Kaggle/Competitions/Titanic/Titanic_RandomForest.ipynb

{"metadata":{"kernelspec":{"name":"ir","display_name":"R","language":"R"},"language_info":{"name":"R","codemirror_mode":"r","pygments_lexer":"r","mimetype":"text/x-r-source","file_extension":".r","version":"4.0.5"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3136,"databundleVersionId":26502,"sourceType":"competition"}],"dockerImageVersionId":30582,"isInternetEnabled":true,"language":"r","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## 0. Given codes from Kaggle","metadata":{"_uuid":"bc64892f-3e22-45ae-8748-aa870d930ca0","_cell_guid":"26b4ae7a-4a1f-4036-8c10-42fa8cdd8821","trusted":true}},{"cell_type":"code","source":"# This R environment comes with many helpful analytics packages installed\n# It is defined by the kaggle/rstats Docker image: https://github.com/kaggle/docker-rstats\n# For example, here's a helpful package to load\n\nlibrary(tidyverse) # metapackage of all tidyverse packages\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nlist.files(path = \"../input/titanic\")\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"265ce384-8590-4972-8f88-d611cd56ef56","_cell_guid":"9fe06cf1-bc28-4916-81ad-976caa8f41a7","collapsed":false,"_execution_state":"idle","jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:17.831830Z","iopub.execute_input":"2023-12-02T22:20:17.835497Z","iopub.status.idle":"2023-12-02T22:20:18.955688Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n\u001b[32m✔\u001b[39m \u001b[34mdplyr    \u001b[39m 1.1.3     \u001b[32m✔\u001b[39m \u001b[34mreadr    \u001b[39m 2.1.4\n\u001b[32m✔\u001b[39m \u001b[34mforcats  \u001b[39m 1.0.0     \u001b[32m✔\u001b[39m \u001b[34mstringr  \u001b[39m 1.5.0\n\u001b[32m✔\u001b[39m \u001b[34mggplot2  \u001b[39m 3.4.4     \u001b[32m✔\u001b[39m \u001b[34mtibble   \u001b[39m 3.2.1\n\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.3     \u001b[32m✔\u001b[39m \u001b[34mtidyr    \u001b[39m 1.3.0\n\u001b[32m✔\u001b[39m \u001b[34mpurrr    \u001b[39m 1.0.2     \n── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n","output_type":"stream"},{"output_type":"display_data","data":{"text/html":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n</style>\n<ol class=list-inline><li>'gender_submission.csv'</li><li>'test.csv'</li><li>'train.csv'</li></ol>\n","text/markdown":"1. 'gender_submission.csv'\n2. 'test.csv'\n3. 'train.csv'\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 'gender\\_submission.csv'\n\\item 'test.csv'\n\\item 'train.csv'\n\\end{enumerate*}\n","text/plain":"[1] \"gender_submission.csv\" \"test.csv\"              \"train.csv\"            "},"metadata":{}}]},{"cell_type":"markdown","source":"## 1. Read Data","metadata":{"_uuid":"c4a5b296-dc3d-4c90-8609-bc8b6014891e","_cell_guid":"ea9f44d3-a462-4633-8056-d338961aac15","trusted":true}},{"cell_type":"code","source":"# 1. Read Data\n\ndf <- read.csv(\"../input/titanic/train.csv\", stringsAsFactors=TRUE)","metadata":{"_uuid":"a5225eb7-52fe-4103-8d05-138b6da48256","_cell_guid":"351e713d-c491-46ba-ba56-9423b8a611d9","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:18.958560Z","iopub.execute_input":"2023-12-02T22:20:18.990591Z","iopub.status.idle":"2023-12-02T22:20:19.018950Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"## 1.1 Skim Data","metadata":{"_uuid":"a36ee7a0-42fd-4f1a-ade9-5841efc28f97","_cell_guid":"06830355-ab3c-4c40-b930-265a122cac10","trusted":true}},{"cell_type":"code","source":"# 1.1 Skim Data\n\nhead(df)","metadata":{"_uuid":"eb61fb79-a8e5-4c40-beca-c64e9cd09576","_cell_guid":"1693a49e-ea39-4f6e-a17d-ac0b4ded1e74","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.021786Z","iopub.execute_input":"2023-12-02T22:20:19.023235Z","iopub.status.idle":"2023-12-02T22:20:19.061429Z"},"trusted":true},"execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/html":"<table class=\"dataframe\">\n<caption>A data.frame: 6 × 12</caption>\n<thead>\n\t<tr><th></th><th scope=col>PassengerId</th><th scope=col>Survived</th><th scope=col>Pclass</th><th scope=col>Name</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>SibSp</th><th scope=col>Parch</th><th scope=col>Ticket</th><th scope=col>Fare</th><th scope=col>Cabin</th><th scope=col>Embarked</th></tr>\n\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>1</th><td>1</td><td>0</td><td>3</td><td>Braund, Mr. Owen Harris                            </td><td>male  </td><td>22</td><td>1</td><td>0</td><td>A/5 21171       </td><td> 7.2500</td><td>    </td><td>S</td></tr>\n\t<tr><th scope=row>2</th><td>2</td><td>1</td><td>1</td><td>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</td><td>female</td><td>38</td><td>1</td><td>0</td><td>PC 17599        </td><td>71.2833</td><td>C85 </td><td>C</td></tr>\n\t<tr><th scope=row>3</th><td>3</td><td>1</td><td>3</td><td>Heikkinen, Miss. Laina                             </td><td>female</td><td>26</td><td>0</td><td>0</td><td>STON/O2. 3101282</td><td> 7.9250</td><td>    </td><td>S</td></tr>\n\t<tr><th scope=row>4</th><td>4</td><td>1</td><td>1</td><td>Futrelle, Mrs. Jacques Heath (Lily May Peel)       </td><td>female</td><td>35</td><td>1</td><td>0</td><td>113803          </td><td>53.1000</td><td>C123</td><td>S</td></tr>\n\t<tr><th scope=row>5</th><td>5</td><td>0</td><td>3</td><td>Allen, Mr. William Henry                           </td><td>male  </td><td>35</td><td>0</td><td>0</td><td>373450          </td><td> 8.0500</td><td>    </td><td>S</td></tr>\n\t<tr><th scope=row>6</th><td>6</td><td>0</td><td>3</td><td>Moran, Mr. James                                   </td><td>male  </td><td>NA</td><td>0</td><td>0</td><td>330877          </td><td> 8.4583</td><td>    </td><td>Q</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 6 × 12\n\n| <!--/--> | PassengerId &lt;int&gt; | Survived &lt;int&gt; | Pclass &lt;int&gt; | Name &lt;fct&gt; | Sex &lt;fct&gt; | Age &lt;dbl&gt; | SibSp &lt;int&gt; | Parch &lt;int&gt; | Ticket &lt;fct&gt; | Fare &lt;dbl&gt; | Cabin &lt;fct&gt; | Embarked &lt;fct&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 1 | 0 | 3 | Braund, Mr. Owen Harris                             | male   | 22 | 1 | 0 | A/5 21171        |  7.2500 | <!----> | S |\n| 2 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female | 38 | 1 | 0 | PC 17599         | 71.2833 | C85  | C |\n| 3 | 3 | 1 | 3 | Heikkinen, Miss. Laina                              | female | 26 | 0 | 0 | STON/O2. 3101282 |  7.9250 | <!----> | S |\n| 4 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel)        | female | 35 | 1 | 0 | 113803           | 53.1000 | C123 | S |\n| 5 | 5 | 0 | 3 | Allen, Mr. William Henry                            | male   | 35 | 0 | 0 | 373450           |  8.0500 | <!----> | S |\n| 6 | 6 | 0 | 3 | Moran, Mr. James                                    | male   | NA | 0 | 0 | 330877           |  8.4583 | <!----> | Q |\n\n","text/latex":"A data.frame: 6 × 12\n\\begin{tabular}{r|llllllllllll}\n  & PassengerId & Survived & Pclass & Name & Sex & Age & SibSp & Parch & Ticket & Fare & Cabin & Embarked\\\\\n  & <int> & <int> & <int> & <fct> & <fct> & <dbl> & <int> & <int> & <fct> & <dbl> & <fct> & <fct>\\\\\n\\hline\n\t1 & 1 & 0 & 3 & Braund, Mr. Owen Harris                             & male   & 22 & 1 & 0 & A/5 21171        &  7.2500 &      & S\\\\\n\t2 & 2 & 1 & 1 & Cumings, Mrs. John Bradley (Florence Briggs Thayer) & female & 38 & 1 & 0 & PC 17599         & 71.2833 & C85  & C\\\\\n\t3 & 3 & 1 & 3 & Heikkinen, Miss. Laina                              & female & 26 & 0 & 0 & STON/O2. 3101282 &  7.9250 &      & S\\\\\n\t4 & 4 & 1 & 1 & Futrelle, Mrs. Jacques Heath (Lily May Peel)        & female & 35 & 1 & 0 & 113803           & 53.1000 & C123 & S\\\\\n\t5 & 5 & 0 & 3 & Allen, Mr. William Henry                            & male   & 35 & 0 & 0 & 373450           &  8.0500 &      & S\\\\\n\t6 & 6 & 0 & 3 & Moran, Mr. James                                    & male   & NA & 0 & 0 & 330877           &  8.4583 &      & Q\\\\\n\\end{tabular}\n","text/plain":"  PassengerId Survived Pclass\n1 1           0        3     \n2 2           1        1     \n3 3           1        3     \n4 4           1        1     \n5 5           0        3     \n6 6           0        3     \n  Name                                                Sex    Age SibSp Parch\n1 Braund, Mr. Owen Harris                             male   22  1     0    \n2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38  1     0    \n3 Heikkinen, Miss. Laina                              female 26  0     0    \n4 Futrelle, Mrs. Jacques Heath (Lily May Peel)        female 35  1     0    \n5 Allen, Mr. William Henry                            male   35  0     0    \n6 Moran, Mr. James                                    male   NA  0     0    \n  Ticket           Fare    Cabin Embarked\n1 A/5 21171         7.2500       S       \n2 PC 17599         71.2833 C85   C       \n3 STON/O2. 3101282  7.9250       S       \n4 113803           53.1000 C123  S       \n5 373450            8.0500       S       \n6 330877            8.4583       Q       "},"metadata":{}}]},{"cell_type":"code","source":"str(df)","metadata":{"_uuid":"db068a70-7971-4c94-90b0-565c92b278c3","_cell_guid":"0f2a0479-be9c-4199-8e14-3a62908baf27","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.065294Z","iopub.execute_input":"2023-12-02T22:20:19.066848Z","iopub.status.idle":"2023-12-02T22:20:19.106738Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"'data.frame':\t891 obs. of  12 variables:\n $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...\n $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ...\n $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...\n $ Name       : Factor w/ 891 levels \"Abbing, Mr. Anthony\",..: 109 191 358 277 16 559 520 629 417 581 ...\n $ Sex        : Factor w/ 2 levels \"female\",\"male\": 2 1 1 1 2 2 2 2 1 1 ...\n $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ...\n $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...\n $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...\n $ Ticket     : Factor w/ 681 levels \"110152\",\"110413\",..: 524 597 670 50 473 276 86 396 345 133 ...\n $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...\n $ Cabin      : Factor w/ 148 levels \"\",\"A10\",\"A14\",..: 1 83 1 57 1 1 131 1 1 1 ...\n $ Embarked   : Factor w/ 4 levels \"\",\"C\",\"Q\",\"S\": 4 2 4 4 4 3 4 4 4 2 ...\n","output_type":"stream"}]},{"cell_type":"code","source":"summary(df)","metadata":{"_uuid":"50a393d9-b025-4f45-8165-2a0be9284c3c","_cell_guid":"631bf341-4ad6-4884-99c8-00a03c7db1ce","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.110668Z","iopub.execute_input":"2023-12-02T22:20:19.112098Z","iopub.status.idle":"2023-12-02T22:20:19.138032Z"},"trusted":true},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"  PassengerId       Survived          Pclass     \n Min.   :  1.0   Min.   :0.0000   Min.   :1.000  \n 1st Qu.:223.5   1st Qu.:0.0000   1st Qu.:2.000  \n Median :446.0   Median :0.0000   Median :3.000  \n Mean   :446.0   Mean   :0.3838   Mean   :2.309  \n 3rd Qu.:668.5   3rd Qu.:1.0000   3rd Qu.:3.000  \n Max.   :891.0   Max.   :1.0000   Max.   :3.000  \n                                                 \n                                    Name         Sex           Age       \n Abbing, Mr. Anthony                  :  1   female:314   Min.   : 0.42  \n Abbott, Mr. Rossmore Edward          :  1   male  :577   1st Qu.:20.12  \n Abbott, Mrs. Stanton (Rosa Hunt)     :  1                Median :28.00  \n Abelson, Mr. Samuel                  :  1                Mean   :29.70  \n Abelson, Mrs. Samuel (Hannah Wizosky):  1                3rd Qu.:38.00  \n Adahl, Mr. Mauritz Nils Martin       :  1                Max.   :80.00  \n (Other)                              :885                NA's   :177    \n     SibSp           Parch             Ticket         Fare       \n Min.   :0.000   Min.   :0.0000   1601    :  7   Min.   :  0.00  \n 1st Qu.:0.000   1st Qu.:0.0000   347082  :  7   1st Qu.:  7.91  \n Median :0.000   Median :0.0000   CA. 2343:  7   Median : 14.45  \n Mean   :0.523   Mean   :0.3816   3101295 :  6   Mean   : 32.20  \n 3rd Qu.:1.000   3rd Qu.:0.0000   347088  :  6   3rd Qu.: 31.00  \n Max.   :8.000   Max.   :6.0000   CA 2144 :  6   Max.   :512.33  \n                                  (Other) :852                   \n         Cabin     Embarked\n            :687    :  2   \n B96 B98    :  4   C:168   \n C23 C25 C27:  4   Q: 77   \n G6         :  4   S:644   \n C22 C26    :  3           \n D          :  3           \n (Other)    :186           "},"metadata":{}}]},{"cell_type":"code","source":"# To remove : Name, Ticket, Cabin\n# To replace : Age, Embarked","metadata":{"_uuid":"760a676a-a222-461f-bac7-d7d44433fa1c","_cell_guid":"0796ab3c-1235-4ab1-820d-1a9fbd4ba54b","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.142015Z","iopub.execute_input":"2023-12-02T22:20:19.143577Z","iopub.status.idle":"2023-12-02T22:20:19.162038Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"## 1.2 Pre-processing","metadata":{"_uuid":"44062d0f-4525-4eab-b489-b333bad1a162","_cell_guid":"781e1e6f-1200-4cbb-af35-9dbcd869d2cf","trusted":true}},{"cell_type":"code","source":"# df <- df[, -c(\"Name\", \"Ticket\", \"Cabin\")] # Why not work?\ndf <- subset(df, select = -c(Name, Ticket, Cabin))\n\nsummary(df)","metadata":{"_uuid":"b1ce6d37-2b7b-4503-8926-1ffe6b141d56","_cell_guid":"65fdc8c6-96ae-44d9-9ed7-8e1075994c1c","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.167192Z","iopub.execute_input":"2023-12-02T22:20:19.169754Z","iopub.status.idle":"2023-12-02T22:20:19.203873Z"},"trusted":true},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"  PassengerId       Survived          Pclass          Sex           Age       \n Min.   :  1.0   Min.   :0.0000   Min.   :1.000   female:314   Min.   : 0.42  \n 1st Qu.:223.5   1st Qu.:0.0000   1st Qu.:2.000   male  :577   1st Qu.:20.12  \n Median :446.0   Median :0.0000   Median :3.000                Median :28.00  \n Mean   :446.0   Mean   :0.3838   Mean   :2.309                Mean   :29.70  \n 3rd Qu.:668.5   3rd Qu.:1.0000   3rd Qu.:3.000                3rd Qu.:38.00  \n Max.   :891.0   Max.   :1.0000   Max.   :3.000                Max.   :80.00  \n                                                               NA's   :177    \n     SibSp           Parch             Fare        Embarked\n Min.   :0.000   Min.   :0.0000   Min.   :  0.00    :  2   \n 1st Qu.:0.000   1st Qu.:0.0000   1st Qu.:  7.91   C:168   \n Median :0.000   Median :0.0000   Median : 14.45   Q: 77   \n Mean   :0.523   Mean   :0.3816   Mean   : 32.20   S:644   \n 3rd Qu.:1.000   3rd Qu.:0.0000   3rd Qu.: 31.00           \n Max.   :8.000   Max.   :6.0000   Max.   :512.33           \n                                                           "},"metadata":{}}]},{"cell_type":"code","source":"Age.mean <- mean(df$Age, na.rm=TRUE)\ndf$Age <- ifelse(is.na(df$Age), Age.mean, df$Age)\n\ndf$Embarked <- replace(df$Embarked, df$Embarked==\"\", \"S\")\n\nsummary(df)","metadata":{"_uuid":"0edc884f-1a65-4a40-b4f7-9ee3b564964f","_cell_guid":"3c09a59d-10db-47e1-8101-4e293787ce36","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.209762Z","iopub.execute_input":"2023-12-02T22:20:19.212796Z","iopub.status.idle":"2023-12-02T22:20:19.254883Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"  PassengerId       Survived          Pclass          Sex           Age       \n Min.   :  1.0   Min.   :0.0000   Min.   :1.000   female:314   Min.   : 0.42  \n 1st Qu.:223.5   1st Qu.:0.0000   1st Qu.:2.000   male  :577   1st Qu.:22.00  \n Median :446.0   Median :0.0000   Median :3.000                Median :29.70  \n Mean   :446.0   Mean   :0.3838   Mean   :2.309                Mean   :29.70  \n 3rd Qu.:668.5   3rd Qu.:1.0000   3rd Qu.:3.000                3rd Qu.:35.00  \n Max.   :891.0   Max.   :1.0000   Max.   :3.000                Max.   :80.00  \n     SibSp           Parch             Fare        Embarked\n Min.   :0.000   Min.   :0.0000   Min.   :  0.00    :  0   \n 1st Qu.:0.000   1st Qu.:0.0000   1st Qu.:  7.91   C:168   \n Median :0.000   Median :0.0000   Median : 14.45   Q: 77   \n Mean   :0.523   Mean   :0.3816   Mean   : 32.20   S:646   \n 3rd Qu.:1.000   3rd Qu.:0.0000   3rd Qu.: 31.00           \n Max.   :8.000   Max.   :6.0000   Max.   :512.33           "},"metadata":{}}]},{"cell_type":"code","source":"str(df)","metadata":{"_uuid":"26011278-fdad-478b-8866-9e3232d792c7","_cell_guid":"4ebc7348-e2a7-4435-bd65-dc06daaba2a5","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.259216Z","iopub.execute_input":"2023-12-02T22:20:19.261061Z","iopub.status.idle":"2023-12-02T22:20:19.285521Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"'data.frame':\t891 obs. of  9 variables:\n $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...\n $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ...\n $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...\n $ Sex        : Factor w/ 2 levels \"female\",\"male\": 2 1 1 1 2 2 2 2 1 1 ...\n $ Age        : num  22 38 26 35 35 ...\n $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...\n $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...\n $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...\n $ Embarked   : Factor w/ 4 levels \"\",\"C\",\"Q\",\"S\": 4 2 4 4 4 3 4 4 4 2 ...\n","output_type":"stream"}]},{"cell_type":"code","source":"# Label Encoding\ndf$Sex <- as.numeric(factor(df$Sex), level=levels(df$Sex))\ndf$Embarked <- as.numeric(factor(df$Embarked), level=levels(df$Embarked))\n\n# Dependent variable should be a factor\ndf$Survived <- as.factor(df$Survived)\n\nstr(df)","metadata":{"_uuid":"5799336f-3224-4945-8eb3-3763195ef285","_cell_guid":"aaaee604-74b5-4a92-8805-3b267cb08afb","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.289532Z","iopub.execute_input":"2023-12-02T22:20:19.291066Z","iopub.status.idle":"2023-12-02T22:20:19.323267Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"'data.frame':\t891 obs. of  9 variables:\n $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...\n $ Survived   : Factor w/ 2 levels \"0\",\"1\": 1 2 2 2 1 1 1 1 2 2 ...\n $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...\n $ Sex        : num  2 1 1 1 2 2 2 2 1 1 ...\n $ Age        : num  22 38 26 35 35 ...\n $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...\n $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...\n $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...\n $ Embarked   : num  3 1 3 3 3 2 3 3 3 1 ...\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## 1.3 Divide into Trainning and Valid Data","metadata":{"_uuid":"e404fa41-733b-4ab4-b517-5739c8c849a6","_cell_guid":"523c5e39-ad5d-4ba7-b96c-9512dab6c7bd","trusted":true}},{"cell_type":"code","source":"# 1.3 Divide into Trainning and Valid Data\n\nn <- nrow(df)\nset.seed(20231202)\nidx <- sample(n, n * 0.8)\n\ndf_train <- df[idx, -1] # remove PassengerId\ndf_valid <- df[-idx, -1]\n\ndim(df_train)\ndim(df_valid)\n\nhead(df_train)\nstr(df_train)","metadata":{"_uuid":"1026be9e-8b66-4a65-a32c-1594a5cc1485","_cell_guid":"1c36a3c4-3679-4c27-b10f-582bacab7174","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.327322Z","iopub.execute_input":"2023-12-02T22:20:19.329555Z","iopub.status.idle":"2023-12-02T22:20:19.446088Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/html":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n</style>\n<ol class=list-inline><li>712</li><li>8</li></ol>\n","text/markdown":"1. 712\n2. 8\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 712\n\\item 8\n\\end{enumerate*}\n","text/plain":"[1] 712   8"},"metadata":{}},{"output_type":"display_data","data":{"text/html":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n</style>\n<ol class=list-inline><li>179</li><li>8</li></ol>\n","text/markdown":"1. 179\n2. 8\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 179\n\\item 8\n\\end{enumerate*}\n","text/plain":"[1] 179   8"},"metadata":{}},{"output_type":"display_data","data":{"text/html":"<table class=\"dataframe\">\n<caption>A data.frame: 6 × 8</caption>\n<thead>\n\t<tr><th></th><th scope=col>Survived</th><th scope=col>Pclass</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>SibSp</th><th scope=col>Parch</th><th scope=col>Fare</th><th scope=col>Embarked</th></tr>\n\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>63</th><td>0</td><td>1</td><td>2</td><td>45.0</td><td>1</td><td>0</td><td>83.4750</td><td>3</td></tr>\n\t<tr><th scope=row>395</th><td>1</td><td>3</td><td>1</td><td>24.0</td><td>0</td><td>2</td><td>16.7000</td><td>3</td></tr>\n\t<tr><th scope=row>714</th><td>0</td><td>3</td><td>2</td><td>29.0</td><td>0</td><td>0</td><td> 9.4833</td><td>3</td></tr>\n\t<tr><th scope=row>797</th><td>1</td><td>1</td><td>1</td><td>49.0</td><td>0</td><td>0</td><td>25.9292</td><td>3</td></tr>\n\t<tr><th scope=row>321</th><td>0</td><td>3</td><td>2</td><td>22.0</td><td>0</td><td>0</td><td> 7.2500</td><td>3</td></tr>\n\t<tr><th scope=row>154</th><td>0</td><td>3</td><td>2</td><td>40.5</td><td>0</td><td>2</td><td>14.5000</td><td>3</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 6 × 8\n\n| <!--/--> | Survived &lt;fct&gt; | Pclass &lt;int&gt; | Sex &lt;dbl&gt; | Age &lt;dbl&gt; | SibSp &lt;int&gt; | Parch &lt;int&gt; | Fare &lt;dbl&gt; | Embarked &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|\n| 63 | 0 | 1 | 2 | 45.0 | 1 | 0 | 83.4750 | 3 |\n| 395 | 1 | 3 | 1 | 24.0 | 0 | 2 | 16.7000 | 3 |\n| 714 | 0 | 3 | 2 | 29.0 | 0 | 0 |  9.4833 | 3 |\n| 797 | 1 | 1 | 1 | 49.0 | 0 | 0 | 25.9292 | 3 |\n| 321 | 0 | 3 | 2 | 22.0 | 0 | 0 |  7.2500 | 3 |\n| 154 | 0 | 3 | 2 | 40.5 | 0 | 2 | 14.5000 | 3 |\n\n","text/latex":"A data.frame: 6 × 8\n\\begin{tabular}{r|llllllll}\n  & Survived & Pclass & Sex & Age & SibSp & Parch & Fare & Embarked\\\\\n  & <fct> & <int> & <dbl> & <dbl> & <int> & <int> & <dbl> & <dbl>\\\\\n\\hline\n\t63 & 0 & 1 & 2 & 45.0 & 1 & 0 & 83.4750 & 3\\\\\n\t395 & 1 & 3 & 1 & 24.0 & 0 & 2 & 16.7000 & 3\\\\\n\t714 & 0 & 3 & 2 & 29.0 & 0 & 0 &  9.4833 & 3\\\\\n\t797 & 1 & 1 & 1 & 49.0 & 0 & 0 & 25.9292 & 3\\\\\n\t321 & 0 & 3 & 2 & 22.0 & 0 & 0 &  7.2500 & 3\\\\\n\t154 & 0 & 3 & 2 & 40.5 & 0 & 2 & 14.5000 & 3\\\\\n\\end{tabular}\n","text/plain":"    Survived Pclass Sex Age  SibSp Parch Fare    Embarked\n63  0        1      2   45.0 1     0     83.4750 3       \n395 1        3      1   24.0 0     2     16.7000 3       \n714 0        3      2   29.0 0     0      9.4833 3       \n797 1        1      1   49.0 0     0     25.9292 3       \n321 0        3      2   22.0 0     0      7.2500 3       \n154 0        3      2   40.5 0     2     14.5000 3       "},"metadata":{}},{"name":"stdout","text":"'data.frame':\t712 obs. of  8 variables:\n $ Survived: Factor w/ 2 levels \"0\",\"1\": 1 2 1 2 1 1 2 1 1 2 ...\n $ Pclass  : int  1 3 3 1 3 3 1 3 1 3 ...\n $ Sex     : num  2 1 2 1 2 2 1 2 2 1 ...\n $ Age     : num  45 24 29 49 22 40.5 39 25 60 33 ...\n $ SibSp   : int  1 0 0 0 0 0 1 0 0 3 ...\n $ Parch   : int  0 2 0 0 0 2 1 0 0 0 ...\n $ Fare    : num  83.47 16.7 9.48 25.93 7.25 ...\n $ Embarked: num  3 3 3 3 3 3 1 3 3 3 ...\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## 2. Model Fitting : Random Forest","metadata":{"_uuid":"ec4247ff-a8b3-4f30-8439-c0aa6d40d1eb","_cell_guid":"79ccc315-03d1-4eca-aca7-41e4cd21e229","trusted":true}},{"cell_type":"code","source":"library(randomForest)\n\nmd <- randomForest(Survived ~ ., data = df_train)\npred <- predict(md, newdata = df_valid, type = \"class\")","metadata":{"_uuid":"d8f2609c-40f3-4f3c-8ccc-c6c39fdab94b","_cell_guid":"39be31f1-4665-490f-bb5a-dfbe0bf9e751","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.450294Z","iopub.execute_input":"2023-12-02T22:20:19.452472Z","iopub.status.idle":"2023-12-02T22:20:19.887501Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stderr","text":"randomForest 4.6-14\n\nType rfNews() to see new features/changes/bug fixes.\n\n\nAttaching package: ‘randomForest’\n\n\nThe following object is masked from ‘package:dplyr’:\n\n    combine\n\n\nThe following object is masked from ‘package:ggplot2’:\n\n    margin\n\n\n","output_type":"stream"}]},{"cell_type":"code","source":"# if not classification\n\n# help(randomForest)\npred.reg <- predict(md, newdata = df_valid, type = \"prob\")\nhead(pred.reg)\nhead(pred.reg[,1])","metadata":{"_uuid":"44a04f61-4a9a-4ca4-bce0-577a7f26b121","_cell_guid":"60151aff-a63c-4663-847f-e27b63a9f0b1","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.891144Z","iopub.execute_input":"2023-12-02T22:20:19.892805Z","iopub.status.idle":"2023-12-02T22:20:19.944192Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/html":"<table class=\"dataframe\">\n<caption>A matrix: 6 × 2 of type dbl</caption>\n<thead>\n\t<tr><th></th><th scope=col>0</th><th scope=col>1</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>8</th><td>0.638</td><td>0.362</td></tr>\n\t<tr><th scope=row>15</th><td>0.270</td><td>0.730</td></tr>\n\t<tr><th scope=row>19</th><td>0.408</td><td>0.592</td></tr>\n\t<tr><th scope=row>21</th><td>0.918</td><td>0.082</td></tr>\n\t<tr><th scope=row>22</th><td>0.936</td><td>0.064</td></tr>\n\t<tr><th scope=row>25</th><td>0.796</td><td>0.204</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA matrix: 6 × 2 of type dbl\n\n| <!--/--> | 0 | 1 |\n|---|---|---|\n| 8 | 0.638 | 0.362 |\n| 15 | 0.270 | 0.730 |\n| 19 | 0.408 | 0.592 |\n| 21 | 0.918 | 0.082 |\n| 22 | 0.936 | 0.064 |\n| 25 | 0.796 | 0.204 |\n\n","text/latex":"A matrix: 6 × 2 of type dbl\n\\begin{tabular}{r|ll}\n  & 0 & 1\\\\\n\\hline\n\t8 & 0.638 & 0.362\\\\\n\t15 & 0.270 & 0.730\\\\\n\t19 & 0.408 & 0.592\\\\\n\t21 & 0.918 & 0.082\\\\\n\t22 & 0.936 & 0.064\\\\\n\t25 & 0.796 & 0.204\\\\\n\\end{tabular}\n","text/plain":"   0     1    \n8  0.638 0.362\n15 0.270 0.730\n19 0.408 0.592\n21 0.918 0.082\n22 0.936 0.064\n25 0.796 0.204"},"metadata":{}},{"output_type":"display_data","data":{"text/html":"<style>\n.dl-inline {width: auto; margin:0; padding: 0}\n.dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n.dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n.dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n</style><dl class=dl-inline><dt>8</dt><dd>0.638</dd><dt>15</dt><dd>0.27</dd><dt>19</dt><dd>0.408</dd><dt>21</dt><dd>0.918</dd><dt>22</dt><dd>0.936</dd><dt>25</dt><dd>0.796</dd></dl>\n","text/markdown":"8\n:   0.63815\n:   0.2719\n:   0.40821\n:   0.91822\n:   0.93625\n:   0.796\n\n","text/latex":"\\begin{description*}\n\\item[8] 0.638\n\\item[15] 0.27\n\\item[19] 0.408\n\\item[21] 0.918\n\\item[22] 0.936\n\\item[25] 0.796\n\\end{description*}\n","text/plain":"    8    15    19    21    22    25 \n0.638 0.270 0.408 0.918 0.936 0.796 "},"metadata":{}}]},{"cell_type":"markdown","source":"## 3. Evaluation","metadata":{"_uuid":"8f9d310d-435f-4832-ad3d-455999349610","_cell_guid":"5e010c5c-9c2d-4b37-99ce-81e2d74f726b","trusted":true}},{"cell_type":"code","source":"# Confusion Matrix\nsummary(pred)\ncm <- table(pred, df[-idx, ]$Survived)\nprint(cm)\n\n# Accuracy\nacc <- (cm[1,1] + cm[2,2]) / nrow(df_valid)\nprint(acc)","metadata":{"_uuid":"d35fccaf-84bf-4106-b5e3-e65b48669a85","_cell_guid":"d018ec89-e368-40a5-b5cf-2d91a35db961","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.946796Z","iopub.execute_input":"2023-12-02T22:20:19.949098Z","iopub.status.idle":"2023-12-02T22:20:19.984412Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/html":"<style>\n.dl-inline {width: auto; margin:0; padding: 0}\n.dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n.dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n.dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n</style><dl class=dl-inline><dt>0</dt><dd>122</dd><dt>1</dt><dd>57</dd></dl>\n","text/markdown":"0\n:   1221\n:   57\n\n","text/latex":"\\begin{description*}\n\\item[0] 122\n\\item[1] 57\n\\end{description*}\n","text/plain":"  0   1 \n122  57 "},"metadata":{}},{"name":"stdout","text":"    \npred   0   1\n   0 101  21\n   1   8  49\n[1] 0.8379888\n","output_type":"stream"}]},{"cell_type":"code","source":"# More elegant way\n\nlibrary(caret)\nconfusionMatrix(cm)","metadata":{"_uuid":"2595fec8-569e-417a-bf3d-4756ecfb66bb","_cell_guid":"a438d322-441a-4f2f-b113-30efb6ba306b","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:19.988362Z","iopub.execute_input":"2023-12-02T22:20:19.989839Z","iopub.status.idle":"2023-12-02T22:20:21.899482Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stderr","text":"Loading required package: lattice\n\n\nAttaching package: ‘caret’\n\n\nThe following object is masked from ‘package:purrr’:\n\n    lift\n\n\nThe following object is masked from ‘package:httr’:\n\n    progress\n\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Confusion Matrix and Statistics\n\n    \npred   0   1\n   0 101  21\n   1   8  49\n                                          \n               Accuracy : 0.838           \n                 95% CI : (0.7757, 0.8887)\n    No Information Rate : 0.6089          \n    P-Value [Acc > NIR] : 2.273e-11       \n                                          \n                  Kappa : 0.6481          \n                                          \n Mcnemar's Test P-Value : 0.02586         \n                                          \n            Sensitivity : 0.9266          \n            Specificity : 0.7000          \n         Pos Pred Value : 0.8279          \n         Neg Pred Value : 0.8596          \n             Prevalence : 0.6089          \n         Detection Rate : 0.5642          \n   Detection Prevalence : 0.6816          \n      Balanced Accuracy : 0.8133          \n                                          \n       'Positive' Class : 0               \n                                          "},"metadata":{}}]},{"cell_type":"markdown","source":"It seems to be performing better than the previous HGB I ran …… Crazy  \nhttps://www.kaggle.com/code/kangrokkim/hgb-histogram-based-gradient-boosting-in-titanic","metadata":{"_uuid":"5d9230be-28ae-4bc4-8307-48cc77de7ae0","_cell_guid":"39f158f4-03e3-4e6f-bb1b-5ba83aeaf1e4","trusted":true}},{"cell_type":"markdown","source":"## 4. Test","metadata":{"_uuid":"2003d509-d5cd-42ad-9c53-7b6092d4bf47","_cell_guid":"ce68b55b-88c7-4a59-b628-e370c47f49e1","trusted":true}},{"cell_type":"code","source":"test <- read.csv(\"../input/titanic/test.csv\", stringsAsFactors=TRUE)\n\nhead(test)","metadata":{"_uuid":"ef33adaf-f089-485c-8882-922c64f5722b","_cell_guid":"ae73116b-d640-44c2-ad27-1f12f60e6514","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:21.902172Z","iopub.execute_input":"2023-12-02T22:20:21.903585Z","iopub.status.idle":"2023-12-02T22:20:21.939384Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/html":"<table class=\"dataframe\">\n<caption>A data.frame: 6 × 11</caption>\n<thead>\n\t<tr><th></th><th scope=col>PassengerId</th><th scope=col>Pclass</th><th scope=col>Name</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>SibSp</th><th scope=col>Parch</th><th scope=col>Ticket</th><th scope=col>Fare</th><th scope=col>Cabin</th><th scope=col>Embarked</th></tr>\n\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>1</th><td>892</td><td>3</td><td>Kelly, Mr. James                            </td><td>male  </td><td>34.5</td><td>0</td><td>0</td><td>330911 </td><td> 7.8292</td><td></td><td>Q</td></tr>\n\t<tr><th scope=row>2</th><td>893</td><td>3</td><td>Wilkes, Mrs. James (Ellen Needs)            </td><td>female</td><td>47.0</td><td>1</td><td>0</td><td>363272 </td><td> 7.0000</td><td></td><td>S</td></tr>\n\t<tr><th scope=row>3</th><td>894</td><td>2</td><td>Myles, Mr. Thomas Francis                   </td><td>male  </td><td>62.0</td><td>0</td><td>0</td><td>240276 </td><td> 9.6875</td><td></td><td>Q</td></tr>\n\t<tr><th scope=row>4</th><td>895</td><td>3</td><td>Wirz, Mr. Albert                            </td><td>male  </td><td>27.0</td><td>0</td><td>0</td><td>315154 </td><td> 8.6625</td><td></td><td>S</td></tr>\n\t<tr><th scope=row>5</th><td>896</td><td>3</td><td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td><td>female</td><td>22.0</td><td>1</td><td>1</td><td>3101298</td><td>12.2875</td><td></td><td>S</td></tr>\n\t<tr><th scope=row>6</th><td>897</td><td>3</td><td>Svensson, Mr. Johan Cervin                  </td><td>male  </td><td>14.0</td><td>0</td><td>0</td><td>7538   </td><td> 9.2250</td><td></td><td>S</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 6 × 11\n\n| <!--/--> | PassengerId &lt;int&gt; | Pclass &lt;int&gt; | Name &lt;fct&gt; | Sex &lt;fct&gt; | Age &lt;dbl&gt; | SibSp &lt;int&gt; | Parch &lt;int&gt; | Ticket &lt;fct&gt; | Fare &lt;dbl&gt; | Cabin &lt;fct&gt; | Embarked &lt;fct&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 892 | 3 | Kelly, Mr. James                             | male   | 34.5 | 0 | 0 | 330911  |  7.8292 | <!----> | Q |\n| 2 | 893 | 3 | Wilkes, Mrs. James (Ellen Needs)             | female | 47.0 | 1 | 0 | 363272  |  7.0000 | <!----> | S |\n| 3 | 894 | 2 | Myles, Mr. Thomas Francis                    | male   | 62.0 | 0 | 0 | 240276  |  9.6875 | <!----> | Q |\n| 4 | 895 | 3 | Wirz, Mr. Albert                             | male   | 27.0 | 0 | 0 | 315154  |  8.6625 | <!----> | S |\n| 5 | 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22.0 | 1 | 1 | 3101298 | 12.2875 | <!----> | S |\n| 6 | 897 | 3 | Svensson, Mr. Johan Cervin                   | male   | 14.0 | 0 | 0 | 7538    |  9.2250 | <!----> | S |\n\n","text/latex":"A data.frame: 6 × 11\n\\begin{tabular}{r|lllllllllll}\n  & PassengerId & Pclass & Name & Sex & Age & SibSp & Parch & Ticket & Fare & Cabin & Embarked\\\\\n  & <int> & <int> & <fct> & <fct> & <dbl> & <int> & <int> & <fct> & <dbl> & <fct> & <fct>\\\\\n\\hline\n\t1 & 892 & 3 & Kelly, Mr. James                             & male   & 34.5 & 0 & 0 & 330911  &  7.8292 &  & Q\\\\\n\t2 & 893 & 3 & Wilkes, Mrs. James (Ellen Needs)             & female & 47.0 & 1 & 0 & 363272  &  7.0000 &  & S\\\\\n\t3 & 894 & 2 & Myles, Mr. Thomas Francis                    & male   & 62.0 & 0 & 0 & 240276  &  9.6875 &  & Q\\\\\n\t4 & 895 & 3 & Wirz, Mr. Albert                             & male   & 27.0 & 0 & 0 & 315154  &  8.6625 &  & S\\\\\n\t5 & 896 & 3 & Hirvonen, Mrs. Alexander (Helga E Lindqvist) & female & 22.0 & 1 & 1 & 3101298 & 12.2875 &  & S\\\\\n\t6 & 897 & 3 & Svensson, Mr. Johan Cervin                   & male   & 14.0 & 0 & 0 & 7538    &  9.2250 &  & S\\\\\n\\end{tabular}\n","text/plain":"  PassengerId Pclass Name                                         Sex    Age \n1 892         3      Kelly, Mr. James                             male   34.5\n2 893         3      Wilkes, Mrs. James (Ellen Needs)             female 47.0\n3 894         2      Myles, Mr. Thomas Francis                    male   62.0\n4 895         3      Wirz, Mr. Albert                             male   27.0\n5 896         3      Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0\n6 897         3      Svensson, Mr. Johan Cervin                   male   14.0\n  SibSp Parch Ticket  Fare    Cabin Embarked\n1 0     0     330911   7.8292       Q       \n2 1     0     363272   7.0000       S       \n3 0     0     240276   9.6875       Q       \n4 0     0     315154   8.6625       S       \n5 1     1     3101298 12.2875       S       \n6 0     0     7538     9.2250       S       "},"metadata":{}}]},{"cell_type":"code","source":"str(test)","metadata":{"_uuid":"d959d8a0-b23b-4d95-b4a5-1f4e057c93ea","_cell_guid":"d7929ee1-3e58-42b9-bc9a-6c9b81109d18","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:21.942027Z","iopub.execute_input":"2023-12-02T22:20:21.943400Z","iopub.status.idle":"2023-12-02T22:20:21.966716Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"'data.frame':\t418 obs. of  11 variables:\n $ PassengerId: int  892 893 894 895 896 897 898 899 900 901 ...\n $ Pclass     : int  3 3 2 3 3 3 3 2 3 3 ...\n $ Name       : Factor w/ 418 levels \"Abbott, Master. Eugene Joseph\",..: 210 409 273 414 182 370 85 58 5 104 ...\n $ Sex        : Factor w/ 2 levels \"female\",\"male\": 2 1 2 2 1 2 1 2 1 2 ...\n $ Age        : num  34.5 47 62 27 22 14 30 26 18 21 ...\n $ SibSp      : int  0 1 0 0 1 0 0 1 0 2 ...\n $ Parch      : int  0 0 0 0 1 0 0 1 0 0 ...\n $ Ticket     : Factor w/ 363 levels \"110469\",\"110489\",..: 153 222 74 148 139 262 159 85 101 270 ...\n $ Fare       : num  7.83 7 9.69 8.66 12.29 ...\n $ Cabin      : Factor w/ 77 levels \"\",\"A11\",\"A18\",..: 1 1 1 1 1 1 1 1 1 1 ...\n $ Embarked   : Factor w/ 3 levels \"C\",\"Q\",\"S\": 2 3 2 3 3 3 2 3 1 3 ...\n","output_type":"stream"}]},{"cell_type":"code","source":"test <- subset(test, select = -c(Name, Ticket, Cabin))\n\ntest$Age <- ifelse(is.na(test$Age), Age.mean, test$Age)\ntest$Embarked <- replace(test$Embarked, test$Embarked==\"\", \"S\")\n\n# Label Encoding\ntest$Sex <- as.numeric(factor(test$Sex), level=levels(test$Sex))\ntest$Embarked <- as.numeric(factor(test$Embarked), level=levels(test$Embarked))\n\nstr(test)\nsummary(test)","metadata":{"_uuid":"c1d03f55-649a-4628-9104-6806eae8ba7f","_cell_guid":"4892de30-d8ac-47f1-a5b2-cce90e12baff","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:21.969393Z","iopub.execute_input":"2023-12-02T22:20:21.970788Z","iopub.status.idle":"2023-12-02T22:20:22.028146Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"'data.frame':\t418 obs. of  8 variables:\n $ PassengerId: int  892 893 894 895 896 897 898 899 900 901 ...\n $ Pclass     : int  3 3 2 3 3 3 3 2 3 3 ...\n $ Sex        : num  2 1 2 2 1 2 1 2 1 2 ...\n $ Age        : num  34.5 47 62 27 22 14 30 26 18 21 ...\n $ SibSp      : int  0 1 0 0 1 0 0 1 0 2 ...\n $ Parch      : int  0 0 0 0 1 0 0 1 0 0 ...\n $ Fare       : num  7.83 7 9.69 8.66 12.29 ...\n $ Embarked   : num  2 3 2 3 3 3 2 3 1 3 ...\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"  PassengerId         Pclass           Sex             Age       \n Min.   : 892.0   Min.   :1.000   Min.   :1.000   Min.   : 0.17  \n 1st Qu.: 996.2   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:23.00  \n Median :1100.5   Median :3.000   Median :2.000   Median :29.70  \n Mean   :1100.5   Mean   :2.266   Mean   :1.636   Mean   :30.15  \n 3rd Qu.:1204.8   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:35.75  \n Max.   :1309.0   Max.   :3.000   Max.   :2.000   Max.   :76.00  \n                                                                 \n     SibSp            Parch             Fare            Embarked    \n Min.   :0.0000   Min.   :0.0000   Min.   :  0.000   Min.   :1.000  \n 1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:  7.896   1st Qu.:2.000  \n Median :0.0000   Median :0.0000   Median : 14.454   Median :3.000  \n Mean   :0.4474   Mean   :0.3923   Mean   : 35.627   Mean   :2.402  \n 3rd Qu.:1.0000   3rd Qu.:0.0000   3rd Qu.: 31.500   3rd Qu.:3.000  \n Max.   :8.0000   Max.   :9.0000   Max.   :512.329   Max.   :3.000  \n                                   NA's   :1                        "},"metadata":{}}]},{"cell_type":"code","source":"# There's a new NA in Fare\n\nFare.mean <- mean(df$Fare, na.rm=TRUE)\ntest$Fare <- ifelse(is.na(test$Fare), Fare.mean, test$Fare)","metadata":{"_uuid":"9a9a7f79-5b28-478e-a8b5-8e11f35f16fb","_cell_guid":"8433bcaf-c420-412c-a780-8a169bb6c5ee","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:22.030972Z","iopub.execute_input":"2023-12-02T22:20:22.032464Z","iopub.status.idle":"2023-12-02T22:20:22.047369Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"pred2 <- predict(md, newdata = test, type = \"class\")\nsummary(pred2)","metadata":{"_uuid":"f98f904e-4bc6-4627-b112-8fcf8c8273ce","_cell_guid":"432da051-4fe4-46fb-9cc8-82435488cb3d","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:22.050006Z","iopub.execute_input":"2023-12-02T22:20:22.051852Z","iopub.status.idle":"2023-12-02T22:20:22.096010Z"},"trusted":true},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/html":"<style>\n.dl-inline {width: auto; margin:0; padding: 0}\n.dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n.dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n.dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n</style><dl class=dl-inline><dt>0</dt><dd>273</dd><dt>1</dt><dd>145</dd></dl>\n","text/markdown":"0\n:   2731\n:   145\n\n","text/latex":"\\begin{description*}\n\\item[0] 273\n\\item[1] 145\n\\end{description*}\n","text/plain":"  0   1 \n273 145 "},"metadata":{}}]},{"cell_type":"markdown","source":"## 5. Submit","metadata":{"_uuid":"ed01c892-a47c-44f2-a224-1bc605d85488","_cell_guid":"6d408965-3951-4223-846a-4a33db53a6f5","trusted":true}},{"cell_type":"code","source":"submission.sample <- read.csv(\"../input/titanic/gender_submission.csv\", stringsAsFactors=TRUE)\nnrow(submission.sample)\nsubmission.sample$Survived <- pred2\n\nhead(submission.sample)\nsummary(submission.sample)","metadata":{"_uuid":"021e7644-95cb-4c55-8d83-a1355c9a467c","_cell_guid":"65a33c50-ea77-4147-afab-80504346c809","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:22.098716Z","iopub.execute_input":"2023-12-02T22:20:22.100154Z","iopub.status.idle":"2023-12-02T22:20:22.140294Z"},"trusted":true},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/html":"418","text/markdown":"418","text/latex":"418","text/plain":"[1] 418"},"metadata":{}},{"output_type":"display_data","data":{"text/html":"<table class=\"dataframe\">\n<caption>A data.frame: 6 × 2</caption>\n<thead>\n\t<tr><th></th><th scope=col>PassengerId</th><th scope=col>Survived</th></tr>\n\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>1</th><td>892</td><td>0</td></tr>\n\t<tr><th scope=row>2</th><td>893</td><td>0</td></tr>\n\t<tr><th scope=row>3</th><td>894</td><td>0</td></tr>\n\t<tr><th scope=row>4</th><td>895</td><td>0</td></tr>\n\t<tr><th scope=row>5</th><td>896</td><td>0</td></tr>\n\t<tr><th scope=row>6</th><td>897</td><td>0</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 6 × 2\n\n| <!--/--> | PassengerId &lt;int&gt; | Survived &lt;fct&gt; |\n|---|---|---|\n| 1 | 892 | 0 |\n| 2 | 893 | 0 |\n| 3 | 894 | 0 |\n| 4 | 895 | 0 |\n| 5 | 896 | 0 |\n| 6 | 897 | 0 |\n\n","text/latex":"A data.frame: 6 × 2\n\\begin{tabular}{r|ll}\n  & PassengerId & Survived\\\\\n  & <int> & <fct>\\\\\n\\hline\n\t1 & 892 & 0\\\\\n\t2 & 893 & 0\\\\\n\t3 & 894 & 0\\\\\n\t4 & 895 & 0\\\\\n\t5 & 896 & 0\\\\\n\t6 & 897 & 0\\\\\n\\end{tabular}\n","text/plain":"  PassengerId Survived\n1 892         0       \n2 893         0       \n3 894         0       \n4 895         0       \n5 896         0       \n6 897         0       "},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"  PassengerId     Survived\n Min.   : 892.0   0:273   \n 1st Qu.: 996.2   1:145   \n Median :1100.5           \n Mean   :1100.5           \n 3rd Qu.:1204.8           \n Max.   :1309.0           "},"metadata":{}}]},{"cell_type":"code","source":"# Save a submission file\nwrite.csv(submission.sample, file = \"submission.csv\", row.names = FALSE)\n\n# No problem?\nsubmission <- read.csv(\"submission.csv\")\nhead(submission)\nstr(submission)","metadata":{"_uuid":"a114c33f-6eb4-42f1-88af-bc179eeca1a6","_cell_guid":"e1345e4e-fc1f-4a98-b633-c85da50460d0","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-02T22:20:22.142896Z","iopub.execute_input":"2023-12-02T22:20:22.144253Z","iopub.status.idle":"2023-12-02T22:20:22.179270Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/html":"<table class=\"dataframe\">\n<caption>A data.frame: 6 × 2</caption>\n<thead>\n\t<tr><th></th><th scope=col>PassengerId</th><th scope=col>Survived</th></tr>\n\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>1</th><td>892</td><td>0</td></tr>\n\t<tr><th scope=row>2</th><td>893</td><td>0</td></tr>\n\t<tr><th scope=row>3</th><td>894</td><td>0</td></tr>\n\t<tr><th scope=row>4</th><td>895</td><td>0</td></tr>\n\t<tr><th scope=row>5</th><td>896</td><td>0</td></tr>\n\t<tr><th scope=row>6</th><td>897</td><td>0</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 6 × 2\n\n| <!--/--> | PassengerId &lt;int&gt; | Survived &lt;int&gt; |\n|---|---|---|\n| 1 | 892 | 0 |\n| 2 | 893 | 0 |\n| 3 | 894 | 0 |\n| 4 | 895 | 0 |\n| 5 | 896 | 0 |\n| 6 | 897 | 0 |\n\n","text/latex":"A data.frame: 6 × 2\n\\begin{tabular}{r|ll}\n  & PassengerId & Survived\\\\\n  & <int> & <int>\\\\\n\\hline\n\t1 & 892 & 0\\\\\n\t2 & 893 & 0\\\\\n\t3 & 894 & 0\\\\\n\t4 & 895 & 0\\\\\n\t5 & 896 & 0\\\\\n\t6 & 897 & 0\\\\\n\\end{tabular}\n","text/plain":"  PassengerId Survived\n1 892         0       \n2 893         0       \n3 894         0       \n4 895         0       \n5 896         0       \n6 897         0       "},"metadata":{}},{"name":"stdout","text":"'data.frame':\t418 obs. of  2 variables:\n $ PassengerId: int  892 893 894 895 896 897 898 899 900 901 ...\n $ Survived   : int  0 0 0 0 0 0 1 0 1 0 ...\n","output_type":"stream"}]}]}