shipwreck.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Dataset & Clean up Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</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    </tr>\n    <tr>\n      <th>885</th>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>39.0</td>\n      <td>0</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>886</th>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>27.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>887</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>19.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>889</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>890</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>32.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>714 rows × 6 columns</p>\n</div>",
      "text/plain": "     Survived  Pclass  Sex   Age  SibSp  Parch\n0           0       3    0  22.0      1      0\n1           1       1    1  38.0      1      0\n2           1       3    1  26.0      0      0\n3           1       1    1  35.0      1      0\n4           0       3    0  35.0      0      0\n..        ...     ...  ...   ...    ...    ...\n885         0       3    1  39.0      0      5\n886         0       2    0  27.0      0      0\n887         1       1    1  19.0      0      0\n889         1       1    0  26.0      0      0\n890         0       3    0  32.0      0      0\n\n[714 rows x 6 columns]"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://cdn.techroulette.xyz/projects/shipwreck/data/m3_data.csv')\n",
    "\n",
    "df = df.drop([\"PassengerId\", \"Name\", \"Ticket\", \"Cabin\", \"Fare\", \"Embarked\"], axis=1)\n",
    "df = df.replace({'male': 0, 'female': 1})\n",
    "df = df.dropna()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare and train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "DecisionTreeClassifier()"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df.pop('Survived')\n",
    "X = df\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.15)\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.8240740740740741"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "\n",
    "accuracy_score(Y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nb> tree.export_graphviz(model, out_file='shipwreck.dot', feature_names=[\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\"], label=\"all\", rounded=True, filled=True, class_names=[\"Died\", \"Survived\"])\n"
   ]
  }
 ],
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