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Titanic prediction step by step
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "26875d60",
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/titanic/train.csv\n",
"/kaggle/input/titanic/test.csv\n",
"/kaggle/input/titanic/gender_submission.csv\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\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",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\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"
]
},
{
"cell_type": "markdown",
"id": "66b97b9b",
"metadata": {
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"exception": false,
"start_time": "2024-06-04T15:09:50.323740",
"status": "completed"
},
"tags": []
},
"source": [
"## Load data\n",
"\n",
"Train data first"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a99e0aa3",
"metadata": {
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},
"tags": []
},
"outputs": [
{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <td>Braund, Mr. Owen Harris</td>\n",
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" <td>22.0</td>\n",
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" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
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" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
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" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
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" </tbody>\n",
"</table>\n",
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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": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data = pd.read_csv(\"/kaggle/input/titanic/train.csv\")\n",
"train_data.head()"
]
},
{
"cell_type": "markdown",
"id": "52024cb8",
"metadata": {
"papermill": {
"duration": 0.00438,
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"exception": false,
"start_time": "2024-06-04T15:09:50.399281",
"status": "completed"
},
"tags": []
},
"source": [
"Then test data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7cdd35be",
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"tags": []
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>892</td>\n",
" <td>3</td>\n",
" <td>Kelly, Mr. James</td>\n",
" <td>male</td>\n",
" <td>34.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330911</td>\n",
" <td>7.8292</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>893</td>\n",
" <td>3</td>\n",
" <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
" <td>female</td>\n",
" <td>47.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>363272</td>\n",
" <td>7.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>894</td>\n",
" <td>2</td>\n",
" <td>Myles, Mr. Thomas Francis</td>\n",
" <td>male</td>\n",
" <td>62.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>240276</td>\n",
" <td>9.6875</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>895</td>\n",
" <td>3</td>\n",
" <td>Wirz, Mr. Albert</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>315154</td>\n",
" <td>8.6625</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>896</td>\n",
" <td>3</td>\n",
" <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
" <td>female</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3101298</td>\n",
" <td>12.2875</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_data = pd.read_csv(\"/kaggle/input/titanic/test.csv\")\n",
"test_data.head()"
]
},
{
"cell_type": "markdown",
"id": "17ec3b91",
"metadata": {
"papermill": {
"duration": 0.004723,
"end_time": "2024-06-04T15:09:50.451717",
"exception": false,
"start_time": "2024-06-04T15:09:50.446994",
"status": "completed"
},
"tags": []
},
"source": [
"## Submission\n",
"\n",
"First predict"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "57d40047",
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"% of women who survived: 0.7420382165605095\n"
]
}
],
"source": [
"women = train_data.loc[train_data.Sex == 'female'][\"Survived\"]\n",
"rate_women = sum(women)/len(women)\n",
"\n",
"print(\"% of women who survived:\", rate_women)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "be25400a",
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-04T15:09:50.492273Z",
"iopub.status.busy": "2024-06-04T15:09:50.491862Z",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"% of men who survived: 0.18890814558058924\n"
]
}
],
"source": [
"men = train_data.loc[train_data.Sex == 'male'][\"Survived\"]\n",
"rate_men = sum(men)/len(men)\n",
"\n",
"print(\"% of men who survived:\", rate_men)"
]
},
{
"cell_type": "markdown",
"id": "18a9d47d",
"metadata": {
"papermill": {
"duration": 0.004718,
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"status": "completed"
},
"tags": []
},
"source": [
"## First model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "53e4c215",
"metadata": {
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"status": "completed"
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"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your submission was successfully saved!\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"y = train_data[\"Survived\"]\n",
"\n",
"features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\"]\n",
"X = pd.get_dummies(train_data[features])\n",
"X_test = pd.get_dummies(test_data[features])\n",
"\n",
"model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=2)\n",
"model.fit(X, y)\n",
"predictions = model.predict(X_test)\n",
"\n",
"output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})\n",
"output.to_csv('submission.csv', index=False)\n",
"print(\"Your submission was successfully saved!\")"
]
}
],
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