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hafizio/titanic_survival_prediction.ipynb

Created December 9, 2019 13:53
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titanic_survival_prediction.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"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": "3.7.4"
},
"colab": {
"name": "titanic_survival_prediction.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/hafizio/6d1c5f96667325f64b2629f3f4c2460f/titanic_survival_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sOqm1rrz_uEB",
"colab_type": "text"
},
"source": [
"## Import Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UZrrIdWk_FMC",
"colab_type": "code",
"outputId": "4dd956f9-cec4-4b8c-a501-4fc47688a86a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
}
},
"source": [
"!git clone https://github.com/hafizio/titanic.git"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Cloning into 'titanic'...\n",
"remote: Enumerating objects: 11, done.\u001b[K\n",
"remote: Counting objects: 100% (11/11), done.\u001b[K\n",
"remote: Compressing objects: 100% (11/11), done.\u001b[K\n",
"remote: Total 11 (delta 0), reused 11 (delta 0), pack-reused 0\u001b[K\n",
"Unpacking objects: 100% (11/11), done.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EfNu-q97977M",
"colab_type": "text"
},
"source": [
"## Import Libraries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UgYdk39R977O",
"colab_type": "code",
"colab": {}
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"import seaborn as sns"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "o1w5G6ZF977Q",
"colab_type": "text"
},
"source": [
"## List Directory"
]
},
{
"cell_type": "code",
"metadata": {
"id": "wn_i0w8_977R",
"colab_type": "code",
"outputId": "8f548112-6918-422c-84cc-d02971e84076",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"!ls"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"sample_data titanic\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8PvPftmwAS6n",
"colab_type": "text"
},
"source": [
"## Change Working Directory"
]
},
{
"cell_type": "code",
"metadata": {
"id": "JVJNuijIAR3G",
"colab_type": "code",
"outputId": "1931f26d-c733-4ce3-8c44-868189974c20",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"os.chdir(\"./titanic\")\n",
"os.getcwd()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'/content/titanic'"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZbCWIVPH977U",
"colab_type": "text"
},
"source": [
"## Read CSV Files"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Oh8RRDmt977U",
"colab_type": "code",
"outputId": "32aaf7b7-4d71-48e6-f31d-fa5d92e3d305",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"source": [
"train_data = pd.read_csv(\"./data/train.csv\")\n",
"test_data = pd.read_csv(\"./data/test.csv\")\n",
"gender_sub = pd.read_csv(\"./data/gender_submission.csv\")\n",
"\n",
"print(train_data.shape)\n",
"print(test_data.shape)\n",
"print(gender_sub.shape)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"(891, 12)\n",
"(418, 11)\n",
"(418, 2)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E6mmxI7S977X",
"colab_type": "text"
},
"source": [
"## Exploratory Data Analysis (EDA)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "DgDYcbZt977X",
"colab_type": "code",
"outputId": "8699672f-b2f4-4d2e-8ef5-d6e1410643cf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 496
}
},
"source": [
"train_data.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <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",
" <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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <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",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass ... Fare Cabin Embarked\n",
"0 1 0 3 ... 7.2500 NaN S\n",
"1 2 1 1 ... 71.2833 C85 C\n",
"2 3 1 3 ... 7.9250 NaN S\n",
"3 4 1 1 ... 53.1000 C123 S\n",
"4 5 0 3 ... 8.0500 NaN S\n",
"\n",
"[5 rows x 12 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 252
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jfDojLGs977Z",
"colab_type": "code",
"outputId": "1799cdec-8468-435d-d485-dd5941bfefe1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 289
}
},
"source": [
"test_data.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<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",
" </tr>\n",
" </thead>\n",
" <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",
" </tr>\n",
" <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",
" <td>Q</td>\n",
" </tr>\n",
" <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 ... Cabin Embarked\n",
"0 892 3 ... NaN Q\n",
"1 893 3 ... NaN S\n",
"2 894 2 ... NaN Q\n",
"3 895 3 ... NaN S\n",
"4 896 3 ... NaN S\n",
"\n",
"[5 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 253
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "siHNMB_m977b",
"colab_type": "code",
"outputId": "73049805-14b6-4c06-bd6a-a080f4fd0351",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"gender_sub.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>892</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>893</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>894</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>895</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>896</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived\n",
"0 892 0\n",
"1 893 1\n",
"2 894 0\n",
"3 895 0\n",
"4 896 1"
]
},
"metadata": {
"tags": []
},
"execution_count": 254
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jjLM_8D3977d",
"colab_type": "code",
"outputId": "d212f587-e7dc-449c-eb81-78d01920ecb3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
}
},
"source": [
"train_data.describe()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>714.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>446.000000</td>\n",
" <td>0.383838</td>\n",
" <td>2.308642</td>\n",
" <td>29.699118</td>\n",
" <td>0.523008</td>\n",
" <td>0.381594</td>\n",
" <td>32.204208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>257.353842</td>\n",
" <td>0.486592</td>\n",
" <td>0.836071</td>\n",
" <td>14.526497</td>\n",
" <td>1.102743</td>\n",
" <td>0.806057</td>\n",
" <td>49.693429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.420000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>223.500000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>20.125000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.910400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>446.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>28.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>14.454200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>668.500000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>38.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>31.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>891.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>80.000000</td>\n",
" <td>8.000000</td>\n",
" <td>6.000000</td>\n",
" <td>512.329200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass ... SibSp Parch Fare\n",
"count 891.000000 891.000000 891.000000 ... 891.000000 891.000000 891.000000\n",
"mean 446.000000 0.383838 2.308642 ... 0.523008 0.381594 32.204208\n",
"std 257.353842 0.486592 0.836071 ... 1.102743 0.806057 49.693429\n",
"min 1.000000 0.000000 1.000000 ... 0.000000 0.000000 0.000000\n",
"25% 223.500000 0.000000 2.000000 ... 0.000000 0.000000 7.910400\n",
"50% 446.000000 0.000000 3.000000 ... 0.000000 0.000000 14.454200\n",
"75% 668.500000 1.000000 3.000000 ... 1.000000 0.000000 31.000000\n",
"max 891.000000 1.000000 3.000000 ... 8.000000 6.000000 512.329200\n",
"\n",
"[8 rows x 7 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 255
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "umJauclU977f",
"colab_type": "code",
"outputId": "edbd72f9-f8bb-49e9-b352-56252f3b24fe",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
}
},
"source": [
"test_data.describe()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<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>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>418.000000</td>\n",
" <td>418.000000</td>\n",
" <td>332.000000</td>\n",
" <td>418.000000</td>\n",
" <td>418.000000</td>\n",
" <td>417.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1100.500000</td>\n",
" <td>2.265550</td>\n",
" <td>30.272590</td>\n",
" <td>0.447368</td>\n",
" <td>0.392344</td>\n",
" <td>35.627188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>120.810458</td>\n",
" <td>0.841838</td>\n",
" <td>14.181209</td>\n",
" <td>0.896760</td>\n",
" <td>0.981429</td>\n",
" <td>55.907576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>892.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.170000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>996.250000</td>\n",
" <td>1.000000</td>\n",
" <td>21.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.895800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1100.500000</td>\n",
" <td>3.000000</td>\n",
" <td>27.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>14.454200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1204.750000</td>\n",
" <td>3.000000</td>\n",
" <td>39.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>31.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1309.000000</td>\n",
" <td>3.000000</td>\n",
" <td>76.000000</td>\n",
" <td>8.000000</td>\n",
" <td>9.000000</td>\n",
" <td>512.329200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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"
]
},
"metadata": {
"tags": []
},
"execution_count": 256
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "v1V3rs4C977h",
"colab_type": "code",
"outputId": "96cbe883-38fd-4a73-a9b6-b1e9b3e73b41",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
}
},
"source": [
"gender_sub.describe()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>418.000000</td>\n",
" <td>418.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1100.500000</td>\n",
" <td>0.363636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>120.810458</td>\n",
" <td>0.481622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>892.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>996.250000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1100.500000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1204.750000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1309.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived\n",
"count 418.000000 418.000000\n",
"mean 1100.500000 0.363636\n",
"std 120.810458 0.481622\n",
"min 892.000000 0.000000\n",
"25% 996.250000 0.000000\n",
"50% 1100.500000 0.000000\n",
"75% 1204.750000 1.000000\n",
"max 1309.000000 1.000000"
]
},
"metadata": {
"tags": []
},
"execution_count": 257
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lPDLscwR977j",
"colab_type": "code",
"outputId": "9e5db0b7-3aa8-4aca-c9e8-cf70cb8b308f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
}
},
"source": [
"# Total survived and dead counts\n",
"sns.countplot(x= \"Survived\", data = train_data)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb34efcf208>"
]
},
"metadata": {
"tags": []
},
"execution_count": 258
},
{
"output_type": "display_data",
"data": {
"image/png": 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6Jya5PclBSX42yWeTbE7yr0me0cYck+TqJNcnedc8//fQImYoSPB5YGWS/0jy4SS/MMI2\nxwK/VFWvAS4CTgNIcjRwdFVNzgysqvuBa4GZ/b4c+FxV/YjpH6T/3ap6HvAW4MNtzDnAuVX1TOCO\nx/wNpREZClr0quoB4HnAemAKuCjJ6x9ls01V9T+tfTHwqtY+DbhkjvEXAa9u7dPbZxwKvBD4RJJr\ngb9hetYC8CLg4619wR59IekxWDLuAqSFoKoeBK4CrkpyPbAW2MFD/+N0yE6bfG/WttuS3J3kWUz/\n4f/tOT5iE/BnSY5kOoCuAJ4I3FdVx+2qrL38OtJec6agRS/J05OsntV1HHAbcCvTf8ABfuNRdnMR\n8FbgsKq6bueVbTbyVaYPC326qh6squ8A/5Xk1FZHkjy7bfIlpmcUAK/d828l7R1DQYJDgY1Jbkpy\nHdPnC94OvAM4J8kk8OCj7OMSpv+IX7ybMRcBr2vvM14LrEvydeBGHvop1DcCZ7VZi7+Ep3njJamS\npM6ZgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTu/wH4gcjVw7UORgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "BzfymYMy977l",
"colab_type": "code",
"outputId": "15eafbed-df95-45ec-bcc9-ae71d77b0a9b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 323
}
},
"source": [
"# Total survived and dead count based on gender\n",
"new_train = train_data\n",
"new_train[\"Died\"] = 1 - new_train[\"Survived\"]\n",
"new_train.groupby(\"Sex\").agg(\"sum\")[[\"Survived\", \"Died\"]].plot(kind='bar',stacked=True)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb34ef96be0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 259
},
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pONZSmQc977n",
"colab_type": "code",
"outputId": "4b7774ff-be13-4bcf-eaf3-03a4df831f46",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
}
},
"source": [
"# Total survived and dead count based on class\n",
"sns.countplot(x='Survived',hue='Pclass',data=new_train)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb34ef87588>"
]
},
"metadata": {
"tags": []
},
"execution_count": 260
},
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "umP3nAZZ977p",
"colab_type": "text"
},
"source": [
"## Combine Train and Test Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "gDKUIzns977p",
"colab_type": "code",
"outputId": "04a84b82-853d-453c-bcd0-60e7be064cbc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 691
}
},
"source": [
"# Drop the survived and died columns and combine the train and test sets\n",
"x_train = train_data.drop([\"Survived\", \"Died\"], axis = 1)\n",
"y_train = train_data[\"Survived\"]\n",
"x_test = test_data\n",
"x_train"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <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",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</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>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",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>886</th>\n",
" <td>887</td>\n",
" <td>2</td>\n",
" <td>Montvila, Rev. Juozas</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>211536</td>\n",
" <td>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>888</td>\n",
" <td>1</td>\n",
" <td>Graham, Miss. Margaret Edith</td>\n",
" <td>female</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>112053</td>\n",
" <td>30.0000</td>\n",
" <td>B42</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>889</td>\n",
" <td>3</td>\n",
" <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>W./C. 6607</td>\n",
" <td>23.4500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>890</td>\n",
" <td>1</td>\n",
" <td>Behr, Mr. Karl Howell</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111369</td>\n",
" <td>30.0000</td>\n",
" <td>C148</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>891</td>\n",
" <td>3</td>\n",
" <td>Dooley, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>370376</td>\n",
" <td>7.7500</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>891 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Pclass ... Cabin Embarked\n",
"0 1 3 ... NaN S\n",
"1 2 1 ... C85 C\n",
"2 3 3 ... NaN S\n",
"3 4 1 ... C123 S\n",
"4 5 3 ... NaN S\n",
".. ... ... ... ... ...\n",
"886 887 2 ... NaN S\n",
"887 888 1 ... B42 S\n",
"888 889 3 ... NaN S\n",
"889 890 1 ... C148 C\n",
"890 891 3 ... NaN Q\n",
"\n",
"[891 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 261
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "H0H81SK2977r",
"colab_type": "code",
"outputId": "748f3ced-0f17-4b70-eec3-9e26dcce07b1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"combined_data = x_train.append(x_test)\n",
"combined_data.shape"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1309, 11)"
]
},
"metadata": {
"tags": []
},
"execution_count": 262
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rul1FuSQ977t",
"colab_type": "code",
"outputId": "3a4b2a6f-8afd-4a7d-ebd8-b0565f165045",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 221
}
},
"source": [
"# Check for total Nan in each columns\n",
"combined_data.isna().sum()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"PassengerId 0\n",
"Pclass 0\n",
"Name 0\n",
"Sex 0\n",
"Age 263\n",
"SibSp 0\n",
"Parch 0\n",
"Ticket 0\n",
"Fare 1\n",
"Cabin 1014\n",
"Embarked 2\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 263
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vcy65Jb7977u",
"colab_type": "text"
},
"source": [
"## Pre-processing"
]
},
{
"cell_type": "code",
"metadata": {
"id": "phg4yvk1977v",
"colab_type": "code",
"outputId": "eec3ec5e-6649-4c44-a34c-a2873a01d2e5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
}
},
"source": [
"# Boxplot to see age whether has outliers\n",
"combined_data.boxplot(column=\"Age\", return_type=\"axes\")"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb34eef05c0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 264
},
{
"output_type": "display_data",
"data": {
"image/png": 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SpFk0aYBn5n8APx43vAYYrpaHgbUtrkuSNInp9sAbmXm8Wj4BNFpUjySppjqTGl9UZmZE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\nygCXpEIZ4JJUqP8DtYAm0rpzBHQAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "wA_4Cllg977w",
"colab_type": "code",
"outputId": "495a91d2-99ba-4198-9ac7-c6e687df7c6e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# Use median to replace Nan in Age attribute as there are outliers\n",
"# Mean is not suitable to be used to replace Nan if there are outliers\n",
"age_median = combined_data[\"Age\"].median()\n",
"age_median"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"28.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 265
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "waRGXnS0977y",
"colab_type": "code",
"outputId": "f6ad6255-7ad6-4d94-eb9a-ad553e25e4fe",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 827
}
},
"source": [
"age_nan = combined_data[\"Age\"]\n",
"replaced_age = age_nan.fillna(age_median)\n",
"combined_data[\"Age\"] = replaced_age\n",
"combined_data"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <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",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</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>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",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>413</th>\n",
" <td>1305</td>\n",
" <td>3</td>\n",
" <td>Spector, Mr. Woolf</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>A.5. 3236</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>414</th>\n",
" <td>1306</td>\n",
" <td>1</td>\n",
" <td>Oliva y Ocana, Dona. Fermina</td>\n",
" <td>female</td>\n",
" <td>39.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17758</td>\n",
" <td>108.9000</td>\n",
" <td>C105</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>415</th>\n",
" <td>1307</td>\n",
" <td>3</td>\n",
" <td>Saether, Mr. Simon Sivertsen</td>\n",
" <td>male</td>\n",
" <td>38.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>SOTON/O.Q. 3101262</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>416</th>\n",
" <td>1308</td>\n",
" <td>3</td>\n",
" <td>Ware, Mr. Frederick</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>359309</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>417</th>\n",
" <td>1309</td>\n",
" <td>3</td>\n",
" <td>Peter, Master. Michael J</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2668</td>\n",
" <td>22.3583</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1309 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Pclass ... Cabin Embarked\n",
"0 1 3 ... NaN S\n",
"1 2 1 ... C85 C\n",
"2 3 3 ... NaN S\n",
"3 4 1 ... C123 S\n",
"4 5 3 ... NaN S\n",
".. ... ... ... ... ...\n",
"413 1305 3 ... NaN S\n",
"414 1306 1 ... C105 C\n",
"415 1307 3 ... NaN S\n",
"416 1308 3 ... NaN S\n",
"417 1309 3 ... NaN C\n",
"\n",
"[1309 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 266
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1Ojz3Jxn977z",
"colab_type": "code",
"outputId": "c4b4b2ec-4e53-48df-b12b-f56b8c086410",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# Find the unique values of Embarked attribute\n",
"combined_data[\"Embarked\"].unique()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['S', 'C', 'Q', nan], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 267
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vTLkl9yX9772",
"colab_type": "code",
"outputId": "543c9285-1412-48c9-bd02-30ef63afe7c0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
}
},
"source": [
"combined_data[\"Embarked\"].value_counts()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"S 914\n",
"C 270\n",
"Q 123\n",
"Name: Embarked, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 268
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "LliCEc4e9775",
"colab_type": "code",
"outputId": "fa8ed52a-f2d7-4e08-8a88-6b64ff7d6817",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 827
}
},
"source": [
"# Replace the Nan with the most occuring values, which is \"S\"\n",
"embarked_nan = combined_data[\"Embarked\"]\n",
"replaced_embarked = embarked_nan.fillna(\"S\")\n",
"combined_data[\"Embarked\"] = replaced_embarked\n",
"combined_data"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <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",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</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>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",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>413</th>\n",
" <td>1305</td>\n",
" <td>3</td>\n",
" <td>Spector, Mr. Woolf</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>A.5. 3236</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>414</th>\n",
" <td>1306</td>\n",
" <td>1</td>\n",
" <td>Oliva y Ocana, Dona. Fermina</td>\n",
" <td>female</td>\n",
" <td>39.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17758</td>\n",
" <td>108.9000</td>\n",
" <td>C105</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>415</th>\n",
" <td>1307</td>\n",
" <td>3</td>\n",
" <td>Saether, Mr. Simon Sivertsen</td>\n",
" <td>male</td>\n",
" <td>38.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>SOTON/O.Q. 3101262</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>416</th>\n",
" <td>1308</td>\n",
" <td>3</td>\n",
" <td>Ware, Mr. Frederick</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>359309</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>417</th>\n",
" <td>1309</td>\n",
" <td>3</td>\n",
" <td>Peter, Master. Michael J</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2668</td>\n",
" <td>22.3583</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1309 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Pclass ... Cabin Embarked\n",
"0 1 3 ... NaN S\n",
"1 2 1 ... C85 C\n",
"2 3 3 ... NaN S\n",
"3 4 1 ... C123 S\n",
"4 5 3 ... NaN S\n",
".. ... ... ... ... ...\n",
"413 1305 3 ... NaN S\n",
"414 1306 1 ... C105 C\n",
"415 1307 3 ... NaN S\n",
"416 1308 3 ... NaN S\n",
"417 1309 3 ... NaN C\n",
"\n",
"[1309 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 269
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Xra6VF009776",
"colab_type": "code",
"outputId": "e1ed76b0-2026-47f2-d88d-99c8bfa3744d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
" # Find the first unique letter of each string in Cabin attribute\n",
"combined_data[\"Cabin\"].astype(str).str[0].unique()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['n', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 270
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "14GnSSv_9777",
"colab_type": "code",
"colab": {}
},
"source": [
"def process_cabin():\n",
" global combined_data \n",
" # replacing missing cabins with U (for Uknown)\n",
" combined_data.Cabin.fillna('T', inplace=True)\n",
" \n",
" # mapping each Cabin value with the cabin letter\n",
" combined_data['Cabin'] = combined_data['Cabin'].map(lambda c: c[0])\n",
" \n",
" # dummy encoding ...\n",
" cabin_dummies = pd.get_dummies(combined_data['Cabin'], prefix='Cabin') \n",
" combined_data = pd.concat([combined_data, cabin_dummies], axis=1)\n",
"\n",
" combined_data.drop('Cabin', axis=1, inplace=True)\n",
"# status('cabin')\n",
" return combined_data"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "EHf1AlwR9779",
"colab_type": "code",
"outputId": "8d1bd135-08ed-4f48-c33b-cbf90ac20c31",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 864
}
},
"source": [
"combined_data = process_cabin()\n",
"combined_data"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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"</style>\n",
"<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",
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" <th>Cabin_A</th>\n",
" <th>Cabin_B</th>\n",
" <th>Cabin_C</th>\n",
" <th>Cabin_D</th>\n",
" <th>Cabin_E</th>\n",
" <th>Cabin_F</th>\n",
" <th>Cabin_G</th>\n",
" <th>Cabin_T</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <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>C</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</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>S</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</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>S</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>...</th>\n",
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" <td>...</td>\n",
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" <tr>\n",
" <th>413</th>\n",
" <td>1305</td>\n",
" <td>3</td>\n",
" <td>Spector, Mr. Woolf</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>A.5. 3236</td>\n",
" <td>8.0500</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>414</th>\n",
" <td>1306</td>\n",
" <td>1</td>\n",
" <td>Oliva y Ocana, Dona. Fermina</td>\n",
" <td>female</td>\n",
" <td>39.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17758</td>\n",
" <td>108.9000</td>\n",
" <td>C</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
" <th>415</th>\n",
" <td>1307</td>\n",
" <td>3</td>\n",
" <td>Saether, Mr. Simon Sivertsen</td>\n",
" <td>male</td>\n",
" <td>38.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>SOTON/O.Q. 3101262</td>\n",
" <td>7.2500</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>416</th>\n",
" <td>1308</td>\n",
" <td>3</td>\n",
" <td>Ware, Mr. Frederick</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>359309</td>\n",
" <td>8.0500</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>417</th>\n",
" <td>1309</td>\n",
" <td>3</td>\n",
" <td>Peter, Master. Michael J</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2668</td>\n",
" <td>22.3583</td>\n",
" <td>C</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1309 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Pclass ... Cabin_G Cabin_T\n",
"0 1 3 ... 0 1\n",
"1 2 1 ... 0 0\n",
"2 3 3 ... 0 1\n",
"3 4 1 ... 0 0\n",
"4 5 3 ... 0 1\n",
".. ... ... ... ... ...\n",
"413 1305 3 ... 0 1\n",
"414 1306 1 ... 0 0\n",
"415 1307 3 ... 0 1\n",
"416 1308 3 ... 0 1\n",
"417 1309 3 ... 0 1\n",
"\n",
"[1309 rows x 18 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 272
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "NrUyRmKR977_",
"colab_type": "code",
"outputId": "63a1384f-0c50-4c58-a4c1-d2b8b1509ee4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"# Use describe to see the percentiles of Fare attribute, median method is chosen to replace Nan\n",
"print(combined_data[\"Fare\"].describe())\n",
"print(\"\")\n",
"fare_median = combined_data[\"Fare\"].median()\n",
"print(\"The median of Fare is : \", fare_median)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"count 1308.000000\n",
"mean 33.295479\n",
"std 51.758668\n",
"min 0.000000\n",
"25% 7.895800\n",
"50% 14.454200\n",
"75% 31.275000\n",
"max 512.329200\n",
"Name: Fare, dtype: float64\n",
"\n",
"The median of Fare is : 14.4542\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "btBvypve978A",
"colab_type": "code",
"colab": {}
},
"source": [
"fare_nan = combined_data[\"Fare\"]\n",
"replaced_fare = fare_nan.fillna(fare_median)\n",
"combined_data[\"Fare\"] = replaced_fare"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bm6yHD2M978C",
"colab_type": "code",
"outputId": "db39a265-6d5c-41c3-dcc0-92fe5aa9be9d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 340
}
},
"source": [
"# Display to see whether there is still Nan in data\n",
"combined_data.isna().sum()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"PassengerId 0\n",
"Pclass 0\n",
"Name 0\n",
"Sex 0\n",
"Age 0\n",
"SibSp 0\n",
"Parch 0\n",
"Ticket 0\n",
"Fare 0\n",
"Embarked 0\n",
"Cabin_A 0\n",
"Cabin_B 0\n",
"Cabin_C 0\n",
"Cabin_D 0\n",
"Cabin_E 0\n",
"Cabin_F 0\n",
"Cabin_G 0\n",
"Cabin_T 0\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 275
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QVLXGWt-f1Cu",
"colab_type": "text"
},
"source": [
"**Reconstruct Features**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7iUf9Y98P6nK",
"colab_type": "code",
"outputId": "ca0d2578-2b91-4738-84b6-d3f83bf4b075",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 496
}
},
"source": [
"# Map string values to binary class for sex\n",
"sex_dict = {'male' : 0, 'female' : 1} \n",
"combined_data['Sex']= combined_data['Sex'].map(sex_dict)\n",
"\n",
"combined_data.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<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>Embarked</th>\n",
" <th>Cabin_A</th>\n",
" <th>Cabin_B</th>\n",
" <th>Cabin_C</th>\n",
" <th>Cabin_D</th>\n",
" <th>Cabin_E</th>\n",
" <th>Cabin_F</th>\n",
" <th>Cabin_G</th>\n",
" <th>Cabin_T</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>0</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>1</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>C</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>1</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>1</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>S</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>0</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>S</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Pclass ... Cabin_G Cabin_T\n",
"0 1 3 ... 0 1\n",
"1 2 1 ... 0 0\n",
"2 3 3 ... 0 1\n",
"3 4 1 ... 0 0\n",
"4 5 3 ... 0 1\n",
"\n",
"[5 rows x 18 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 276
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "FXsWejp9CHzH",
"colab_type": "code",
"colab": {}
},
"source": [
"# Categorise Names into Designation\n",
"designation_dict = {\n",
" \"Mr\": 1,\n",
" \"Miss\": 2,\n",
" \"Mrs\": 3,\n",
" \"Master\": 4,\n",
" \"Rare\": 5\n",
"}\n",
"\n",
"combined_data['Designation'] = combined_data.Name.str.extract('([A-Za-z]+)\\.', expand = False)\n",
"\n",
"# Replace titles with a more common title or as Rare\n",
"combined_data['Designation'] = combined_data['Designation'].replace(\n",
" ['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'],\n",
" 'Rare')\n",
"combined_data['Designation'] = combined_data['Designation'].replace('Mlle', 'Miss')\n",
"combined_data['Designation'] = combined_data['Designation'].replace('Ms', 'Miss')\n",
"combined_data['Designation'] = combined_data['Designation'].replace('Mme', 'Mrs')\n",
"\n",
"# Convert titles into categorical feature\n",
"combined_data['Designation'] = combined_data['Designation'].map(designation_dict)\n",
"\n",
"# Replace NaN with 0, to be safe\n",
"combined_data['Designation'] = combined_data['Designation'].fillna(0)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "aYICVsIwGoIT",
"colab_type": "code",
"outputId": "f10d4383-870f-48cb-e0a6-16acbd02ab07",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 153
}
},
"source": [
"# Make Age into a categorical feature\n",
"combined_data['Age'] = combined_data['Age'].astype(int)\n",
"combined_data.loc[combined_data['Age'] <= 11, 'Age'] = 0\n",
"combined_data.loc[(combined_data['Age'] > 11) & (combined_data['Age'] <= 18), 'Age'] = 1\n",
"combined_data.loc[(combined_data['Age'] > 18) & (combined_data['Age'] <= 22), 'Age'] = 2\n",
"combined_data.loc[(combined_data['Age'] > 22) & (combined_data['Age'] <= 27), 'Age'] = 3\n",
"combined_data.loc[(combined_data['Age'] > 27) & (combined_data['Age'] <= 33), 'Age'] = 4\n",
"combined_data.loc[(combined_data['Age'] > 33) & (combined_data['Age'] <= 40), 'Age'] = 5\n",
"combined_data.loc[(combined_data['Age'] > 40) & (combined_data['Age'] <= 66), 'Age'] = 6\n",
"combined_data.loc[ combined_data['Age'] > 66, 'Age'] = 6\n",
"\n",
"combined_data['Age'].value_counts()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"4 442\n",
"6 224\n",
"3 170\n",
"5 139\n",
"2 138\n",
"1 105\n",
"0 91\n",
"Name: Age, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 278
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PjELas09JHV6",
"colab_type": "code",
"outputId": "7576204b-1473-41b7-c3ff-4a0bf01e26be",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
}
},
"source": [
"# Make Fare into a categorical feature\n",
"combined_data.loc[combined_data['Fare'] <= 7.91, 'Fare'] = 0\n",
"combined_data.loc[(combined_data['Fare'] > 7.91) & (combined_data['Fare'] <= 14.454), 'Fare'] = 1\n",
"combined_data.loc[(combined_data['Fare'] > 14.454) & (combined_data['Fare'] <= 31), 'Fare'] = 2\n",
"combined_data.loc[(combined_data['Fare'] > 31) & (combined_data['Fare'] <= 99), 'Fare'] = 3\n",
"combined_data.loc[(combined_data['Fare'] > 99) & (combined_data['Fare'] <= 250), 'Fare'] = 4\n",
"combined_data.loc[combined_data['Fare'] > 250, 'Fare'] = 5\n",
"\n",
"combined_data['Fare'] = combined_data['Fare'].astype(int)\n",
"\n",
"combined_data['Fare'].value_counts()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 337\n",
"2 332\n",
"1 310\n",
"3 246\n",
"4 67\n",
"5 17\n",
"Name: Fare, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 279
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3d1cOxeffnRq",
"colab_type": "text"
},
"source": [
"**Dataset Normalisation**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "l2pvJUZRNzlv",
"colab_type": "code",
"colab": {}
},
"source": [
"# Drop columns that we think are irrelevant features\n",
"normalised_data = combined_data.drop([\"PassengerId\", \"Name\", \"Ticket\", \"Embarked\"], axis = 1)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VRkh2cKu978I",
"colab_type": "code",
"colab": {}
},
"source": [
"x_train = normalised_data[:891]\n",
"x_test = normalised_data[891:]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VLZlcXE_EDNW",
"colab_type": "code",
"outputId": "431ce858-0303-4abf-d91e-edd3cca6b762",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 224
}
},
"source": [
"# Verify that we have the normalised data for training\n",
"x_train.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" <th>Cabin_A</th>\n",
" <th>Cabin_B</th>\n",
" <th>Cabin_C</th>\n",
" <th>Cabin_D</th>\n",
" <th>Cabin_E</th>\n",
" <th>Cabin_F</th>\n",
" <th>Cabin_G</th>\n",
" <th>Cabin_T</th>\n",
" <th>Designation</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pclass Sex Age SibSp ... Cabin_F Cabin_G Cabin_T Designation\n",
"0 3 0 2 1 ... 0 0 1 1\n",
"1 1 1 5 1 ... 0 0 0 3\n",
"2 3 1 3 0 ... 0 0 1 2\n",
"3 1 1 5 1 ... 0 0 0 3\n",
"4 3 0 5 0 ... 0 0 1 1\n",
"\n",
"[5 rows x 15 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 282
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Az4j-ZRjPG9C",
"colab_type": "code",
"outputId": "5cbf2174-3c85-4a27-e72e-29cbdafdfb17",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 224
}
},
"source": [
"# Verify that we have the normalised data for test\n",
"x_test.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" <th>Cabin_A</th>\n",
" <th>Cabin_B</th>\n",
" <th>Cabin_C</th>\n",
" <th>Cabin_D</th>\n",
" <th>Cabin_E</th>\n",
" <th>Cabin_F</th>\n",
" <th>Cabin_G</th>\n",
" <th>Cabin_T</th>\n",
" <th>Designation</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pclass Sex Age SibSp ... Cabin_F Cabin_G Cabin_T Designation\n",
"0 3 0 5 0 ... 0 0 1 1\n",
"1 3 1 6 1 ... 0 0 1 3\n",
"2 2 0 6 0 ... 0 0 1 1\n",
"3 3 0 3 0 ... 0 0 1 1\n",
"4 3 1 2 1 ... 0 0 1 3\n",
"\n",
"[5 rows x 15 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 283
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kpJi5GpjRS9_",
"colab_type": "text"
},
"source": [
"## Building Prediction Models"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1j6cLZAFB8dF",
"colab_type": "text"
},
"source": [
"##### Random Forest\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hYXG2AxGwslD",
"colab_type": "text"
},
"source": [
"**Model Training**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "DS3nWueQCxBc",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import confusion_matrix"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1W677Mr4Cq14",
"colab_type": "code",
"outputId": "5eb0475c-3143-44d2-dfc1-0be1f69f463f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"random_forest = RandomForestClassifier(n_estimators = 200)\n",
"\n",
"random_forest.fit(x_train, y_train)\n",
"\n",
"y_prediction = random_forest.predict(x_test)\n",
"random_forest.score(x_train, y_train)\n",
"accuracy_for_random_forest = round(random_forest.score(x_train, y_train) * 100, 2)\n",
"accuracy_for_random_forest"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"91.02"
]
},
"metadata": {
"tags": []
},
"execution_count": 285
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PBZ0D8IFd2XE",
"colab_type": "text"
},
"source": [
"**K-Fold Cross Validation**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "_FrQxYkmdozA",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.model_selection import cross_val_score"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NuNDFbqhd-fT",
"colab_type": "code",
"outputId": "150f228c-1ce1-49f6-b384-137d329783b9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
}
},
"source": [
"scores = cross_val_score(random_forest, x_train, y_train, cv = 10, scoring = \"accuracy\")\n",
"print(\"Scores:\", scores)\n",
"print(\"Mean:\", scores.mean())\n",
"print(\"Standard Deviation:\", scores.std())\n",
"\n",
"mean_accuracy_for_random_forest = round(scores.mean() * 100, 2)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Scores: [0.76666667 0.83333333 0.71910112 0.83146067 0.88764045 0.85393258\n",
" 0.83146067 0.83146067 0.87640449 0.86363636]\n",
"Mean: 0.8295097037793667\n",
"Standard Deviation: 0.048481699508097505\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kAtsXAGewyXl",
"colab_type": "text"
},
"source": [
"**Feature Importance**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UO-PT1UiyMLF",
"colab_type": "code",
"outputId": "00d900d2-3dee-4362-b0c7-7ce5a105477b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 545
}
},
"source": [
"importances = pd.DataFrame({\n",
" 'Feature': x_train.columns,\n",
" 'Importance': np.round(random_forest.feature_importances_, 3)\n",
"})\n",
"\n",
"importances = importances.sort_values('Importance', ascending = False).set_index('Feature')\n",
"importances"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Importance</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Feature</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Designation</th>\n",
" <td>0.210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sex</th>\n",
" <td>0.189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age</th>\n",
" <td>0.150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fare</th>\n",
" <td>0.108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pclass</th>\n",
" <td>0.094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SibSp</th>\n",
" <td>0.089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cabin_T</th>\n",
" <td>0.053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Parch</th>\n",
" <td>0.051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cabin_E</th>\n",
" <td>0.014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cabin_B</th>\n",
" <td>0.012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cabin_C</th>\n",
" <td>0.009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cabin_D</th>\n",
" <td>0.009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cabin_A</th>\n",
" <td>0.005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cabin_F</th>\n",
" <td>0.003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cabin_G</th>\n",
" <td>0.003</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Importance\n",
"Feature \n",
"Designation 0.210\n",
"Sex 0.189\n",
"Age 0.150\n",
"Fare 0.108\n",
"Pclass 0.094\n",
"SibSp 0.089\n",
"Cabin_T 0.053\n",
"Parch 0.051\n",
"Cabin_E 0.014\n",
"Cabin_B 0.012\n",
"Cabin_C 0.009\n",
"Cabin_D 0.009\n",
"Cabin_A 0.005\n",
"Cabin_F 0.003\n",
"Cabin_G 0.003"
]
},
"metadata": {
"tags": []
},
"execution_count": 288
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6Eas7fhn0KAc",
"colab_type": "code",
"outputId": "5dc3cbda-fd86-489c-badd-1a341a576b35",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 346
}
},
"source": [
"importances.plot.bar()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb34ee3eda0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 298
},
{
"output_type": "display_data",
"data": {
"image/png": 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2EPBN4ECSQd+/KWnHdPHPgc8CI9PH+C6/CzMz67RazgDGAQsjYnFEvAVcB0wsXyEilkTE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\nmVlBOQGYmRWUE4CZWUH9f6gYhqTQZ4AdAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "astQLJLTU2Rq",
"colab_type": "text"
},
"source": [
"##### Logistic Regression"
]
},
{
"cell_type": "code",
"metadata": {
"id": "KwmCEzf9HFER",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.linear_model import LogisticRegression"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "U9BpGG-9VBiV",
"colab_type": "code",
"outputId": "951dd8b0-2d9b-401c-d9c2-e967b451e0d0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 88
}
},
"source": [
"logistic_regression = LogisticRegression()\n",
"logistic_regression.fit(x_train, y_train)\n",
"\n",
"y_prediction = logistic_regression.predict(x_test)\n",
"\n",
"accuracy_for_logistic_regression = round(logistic_regression.score(x_train, y_train) * 100, 2)\n",
"accuracy_for_logistic_regression"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"81.71"
]
},
"metadata": {
"tags": []
},
"execution_count": 291
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cwZ6If7nvu0A",
"colab_type": "text"
},
"source": [
"**K-Fold Cross Validation**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sJZQqCObvRZn",
"colab_type": "code",
"outputId": "7d0f9621-15e4-486a-9a3c-2d87c670c969",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 445
}
},
"source": [
"scores = cross_val_score(logistic_regression, x_train, y_train, cv = 10, scoring = \"accuracy\")\n",
"print(\"Scores:\", scores)\n",
"print(\"Mean:\", scores.mean())\n",
"print(\"Standard Deviation:\", scores.std())\n",
"\n",
"mean_accuracy_for_logistic_regression = round(scores.mean() * 100, 2)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Scores: [0.77777778 0.8 0.7752809 0.86516854 0.80898876 0.76404494\n",
" 0.84269663 0.80898876 0.83146067 0.80681818]\n",
"Mean: 0.8081225173079106\n",
"Standard Deviation: 0.03000945243665205\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "klWF2ft6Z8D5",
"colab_type": "text"
},
"source": [
"## Comparing Prediction Models"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8_FgJg3uLa1i",
"colab_type": "text"
},
"source": [
"**Overall Accuracy**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "i3N0BZZaZ_A6",
"colab_type": "code",
"outputId": "e9cac681-8d99-4df6-d182-911021094bb1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 142
}
},
"source": [
"result_dict = {\n",
" 'Model': ['Random Forest', 'Logistic Regression'],\n",
" 'Score': [mean_accuracy_for_random_forest, mean_accuracy_for_logistic_regression]\n",
"}\n",
"\n",
"accuracy_results = pd.DataFrame(result_dict)\n",
"result_df = accuracy_results.sort_values(by='Score', ascending = False)\n",
"result_df = result_df.set_index('Score')\n",
"result_df.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Score</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>82.95</th>\n",
" <td>Random Forest</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80.81</th>\n",
" <td>Logistic Regression</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model\n",
"Score \n",
"82.95 Random Forest\n",
"80.81 Logistic Regression"
]
},
"metadata": {
"tags": []
},
"execution_count": 293
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "axAM76NRLfZb",
"colab_type": "text"
},
"source": [
"**Confusion Matrix**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "qE3hGLB1Lul8",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.model_selection import cross_val_predict\n",
"from sklearn.metrics import confusion_matrix"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yM3TMespLw0E",
"colab_type": "code",
"outputId": "145adcb1-f872-4158-be32-97f9910fd869",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 327
}
},
"source": [
"predictions = cross_val_predict(random_forest, x_train, y_train, cv = 3)\n",
"confusion_matrix = confusion_matrix(y_train, predictions)\n",
"\n",
"ax = plt.subplot()\n",
"sns.heatmap(confusion_matrix, annot = True, ax = ax, cmap = 'Greens', fmt = 'g')\n",
"\n",
"ax.set_xlabel('Predicted Labels')\n",
"ax.set_ylabel('True Labels')\n",
"ax.set_title('Confusion Matrix for Random Forest\\n')\n",
"ax.xaxis.set_ticklabels(['Survived', 'Not Survived'])\n",
"ax.yaxis.set_ticklabels(['Not Survived', 'Survived'])"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[Text(0, 0.5, 'Not Survived'), Text(0, 1.5, 'Survived')]"
]
},
"metadata": {
"tags": []
},
"execution_count": 295
},
{
"output_type": "display_data",
"data": {
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feZX9L9iPOR/M4a6f3c0Bl+xPXV1xfv7XWpH6uP2udiCvTXyNwRtvQLfu3ejS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\nRK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "W-7w8YOmR0r6",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.model_selection import cross_val_predict\n",
"from sklearn.metrics import confusion_matrix"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VZ5QhKBgQfQA",
"colab_type": "code",
"outputId": "54a59f11-ab4f-4391-be42-4bdb1e10dec6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 449
}
},
"source": [
"predictions = cross_val_predict(logistic_regression, x_train, y_train, cv = 3)\n",
"predictions\n",
"confusion_matrix = confusion_matrix(y_train, predictions)\n",
"\n",
"ax = plt.subplot()\n",
"sns.heatmap(confusion_matrix, annot = True, ax = ax, cmap = 'Greens', fmt = 'g')\n",
"\n",
"ax.set_xlabel('Predicted')\n",
"ax.set_ylabel('True')\n",
"ax.set_title('Confusion Matrix for Logistic Regression\\n')\n",
"ax.xaxis.set_ticklabels(['Survived', 'Not Survived'])\n",
"ax.yaxis.set_ticklabels(['Not Survived', 'Survived'])"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[Text(0, 0.5, 'Not Survived'), Text(0, 1.5, 'Survived')]"
]
},
"metadata": {
"tags": []
},
"execution_count": 297
},
{
"output_type": "display_data",
"data": {
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BuWisg5g9ezYzZ8ykU6dOTJ8xg76L9eWCERdy5tl/mHNQrXfvRdu4lcXWHnrS5cojVdIj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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
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