-01T06:45:27.491110Z","shell.execute_reply.started":"2024-04-01T06:45:27.464242Z"},"trusted":true},"outputs":[],"source":["train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.494326Z","iopub.status.busy":"2024-04-01T06:45:27.493637Z","iopub.status.idle":"2024-04-01T06:45:27.541999Z","shell.execute_reply":"2024-04-01T06:45:27.541210Z","shell.execute_reply.started":"2024-04-01T06:45:27.494251Z"},"jupyter":{"source_hidden":true},"trusted":true},"outputs":[],"source":["train_df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["### 2. Without looking above, try checking the first few rows of test_df"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### 3. Now try checking for a description of test_df's data"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["# Variable Description\n","
    \n","
  1. PassengerId: unique id number to each passenger
    2. \n","
  2. Survived: passenger survive(1) or died(0)
    3. \n","
  3. Pclass: passenger class
    4. \n","
  4. Name: name
    5. \n","
  5. Sex: gender of passenger
    6. \n","
  6. Age: age of passenger
    7. \n","
  7. SibSp: number of siblings/spouses
    8. \n","
  8. Parch: number of parents/children
    9. \n","
  9. Ticket: ticket number
    10. \n","
  10. Fare: amount of money spent on ticket
    11. \n","
  11. Cabin: cabin category
    12. \n","
  12. Embarked: port where passenger embarked ( C = Cherbourg, Q = Queenstown, S = Southampton )
    13. \n","
\n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.544377Z","iopub.status.busy":"2024-04-01T06:45:27.543901Z","iopub.status.idle":"2024-04-01T06:45:27.557229Z","shell.execute_reply":"2024-04-01T06:45:27.555972Z","shell.execute_reply.started":"2024-04-01T06:45:27.544320Z"},"trusted":true},"outputs":[],"source":["train_df.info()"]},{"cell_type":"markdown","metadata":{},"source":["### Slice Rows and Columsn of DF (Assigmennt)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:53:12.214069Z","iopub.status.busy":"2024-04-01T06:53:12.213708Z","iopub.status.idle":"2024-04-01T06:53:12.223150Z","shell.execute_reply":"2024-04-01T06:53:12.222195Z","shell.execute_reply.started":"2024-04-01T06:53:12.214014Z"},"trusted":true},"outputs":[],"source":["# Printing the Second Row\n","train_df.iloc[2]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Print the 5th Row"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:54:14.398373Z","iopub.status.busy":"2024-04-01T06:54:14.398006Z","iopub.status.idle":"2024-04-01T06:54:14.407886Z","shell.execute_reply":"2024-04-01T06:54:14.406590Z","shell.execute_reply.started":"2024-04-01T06:54:14.398326Z"},"trusted":true},"outputs":[],"source":["# Print the Sex Column\n","train_df['Sex']"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:54:24.550687Z","iopub.status.busy":"2024-04-01T06:54:24.550286Z","iopub.status.idle":"2024-04-01T06:54:24.555255Z","shell.execute_reply":"2024-04-01T06:54:24.553923Z","shell.execute_reply.started":"2024-04-01T06:54:24.550616Z"},"trusted":true},"outputs":[],"source":["# Print the Name Column"]},{"cell_type":"markdown","metadata":{},"source":["## Visualization (Assignment)"]},{"cell_type":"markdown","metadata":{},"source":["### Age -- Survived"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:13:34.450088Z","iopub.status.busy":"2024-04-01T07:13:34.449302Z","iopub.status.idle":"2024-04-01T07:13:34.932717Z","shell.execute_reply":"2024-04-01T07:13:34.930449Z","shell.execute_reply.started":"2024-04-01T07:13:34.450021Z"},"trusted":true},"outputs":[],"source":["plt.figure(figsize=(8, 6))\n","# Plot 1: Survivors vs Non Survivors\n","\n","# Creating a plot for the Survived Column\n","sns.countplot(x='Survived', data=train_df)\n","\n","plt.title('Survivors vs Non Survivors')\n","plt.xlabel('Survived')\n","plt.ylabel('Count')\n","plt.xticks([0, 1], ['No', 'Yes']) # Setting custom tick labels\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try Plotting Passenger Class"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:14:31.768779Z","iopub.status.busy":"2024-04-01T07:14:31.768341Z","iopub.status.idle":"2024-04-01T07:14:32.062495Z","shell.execute_reply":"2024-04-01T07:14:32.060660Z","shell.execute_reply.started":"2024-04-01T07:14:31.768690Z"},"trusted":true},"outputs":[],"source":["plt.figure(figsize=(8, 6))\n","\n","# Make the plot for Pclass here:\n","\n","\n","plt.title('Count of Passengers In each Passenger Class')\n","plt.xlabel('Passenger Class')\n","plt.ylabel('Count')\n","plt.xticks([0, 1, 2], ['1st', '2nd', '3rd']) # Setting custom tick labels\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try it for \"Embarked\""]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### Try Making a histogram for \"Fare\""]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### Here is the distplot for \"Fare\", refer to it after you tried it yourself:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:18:24.402882Z","iopub.status.busy":"2024-04-01T07:18:24.402274Z","iopub.status.idle":"2024-04-01T07:18:24.798062Z","shell.execute_reply":"2024-04-01T07:18:24.796669Z","shell.execute_reply.started":"2024-04-01T07:18:24.402828Z"},"trusted":true},"outputs":[],"source":["sns.histplot(train_df['Fare'], bins=20, color='orange')\n","plt.title('Distribution of Passenger Fares')\n","plt.xlabel('Fare')\n","plt.ylabel('Frequency')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Make a histogram for \"Age\" (Assignment)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:19:53.874413Z","iopub.status.busy":"2024-04-01T07:19:53.873686Z","iopub.status.idle":"2024-04-01T07:19:54.244996Z","shell.execute_reply":"2024-04-01T07:19:54.243521Z","shell.execute_reply.started":"2024-04-01T07:19:53.874351Z"},"trusted":true},"outputs":[],"source":["# Create the plot below"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Fill Missing: Age Feature"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:50.370496Z","iopub.status.busy":"2024-04-01T06:27:50.369419Z","iopub.status.idle":"2024-04-01T06:27:50.427731Z","shell.execute_reply":"2024-04-01T06:27:50.426655Z","shell.execute_reply.started":"2024-04-01T06:27:50.370387Z"},"trusted":true},"outputs":[],"source":["train_df[train_df[\"Age\"].isnull()]"]},{"cell_type":"markdown","metadata":{},"source":["### Try Checking for Null Values in Test Df"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["test_df.isnull().sum()"]},{"cell_type":"markdown","metadata":{},"source":["Run this to fix the Null Values"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:21:48.194895Z","iopub.status.busy":"2024-04-01T07:21:48.194020Z","iopub.status.idle":"2024-04-01T07:21:49.449282Z","shell.execute_reply":"2024-04-01T07:21:49.447918Z","shell.execute_reply.started":"2024-04-01T07:21:48.194825Z"},"trusted":true},"outputs":[],"source":["index_nan_age = list(train_df[\"Age\"][train_df[\"Age\"].isnull()].index)\n","for i in index_nan_age:\n"," age_pred = train_df[\"Age\"][((train_df[\"SibSp\"] == train_df.iloc[i][\"SibSp\"]) &(train_df[\"Parch\"] == train_df.iloc[i][\"Parch\"])& (train_df[\"Pclass\"] == train_df.iloc[i][\"Pclass\"]))].median()\n"," age_med = train_df[\"Age\"].median()\n"," if not np.isnan(age_pred):\n"," train_df[\"Age\"].iloc[i] = age_pred\n"," else:\n"," train_df[\"Age\"].iloc[i] = age_med\n","\n","index_nan_age = list(test_df[\"Age\"][test_df[\"Age\"].isnull()].index)\n","for i in index_nan_age:\n"," age_pred = test_df[\"Age\"][((test_df[\"SibSp\"] == test_df.iloc[i][\"SibSp\"]) &(test_df[\"Parch\"] == test_df.iloc[i][\"Parch\"])& (test_df[\"Pclass\"] == test_df.iloc[i][\"Pclass\"]))].median()\n"," age_med = test_df[\"Age\"].median()\n"," if not np.isnan(age_pred):\n"," test_df[\"Age\"].iloc[i] = age_pred\n"," else:\n"," test_df[\"Age\"].iloc[i] = age_med"]},{"cell_type":"markdown","metadata":{},"source":["## Analysing the correlation between the different columns"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:24:33.644174Z","iopub.status.busy":"2024-04-01T07:24:33.643621Z","iopub.status.idle":"2024-04-01T07:24:34.404306Z","shell.execute_reply":"2024-04-01T07:24:34.402938Z","shell.execute_reply.started":"2024-04-01T07:24:33.643935Z"},"trusted":true},"outputs":[],"source":["numerical_columns = train_df.select_dtypes(include=[np.number]).columns\n","sns.heatmap(train_df[numerical_columns].corr(), annot=True)"]},{"cell_type":"markdown","metadata":{},"source":["We see that Fare and Parch are positively correlated with Survived. Similarly, Fare and Class are negatively correlated, in the sense that the higher the higher the Fare, the lower the Class number (Remember that Class 1 < Class 2 < Class 3 in face value)."]},{"cell_type":"markdown","metadata":{},"source":["## Embarked"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.378151Z","iopub.status.busy":"2024-04-01T06:27:55.377756Z","iopub.status.idle":"2024-04-01T06:27:55.384785Z","shell.execute_reply":"2024-04-01T06:27:55.384101Z","shell.execute_reply.started":"2024-04-01T06:27:55.378107Z"},"trusted":true},"outputs":[],"source":["train_df[\"Embarked\"].head()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.386265Z","iopub.status.busy":"2024-04-01T06:27:55.385875Z","iopub.status.idle":"2024-04-01T06:27:55.635178Z","shell.execute_reply":"2024-04-01T06:27:55.633609Z","shell.execute_reply.started":"2024-04-01T06:27:55.386223Z"},"trusted":true},"outputs":[],"source":["sns.countplot(x = \"Embarked\", data = train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.638242Z","iopub.status.busy":"2024-04-01T06:27:55.637447Z","iopub.status.idle":"2024-04-01T06:27:55.699106Z","shell.execute_reply":"2024-04-01T06:27:55.698208Z","shell.execute_reply.started":"2024-04-01T06:27:55.638150Z"},"trusted":true},"outputs":[],"source":["train_df = pd.get_dummies(train_df, columns=[\"Embarked\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["test_df = pd.get_dummies(test_df, columns=[\"Embarked\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Ticket (Assignment)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.700760Z","iopub.status.busy":"2024-04-01T06:27:55.700330Z","iopub.status.idle":"2024-04-01T06:27:55.708542Z","shell.execute_reply":"2024-04-01T06:27:55.707466Z","shell.execute_reply.started":"2024-04-01T06:27:55.700715Z"},"trusted":true},"outputs":[],"source":["train_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.710291Z","iopub.status.busy":"2024-04-01T06:27:55.709980Z","iopub.status.idle":"2024-04-01T06:27:55.722810Z","shell.execute_reply":"2024-04-01T06:27:55.721839Z","shell.execute_reply.started":"2024-04-01T06:27:55.710231Z"},"trusted":true},"outputs":[],"source":["example_ticket = \"A/5. 2151\"\n","example_ticket.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.726116Z","iopub.status.busy":"2024-04-01T06:27:55.725689Z","iopub.status.idle":"2024-04-01T06:27:55.738095Z","shell.execute_reply":"2024-04-01T06:27:55.737043Z","shell.execute_reply.started":"2024-04-01T06:27:55.726039Z"},"trusted":true},"outputs":[],"source":["tickets = []\n","for i in list(train_df.Ticket):\n"," if not i.isdigit():\n"," tickets.append(i.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0])\n"," else:\n"," tickets.append(\"x\")\n","train_df[\"Ticket\"] = tickets\n","\n","# Do the same for the test set"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.740389Z","iopub.status.busy":"2024-04-01T06:27:55.739797Z","iopub.status.idle":"2024-04-01T06:27:55.755416Z","shell.execute_reply":"2024-04-01T06:27:55.754317Z","shell.execute_reply.started":"2024-04-01T06:27:55.740333Z"},"trusted":true},"outputs":[],"source":["train_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["test_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.790832Z","iopub.status.busy":"2024-04-01T06:27:55.790500Z","iopub.status.idle":"2024-04-01T06:27:55.841011Z","shell.execute_reply":"2024-04-01T06:27:55.839862Z","shell.execute_reply.started":"2024-04-01T06:27:55.790770Z"},"trusted":true},"outputs":[],"source":["train_df = pd.get_dummies(train_df, columns= [\"Ticket\"], prefix = \"TcktName\")\n","train_df.head(10)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["test_df = pd.get_dummies(test_df, columns= [\"Ticket\"], prefix = \"TcktName\")\n","test_df.head(10)"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Pclass"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.843395Z","iopub.status.busy":"2024-04-01T06:27:55.842833Z","iopub.status.idle":"2024-04-01T06:27:56.089225Z","shell.execute_reply":"2024-04-01T06:27:56.087578Z","shell.execute_reply.started":"2024-04-01T06:27:55.843168Z"},"trusted":true},"outputs":[],"source":["sns.countplot(x = \"Pclass\", data = train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.092270Z","iopub.status.busy":"2024-04-01T06:27:56.091722Z","iopub.status.idle":"2024-04-01T06:27:56.162888Z","shell.execute_reply":"2024-04-01T06:27:56.161841Z","shell.execute_reply.started":"2024-04-01T06:27:56.092186Z"},"trusted":true},"outputs":[],"source":["train_df[\"Pclass\"] = train_df[\"Pclass\"].astype(\"category\")\n","train_df = pd.get_dummies(train_df, columns= [\"Pclass\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["test_df[\"Pclass\"] = test_df[\"Pclass\"].astype(\"category\")\n","test_df = pd.get_dummies(test_df, columns= [\"Pclass\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Sex"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.164709Z","iopub.status.busy":"2024-04-01T06:27:56.164391Z","iopub.status.idle":"2024-04-01T06:27:56.205775Z","shell.execute_reply":"2024-04-01T06:27:56.204761Z","shell.execute_reply.started":"2024-04-01T06:27:56.164639Z"},"trusted":true},"outputs":[],"source":["train_df[\"Sex\"] = train_df[\"Sex\"].astype(\"category\")\n","train_df = pd.get_dummies(train_df, columns=[\"Sex\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["test_df[\"Sex\"] = test_df[\"Sex\"].astype(\"category\")\n","test_df = pd.get_dummies(test_df, columns=[\"Sex\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Drop Passenger ID and Cabin (Assignment)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.207602Z","iopub.status.busy":"2024-04-01T06:27:56.207299Z","iopub.status.idle":"2024-04-01T06:27:56.215886Z","shell.execute_reply":"2024-04-01T06:27:56.214401Z","shell.execute_reply.started":"2024-04-01T06:27:56.207550Z"},"trusted":true},"outputs":[],"source":["train_df.drop(labels = [\"PassengerId\", \"Cabin\"], axis = 1, inplace = True)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.217917Z","iopub.status.busy":"2024-04-01T06:27:56.217536Z","iopub.status.idle":"2024-04-01T06:27:56.228150Z","shell.execute_reply":"2024-04-01T06:27:56.227230Z","shell.execute_reply.started":"2024-04-01T06:27:56.217854Z"},"trusted":true},"outputs":[],"source":["train_df.columns"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Drop the PassengerId and Cabin columns from the test set"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Print the columns of the test set"]},{"cell_type":"markdown","metadata":{},"source":["
\n","# Modeling"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.230086Z","iopub.status.busy":"2024-04-01T06:27:56.229809Z","iopub.status.idle":"2024-04-01T06:27:56.238557Z","shell.execute_reply":"2024-04-01T06:27:56.237679Z","shell.execute_reply.started":"2024-04-01T06:27:56.230040Z"},"trusted":true},"outputs":[],"source":["from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.svm import SVC\n","from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.metrics import accuracy_score"]},{"cell_type":"markdown","metadata":{},"source":["## Train - Test Split (Assignment)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.240103Z","iopub.status.busy":"2024-04-01T06:27:56.239830Z","iopub.status.idle":"2024-04-01T06:27:56.256809Z","shell.execute_reply":"2024-04-01T06:27:56.255463Z","shell.execute_reply.started":"2024-04-01T06:27:56.240056Z"},"trusted":true},"outputs":[],"source":["train_df_len = len(train_df)\n","train_df_len"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.314730Z","iopub.status.busy":"2024-04-01T06:27:56.313986Z","iopub.status.idle":"2024-04-01T06:27:56.333564Z","shell.execute_reply":"2024-04-01T06:27:56.332507Z","shell.execute_reply.started":"2024-04-01T06:27:56.314635Z"},"trusted":true},"outputs":[],"source":["\n","train = train_df[:train_df_len]\n","test = test_df\n","\n","# Select all numerical values from train and test\n","numeric_train = train.select_dtypes(include=[np.number])\n","numeric_test = test.select_dtypes(include=[np.number]) \n","\n","\n","X_train = numeric_train.drop(labels=[\"Survived\",], axis=1)\n","y_train = numeric_train[\"Survived\"]\n","\n","# Split the train data into train and test sets with a 1/3 ratio\n","X_train, X_test, y_train, y_test = # Use the train_test_split function here\n","\n","\n","print(\"X_train\", len(X_train))\n","print(\"X_test\", len(X_test))\n","print(\"y_train\", len(y_train))\n","print(\"y_test\", len(y_test))\n","print(\"test\", len(numeric_test))"]},{"cell_type":"markdown","metadata":{},"source":["## Simple Logistic Regression (Assignment)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.335970Z","iopub.status.busy":"2024-04-01T06:27:56.335281Z","iopub.status.idle":"2024-04-01T06:27:56.368083Z","shell.execute_reply":"2024-04-01T06:27:56.366489Z","shell.execute_reply.started":"2024-04-01T06:27:56.335561Z"},"trusted":true},"outputs":[],"source":["logreg = LogisticRegression()\n","logreg.fit(X_train, y_train)\n","acc_log_train = round(logreg.score(X_train, y_train)*100,2) \n","acc_log_test = round(logreg.score(X_test,y_test)*100,2)\n","# Print the accuracy on the training and test set"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Hyperparameter Tuning -- Grid Search -- Cross Validation\n","We will compare 5 ml classifier and evaluate mean accuracy of each of them by stratified cross validation.\n","\n","* Decision Tree\n","* SVM\n","* Random Forest\n","* KNN\n","* Logistic Regression"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.371066Z","iopub.status.busy":"2024-04-01T06:27:56.370400Z","iopub.status.idle":"2024-04-01T06:27:56.401742Z","shell.execute_reply":"2024-04-01T06:27:56.396867Z","shell.execute_reply.started":"2024-04-01T06:27:56.370802Z"},"trusted":true},"outputs":[],"source":["random_state = 42\n","classifier = [DecisionTreeClassifier(random_state = random_state),\n"," SVC(random_state = random_state),\n"," RandomForestClassifier(random_state = random_state),\n"," LogisticRegression(random_state = random_state),\n"," KNeighborsClassifier()]\n","\n","dt_param_grid = {\"min_samples_split\" : range(10,500,20),\n"," \"max_depth\": range(1,20,2)}\n","\n","svc_param_grid = {\"kernel\" : [\"rbf\"],\n"," \"gamma\": [0.001, 0.01, 0.1, 1],\n"," \"C\": [1,10,50,100,200,300,1000]}\n","\n","rf_param_grid = {\"max_features\": [1,3,10],\n"," \"min_samples_split\":[2,3,10],\n"," \"min_samples_leaf\":[1,3,10],\n"," \"bootstrap\":[False],\n"," \"n_estimators\":[100,300],\n"," \"criterion\":[\"gini\"]}\n","\n","logreg_param_grid = {\"C\":np.logspace(-3,3,7),\n"," \"penalty\": [\"l1\",\"l2\"]}\n","\n","knn_param_grid = {\"n_neighbors\": np.linspace(1,19,10, dtype = int).tolist(),\n"," \"weights\": [\"uniform\",\"distance\"],\n"," \"metric\":[\"euclidean\",\"manhattan\"]}\n","classifier_param = [dt_param_grid,\n"," svc_param_grid,\n"," rf_param_grid,\n"," logreg_param_grid,\n"," knn_param_grid]"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.413811Z","iopub.status.busy":"2024-04-01T06:27:56.404322Z","iopub.status.idle":"2024-04-01T06:29:38.718970Z","shell.execute_reply":"2024-04-01T06:29:38.717807Z","shell.execute_reply.started":"2024-04-01T06:27:56.413658Z"},"trusted":true},"outputs":[],"source":["cv_result = []\n","best_estimators = []\n","for i in range(len(classifier)):\n"," clf = GridSearchCV(classifier[i], param_grid=classifier_param[i], cv = StratifiedKFold(n_splits = 10), scoring = \"accuracy\", n_jobs = -1,verbose = 1)\n"," clf.fit(X_train,y_train)\n"," cv_result.append(clf.best_score_)\n"," best_estimators.append(clf.best_estimator_)\n"," print(cv_result[i])"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:38.722928Z","iopub.status.busy":"2024-04-01T06:29:38.722207Z","iopub.status.idle":"2024-04-01T06:29:39.075423Z","shell.execute_reply":"2024-04-01T06:29:39.073987Z","shell.execute_reply.started":"2024-04-01T06:29:38.722582Z"},"trusted":true},"outputs":[],"source":["cv_results = pd.DataFrame({\"Cross Validation Means\":cv_result, \"ML Models\":[\"DecisionTreeClassifier\", \"SVM\",\"RandomForestClassifier\",\n"," \"LogisticRegression\",\n"," \"KNeighborsClassifier\"]})\n","\n","g = sns.barplot(x=\"Cross Validation Means\",y= \"ML Models\", data=cv_results)\n","g.set_xlabel(\"Mean Accuracy\")\n","g.set_title(\"Cross Validation Scores\")"]},{"cell_type":"markdown","metadata":{},"source":["## Ensemble Modeling (Assignment)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:39.078654Z","iopub.status.busy":"2024-04-01T06:29:39.077840Z","iopub.status.idle":"2024-04-01T06:29:39.862871Z","shell.execute_reply":"2024-04-01T06:29:39.860937Z","shell.execute_reply.started":"2024-04-01T06:29:39.078554Z"},"trusted":true},"outputs":[],"source":["votingC = VotingClassifier(estimators = [(\"dt\",best_estimators[0]),\n"," (\"rfc\",best_estimators[2]),\n"," (\"lr\",best_estimators[3])],\n"," voting = \"soft\", n_jobs = -1)\n","votingC = votingC.fit(X_train, y_train)\n","\n","# Print the accuracy score of the voting classifier"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Drop the null values which are going to cause you an error in the next cell"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Prediction and Submission"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:39.865981Z","iopub.status.busy":"2024-04-01T06:29:39.865330Z","iopub.status.idle":"2024-04-01T06:29:39.977357Z","shell.execute_reply":"2024-04-01T06:29:39.973301Z","shell.execute_reply.started":"2024-04-01T06:29:39.865906Z"},"trusted":true},"outputs":[],"source":["test_survived = pd.Series(votingC.predict(numeric_test), name=\"Survived\").astype(int)\n","results = pd.concat([test_PassengerId, test_survived], axis=1)\n","results.to_csv(\"titanic.csv\", index=False)\n","print(results)"]},{"cell_type":"markdown","metadata":{},"source":["# Congratulations on finishing the assignment!!\n","\n","### The submission is the titanic.csv which was just created, and this file which you have modified."]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"databundleVersionId":26502,"sourceId":3136,"sourceType":"competition"}],"dockerImageVersionId":29852,"isGpuEnabled":false,"isInternetEnabled":false,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.7"}},"nbformat":4,"nbformat_minor":4} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "# Ignore this" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", + "\n", + "import seaborn as sns\n", + "\n", + "from collections import Counter\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "# Load and Check Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DataFrames hold the dataset in a tabular format for easy manipulation and analysis.
\n", + "CSV data is read into 'df' using Pandas' read_csv() function." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "_kg_hide-input": true + }, + "outputs": [], + "source": [ + "train_df = pd.read_csv(\"/Downloads/train.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Try to read the test .csv file into test_df" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "test_df = pd.read_csv(\"/Downloads/test.csv\")\n", + "test_PassengerId = test_df[\"PassengerId\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", + "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Columns of train_df are: \n" + ] + }, + { + "data": { + "text/plain": [ + "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n", + " 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n", + " dtype='object')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"The Columns of train_df are: \")\n", + "train_df.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### We can use head() to see the first few rows in the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
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3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Age SibSp \\\n", + "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", + "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", + "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", + "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", + "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", + "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", + "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", + "\n", + " Parch Fare \n", + "count 891.000000 891.000000 \n", + "mean 0.381594 32.204208 \n", + "std 0.806057 49.693429 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 7.910400 \n", + "50% 0.000000 14.454200 \n", + "75% 0.000000 31.000000 \n", + "max 6.000000 512.329200 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Without looking above, try checking the first few rows of test_df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
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" + ], + "text/plain": [ + " PassengerId Pclass Name Sex \\\n", + "0 892 3 Kelly, Mr. James male \n", + "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n", + "2 894 2 Myles, Mr. Thomas Francis male \n", + "3 895 3 Wirz, Mr. Albert male \n", + "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", + "\n", + " Age SibSp Parch Ticket Fare Cabin Embarked \n", + "0 34.5 0 0 330911 7.8292 NaN Q \n", + "1 47.0 1 0 363272 7.0000 NaN S \n", + "2 62.0 0 0 240276 9.6875 NaN Q \n", + "3 27.0 0 0 315154 8.6625 NaN S \n", + "4 22.0 1 1 3101298 12.2875 NaN S " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Now try checking for a description of test_df's data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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max1309.0000003.00000076.0000008.0000009.000000512.329200
\n", + "
" + ], + "text/plain": [ + " PassengerId Pclass Age SibSp Parch Fare\n", + "count 418.000000 418.000000 332.000000 418.000000 418.000000 417.000000\n", + "mean 1100.500000 2.265550 30.272590 0.447368 0.392344 35.627188\n", + "std 120.810458 0.841838 14.181209 0.896760 0.981429 55.907576\n", + "min 892.000000 1.000000 0.170000 0.000000 0.000000 0.000000\n", + "25% 996.250000 1.000000 21.000000 0.000000 0.000000 7.895800\n", + "50% 1100.500000 3.000000 27.000000 0.000000 0.000000 14.454200\n", + "75% 1204.750000 3.000000 39.000000 1.000000 0.000000 31.500000\n", + "max 1309.000000 3.000000 76.000000 8.000000 9.000000 512.329200" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Variable Description\n", + "
    \n", + "
  1. PassengerId: unique id number to each passenger
    2. \n", + "
  2. Survived: passenger survive(1) or died(0)
    3. \n", + "
  3. Pclass: passenger class
    4. \n", + "
  4. Name: name
    5. \n", + "
  5. Sex: gender of passenger
    6. \n", + "
  6. Age: age of passenger
    7. \n", + "
  7. SibSp: number of siblings/spouses
    8. \n", + "
  8. Parch: number of parents/children
    9. \n", + "
  9. Ticket: ticket number
    10. \n", + "
  10. Fare: amount of money spent on ticket
    11. \n", + "
  11. Cabin: cabin category
    12. \n", + "
  12. Embarked: port where passenger embarked ( C = Cherbourg, Q = Queenstown, S = Southampton )
    13. \n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 891 entries, 0 to 890\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 PassengerId 891 non-null int64 \n", + " 1 Survived 891 non-null int64 \n", + " 2 Pclass 891 non-null int64 \n", + " 3 Name 891 non-null object \n", + " 4 Sex 891 non-null object \n", + " 5 Age 714 non-null float64\n", + " 6 SibSp 891 non-null int64 \n", + " 7 Parch 891 non-null int64 \n", + " 8 Ticket 891 non-null object \n", + " 9 Fare 891 non-null float64\n", + " 10 Cabin 204 non-null object \n", + " 11 Embarked 889 non-null object \n", + "dtypes: float64(2), int64(5), object(5)\n", + "memory usage: 83.7+ KB\n" + ] + } + ], + "source": [ + "train_df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Slice Rows and Columsn of DF (Assigmennt)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId 3\n", + "Survived 1\n", + "Pclass 3\n", + "Name Heikkinen, Miss. Laina\n", + "Sex female\n", + "Age 26.0\n", + "SibSp 0\n", + "Parch 0\n", + "Ticket STON/O2. 3101282\n", + "Fare 7.925\n", + "Cabin NaN\n", + "Embarked S\n", + "Name: 2, dtype: object" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Printing the Second Row\n", + "train_df.iloc[2]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId 6\n", + "Survived 0\n", + "Pclass 3\n", + "Name Moran, Mr. James\n", + "Sex male\n", + "Age NaN\n", + "SibSp 0\n", + "Parch 0\n", + "Ticket 330877\n", + "Fare 8.4583\n", + "Cabin NaN\n", + "Embarked Q\n", + "Name: 5, dtype: object" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Print the 5th Row\n", + "train_df.iloc[5]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 male\n", + "1 female\n", + "2 female\n", + "3 female\n", + "4 male\n", + " ... \n", + "886 male\n", + "887 female\n", + "888 female\n", + "889 male\n", + "890 male\n", + "Name: Sex, Length: 891, dtype: object" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Print the Sex Column\n", + "train_df['Sex']" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Braund, Mr. Owen Harris\n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th...\n", + "2 Heikkinen, Miss. Laina\n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel)\n", + "4 Allen, Mr. William Henry\n", + " ... \n", + "886 Montvila, Rev. Juozas\n", + "887 Graham, Miss. Margaret Edith\n", + "888 Johnston, Miss. Catherine Helen \"Carrie\"\n", + "889 Behr, Mr. Karl Howell\n", + "890 Dooley, Mr. Patrick\n", + "Name: Name, Length: 891, dtype: object" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Print the Name Column\n", + "train_df['Name']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization (Assignment)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Age -- Survived" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "# Plot 1: Survivors vs Non Survivors\n", + "\n", + "# Creating a plot for the Survived Column\n", + "sns.countplot(x='Survived', data=train_df)\n", + "\n", + "plt.title('Survivors vs Non Survivors')\n", + "plt.xlabel('Survived')\n", + "plt.ylabel('Count')\n", + "plt.xticks([0, 1], ['No', 'Yes']) # Setting custom tick labels\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Try Plotting Passenger Class" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "\n", + "# Make the plot for Pclass here:\n", + "sns.countplot(x='Pclass', data=train_df)\n", + "\n", + "plt.title('Count of Passengers In each Passenger Class')\n", + "plt.xlabel('Passenger Class')\n", + "plt.ylabel('Count')\n", + "plt.xticks([0, 1, 2], ['1st', '2nd', '3rd']) # Setting custom tick labels\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Try it for \"Embarked\"" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "\n", + "sns.countplot(x='Embarked', data=train_df)\n", + "\n", + "plt.title('Embarked Histogram')\n", + "plt.xlabel('S vs C vs Q')\n", + "plt.ylabel('Count')\n", + "plt.xticks([0, 1, 2], ['S', 'C', 'Q']) \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Try Making a histogram for \"Fare\"" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.histplot(train_df['Fare'], bins=20, color='green')\n", + "\n", + "plt.title('Fare Histogram')\n", + "plt.xlabel('Fare')\n", + "plt.ylabel('Frequency')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Here is the distplot for \"Fare\", refer to it after you tried it yourself:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.histplot(train_df['Fare'], bins=20, color='orange')\n", + "plt.title('Distribution of Passenger Fares')\n", + "plt.xlabel('Fare')\n", + "plt.ylabel('Frequency')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make a histogram for \"Age\" (Assignment)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create the plot below\n", + "sns.histplot(train_df['Age'], bins=15, color='blue')\n", + "plt.title('Age Histogram')\n", + "plt.xlabel('Age')\n", + "plt.ylabel('Frequency')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "## Fill Missing: Age Feature" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
171812Williams, Mr. Charles EugenemaleNaN0024437313.0000NaNS
192013Masselmani, Mrs. FatimafemaleNaN0026497.2250NaNC
262703Emir, Mr. Farred ChehabmaleNaN0026317.2250NaNC
282913O'Dwyer, Miss. Ellen \"Nellie\"femaleNaN003309597.8792NaNQ
.......................................
85986003Razi, Mr. RaihedmaleNaN0026297.2292NaNC
86386403Sage, Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500NaNS
86886903van Melkebeke, Mr. PhilemonmaleNaN003457779.5000NaNS
87887903Laleff, Mr. KristomaleNaN003492177.8958NaNS
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
\n", + "

177 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass Name \\\n", + "5 6 0 3 Moran, Mr. James \n", + "17 18 1 2 Williams, Mr. Charles Eugene \n", + "19 20 1 3 Masselmani, Mrs. Fatima \n", + "26 27 0 3 Emir, Mr. Farred Chehab \n", + "28 29 1 3 O'Dwyer, Miss. Ellen \"Nellie\" \n", + ".. ... ... ... ... \n", + "859 860 0 3 Razi, Mr. Raihed \n", + "863 864 0 3 Sage, Miss. Dorothy Edith \"Dolly\" \n", + "868 869 0 3 van Melkebeke, Mr. Philemon \n", + "878 879 0 3 Laleff, Mr. Kristo \n", + "888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", + "5 male NaN 0 0 330877 8.4583 NaN Q \n", + "17 male NaN 0 0 244373 13.0000 NaN S \n", + "19 female NaN 0 0 2649 7.2250 NaN C \n", + "26 male NaN 0 0 2631 7.2250 NaN C \n", + "28 female NaN 0 0 330959 7.8792 NaN Q \n", + ".. ... ... ... ... ... ... ... ... \n", + "859 male NaN 0 0 2629 7.2292 NaN C \n", + "863 female NaN 8 2 CA. 2343 69.5500 NaN S \n", + "868 male NaN 0 0 345777 9.5000 NaN S \n", + "878 male NaN 0 0 349217 7.8958 NaN S \n", + "888 female NaN 1 2 W./C. 6607 23.4500 NaN S \n", + "\n", + "[177 rows x 12 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df[train_df[\"Age\"].isnull()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Try Checking for Null Values in Test Df" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId 0\n", + "Pclass 0\n", + "Name 0\n", + "Sex 0\n", + "Age 86\n", + "SibSp 0\n", + "Parch 0\n", + "Ticket 0\n", + "Fare 1\n", + "Cabin 327\n", + "Embarked 0\n", + "dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_df.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run this to fix the Null Values" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "index_nan_age = list(train_df[\"Age\"][train_df[\"Age\"].isnull()].index)\n", + "for i in index_nan_age:\n", + " age_pred = train_df[\"Age\"][((train_df[\"SibSp\"] == train_df.iloc[i][\"SibSp\"]) &(train_df[\"Parch\"] == train_df.iloc[i][\"Parch\"])& (train_df[\"Pclass\"] == train_df.iloc[i][\"Pclass\"]))].median()\n", + " age_med = train_df[\"Age\"].median()\n", + " if not np.isnan(age_pred):\n", + " train_df[\"Age\"].iloc[i] = age_pred\n", + " else:\n", + " train_df[\"Age\"].iloc[i] = age_med\n", + "\n", + "index_nan_age = list(test_df[\"Age\"][test_df[\"Age\"].isnull()].index)\n", + "for i in index_nan_age:\n", + " age_pred = test_df[\"Age\"][((test_df[\"SibSp\"] == test_df.iloc[i][\"SibSp\"]) &(test_df[\"Parch\"] == test_df.iloc[i][\"Parch\"])& (test_df[\"Pclass\"] == test_df.iloc[i][\"Pclass\"]))].median()\n", + " age_med = test_df[\"Age\"].median()\n", + " if not np.isnan(age_pred):\n", + " test_df[\"Age\"].iloc[i] = age_pred\n", + " else:\n", + " test_df[\"Age\"].iloc[i] = age_med" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysing the correlation between the different columns" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "numerical_columns = train_df.select_dtypes(include=[np.number]).columns\n", + "sns.heatmap(train_df[numerical_columns].corr(), annot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that Fare and Parch are positively correlated with Survived. Similarly, Fare and Class are negatively correlated, in the sense that the higher the higher the Fare, the lower the Class number (Remember that Class 1 < Class 2 < Class 3 in face value)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Embarked" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 S\n", + "1 C\n", + "2 S\n", + "3 S\n", + "4 S\n", + "Name: Embarked, dtype: object" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df[\"Embarked\"].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked_CEmbarked_QEmbarked_S
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNFalseFalseTrue
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85TrueFalseFalse
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNFalseFalseTrue
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123FalseFalseTrue
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked_CEmbarked_QEmbarked_S
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNFalseTrueFalse
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNFalseFalseTrue
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNFalseTrueFalse
38953Wirz, Mr. Albertmale27.0003151548.6625NaNFalseFalseTrue
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNFalseFalseTrue
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" + ], + "text/plain": [ + " PassengerId Pclass Name Sex \\\n", + "0 892 3 Kelly, Mr. James male \n", + "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n", + "2 894 2 Myles, Mr. Thomas Francis male \n", + "3 895 3 Wirz, Mr. Albert male \n", + "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", + "\n", + " Age SibSp Parch Ticket Fare Cabin Embarked_C Embarked_Q \\\n", + "0 34.5 0 0 330911 7.8292 NaN False True \n", + "1 47.0 1 0 363272 7.0000 NaN False False \n", + "2 62.0 0 0 240276 9.6875 NaN False True \n", + "3 27.0 0 0 315154 8.6625 NaN False False \n", + "4 22.0 1 1 3101298 12.2875 NaN False False \n", + "\n", + " Embarked_S \n", + "0 False \n", + "1 True \n", + "2 False \n", + "3 True \n", + "4 True " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_df = pd.get_dummies(test_df, columns=[\"Embarked\"])\n", + "test_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ticket (Assignment)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 330911\n", + "1 363272\n", + "2 240276\n", + "3 315154\n", + "4 3101298\n", + "Name: Ticket, dtype: object" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_df[\"Ticket\"].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'A5'" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example_ticket = \"A/5. 2151\"\n", + "example_ticket.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "tickets = []\n", + "for i in list(train_df.Ticket):\n", + " if not i.isdigit():\n", + " tickets.append(i.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0])\n", + " else:\n", + " tickets.append(\"x\")\n", + "train_df[\"Ticket\"] = tickets\n", + "\n", + "# Do the same for the test set\n", + "tickets = []\n", + "for i in list(test_df.Ticket):\n", + " if not i.isdigit():\n", + " tickets.append(i.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0])\n", + " else:\n", + " tickets.append(\"x\")\n", + "test_df[\"Ticket\"] = tickets\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 A5\n", + "1 PC\n", + "2 STONO2\n", + "3 x\n", + "4 x\n", + "5 x\n", + "6 x\n", + "7 x\n", + "8 x\n", + "9 x\n", + "10 PP\n", + "11 x\n", + "12 A5\n", + "13 x\n", + "14 x\n", + "15 x\n", + "16 x\n", + "17 x\n", + "18 x\n", + "19 x\n", + "Name: Ticket, dtype: object" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df[\"Ticket\"].head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 x\n", + "1 x\n", + "2 x\n", + "3 x\n", + "4 x\n", + "5 x\n", + "6 x\n", + "7 x\n", + "8 x\n", + "9 A4\n", + "10 x\n", + "11 x\n", + "12 x\n", + "13 x\n", + "14 WEP\n", + "15 SCPARIS\n", + "16 x\n", + "17 x\n", + "18 STONO2\n", + "19 x\n", + "Name: Ticket, dtype: object" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_df[\"Ticket\"].head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchFareCabin...TcktName_SOPPTcktName_SOTONO2TcktName_SOTONOQTcktName_SPTcktName_STONOTcktName_STONO2TcktName_SWPPTcktName_WCTcktName_WEPTcktName_x
0103Braund, Mr. Owen Harrismale22.0107.2500NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.01071.2833C85...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
2313Heikkinen, Miss. Lainafemale26.0007.9250NaN...FalseFalseFalseFalseFalseTrueFalseFalseFalseFalse
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01053.1000C123...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
4503Allen, Mr. William Henrymale35.0008.0500NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
5603Moran, Mr. Jamesmale26.0008.4583NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
6701McCarthy, Mr. Timothy Jmale54.00051.8625E46...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
7803Palsson, Master. Gosta Leonardmale2.03121.0750NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00211.1333NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01030.0708NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
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PassengerIdPclassNameSexAgeSibSpParchFareCabinEmbarked_C...TcktName_SOCTcktName_SOPPTcktName_SOTONO2TcktName_SOTONOQTcktName_STONOTcktName_STONO2TcktName_STONOQTcktName_WCTcktName_WEPTcktName_x
08923Kelly, Mr. Jamesmale34.5007.8292NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
18933Wilkes, Mrs. James (Ellen Needs)female47.0107.0000NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
28942Myles, Mr. Thomas Francismale62.0009.6875NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
38953Wirz, Mr. Albertmale27.0008.6625NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.01112.2875NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
58973Svensson, Mr. Johan Cervinmale14.0009.2250NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
68983Connolly, Miss. Katefemale30.0007.6292NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
78992Caldwell, Mr. Albert Francismale26.01129.0000NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
89003Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.0007.2292NaNTrue...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
99013Davies, Mr. John Samuelmale21.02024.1500NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
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10 rows × 40 columns

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" + ], + "text/plain": [ + " PassengerId Pclass Name Sex \\\n", + "0 892 3 Kelly, Mr. James male \n", + "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n", + "2 894 2 Myles, Mr. Thomas Francis male \n", + "3 895 3 Wirz, Mr. Albert male \n", + "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", + "5 897 3 Svensson, Mr. Johan Cervin male \n", + "6 898 3 Connolly, Miss. Kate female \n", + "7 899 2 Caldwell, Mr. Albert Francis male \n", + "8 900 3 Abrahim, Mrs. Joseph (Sophie Halaut Easu) female \n", + "9 901 3 Davies, Mr. John Samuel male \n", + "\n", + " Age SibSp Parch Fare Cabin Embarked_C ... TcktName_SOC \\\n", + "0 34.5 0 0 7.8292 NaN False ... False \n", + "1 47.0 1 0 7.0000 NaN False ... False \n", + "2 62.0 0 0 9.6875 NaN False ... False \n", + "3 27.0 0 0 8.6625 NaN False ... False \n", + "4 22.0 1 1 12.2875 NaN False ... False \n", + "5 14.0 0 0 9.2250 NaN False ... False \n", + "6 30.0 0 0 7.6292 NaN False ... False \n", + "7 26.0 1 1 29.0000 NaN False ... False \n", + "8 18.0 0 0 7.2292 NaN True ... False \n", + "9 21.0 2 0 24.1500 NaN False ... False \n", + "\n", + " TcktName_SOPP TcktName_SOTONO2 TcktName_SOTONOQ TcktName_STONO \\\n", + "0 False False False False \n", + "1 False False False False \n", + "2 False False False False \n", + "3 False False False False \n", + "4 False False False False \n", + "5 False False False False \n", + "6 False False False False \n", + "7 False False False False \n", + "8 False False False False \n", + "9 False False False False \n", + "\n", + " TcktName_STONO2 TcktName_STONOQ TcktName_WC TcktName_WEP TcktName_x \n", + "0 False False False False True \n", + "1 False False False False True \n", + "2 False False False False True \n", + "3 False False False False True \n", + "4 False False False False True \n", + "5 False False False False True \n", + "6 False False False False True \n", + "7 False False False False True \n", + "8 False False False False True \n", + "9 False False False False False \n", + "\n", + "[10 rows x 40 columns]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_df = pd.get_dummies(test_df, columns= [\"Ticket\"], prefix = \"TcktName\")\n", + "test_df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "## Pclass" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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LFy/qfz94Ozg4uP6TAQAA1EKdYubjjz/WX/7yFx0/flyS5HK55OHh4f7P3NzcBh0SAADgUuoUM3PmzFGPHj20bNkyLjUBAABL1SlmTpw4oZUrV6pDhw4NPQ8AAMCvUqf3mYmNjZXdbm/oWQAAAH61Op2Zuf322/Xss8/qP//5jzp27FjlT7QlKTExsUGGAwAAuJw63wDcvXt3nTp1SqdOnaqy5uHh0SCDAQAA1EadYmbNmjUNPQcAAECd1Clm/va3v9W4PnLkyLr8WAAAgF+tTjGzaNGiKt87nU6dOnVKnp6e6tGjBzEDAAAaTZ1i5t///ne1bT/88IOeeeYZde3atd5DAQAA1Fad/jT7l7Rs2VJTpkzRq6++2lA/EgAA4LIaLGYk6eDBg6qsrGzIHwkAAFCjOl1mGjt2bLU/wf7hhx/05Zdfavz48Q0xFwAAQK3UKWZ69uxZbZuXl5emTZum3r1713soAACA2qpTzPz8HX7Pnz8vp9Op1q1bN9hQAAAAtVWnmJGk1atXa+XKlSouLpYktWnTRmPGjOGjDAAAQKOqU8xkZGRo7dq1+sMf/qDo6GhVVlZqz549WrJkiby8vJSQkNDQcwIAAPyiOsXMW2+9pdTUVA0cONC9LSwsTEFBQUpNTSVmAABAo6nTn2afP39enTp1qra9c+fOOn36dH1nAgAAqLU6xUx0dLRWrVpV5T1lnE6nXnnlFfXo0aPBhgMAALicOl1mSk5O1u9+9zvt3LlT4eHhkqT9+/fL4XBo5cqVDTogAABATeoUMyEhIZo5c6ZKSkp05MgReXt768MPP9SiRYt06623NvSMAAAAl1Sny0xr1qzR7Nmzdd1112n27NlKTk7W2LFjNW3aNL311lsNPSMAAMAl1SlmXn31Vb3wwgu677773NtmzJih9PR0ZWZmNthwAAAAl1OnmDlz5oxuvvnmats7d+7sfhM9AACAxlCnmImJidHixYtVVlbm3lZRUaHly5crOjq6wYYDAAC4nDrdAPzMM89owoQJ6tOnj/v9Zr755hsFBARo6dKlDTkfAABAjeoUMzfffLO2bNmijz76SF9//bU8PT3VqVMn9enTRzabraFnBAAAuKQ6f9Ckl5eX7rrrroacBQAA4Fer0z0zAAAATQUxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKPV+bOZmpuYp1+3egQ0Ifb0cVaPAAD4fzgzAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMZmnMnDx5UklJSYqLi1Pfvn2VlpamiooKSVJBQYHGjx+vqKgoDR06VDt27Kiy786dOzVs2DBFRkZq3LhxKigosOIQAACAxSyLGZfLpaSkJJWVlemNN97QggUL9OGHH2rhwoVyuVyaPHmyAgIClJWVpREjRigxMVGFhYWSpMLCQk2ePFnx8fHauHGj2rRpo0mTJsnlcll1OAAAwCKWvc/MkSNHlJ2drY8//lgBAQGSpKSkJM2bN0933nmnCgoKtG7dOrVo0UIhISH65JNPlJWVpSlTpmjDhg3q3r27JkyYIElKS0vTHXfcoV27dqlnz55WHRIAALCAZWdmAgMDtXLlSnfI/OT8+fPKyclRt27d1KJFC/f2mJgYZWdnS5JycnIUGxvrXvP19VV4eLh7HQAANB+WnZnx8/NT37593d9XVlZq7dq16tWrl4qKitS2bdsqj/f399eJEyck6bLrl+J0OuV0OhvoCNCc8ToCgMur77+Vtd2/yXycQXp6ug4cOKCNGzfqtddek5eXV5V1Ly8vORwOSVJZWVmN65dy6NChhh0azRZnAQHg8hrr38omETPp6elavXq1FixYoNDQUHl7e6ukpKTKYxwOh3x8fCRJ3t7e1cLF4XDIz8+vxucJDQ2tcunqV1m3v2774aoUFRVl9QhA08S/lfiZ+v5bWVpaWqsTEZbHzNy5c/Xmm28qPT1dgwYNkiQFBQUpPz+/yuOKi4vdl5aCgoJUXFxcbT0sLKzG57LZbLLZbA04PZorXkcAcHn1/beytvtb+j4zS5Ys0bp16/Tiiy/q3nvvdW+PjIzU/v37VV5e7t5mt9sVGRnpXrfb7e61srIyHThwwL0OAACaD8ti5vDhw1q6dKkef/xxxcTEqKioyP0VFxendu3aKTk5WXl5ecrMzNTevXs1evRoSdKoUaO0Z88eZWZmKi8vT8nJyWrfvj1/lg0AQDNkWcx88MEHcjqdWrZsmfr06VPly2azaenSpSoqKlJ8fLzeeecdZWRkKDg4WJLUvn17LV68WFlZWRo9erRKSkqUkZEhDw8Pqw4HAABYxLJ7ZhISEpSQkHDJ9Y4dO2rt2rWXXO/Xr5/69et3JUYDAAAG4YMmAQCA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRLH8HYAB1F/P061aPgCbEnj7O6hEAS3BmBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYrUnEjMPh0LBhw/TZZ5+5txUUFGj8+PGKiorS0KFDtWPHjir77Ny5U8OGDVNkZKTGjRungoKCxh4bAAA0AZbHTEVFhaZOnaq8vDz3NpfLpcmTJysgIEBZWVkaMWKEEhMTVVhYKEkqLCzU5MmTFR8fr40bN6pNmzaaNGmSXC6XVYcBAAAsYmnM5Ofn64EHHtA333xTZfunn36qgoICzZkzRyEhIZo4caKioqKUlZUlSdqwYYO6d++uCRMm6JZbblFaWpqOHTumXbt2WXEYAADAQpbGzK5du9SzZ0+tX7++yvacnBx169ZNLVq0cG+LiYlRdna2ez02Nta95uvrq/DwcPc6AABoPjytfPKHHnroF7cXFRWpbdu2Vbb5+/vrxIkTtVoHAADNh6UxcyllZWXy8vKqss3Ly0sOh6NW65fidDrldDobdlg0S7yO0BTxukRTU9/XZG33b5Ix4+3trZKSkirbHA6HfHx83Ov/Gy4Oh0N+fn41/txDhw416JxovrikiaaI1yWamsZ6TTbJmAkKClJ+fn6VbcXFxe5LS0FBQSouLq62HhYWVuPPDQ0NrXIfzq+ybn/d9sNVKSoqyuoRfsTrEj/TJF6XvCbxM/V9TZaWltbqRESTjJnIyEhlZmaqvLzcfTbGbrcrJibGvW63292PLysr04EDB5SYmFjjz7XZbLLZbFducDQbvI7QFPG6RFNT39dkbfe3/H1mfklcXJzatWun5ORk5eXlKTMzU3v37tXo0aMlSaNGjdKePXuUmZmpvLw8JScnq3379urZs6fFkwMAgMbWJGPGZrNp6dKlKioqUnx8vN555x1lZGQoODhYktS+fXstXrxYWVlZGj16tEpKSpSRkSEPDw+LJwcAAI2tyVxm+vLLL6t837FjR61du/aSj+/Xr5/69et3pccCAABNXJM8MwMAAFBbxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjGR0zFRUVmjlzpmJjY9WnTx+tWrXK6pEAAEAj87R6gPqYP3++9u3bp9WrV6uwsFAzZsxQcHCwBg8ebPVoAACgkRgbM6WlpdqwYYNefvllhYeHKzw8XHl5eXrjjTeIGQAAmhFjLzMdPHhQFy9eVHR0tHtbTEyMcnJyVFlZaeFkAACgMRkbM0VFRbrhhhvk5eXl3hYQEKCKigqVlJRYNxgAAGhUxl5mKisrqxIyktzfOxyOKtt/OlPzww8/yOl01un5br7Bp0774er0/fffWz2CJF6XqKopvC55TeLn6vuaLC8vl6TLXnExNma8vb2rRctP3/v4VP0fU0VFhSTpm2++qfPz/XlQSJ33xdXn0KFDVo8gidclqmoKr0tek/i5hnpNVlRUqFWrVpdcNzZmgoKCdObMGV28eFGenj8eRlFRkXx8fOTn51flsa1bt1anTp3k7e2ta64x9soaAADNSmVlpSoqKtS6desaH2dszISFhcnT01PZ2dmKjY2VJNntdkVERFQLFk9PT/n7+1sxJgAAqIeazsj8xNjTFL6+vho5cqRmz56tvXv3atu2bVq1apXGjRtn9WgAAKARGRszkpScnKzw8HA9/PDDevbZZzVlyhTdc889Vo91VXM4HBo2bJg+++wzq0dBM3fy5EklJSUpLi5Offv2VVpamvv+OMAqR48e1aOPPqro6Gj1799fK1eutHqkZsHYy0zSj2dn5s2bp3nz5lk9SrN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" + ], + "text/plain": [ + " PassengerId Name Sex Age \\\n", + "0 892 Kelly, Mr. James male 34.5 \n", + "1 893 Wilkes, Mrs. James (Ellen Needs) female 47.0 \n", + "2 894 Myles, Mr. Thomas Francis male 62.0 \n", + "3 895 Wirz, Mr. Albert male 27.0 \n", + "4 896 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 \n", + "\n", + " SibSp Parch Fare Cabin Embarked_C Embarked_Q ... TcktName_SOTONOQ \\\n", + "0 0 0 7.8292 NaN False True ... False \n", + "1 1 0 7.0000 NaN False False ... False \n", + "2 0 0 9.6875 NaN False True ... False \n", + "3 0 0 8.6625 NaN False False ... False \n", + "4 1 1 12.2875 NaN False False ... False \n", + "\n", + " TcktName_STONO TcktName_STONO2 TcktName_STONOQ TcktName_WC \\\n", + "0 False False False False \n", + "1 False False False False \n", + "2 False False False False \n", + "3 False False False False \n", + "4 False False False False \n", + "\n", + " TcktName_WEP TcktName_x Pclass_1 Pclass_2 Pclass_3 \n", + "0 False True False False True \n", + "1 False True False False True \n", + "2 False True False True False \n", + "3 False True False False True \n", + "4 False True False False True \n", + "\n", + "[5 rows x 42 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_df[\"Pclass\"] = test_df[\"Pclass\"].astype(\"category\")\n", + "test_df = pd.get_dummies(test_df, columns= [\"Pclass\"])\n", + "test_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "## Sex" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedNameAgeSibSpParchFareCabinEmbarked_CEmbarked_Q...TcktName_STONO2TcktName_SWPPTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3Sex_femaleSex_male
010Braund, Mr. Owen Harris22.0107.2500NaNFalseFalse...FalseFalseFalseFalseFalseFalseFalseTrueFalseTrue
121Cumings, Mrs. John Bradley (Florence Briggs Th...38.01071.2833C85TrueFalse...FalseFalseFalseFalseFalseTrueFalseFalseTrueFalse
231Heikkinen, Miss. Laina26.0007.9250NaNFalseFalse...TrueFalseFalseFalseFalseFalseFalseTrueTrueFalse
341Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.1000C123FalseFalse...FalseFalseFalseFalseTrueTrueFalseFalseTrueFalse
450Allen, Mr. William Henry35.0008.0500NaNFalseFalse...FalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
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5 rows × 47 columns

\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Name \\\n", + "0 1 0 Braund, Mr. Owen Harris \n", + "1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n", + "2 3 1 Heikkinen, Miss. Laina \n", + "3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n", + "4 5 0 Allen, Mr. William Henry \n", + "\n", + " Age SibSp Parch Fare Cabin Embarked_C Embarked_Q ... \\\n", + "0 22.0 1 0 7.2500 NaN False False ... \n", + "1 38.0 1 0 71.2833 C85 True False ... \n", + "2 26.0 0 0 7.9250 NaN False False ... \n", + "3 35.0 1 0 53.1000 C123 False False ... \n", + "4 35.0 0 0 8.0500 NaN False False ... \n", + "\n", + " TcktName_STONO2 TcktName_SWPP TcktName_WC TcktName_WEP TcktName_x \\\n", + "0 False False False False False \n", + "1 False False False False False \n", + "2 True False False False False \n", + "3 False False False False True \n", + "4 False False False False True \n", + "\n", + " Pclass_1 Pclass_2 Pclass_3 Sex_female Sex_male \n", + "0 False False True False True \n", + "1 True False False True False \n", + "2 False False True True False \n", + "3 True False False True False \n", + "4 False False True False True \n", + "\n", + "[5 rows x 47 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df[\"Sex\"] = train_df[\"Sex\"].astype(\"category\")\n", + "train_df = pd.get_dummies(train_df, columns=[\"Sex\"])\n", + "train_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdNameAgeSibSpParchFareCabinEmbarked_CEmbarked_QEmbarked_S...TcktName_STONO2TcktName_STONOQTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3Sex_femaleSex_male
0892Kelly, Mr. James34.5007.8292NaNFalseTrueFalse...FalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
1893Wilkes, Mrs. James (Ellen Needs)47.0107.0000NaNFalseFalseTrue...FalseFalseFalseFalseTrueFalseFalseTrueTrueFalse
2894Myles, Mr. Thomas Francis62.0009.6875NaNFalseTrueFalse...FalseFalseFalseFalseTrueFalseTrueFalseFalseTrue
3895Wirz, Mr. Albert27.0008.6625NaNFalseFalseTrue...FalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
4896Hirvonen, Mrs. Alexander (Helga E Lindqvist)22.01112.2875NaNFalseFalseTrue...FalseFalseFalseFalseTrueFalseFalseTrueTrueFalse
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5 rows × 43 columns

\n", + "
" + ], + "text/plain": [ + " PassengerId Name Age SibSp \\\n", + "0 892 Kelly, Mr. James 34.5 0 \n", + "1 893 Wilkes, Mrs. James (Ellen Needs) 47.0 1 \n", + "2 894 Myles, Mr. Thomas Francis 62.0 0 \n", + "3 895 Wirz, Mr. Albert 27.0 0 \n", + "4 896 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 22.0 1 \n", + "\n", + " Parch Fare Cabin Embarked_C Embarked_Q Embarked_S ... \\\n", + "0 0 7.8292 NaN False True False ... \n", + "1 0 7.0000 NaN False False True ... \n", + "2 0 9.6875 NaN False True False ... \n", + "3 0 8.6625 NaN False False True ... \n", + "4 1 12.2875 NaN False False True ... \n", + "\n", + " TcktName_STONO2 TcktName_STONOQ TcktName_WC TcktName_WEP TcktName_x \\\n", + "0 False False False False True \n", + "1 False False False False True \n", + "2 False False False False True \n", + "3 False False False False True \n", + "4 False False False False True \n", + "\n", + " Pclass_1 Pclass_2 Pclass_3 Sex_female Sex_male \n", + "0 False False True False True \n", + "1 False False True True False \n", + "2 False True False False True \n", + "3 False False True False True \n", + "4 False False True True False \n", + "\n", + "[5 rows x 43 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_df[\"Sex\"] = test_df[\"Sex\"].astype(\"category\")\n", + "test_df = pd.get_dummies(test_df, columns=[\"Sex\"])\n", + "test_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Drop Passenger ID and Cabin (Assignment)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "train_df.drop(labels = [\"PassengerId\", \"Cabin\"], axis = 1, inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Survived', 'Name', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_C',\n", + " 'Embarked_Q', 'Embarked_S', 'TcktName_A4', 'TcktName_A5', 'TcktName_AS',\n", + " 'TcktName_C', 'TcktName_CA', 'TcktName_CASOTON', 'TcktName_FC',\n", + " 'TcktName_FCC', 'TcktName_Fa', 'TcktName_LINE', 'TcktName_PC',\n", + " 'TcktName_PP', 'TcktName_PPP', 'TcktName_SC', 'TcktName_SCA4',\n", + " 'TcktName_SCAH', 'TcktName_SCOW', 'TcktName_SCPARIS',\n", + " 'TcktName_SCParis', 'TcktName_SOC', 'TcktName_SOP', 'TcktName_SOPP',\n", + " 'TcktName_SOTONO2', 'TcktName_SOTONOQ', 'TcktName_SP', 'TcktName_STONO',\n", + " 'TcktName_STONO2', 'TcktName_SWPP', 'TcktName_WC', 'TcktName_WEP',\n", + " 'TcktName_x', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female',\n", + " 'Sex_male'],\n", + " dtype='object')" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop the PassengerId and Cabin columns from the test set\n", + "test_df.drop(labels = [\"PassengerId\", \"Cabin\"], axis = 1, inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Name', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_C', 'Embarked_Q',\n", + " 'Embarked_S', 'TcktName_A', 'TcktName_A4', 'TcktName_A5',\n", + " 'TcktName_AQ3', 'TcktName_AQ4', 'TcktName_C', 'TcktName_CA',\n", + " 'TcktName_FC', 'TcktName_FCC', 'TcktName_LP', 'TcktName_PC',\n", + " 'TcktName_PP', 'TcktName_SC', 'TcktName_SCA3', 'TcktName_SCA4',\n", + " 'TcktName_SCAH', 'TcktName_SCPARIS', 'TcktName_SCParis', 'TcktName_SOC',\n", + " 'TcktName_SOPP', 'TcktName_SOTONO2', 'TcktName_SOTONOQ',\n", + " 'TcktName_STONO', 'TcktName_STONO2', 'TcktName_STONOQ', 'TcktName_WC',\n", + " 'TcktName_WEP', 'TcktName_x', 'Pclass_1', 'Pclass_2', 'Pclass_3',\n", + " 'Sex_female', 'Sex_male'],\n", + " dtype='object')" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Print the columns of the test set\n", + "test_df.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "# Modeling" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.svm import SVC\n", + "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train - Test Split (Assignment)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "891" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df_len = len(train_df)\n", + "train_df_len" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train 594\n", + "X_test 297\n", + "y_train 594\n", + "y_test 297\n", + "test 418\n" + ] + } + ], + "source": [ + "\n", + "train = train_df[:train_df_len]\n", + "test = test_df\n", + "\n", + "# Select all numerical values from train and test\n", + "numeric_train = train.select_dtypes(include=[np.number])\n", + "numeric_test = test.select_dtypes(include=[np.number]) \n", + "\n", + "\n", + "X_train = numeric_train.drop(labels=[\"Survived\",], axis=1)\n", + "y_train = numeric_train[\"Survived\"]\n", + "\n", + "# Split the train data into train and test sets with a 1/3 ratio\n", + "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=1/3) # Use the train_test_split function here\n", + "\n", + "\n", + "print(\"X_train\", len(X_train))\n", + "print(\"X_test\", len(X_test))\n", + "print(\"y_train\", len(y_train))\n", + "print(\"y_test\", len(y_test))\n", + "print(\"test\", len(numeric_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple Logistic Regression (Assignment)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on the training set: 70.03\n", + "Accuracy on the test set: 69.36\n" + ] + } + ], + "source": [ + "logreg = LogisticRegression()\n", + "logreg.fit(X_train, y_train)\n", + "acc_log_train = round(logreg.score(X_train, y_train)*100,2) \n", + "acc_log_test = round(logreg.score(X_test,y_test)*100,2)\n", + "# Print the accuracy on the training and test set\n", + "print(\"Accuracy on the training set:\", acc_log_train)\n", + "print(\"Accuracy on the test set:\", acc_log_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "## Hyperparameter Tuning -- Grid Search -- Cross Validation\n", + "We will compare 5 ml classifier and evaluate mean accuracy of each of them by stratified cross validation.\n", + "\n", + "* Decision Tree\n", + "* SVM\n", + "* Random Forest\n", + "* KNN\n", + "* Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "random_state = 42\n", + "classifier = [DecisionTreeClassifier(random_state = random_state),\n", + " SVC(random_state = random_state),\n", + " RandomForestClassifier(random_state = random_state),\n", + " LogisticRegression(random_state = random_state),\n", + " KNeighborsClassifier()]\n", + "\n", + "dt_param_grid = {\"min_samples_split\" : range(10,500,20),\n", + " \"max_depth\": range(1,20,2)}\n", + "\n", + "svc_param_grid = {\"kernel\" : [\"rbf\"],\n", + " \"gamma\": [0.001, 0.01, 0.1, 1],\n", + " \"C\": [1,10,50,100,200,300,1000]}\n", + "\n", + "rf_param_grid = {\"max_features\": [1,3,10],\n", + " \"min_samples_split\":[2,3,10],\n", + " \"min_samples_leaf\":[1,3,10],\n", + " \"bootstrap\":[False],\n", + " \"n_estimators\":[100,300],\n", + " \"criterion\":[\"gini\"]}\n", + "\n", + "logreg_param_grid = {\"C\":np.logspace(-3,3,7),\n", + " \"penalty\": [\"l1\",\"l2\"]}\n", + "\n", + "knn_param_grid = {\"n_neighbors\": np.linspace(1,19,10, dtype = int).tolist(),\n", + " \"weights\": [\"uniform\",\"distance\"],\n", + " \"metric\":[\"euclidean\",\"manhattan\"]}\n", + "classifier_param = [dt_param_grid,\n", + " svc_param_grid,\n", + " rf_param_grid,\n", + " logreg_param_grid,\n", + " knn_param_grid]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 10 folds for each of 250 candidates, totalling 2500 fits\n", + "0.7035875706214688\n", + "Fitting 10 folds for each of 28 candidates, totalling 280 fits\n", + "0.720367231638418\n", + "Fitting 10 folds for each of 54 candidates, totalling 540 fits\n", + "0.7492090395480225\n", + "Fitting 10 folds for each of 14 candidates, totalling 140 fits\n", + "0.7019774011299434\n", + "Fitting 10 folds for each of 40 candidates, totalling 400 fits\n", + "0.7188983050847456\n" + ] + } + ], + "source": [ + "cv_result = []\n", + "best_estimators = []\n", + "for i in range(len(classifier)):\n", + " clf = GridSearchCV(classifier[i], param_grid=classifier_param[i], cv = StratifiedKFold(n_splits = 10), scoring = \"accuracy\", n_jobs = -1,verbose = 1)\n", + " clf.fit(X_train,y_train)\n", + " cv_result.append(clf.best_score_)\n", + " best_estimators.append(clf.best_estimator_)\n", + " print(cv_result[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Cross Validation Scores')" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+fYzq1as/9zzFxMQYixYtMpo3b25UqFDBKFWqlFG1alWjb9++xrlz56zG3rlzxxg8eLDh6elpuLm5GU2aNDF27dplWR8dHW1MnTrV8PX1NUqVKmVUr17dCA4ONh4+fPjCmqKioozJkydbtn3nnXeMIUOGGDdv3rSM+bc/EyKpzcYwEjhLXURERETkP0JzYkVERETEdBRiRURERMR0FGJFRERExHQUYkVERETEdBRiRURERMR0FGJFRERExHT0YQfyyoqOjub27dukS5fuhZ/iIyIiIv8NsbGxPHr0iGzZsmFn9+yoqhArr6zbt28TERGR2mWIiIjISyhUqBA5cuR45nqFWHllpUuXDoC33nqLTJkypXI1KSsmJoY//viD4sWLY2trm9rlpCj1rt7V++tDvb+avT948ICIiAjL3/FnUYiVV1bcFIL06dOTMWPGVK4mZcXExACQMWPGV+5/bi+i3tW7en99qPdXu/cXTQXUREERERERMR2FWBERERExHYVYERERETEdhVgRERERMR0bwzCM1C5CJDlERkZy7NgxRqwL59zNh6ldjoiISLLYG/RxapeQpOL+fru4uDz3jdm6EisiIiIipqMQKyIiIiKmoxArIiIiIqajECsiIiIipqMQKyIiIiKmoxArIiIiIqajECsiIiIipqMQKyIiIiKmoxArIiIiIqajECsiIiIipqMQKyIiIiKmoxArIiIiIqajECsiIiIipqMQKyIiIiKmoxArIiIiIqajECsiIiIipqMQKyIiIiKmoxArIiIiIqajECsiIiIipqMQKyIiIiKmoxArIiIiIqajECsiIiIipqMQKyIiIiKmoxArIiIiIqZjihDr4+ODs7Mzzs7OlChRAnd3d5o2bcr27dv/9b5DQ0Px8fFJsnEvsmvXLksvT/vq27fvvz7Gs2zdupWWLVtSrlw5KleuTJcuXTh16pRlfUhICC1btky248OT5zI0NBSAGzdu8NFHH1GmTBn69OlDy5YtCQkJSdbji4iIyKvBLrULSKj+/ftTu3ZtYmNjuX37NmFhYXTo0IGZM2fy9ttvv/R+a9eujbe3d5KNexF3d3d27Nhheezl5UVISAju7u4ApE+f/l8f42nmzp3L+PHjCQgIYOjQoURFRTFjxgxatGjBokWLcHJySpbj/tPSpUvJmDEjACtXriQiIoKwsDCyZ89OmjRpsLe3T5E6RERExNxME2KzZMlCrly5AMiTJw+9e/fm2rVrjB49mlWrVr30ftOnT5+g4JjQcS+SNm1aSx9xsmXLFm9ZUjp//jxBQUGMHDmS999/37I8KCiIxo0bM3nyZP73v/8l2/H/ztHR0fL9vXv3KFSoEEWKFEmRY4uIiMirwxTTCZ6lSZMm/PHHH5w9e5Y7d+7wxRdf4OHhgZeXF4GBgTx8+NAy9tChQzRr1oyyZcvy7rvvsmbNGiD+NIHg4GC8vLxwdXWlZcuWnDx58qnjwsPDadeuHR4eHlStWpXJkycTGxsLPHlZvmfPngwZMgQPDw88PT2ZMWNGgvtq2bIlgYGB+Pr64u3tzb179/jzzz/p2LEjZcuWxcfHh8mTJxMTE2PZZs+ePXz44Ye4urri7+/PunXrLOtWr16Ng4MD/v7+VsdJkyYNX375Jd27d39qHUuWLKFWrVqULl2aSpUqMWzYMMsxL126RNu2bXF3d8fT05PAwECioqIAOH78OE2bNqVs2bKWcxMnbjpBSEgIISEh/Pbbbzg7O7Nr16540wkWLVqEj48P7u7utGzZkhMnTljtJygoCC8vL+rXr49hGAk+vyIiImJ+prkS+zRxV/BOnTpFWFgYUVFRLFy4kEePHjFixAiGDx/OqFGjuH79Om3btqVevXqMHDmSAwcO0KdPn3hXADds2MD333/PlClTyJ07N+PHj6dfv34sXbrUatyNGzdo3rw5Pj4+LFmyhDNnzjBw4EAyZ85M69atAVi3bh3Nmzdn+fLlbNiwgaCgIPz8/BL8sn1oaCizZs0ibdq0ZMqUiVatWlGiRAmWL1/OtWvXGDx4MDY2NnTp0oVr167RoUMHevToQdWqVTlw4AB9+/YlR44clC9fnuPHj1O6dGnSpIn/b5ZnXQXdvXs3I0aMICgoiJIlS3LkyBG++OILPD09qVmzJoGBgWTMmJGwsDCuX79OQEAAhQsXpkWLFvTu3Zty5coRFBTEmTNnCAgIoEyZMlSrVs2y/7Zt2xIZGcn+/fsJCQkhW7ZsVsffvHkzkydPJjAwECcnJ8LCwvj4449Zv369ZeyqVauYNWsWhmFgY2OToPMqIiIirwZTh9gsWbIA8Mcff7Bx40Z2795tWRYYGEj9+vXp168fa9asIVu2bAwcOJA0adJQuHBhbt++bXWlFuDixYvY29uTN29e8ubNy6BBgzh9+nS8465evZoMGTIQGBiInZ0dRYoU4dq1a0yZMsUSYh0cHOjTpw+2tra0b9+eGTNmcOTIkQSHWG9vbzw8PADYuXMnly5dYsmSJZb6+/TpQ79+/ejSpQsLFizg7bff5qOPPgKgYMGCHDt2jLlz51K+fHnu3r1r9TJ+QmTMmJGRI0dSs2ZNAPLnz8/s2bM5efIkNWvW5OLFi5QqVYq8efNSsGBBpk+fTtasWS3n0dfXl3z58lGgQAFmz55N/vz5rfafKVMmMmbMiL29/VOnUsycOZMOHTpQvXp1ALp37862bdtYuXKl5c1n9erVw9nZOVF9iYiIyKvB1CH23r17ADg7OxMbG8s777xjtT42NpazZ89y5swZSpYsaXUlsk2bNgBWIbVOnTrMnz8fX19f3Nzc8PPzo2HDhvGOGx4eTqlSpbCz+7/T5+7uzrVr17hz5w7wJPTZ2tpa1mfKlIno6OgE95YvXz6r4926dYty5cpZ9fbw4UNu3rzJ6dOn2bJli+XNYQBRUVGWwOzg4GCpK6FKly5N+vTpmTRpEqdOneLEiROcPXsWLy8vANq3b0///v3ZsGED77zzDrVr16ZkyZIAdOjQgeDgYL7//nu8vb15//33Ez3nNzw8nKCgIIKDgy3LHj16REREhOXx38+RiIiIvF5MHWLj5kieO3eOLFmysGzZsnhj8uTJYxU2nydXrlysXbuWn3/+mS1btjBr1iwWL15MWFiY1bh06dLF2zZuPmzcnNGnvcs+MfM2/36M6OhoChcuzNSpU+ONy5IlC9HR0fj7+9OxY0erdXF9lypVitmzZz/1ZfcffviB7du3M3r0aKvl27dvp0uXLtSvX5+qVavSpUsXhg0bZllfr149PD092bhxI1u3biUgIIBPPvmEHj168Omnn/Lee++xceNGNm/eTKtWrQgMDKRRo0YJ7j8mJob+/fvj6elptTxz5sxPPUciIiLyejH1G7uWLVtGqVKlqFq1Knfv3sXGxoaCBQtSsGBBHj58yNixY3n8+DGFChXixIkTViGye/fuzJw502p/W7duZcmSJXh7ezNs2DBWrFhBREQEf/zxh9U4Jycnjh49ankjE8D+/ftxdHTEwcEhyft0cnLi0qVLODo6Wvq7cOECkyZNwsbGBicnJ86ePWtZV7BgQTZt2mS5a0OtWrW4desWq1evttpvTEwMs2fPJjIyMt4xlyxZQoMGDRg+fDiNGjWiSJEinDt3znIOx48fz/Xr12nWrBlff/013bt3Z/369Zb5yGnTpqVNmzZ8++23NG7c2OqNZgnt+fLly1Y9ffXVVxw4cODlTqKIiIi8UkwTYu/evcu1a9e4evUqJ06cYOTIkfzwww/07duXIkWKULVqVXr16sWhQ4c4evQo/fr1IzIykqxZs+Lv78+tW7cYO3YsERERhIaGsmnTJqpUqWJ1jNjYWMaOHcuGDRu4cOECoaGhZMiQgUKFClmN8/f35/HjxwwePJjw8HA2btxISEgIzZo1S5Y3GHl5eZEvXz6++OILTpw4wZ49exg0aBAZMmTA1taW5s2bc+TIEcaPH09ERASrVq0iODiYvHnzAk9edu/atSsDBgxgzpw5REREcOjQIbp168a5c+fo2bNnvGM6ODiwf/9+Tpw4wcmTJ+nbty/Xrl3j8ePHwJNpGMOHD+f48eOcPHmSn376iZIlS5IuXTr27dtHYGAgp0+f5vDhw+zZs8cy1SCh2rRpw9y5cwkLC+PcuXMEBQWxdu1a3Y5LREREABNNJxg1ahSjRo3CxsYGR0dHSpYsyZw5cyhfvjwAY8eOZcSIEbRu3Ro7OzuqVq3KwIEDAciaNStff/01o0aN4ttvv6VAgQL873//w8XFhWPHjlmO4ePjQ0BAAKNHj+batWuWl/D/+c75zJkzM3PmTEaOHEn9+vVxdHSkVatWdOjQIVl6t7W1Zdq0aQQGBtK4cWMyZsxIrVq16NOnD/AkpH711VeMGzeOWbNmkSdPHvr27Uu9evUs++jYsSNvvPEG3377LZMmTSJ9+vSUK1eOhQsX8tZbb8U7ZteuXenXrx9NmjQhc+bMVKtWjWbNmlnO19ChQxk2bBgtW7YkOjoab29vBgwYADy5Sjt8+HAaNmyInZ0dtWrVonPnzonquXbt2vz1119MmjSJv/76i6JFizJt2rR4/6AQERGR15ONoRtsyisqMjKSY8eOMWJdOOduPnzxBiIiIia0N+jj1C4hScX9/XZxcbF8yufTmGY6gYiIiIhIHIVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdu9QuQCS5zev2HlmyZEntMlJUTEwMBw4cwM3NDVtb29QuJ0Wpd/Wu3l8f6v1J768rXYkVEREREdNRiBURERER01GIFRERERHTUYgVEREREdNRiBURERER01GIFRERERHTUYgVEREREdNRiBURERER01GIFRERERHTUYgVEREREdNRiBURERER01GIFRERERHTUYgVEREREdOxMQzDSO0iRJJDZGQkx44dY8S6cM7dfJja5YiIiKS6vUEfp3YJLxT399vFxYWMGTM+c5yuxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxEqyiIqKIiQkBF9fX0qXLo23tzejR4/m3r17TJgwgWrVqmEYRrztLl++TIkSJTh06BAhISE4OzvTr1+/eOMMw8DLywtnZ+eUaEdERET+YxRiJVmMGzeO9evXM2LECH788UdGjx7Nzz//TK9evahbty6XL1/m0KFD8bb78ccfeeutt3B1dQXA3t6en376idjYWKtxBw4c4K+//kqRXkREROS/RyFWksXy5cv57LPP8PT0JH/+/Hh6ejJ06FC2bNlC1qxZcXZ2Zt26dfG2W7t2LXXq1LE8LlmyJA8ePODAgQNW4zZu3Iibm1sydyEiIiL/VQqxkixsbGz49ddfra6guru7s2bNGrJnz07dunXZsGGD1TaXLl3i4MGD1K1b17IsXbp0eHl5sXnzZquxGzduxM/PL3mbEBERkf8shVhJFh9//DHffvstPj4+DBkyhHXr1vHw4UOKFi2Kvb09derU4fz58xw/ftyyzY8//oiLiwtFihSx2pevr69ViD116hQPHz6kdOnSKdaPiIiI/LcoxEqy6NKlC0FBQbzxxhssXryYgIAAqlatyrJlywDIly8f7u7urF+/3rLN2rVr8ff3j7evatWqERERwdmzZ4EnV2F9fX2xsbFJmWZERETkP0chVpJNvXr1WLRoEb/88gvjxo2jWLFiDBgwgCNHjgBQt25dS4i9ePEiR48etZoPGyd79uyUK1fOcjV248aN1KhRI+UaERERkf8chVhJcsePH2fMmDGWx9mzZ8ff359vv/2WN954g19//RWA9957jzNnznDmzBnWrl1LuXLlyJMnz1P3GTel4MqVK5w/f54KFSqkSC8iIiLy36QQK0kuJiaG2bNn8/vvv1stT5s2LenTp8fR0REAR0dHKleuzKZNm9i4ceNTpxLE8fX1Zd++fSxfvhxvb2/s7OyStQcRERH5b1OIlSRXqlQpvL296dy5M6tWreLChQscOHCAIUOG8PjxY2rWrGkZ6+/vz4oVKzh27BjvvvvuM/dZoEABChcuzPTp0zWVQERERBRiJXlMmDCB999/n8mTJ/Pee+/RoUMH7t27x/z588mcObNlnJ+fH2fPnuXtt98mW7Zsz92nj48PMTExVKlSJbnLFxERkf84G+Npn/0p8gqIjIzk2LFjjFgXzrmbD1O7HBERkVS3N+jj1C7hheL+fru4uJAxY8ZnjtOVWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHbvULkAkuc3r9h5ZsmRJ7TJSVExMDAcOHMDNzQ1bW9vULidFqXf1rt5fH+r99ew9jq7EioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6bxUiN22bRvXr18HYOnSpXz66adMmDCBx48fJ2lxIiIiIiJPk+gQO2XKFD777DMuXLjA7t27GTx4MG+++SYbNmxg9OjRyVGjiIiIiIgVG8MwjMRsUK1aNUaOHImXlxcDBgzgwoULzJ07l8OHD9O+fXt27dqVXLWKJEpkZCTHjh1jxLpwzt18mNrliIiIvDL2Bn2cbPuO+/vt4uJCxowZnzku0Vdib9++TeHChTEMg61bt1K9enUAMmfOTExMzMtXLCIiIiKSQHaJ3aBEiRLMmjULBwcHbty4QY0aNbhy5QrBwcG4ubklQ4kiIiIiItYSfSV26NCh7Nmzh7lz59KzZ0/y5cvHzJkzuXjxIkOGDEmOGkVERERErLzUldgVK1ZYLfviiy9ImzZtkhUlIiIiIvI8CQqxYWFhCd5h/fr1X7IUEREREZGESVCInTRpUoJ2ZmNjoxArIiIiIskuQSF28+bNyV2HiIiIiEiCvdQndt29e5cFCxYwcuRIbty4wZYtWzh//nxS1yYiIiIi8lSJDrF//PEHNWvWZNmyZSxcuJD79++zfv166tWrx+7du5OjRhERERERK4kOsSNGjKBZs2aEhoZib28PwOjRo2nevDljx45N8gJFRERERP4p0SH28OHDT33zVtOmTTl16lRS1CQiIiIi8lyJDrGOjo6cOXMm3vJ9+/aRI0eOJClKREREROR5Ev1hB5988gkDBw6kY8eOGIbBr7/+yvLly5k7dy49evRIjhpFRERERKwkOsQ2bdqU3LlzM2vWLNKnT8/YsWNxcnIiMDCQ2rVrJ0eNIiIiIiJWEh1iAXx8fPDx8UnqWkREREREEiRBIXby5MkJ3mHXrl1fuhgRERERkYRIUIjdtWuX5fvY2Fj27t1L7ty5cXFxwd7enuPHj/Pnn3/yzjvvJFuhIiIiIiJxEhRiv/32W8v3gYGBFClShMGDB2Nn92RzwzAYM2YMf/31V/JUKSIiIiLyN4meExsaGkpoaKglwALY2NjQtGlTPvjggyQtTkRERETkaRJ9n9jcuXOzffv2eMvXr19PgQIFkqQoEREREZHnSfSV2F69etGjRw+2bNlCiRIlgCef4nXkyBGmTZuW5AWKiIiIiPxToq/E1qhRg7CwMFxcXDh9+jSnT5/Gzc2NlStX4unpmRw1ioiIiIhYean7xBYtWpRu3bpx9uxZYmNjeeutt8icOXNS1yYiIiIi8lSJDrFRUVEEBQXx3XffERMTA4CtrS3+/v4MGzaMtGnTJnmRIiIiIiJ/l+jpBF9++SVbtmxh2rRp/Pbbb+zatYspU6awZ88exo8fn6h9+fj44OzsbPkqUaIEFStWpFOnTvz555+JLS3BxwwNDU3y/e7atcuql79/LVmyJMmP9yLHjh1j3759Vsv+/PNPBg4cyDvvvIObmxv169cnLCzMsv7ChQs4Oztz4cKFZKsrJCSEli1bWj0uV64c5cuXZ968efokOBEREUmQRF+JXb16NRMnTqRSpUqWZdWqVSNdunT06tWLPn36JGp//fv3p3bt2sCTD1I4deoUQ4YMoU+fPsybNy+x5aW6HTt2xFuWJUuWFK+jS5cudO3aFQ8PDwAiIiJo3rw5Hh4eTJw4kRw5crBz506GDBnCjRs3aNu2bYrU1bZtW0uIvX37NpMnTyYwMJAqVaqQI0cO6tatmyJ1iIiIiLklOsQahkGOHDniLXd0dOT+/fuJLiBLlizkypXL8jhPnjwEBATwxRdfcPfu3VQJgP/G33v5Lxk2bBglSpQgJCQEGxsbAN566y0eP35McHAwDRs2TJE6MmXKZPn+3r17AHh6epIvXz4A0qdPnyJ1iIiIiLklejpB5cqVGTdunCWAANy5c4fg4GCrq7P/Rty82jRp0nDq1CnatWuHu7s7ZcqUoXnz5oSHhwNPXsL38fHhu+++o2rVqri5ufHFF1/w+PFjy74WLVqEt7c3Hh4eTJ061eo4sbGxzJw5E19fX1xdXWnZsiUnTpywrHd2dmbt2rW89957lC1bls8//5zz58/z8ccfU7ZsWZo3b86VK1cS3Nfly5f57LPPqFixIpUqVWLEiBGWWkNDQ2natCldunShXLlyrFy5EsMwmDJlCl5eXpQvX56OHTty6dIly/5++OEH3n33XcqUKUPt2rXZuHEjAC1btuTixYv069ePvn37cvnyZXbu3Enr1q0tATZOw4YNmTFjBhkzZoxX7/POPUBwcDBeXl6Wc3fy5EngybzpgQMHUqlSJdzd3enYsaPlPMVNJ7hw4YJl6oCfnx99+/YlNDTUajrBH3/8QcuWLXF1deXdd99lwYIFlnUhISF07tyZFi1aULFiRXbv3p3g50FERETML9Ehtn///pw5c4aqVavy4Ycf8uGHH1KtWjUuXbrEoEGD/nVB586dY/r06VStWpUMGTLQsWNH8uXLx4oVK1i0aBExMTEEBQVZxl+9epV169Yxc+ZMQkJCWL9+vWWe5/bt2xk5ciTdu3fn+++/5/Dhw1y8eNGy7ZQpU/jmm2/o378/y5cvJ1++fLRv357IyEjLmEmTJjFmzBi+/vpr1q9fT7NmzWjWrBmLFi3i2rVrzJgxI0F9PX78mFatWvHgwQO+/fZbJkyYwNatWxk7dqxlzP79+ylatCiLFy/Gy8uL+fPns2rVKv73v//x/fffkyNHDtq2bUtUVBTXr1+nd+/edOjQgR9//JEGDRrw+eefc+vWLUJCQnjjjTfo378/AwYM4MSJExiGQZkyZeLVlSFDBsqXL2/1CWzwJOA/79xv2LCB77//ngkTJrB69Wpy5sxJv379AFiwYAG//fYb33zzDUuXLuX+/fuMGjXKav9vvvmmZa7wkiVLGDBggNX6hw8f8sknn1gCfZ8+fZg6darVHN5NmzZRt25d5s6di6ura4KeBxEREXk1JHo6QZ48eVi9ejXbtm3j9OnTpEuXDicnJ6pUqUKaNInOxAwZMoTAwEAAoqOjsbe3x9fXl/79+/Pw4UOaNm1K8+bNLVcKP/jgA2bOnGnZPu6qX7FixXB2dqZq1aocPnyYxo0bs2TJEvz9/alfvz4Ao0aNolq1asCTaRHz58/n888/x9fXF4DAwEBq1KjBypUradq0KQCtW7embNmyALi4uODk5MR7770HQM2aNTl+/LhVP+7u7laP/fz8CAoKYvv27Vy5coXFixeTLVs2AAYPHkynTp3o0aMH8OTjezt16mR5SX3mzJkMGTLEcoV7+PDheHl5sX37dt544w2ioqJ44403yJcvH23btsXZ2Zl06dKRIUMGbG1tyZIlC1myZOHOnTtA4ubmvujcX7x4EXt7e/LmzUvevHkZNGgQp0+fBp68QSxdunTky5cPBwcHxowZw61bt6z2b2tri6OjI/BkKso/a1u1ahU5cuSge/fuABQqVIiLFy8yb948y/OZM2dOmjVrluCeRERE5NXxUveJjQuaceHv3wgICKBmzZrcv3+fkJAQLl68SM+ePcmePTsAzZo1IywsjCNHjnD69Gl+//13cubMabWPggULWr7PnDkz0dHRAISHh1vCKED27NktH417/fp1bt26ZQmocX2VLl3a6iXzv3+Ubvr06S1zN+Me/33qAmB1pRCwBMDw8HAKFSpkCbAAHh4eREdHc+7cOQBy5MhhCbD379/n8uXL9OjRw+ofBw8fPiQiIoLq1avj7e1NmzZtcHJywtfXl0aNGpEhQ4Z459jBwQF4Mu0jLji+SMaMGZ977uvUqcP8+fPx9fXFzc0NPz8/y7zaJk2asGbNGry8vKhYsSJ+fn58+OGHCTpunNOnT3P8+HGrfxTExMRga2trefz350JEREReLwkKsS4uLgne4bFjxxJVQI4cOSwhdOLEiTRs2JDOnTvz/fff8/jxYxo2bEj27Nnx8fGhbt26nD59mm+++cZqH/+8N61hGE/9Hp4EVYB06dI9tZ6YmBhiY2Mtj/8emoAXXm3+e6D+u6cdL+4+u3H//fuYuGUTJ07EycnJarts2bJhY2PD119/zaFDh9i0aRMbNmzgu+++47vvvov3fJUqVQobGxuOHDnCO++8Y7UuMjKSLl260KdPH6sPrLh///5zz32uXLlYu3YtP//8M1u2bGHWrFksXryYsLAwihUrxubNm9m6dStbt24lODiY1atXW81pfZHo6Gg8PT0ZPHjwM8c86zkUERGRV1+CQmyOHDm4fv06ZcuWpWbNmpZQlNTSpk3LiBEjaNKkCXPmzKFo0aJcvXqVVatWWeZs7tixI14wfZZixYpx+PBhy+N79+5x9uxZ4MlL6zlz5uTAgQOUKFECeDI14ejRo1SpUiWJOwMnJyciIiK4deuW5crogQMHsLOz46233uKPP/6wGp81a1Zy5MjBtWvX8Pb2Bp7Mq/38889p164dWbNmZenSpfTp0wdXV1e6d+9OnTp12L59e7wQ6+joSJUqVZg7dy5Vq1a1eu6WLVvGnj17ePPNN7l7965l+e7du5977rdu3cqlS5do3rw53t7edO3aFS8vL/744w/OnDlD2rRpqV27Nu+99x4HDhygSZMmXL9+PVHna9OmTeTPn9/yD4kVK1Zw+PBhBg4cmOD9iIiIyKspQSF2x44dHDhwgI0bN7J48WIePXqEr68vfn5+VKxY8aXmwj6Lq6srDRs2ZOrUqcycOZPIyEg2btxI6dKl2blzJwsWLEjwR9x+9NFHtG7dmgoVKlCuXDmmTJnCw4cPLetbt27NpEmTyJ07NwULFmTGjBk8evTIct/apFSlShUKFChA79696dmzJzdv3iQwMJC6deuSNWvWp27TunVrJkyYQI4cOShcuDBTp05l3759jBw5ksePH7Nw4UKyZMmCv78/p06d4uLFi5QsWRJ4Mh3g9OnTltDcr18/mjVrxmeffUb79u3JkiULW7ZsYcKECfTs2ZNs2bJZhVgHB4fnnvvY2FjGjh1Lrly5cHFxYc2aNWTIkIFChQpx6NAhvvrqK7Jnz07+/PlZtWoVb7zxhmWKSELUq1ePyZMnM3jwYNq2bcuFCxcYOXIkbdq0+RfPgoiIiLwqEjwn1s3NDTc3N3r16kV4eDgbN27kf//7HxcuXMDb2xs/Pz+8vLyS5CXeHj16sG7dOhYuXEiXLl0YNmwYjx49wtnZmcGDBzNgwIAE3dqqfPnyjB49mgkTJnDjxg0aNGhgdZWybdu23Lt3j0GDBnHv3j3c3d359ttvEzxvNDFsbW2ZOnUqgYGBNG7cmEyZMuHv78/nn3/+zG3atWvH/fv3GTx4MPfu3aN06dLMmjXLMq82JCSEcePG8dVXX5EjRw4+//xzvLy8gCdziceNG0dERASTJ0+maNGifPfdd4SEhNCpUyfu379P4cKFGTlyJP7+/vGO7e7u/txz7+PjQ0BAAKNHj+batWuWkJ0tWzZatGjB5cuX+eKLL7h9+zalS5dm2rRp8aZmPE/mzJmZMWMGo0aNon79+jg4ONCiRQs6dOiQyDMvIiIiryIbI6GvzT/DlStXCAsLY/r06cTGxrJ///6kqk3kX4mMjOTYsWOMWBfOuZsPX7yBiIiIJMjeoI+Tbd9xf79dXFyeeh/7OC91dwKA8+fPs2nTJjZv3sy+ffss75AXEREREUluiQqxBw4cYPPmzWzatImIiAg8PDzw9fVl5MiRVreiEhERERFJTgkKsQMGDOCnn34iMjISLy8vPv30U6pVq2Z5l72IiIiISEpKUIhdtmwZdnZ2lCpVips3b7Js2TKWLVv21LHz5s1L0gJFRERERP4pQSG2a9euyV2HiIiIiEiCKcSKiIiIiOkk3acUiIiIiIikEIVYERERETEdhVgRERERMZ0kC7EnTpygd+/eSbU7EREREZFnSrIQe/XqVVatWpVUuxMREREReSZNJxARERER01GIFRERERHTUYgVEREREdNJ0IcdtGzZEhsbm+eOuXXrVlLUIyIiIiLyQgkKsZUqVUrQzmrWrPmvihERERERSQh97KyIiIiImE6CQuzkyZMTvEMFXhERERFJbgkOsWnSpMHFxYVMmTJhGMZTx71o3qyIiIiISFJIUIgdMmQIGzdu5MCBA1SoUAFfX198fX1xdHRM7vpEREREROJJUIht1qwZzZo14969e/z0009s2LCBoKAgihcvjp+fHzVq1CBfvnzJXauIiIiICJDAEBsnc+bM1KlThzp16vD48WN27tzJpk2baNq0KTlz5sTPz48uXbokV60iIiIiIgDYGM+a4JoAsbGx7N27l02bNrFkyRJiYmI4cOBAEpYn8vIiIyM5duwYxYsXJ0uWLKldToqK+110c3PD1tY2tctJUepdvav314d6fzV7j/v77eLiQsaMGZ85LlFXYgHu37/P9u3b2bx5M9u2bQPA29ub0aNH4+Xl9fIVi4iIiIgkUIJC7OXLl9m0aRObN2/mt99+I0+ePPj4+DBp0iTKlSv3yv0LQERERET+2xIUYqtXr46dnR0VKlSgT58+FC9e3LJu3759VmMrVKiQtBWKiIiIiPxDgkKsYRhERUXxyy+/8MsvvzxznI2NDceOHUuy4kREREREniZBIfb48ePJXYeIiIiISIKlSe0CREREREQSSyFWRERERExHIVZERERETEchVkRERERMRyFWRERERExHIVZERERETEchVkRERERMRyFWRERERExHIVZERERETMfGMAwjtYsQSQ6RkZEcO3aMEevCOXfzYWqXIyIikuR2j2mBra1tapeRpOL+fru4uJAxY8ZnjtOVWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMZ3XNsQ6Ozuza9euVN33+fPn+emnnwC4cOECzs7OXLhw4YXb+fj44OzsbPkqUaIEFStWpFOnTvz555//uv6UFBoaio+PT2qXISIiIibz2obY5LRjxw7c3d1fOK5///4cOnQIgDfffJMdO3bw5ptvJugY/fv3Z8eOHezYsYOffvqJ8ePHc/LkSfr06fOvak9ptWvXZunSpaldhoiIiJiMXWoX8CrKlStXorextbVN1HZZsmSxGp8nTx4CAgL44osvuHv3LlmyZEl0Dakhffr0pE+fPrXLEBEREZPRldhn2LJlCx988AGurq7Url2b9evXW9bFxsYybtw4KlWqRKVKlZg6dSo1atSwTCH4+3SCnTt38v7771OmTBl8fX1ZtGgRAH379mX37t1MnjyZli1bxptOcP36dbp3746HhwdVqlQhODgYwzCeW3PatGkBSJPmydN6584dvvjiCzw8PPDy8iIwMJCHDx9axh85coTGjRvj6upK06ZNmThxIi1btgQgJCSEzp0706JFCypWrMju3bt5/PgxI0aMsPTdq1cvbt26ZdnfvHnzqF69OmXKlOHDDz9kz549lnXBwcF4eXnh6upKy5YtOXnyJBB/OkF4eDjt2rXDw8ODqlWrMnnyZGJjYy019ezZkyFDhuDh4YGnpyczZsxI6FMqIiIirxCF2KfYuXMn3bp14/3332fFihU0atSIHj16cOTIEQC+/vprwsLC+N///sfs2bPZunUr58+fj7efmJgYunfvTq1atVi7di2fffYZw4YN49SpUwwYMAB3d3fatm1LSEhIvG27dOnCtWvXmD9/PhMmTCA0NJQFCxY8s+Zz584xffp0qlatSqZMmQAYMGAAd+/eZeHChUydOpXDhw8zfPhwAO7evUv79u0pVaoUYWFh1K1bl+nTp1vtc9OmTdStW5e5c+fi6upKcHAwR44cYcaMGcybN4979+7x2WefAfD7778zduxYhgwZwtq1aylfvjzdu3cnNjaWDRs28P333zNhwgRWr15Nzpw56devX7webty4QfPmzcmdOzdLlixhyJAhzJ8/n3nz5lnGrFu3jnTp0rF8+XLatWvHuHHjOHPmzIueUhEREXnFaDrBUyxYsIB3332X1q1bA+Dk5MShQ4f45ptvCA4O5rvvvqN79+54eXkBMGbMGN577714+7l79y63bt0iZ86c5M+fn/z585M7d25y5cpFlixZsLe3J2PGjDg4OHDv3j3LdsePH2f//v1s3LiRAgUKADB06FAiIyMtY4YMGUJgYCAA0dHR2Nvb4+vrS//+/YEnoXbjxo3s3r3bMrUgMDCQ+vXr069fP3744QcyZszIwIEDsbW1pXDhwuzbt49r165ZjpEzZ06aNWsGwIMHD5g/fz7Lli3D2dkZgLFjx1KpUiVOnDjBxYsXsbGxIW/evOTPn5/u3btTvXp1YmNjuXjxIvb29uTNm5e8efMyaNAgTp8+He98rV69mgwZMhAYGIidnR1FihTh2rVrTJkyxfJcODg40KdPH2xtbWnfvj0zZszgyJEjODk5Jf6JFhEREdNSiH2K8PBwmjZtarXM3d2dZcuWcePGDa5evUqZMmUs6woXLky2bNni7cfBwYFmzZoxcOBApk6dSvXq1WnQoMFTx/7dmTNncHBwsARYAD8/P6sxAQEB1KxZk/v37xMSEsLFixfp2bMn2bNnt/QQGxvLO++8Y7VdbGwsZ8+e5cSJE5QqVQpbW1vLOjc3NzZs2GB5nC9fPsv358+fJyoqKt55iY2NJSIignfeeYfixYvj7+9PyZIl8fX1pVGjRtjZ2VGnTh3mz5+Pr68vbm5u+Pn50bBhw3h9h4eHU6pUKezs/u/H0t3dnWvXrnHnzh0A8ufPb1VzpkyZiI6Ofu75FBERkVePQuxTpEuXLt6y2NhYYmNjLQHrn/NTnzVfdejQobRo0YKNGzeyceNGvv/+e6ZOnUq1atWeeXx7e/sX1pgjRw4KFiwIwMSJE2nYsCGdO3fm+++/x97enpiYGLJkycKyZcvibZsnTx5sbW1f2MPfz0NMTAwA3333HRkzZoxXS4YMGViyZAm7d+9my5YthIaGsnDhQkJDQ8mTJw9r167l559/ZsuWLcyaNYvFixcTFhb2zOPFiZsPG3f8p52bF80VFhERkVeP5sQ+hZOTEwcPHrRatn//fpycnMiaNSu5c+fm6NGjlnXnz5+3XCn8u2vXrjFs2DAKFixIp06dWLZsGZUrV2bz5s3PPX7BggW5deuW1T1f582bR+fOnZ86Pm3atIwYMYJjx44xZ84cSw93797FxsaGggULUrBgQR4+fMjYsWN5/PgxxYoV49ixY5aQCFj19E8FChTA1taWW7duWfaXOXNmRo8ezfXr19m/fz9ff/01lStXpl+/fvz44488evSIvXv3snXrVpYsWYK3tzfDhg1jxYoVRERE8Mcff1gdw8nJiaNHjxIVFWV13h0dHXFwcHjuORMREZHXy2sdYg8dOsS2bdusvh48eEDr1q1Zt24dc+fOJSIigjlz5rBhwwbL/NCWLVsyadIkdu7cyfHjxy1vUrKxsbHaf7Zs2diwYQOjRo3i3Llz/Pbbbxw/fpySJUsCkDFjRiIiIrh+/brVdsWKFaNy5coMGDCAEydOsGvXLqZPn06VKlWe2YurqysNGzZk6tSpXLlyhSJFilC1alV69erFoUOHOHr0KP369SMyMpKsWbNSp04d7t27x+jRozlz5gyLFy/mhx9+eOb+M2fOTKNGjRg6dCi7du3i1KlT9O7dm7Nnz5I/f37Sp0/PlClTWLJkCRcuXGDNmjVERkbi7OxMbGwsY8eOZcOGDVy4cIHQ0FAyZMhAoUKFrI7h7+/P48ePGTx4MOHh4WzcuJGQkBCaNWsW79yKiIjI6+21nk4wbty4eMvWr19P2bJlGTt2LCEhIQQFBeHk5MSECRPw9PQEoG3btly9epVu3bpha2vLp59+yp49e+K91J02bVqmTp3KqFGjqFevHpkyZaJhw4Y0atQIgEaNGtG/f3/at28f7w4FQUFBDBs2jCZNmpA5c2aaNGlC8+bNn9tPjx49WLduHUFBQYwbN46xY8cyYsQIWrdujZ2dHVWrVmXgwIHAk7mkX331FcOGDWPhwoWUKVMGf39/rl69+sz99+3bly+//JKAgACioqKoUKEC06dPx9bWFhcXF0aOHMnUqVMZPnw4efPmJSgoiCJFilCkSBECAgIYPXo0165do3DhwkydOjXe3ODMmTMzc+ZMRo4cSf369XF0dKRVq1Z06NDhuX2LiIjI68fG0ITCRNu2bRulS5fG0dEReHJrKE9PTzZt2kT+/PlTubqEOX/+PFeuXKF8+fKWZcOGDePBgweMGTMmFStLOpGRkRw7dowR68I5d/PhizcQERExmd1jWli94flVEPf328XFJd77cP7utZ5O8LK+//57+vfvz6lTpwgPD2fo0KGUKVPGNAEW4N69e7Rp04Yff/yRixcvsn79elasWEGtWrVSuzQRERGRF3qtpxO8rMGDBzNs2DCaNm2KYRh4enoyZcqU1C4rUVxcXBg8eDDBwcH8+eef5M2bl379+uHt7Z3apYmIiIi8kELsS8iTJw9Tp05N7TL+tUaNGlnm54qIiIiYiaYTiIiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjp2KV2ASLJbV6398iSJUtql5GiYmJiOHDgAG5ubtja2qZ2OSlKvat39f76UO8HUruMVKUrsSIiIiJiOgqxIiIiImI6CrEiIiIiYjoKsSIiIiJiOgqxIiIiImI6CrEiIiIiYjoKsSIiIiJiOgqxIiIiImI6CrEiIiIiYjoKsSIiIiJiOgqxIiIiImI6CrEiIiIiYjoKsSIiIiJiOgqxIiIiImI6NoZhGKldhEhyiIyM5NixY4xYF865mw9TuxwREZFUtTfo49QuIUHi/n67uLiQMWPGZ47TlVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExnVcmxDo7O7Nr1y6rZdu2baNUqVKEhIQA4OPjQ7NmzTAMw2rcrl27cHZ2TtBxEjM2NDQUHx+fZ67v27cvffv2TdC+ksrWrVtp2bIl5cqVo3LlynTp0oVTp05Z1oeEhNCyZctkrcHHx4fQ0FAAbty4wUcffUSZMmXo06cPLVu2tDxfIiIiIs9il9oFJJeDBw/y2Wef0bx5c7p162ZZvm/fPpYtW0bDhg1far/u7u7s2LEjqcpMUXPnzmX8+PEEBAQwdOhQoqKimDFjBi1atGDRokU4OTmlSB1Lly4lY8aMAKxcuZKIiAjCwsLInj07adKkwd7ePkXqEBEREfN6Za7E/t3p06f59NNPqVWrFv3797daly9fPsaNG8etW7deat9p06YlV65cSVBlyjp//jxBQUEMGzaMtm3bUqRIEUqUKEFQUBAFChRg8uTJKVaLo6Mj6dOnB+DevXsUKlSIIkWK4OjoiIODA5kyZUqxWkRERMScXrkQe+XKFdq3b0+lSpUYMWIENjY2VuvbtWtH+vTpGTdu3DP3cefOHb744gs8PDzw8vIiMDCQhw8fAvGnE5w/f57WrVtTtmxZ/P39mTVrltUUAsMwCAkJoVKlSpQvX54vv/zS6lj37t2jc+fOlClTBn9/f3799VfLukePHhEUFES1atVwc3OjY8eO/PnnnwBcuHABZ2dnpkyZQoUKFRg+fDh37tyhW7dulC9fngoVKtCrVy/u3bsHwOrVq3FwcMDf39/q+GnSpOHLL7+ke/fuTz0XS5YsoVatWpQuXZpKlSoxbNgwYmJiALh06RJt27bF3d0dT09PAgMDiYqKAuD48eM0bdqUsmXLUrVqVauQHDedICQkhJCQEH777TfLdJB/TidYtGgRPj4+uLu707JlS06cOGG1n6CgILy8vKhfv368aSIiIiLy6nqlQuzdu3dp3749t27dYsyYMdja2sYbkyFDBgYMGMDSpUvZv3//U/czYMAA7t69y8KFC5k6dSqHDx9m+PDh8cZFR0fToUMHsmbNyrJly/j000/jXdG8dOkSZ86cYdGiRQwfPpzZs2ezbds2y/oNGzZQvHhxwsLCqFKlCl27duXu3bsADBkyhA0bNvDll1+yaNEioqOj6dy5M7GxsZbt46ZHfPzxx0yaNIlr166xcOFC5s2bx/Hjx5k6dSrwJFSWLl2aNGniP+VFihShQIEC8Zbv3r2bESNG8Pnnn/Pjjz8ybNgwli5dyqZNmwAIDAwkY8aMhIWFMWXKFNatW8fixYsB6N27Ny4uLqxevZqRI0cyc+ZMfvrpJ6v9t23b1hKCd+zYgbu7u9X6zZs3M3nyZAYNGsTy5cspV64cH3/8Mbdv37aMWbVqFbNmzWLMmDHx/sEiIiIir65XKsQOGTKEtGnTEhsbyzfffPPMcTVq1KBatWoMHTrUclUxzrlz59i4cSNBQUE4Ozvj6upKYGAgy5cvt4TLOL/++it//vkno0aNomjRovj7+/PRRx9ZjbG3t2fEiBE4OTlRu3ZtSpQowfHjxy3rS5cuTffu3SlSpAi9e/fGwcGB1atXc/v2bVasWMHgwYOpXLkyJUqUYNy4cZw5c4aff/7Zsn2rVq146623KFSoEBcvXiRTpkzkz58fFxcXJk6cSIMGDYAnAT9z5syJOp8ZM2Zk5MiR1KxZk/z581OrVi1KlizJyZMnAbh48SJZsmQhb968eHh4MH36dKpVq2ZZ5+DgQL58+XjnnXeYPXs2JUuWtNp/pkyZyJgxI/b29uTKlYu0adNarZ85cyYdOnSgevXqFCpUiO7du5MvXz5WrlxpGVOvXj2cnZ0pUaJEonoTERERc3ulQqyjoyPffPMNAQEBfPXVV1Zh8Z8GDhxIREQE3377rdXy8PBwYmNjeeedd3B3d8fd3Z2mTZsSGxvL2bNnrcaeOHECJycnq3Do5uZmNSZHjhyWNzEBZMmShcePH1seu7q6Wr5PkyYNLi4uhIeHExERQWxsLGXLlrWsd3BwwMnJifDwcMuyfPnyWb7/+OOP2bdvH56ennTq1InDhw9TqFAhy7Z37tx55vl4mtKlS1OiRAkmTZpEQEAA7777LgcPHrRcCW7fvj2rVq3C09OTzz//nEuXLpE/f34AOnTowLRp0/Dy8qJ///48fvw40XOJw8PDCQoKsjwP7u7uHD9+nIiIiKf2LyIiIq+PVyrE9uvXj2zZstGqVSuKFy9Ov379iI6OfurYAgUK0KFDByZNmsTVq1cty2NiYsiSJQthYWFWX+vXr6do0aJW+7C1tY03D/Ofj582peHvY/65PjY2Fnt7e9KlS/fUumNiYqymE/x9nKenJz/99JPlivTgwYPp06cPAKVKleL3339/6rzRH374gX79+sVbvn37dj788EP++usvqlatyqRJk/Dw8LCsr1evHlu2bKFnz57cv3+fgIAAxo8fD8Cnn37Khg0b+OSTTzh//jytWrViyZIlT+3pWWJiYujfv7/V87B27Vo6d+781P5FRETk9fFKhdi4QGhra8vIkSP5448/+Prrr585vn379uTOndsSvACcnJy4e/cuNjY2FCxYkIIFC/Lw4UPGjh1rdQUVoFixYkRERFjePAVw9OjRRNX89zcqRUdH8/vvv1O4cGEKFCiAnZ0dBw4csKy/efMmZ8+efeatsObMmcPRo0f54IMPmDhxIqNHj2b9+vUA1KpVi1u3brF69WqrbWJiYpg9ezaRkZHx9rdkyRIaNGjA8OHDadSoEUWKFOHcuXOWIDx+/HiuX79Os2bN+Prrr+nevTvr16/n0aNHjBgxgrRp09KmTRu+/fZbGjduzLp16xJ1bpycnLh8+bLleShYsCBfffWV1TkRERGR19MrFWL/zsXFhdatWzNt2jSroPh3adOmZciQIVy8eNGyrEiRIlStWpVevXpx6NAhjh49Sr9+/YiMjCRr1qxW23t6evLmm28yaNAgwsPD+fHHH5k3b16i6tyzZw/Tpk0jPDycESNGEBUVRd26dcmUKRONGjUiMDCQXbt2cfz4cb744gveeOMNqlSp8tR9Xb58meHDh3PgwAEiIiJYt26dZR5qvnz56Nq1KwMGDGDOnDlERERw6NAhunXrxrlz5+jZs2e8/Tk4OLB//35OnDjByZMn6du3L9euXbOE+dOnTzN8+HCOHz/OyZMn+emnnyhZsiTp0qVj3759BAYGcvr0aQ4fPsyePXvizYl9kTZt2jB37lzCwsI4d+4cQUFBrF27liJFiiRqPyIiIvLqeWVDLEC3bt148803nzutwNPTk7p161otGzt2LPnz56d169a0adMGJycngoOD422bJk0aQkJCuHLlCu+//z5Tp07lww8/TNTN+uvXr8+ePXt4//33OXToEF9//TUZMmQAoE+fPrz99tsEBATQrFkz0qVLx5w5c+K9ASrOZ599hoeHB506deL9998nMjKSoKAgy/qOHTsyfPhwVq1axYcffkjHjh2xtbVl4cKFvPXWW/H217VrV3LkyEGTJk1o06YN6dKlo1mzZhw7dgyAoUOHkjNnTlq2bEnjxo3JnTs3AwYMAJ5cpX3w4AENGzakXbt2lC9f3moaQELUrl2bHj16MGnSJOrWrcvOnTuZNm2aZZ6viIiIvL5sDN1c86Vdv36d33//napVq1qWxd1K6p9vGJOUFxkZybFjxxixLpxzNx+mdjkiIiKpam/Qx6ldQoLE/f12cXGxenP8P73SV2JTQqdOnfjuu++4ePEiv/zyC3PnzqVWrVqpXZaIiIjIK80utQswsxw5cjBhwgTLm6hy5szJRx99RPPmzVO7NBEREZFXmkLsv+Tn54efn19qlyEiIiLyWtF0AhERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdhVgRERERMR2FWBERERExHYVYERERETEdu9QuQCS5zev2HlmyZEntMlJUTEwMBw4cwM3NDVtb29QuJ0Wpd/Wu3l8f6v317D2OrsSKiIiIiOkoxIqIiIiI6SjEioiIiIjpKMSKiIiIiOkoxIqIiIiI6SjEioiIiIjp6BZb8sqKjY0F4OHDh6/d7UdiYmIAiIyMVO+vEfWu3tX76+NV7v3BgwfA//0dfxYbwzCMlChIJKVdv36diIiI1C5DREREXkKhQoXIkSPHM9crxMorKzo6mtu3b5MuXTrSpNHMGRERETOIjY3l0aNHZMuWDTu7Z08aUIgVEREREdPR5SkRERERMR2FWBERERExHYVYMbVHjx7Rv39/ypcvj5eXF998880zx/7+++80atSIsmXL0qBBA44cOZKClSa9xPQeZ8+ePfj6+qZAdckrMb1v3bqV999/H3d3d/z9/dm0aVMKVpr0EtP7ypUreffdd3F1daVp06YcOnQoBStNei/zM3/hwgXc3d3ZtWtXClSYfBLTe6dOnXB2drb62rJlSwpWm7QS0/uJEydo1qwZrq6u+Pv78+uvv6ZgpUkvob23bNky3nPu7OxMv379UrjiFGaImNjw4cMNf39/48iRI8b69esNd3d3Y+3atfHG3b9/36hSpYoxZswY49SpU0ZgYKDx9ttvG/fv30+FqpNGQnuPc/z4cePtt982qlevnoJVJo+E9n7s2DGjVKlSxty5c42IiAhj/vz5RqlSpYxjx46lQtVJI6G9//bbb0bp0qWNsLAw49y5c8aYMWOMihUrGvfu3UuFqpNGYn/mDcMw2rVrZxQvXtz49ddfU6jK5JGY3mvUqGGsWLHCuHr1quXr0aNHKVxx0klo73fu3DHefvttY+DAgUZERIQxceJEo1y5csZff/2VClUnjYT2fvPmTavne8OGDUapUqWMQ4cOpULVKUchVkzr/v37RpkyZaz+OE2ZMsX46KOP4o1dsmSJ4ePjY8TGxhqGYRixsbFGjRo1jGXLlqVYvUkpMb0bhmEsXLjQcHNzM/z9/U0fYhPTe1BQkNGuXTurZW3btjWCg4OTvc7kkJjef/jhB2Pq1KmWx3fv3jWKFy9uHDx4MEVqTWqJ/Zk3DMNYsWKF0bRpU9OH2MT0/ujRI8PFxcU4ffp0SpaYbBLT+9y5cw0/Pz8jOjrasuzDDz80tm7dmiK1JrWX+Zk3DMOIjo42ateubYwfPz6ZK0x9mk4gpnX8+HGio6Nxd3e3LCtXrhwHDx6Md4PkgwcPUq5cOWxsbACwsbHBw8ODAwcOpGTJSSYxvQNs27aNL7/8ktatW6dglckjMb1/8MEH9OrVK94+7t69m+x1JofE9P7ee+/RqVMn4MkHfsyZM4ccOXJQpEiRFK05qST2Z/7mzZsEBQUxfPjwlCwzWSSm99OnT2NjY0OBAgVSusxkkZjed+/eja+vr9WN/5ctW0a1atVSrN6klNif+TihoaHcvn2bTz75JCXKTFUKsWJa165dI3v27KRNm9ayLGfOnDx69Ihbt27FG5s7d26rZTly5ODy5cspUWqSS0zvAFOnTqVmzZopWGHySUzvRYoUoUSJEpbHJ0+eZOfOnXh6eqZUuUkqsc87wM6dO3F3d2fy5Mn079+fTJkypVC1SSuxvY8ZM4YPPviAYsWKpWCVySMxvZ8+fZrMmTPTu3dvvLy8aNiwIT/99FMKV5x0EtP7+fPncXR0ZNCgQVSpUoXGjRuzd+/eFK446bzM77thGMycOZOPP/7YtL/riaEQK6b14MEDq19uwPL48ePHCRr7z3FmkZjeXzUv2/uNGzfo1q0bHh4epn1z28v0XqxYMUJDQwkICKBv376mffUhMb3/8ssv7N27l86dO6dYfckpMb2fPn2ahw8f4uXlxcyZM6lWrRqdOnXi8OHDKVZvUkpM75GRkUyfPp1cuXIxY8YMKlSoQLt27fjzzz9TrN6k9DK/77t27eLy5cs0btw42ev7L3j2xyCI/MelS5cu3i9y3OP06dMnaOw/x5lFYnp/1bxM73/99Rdt2rTBMAwmTZpk2k9we5nec+bMSc6cOXFxceHgwYMsWrQINze35C41ySW094cPHzJ48GCGDBnyyvwuJOZ579y5My1btiRbtmwAlChRgqNHj7J48WLKlCmTMgUnocT0bmtri4uLCwEBAQCULFmSn3/+mRUrVtCxY8eUKTgJvczv+7p163jnnXdwcHBI7vL+E8z5f3IRIE+ePNy8eZPo6GjLsmvXrpE+fXqyZs0ab+xff/1lteyvv/6KN8XALBLT+6smsb1fuXKFFi1a8PjxY+bNm4ejo2NKlpukEtP7oUOHOHr0qNWyIkWKcPPmzRSpNakltPdDhw5x/vx5AgICcHd3t8wn/OSTTxg8eHCK150UEvO8p0mTxhJg4xQuXJgrV66kSK1JLTG958qVi8KFC1stK1SokGmvxL7M/+e3b99u2leaXoZCrJiWi4sLdnZ2Vi+P7t27lzJlysS70la2bFn279+P8f8/ZdkwDPbt20fZsmVTsuQkk5jeXzWJ6T0yMpL27duTJk0a5s+fT548eVK42qSVmN6XLl1KcHCw1bKjR4/G+yNvFgnt3dXVlfXr1xMWFmb5AhgxYgSfffZZCledNBLzvPft2zfevUGPHz/+yj/vAG5ubpw4ccJq2enTp8mXL19KlJrkEvv/+Rs3bnD+/HnKlSuXglWmstS9OYLIvzNo0CCjTp06xsGDB40NGzYYHh4exrp16wzDMIyrV68aDx48MAzjye2FKleubAQGBhonT540AgMDjSpVqpj6PrEJ7f3vli1bZvpbbBlGwnsPDg42XF1djYMHD1rdQ/HOnTupWf6/ktDejxw5YpQsWdKYM2eOcebMGWPixImGm5ubcfny5dQs/195mZ95wzBMf4stw0h47+vWrTNKlSplLF++3IiIiDBCQkIMV1dX4/z586lZ/r+S0N4vXLhguLm5GZMmTTIiIiKMCRMmvFY/87/++qtRpkwZy60kXwcKsWJqkZGRRu/evQ03NzfDy8vLmD17tmVd8eLFre4De/DgQaN+/fpGmTJljIYNGxpHjx5NhYqTTmJ6j/OqhNiE9v7uu+8axYsXj/fVp0+fVKr830vM875582ajbt26RpkyZYwPP/zQ2Lt3bypUnHRe5mc+bp3ZQ2xiel+8eLFRs2ZNo3Tp0sYHH3xg7N69OxUqTjqJ6X3Pnj3GBx98YJQuXdp4//33X6ve16xZY1SpUiUVqkw9Nobx/19fFRERERExiVd78pyIiIiIvJIUYkVERETEdBRiRURERMR0FGJFRERExHQUYkVERETEdBRiRURERMR0FGJFRERExHQUYkVEXmPOzs44Oztz6dKleOsWLlyIs7MzISEhqVDZEyEhITg7O7Nz585Uq0FE/psUYkVEXnP29vZs3rw53vKNGzdiY2OTChX9n9WrV/PWW28RFhaWqnWIyH+PQqyIyGuufPny8ULsvXv32L9/PyVLlkylquDo0aOcO3eOTp06sX79eu7fv59qtYjIf49CrIjIa87X15fdu3dz7949y7KtW7dSvnx5MmXKZDV20aJF+Pj44O7uTsuWLTlx4oRl3ZUrVwgICKBChQqULl2aDz74gL179wJw4cIFnJ2dWb9+PX5+fpQpU4YOHTpw69atZ9a1evVqSpQowbvvvktUVBTr16+3Wn/9+nW6d++Oh4cHVapUITg4mLhPUj979izt2rXD3d0db29v5s2bB8CuXbtwdna22k/fvn3p27cv8GT6QufOnWnRogUVK1Zk9+7dz+3recdq06YNI0aMsDpWx44dmTBhwjN7FpGEU4gVEXnNFS9enDx58rBt2zbLsg0bNuDn52c1bvPmzUyePJlBgwaxfPlyypUrx8cff8zt27cB6NWrFzExMSxatIiwsDDy5MnD0KFDrfbx1VdfERwczPz58zl8+DCzZ89+ak2GYbB27VqqV69OpkyZ8PT0ZPny5VZjunTpwrVr15g/fz4TJkwgNDSUBQsW8OjRI9q2bUumTJlYvHgxgwcPZvz48WzZsiVB52PTpk3UrVuXuXPn4urq+ty+nnesOnXqsH79ekuwvnv3Ljt27KBOnToJqkNEnk8hVkRE8PX1tUwpePz4MT///DO+vr5WY2bOnEmHDh2oXr06hQoVonv37uTLl4+VK1diGAZ+fn4MGjSIIkWKULRoUVq0aMGpU6es9hEQEICrqytly5bF39+fw4cPP7WevXv38ueff1qCdM2aNdm9ezcXL14E4Pjx4+zfv58xY8ZQsmRJKlSowNChQ8maNSs7duzgxo0bjBo1imLFiuHj48PAgQNJkyZhf/Jy5sxJs2bNcHFxIV26dM/t63nHqlmzJjdu3GDfvn3AkznGTk5OFCtWLIHPiog8j11qFyAiIqnP19eXgIAAoqOj2blzJ8WLFydHjhxWY8LDwwkKCiI4ONiy7NGjR0RERGBjY0OzZs344Ycf2LdvH2fOnOHIkSPExsZa7aNgwYKW7zNnzkxUVNRT61mzZg358uWzzMn19fVl8ODBrFixgs6dO3PmzBkcHBwoUKCAZZu4wDtz5kycnJzInDmzZV2DBg2AJ9MJXiRfvnyW71/U15kzZ555LIB33nmHH3/8kXLlyrF27Vpq1679wuOLSMIoxIqICOXKlQOeXAHduHEjNWrUiDcmJiaG/v374+npabU8c+bMxMbG0rZtW+7cuUPt2rXx8fEhKiqKrl27Wo21t7d/YS0xMTH8+OOP3Lx50+qNZbGxsZYQ+7z92Nk9+0/b0+62EB0dbbVNunTprI75vL6edyyAunXr8uWXX9KtWzd++eUXBg4c+NzxIpJwCrEiIoKdnR3VqlVj8+bNbNmyhU8//TTeGCcnJy5fvmx1NbVfv374+flRoEABfvvtN3bu3ImjoyMACxYsALDMCU2onTt3cuPGDUJCQihUqJBl+c8//8yYMWPYt28fBQsW5NatW/z555+8+eabAMybN49ff/2Vxo0bc/bsWR48eECGDBkA+PLLL4mKirLMR713757l6umFCxesjvN3p06dem5fhQoVeuaxBg4ciI+PDwMGDGDWrFk4Ozvz1ltvJepciMizaU6siIgAT16yX7JkCTly5LB6mT5OmzZtmDt3LmFhYZw7d46goCDWrl1LkSJFyJo1K2nSpGHNmjVcvHiRH3/80fIhCY8fP05UHWvWrKFYsWLUrFmT4sWLW76aN2+Og4MDYWFhFCtWjMqVKzNgwABOnDjBrl27mD59OlWqVMHLy4ucOXMyePBgwsPD2bRpE4sWLcLLy4tixYqRPn16vvrqK86fP8/MmTP5/fffn1nLi/p63rEA0qdPj6+vL7Nnz9YbukSSmEKsiIgA4OXlRXR0dLy7EsSpXbs2PXr0YNKkSdStW5edO3cybdo0ChUqxBtvvMHQoUOZMWMGdevWZfr06QwcOBA7O7vnhsR/evz4MRs2bKBhw4bx1qVLl44PP/yQtWvX8ujRI4KCgsiQIQNNmjShZ8+eNGnShObNm2NnZ8fUqVO5evUqH3zwASNHjqR37954e3uTOXNmAgMDWbNmDXXr1uX48eO0aNHimfW8qK/nHevv5+3x48eaDyuSxGyMxL7OIyIiIgm2ePFiVq5cyfz581O7FJFXiubEioiIJIOzZ89y5MgRpk2bRvfu3VO7HJFXjqYTiIiIJIMLFy4wYMAAPDw88Pf3T+1yRF45mk4gIiIiIqajK7EiIiIiYjoKsSIiIiJiOgqxIiIiImI6CrEiIiIiYjoKsSIiIiJiOgqxIiIiImI6CrEiIiIiYjoKsSIiIiJiOgqxIiIiImI6/w8jO8+98t5h+gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cv_results = pd.DataFrame({\"Cross Validation Means\":cv_result, \"ML Models\":[\"DecisionTreeClassifier\", \"SVM\",\"RandomForestClassifier\",\n", + " \"LogisticRegression\",\n", + " \"KNeighborsClassifier\"]})\n", + "\n", + "g = sns.barplot(x=\"Cross Validation Means\",y= \"ML Models\", data=cv_results)\n", + "g.set_xlabel(\"Mean Accuracy\")\n", + "g.set_title(\"Cross Validation Scores\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ensemble Modeling (Assignment)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy Score: 0.7138047138047138\n" + ] + } + ], + "source": [ + "votingC = VotingClassifier(estimators = [(\"dt\",best_estimators[0]),\n", + " (\"rfc\",best_estimators[2]),\n", + " (\"lr\",best_estimators[3])],\n", + " voting = \"soft\", n_jobs = -1)\n", + "votingC = votingC.fit(X_train, y_train)\n", + "\n", + "# Print the accuracy score of the voting classifier\n", + "y_pred = votingC.predict(X_test)\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(\"Accuracy Score:\", accuracy)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop the null values which are going to cause you an error in the next cell\n", + "X_train.dropna(inplace=True)\n", + "y_train.dropna(inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "## Prediction and Submission" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PassengerId Survived\n", + "0 892 0.0\n", + "1 893 0.0\n", + "2 894 0.0\n", + "3 895 0.0\n", + "4 896 0.0\n", + ".. ... ...\n", + "413 1305 NaN\n", + "414 1306 NaN\n", + "415 1307 NaN\n", + "416 1308 NaN\n", + "417 1309 NaN\n", + "\n", + "[418 rows x 2 columns]\n" + ] + } + ], + "source": [ + "test_survived = pd.Series(votingC.predict(X_test), name=\"Survived\").astype(int)\n", + "results = pd.concat([test_PassengerId, test_survived], axis=1)\n", + "results.to_csv(\"titanic.csv\", index=False)\n", + "print(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Congratulations on finishing the assignment!!\n", + "\n", + "### The submission is the titanic.csv which was just created, and this file which you have modified." + ] + } + ], + "metadata": { + "kaggle": { + "accelerator": "none", + "dataSources": [ + { + "databundleVersionId": 26502, + "sourceId": 3136, + "sourceType": "competition" + } + ], + "dockerImageVersionId": 29852, + "isGpuEnabled": false, + "isInternetEnabled": false, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}