01-Logistic Regression with Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Logistic Regression with Python\n",
    "\n",
    "For this lecture we will be working with the [Titanic Data Set from Kaggle](https://www.kaggle.com/c/titanic). This is a very famous data set and very often is a student's first step in machine learning! \n",
    "\n",
    "We'll be trying to predict a classification- survival or deceased.\n",
    "Let's begin our understanding of implementing Logistic Regression in Python for classification.\n",
    "\n",
    "We'll use a \"semi-cleaned\" version of the titanic data set, if you use the data set hosted directly on Kaggle, you may need to do some additional cleaning not shown in this lecture notebook.\n",
    "\n",
    "## Import Libraries\n",
    "Let's import some libraries to get started!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "Let's start by reading in the titanic_train.csv file into a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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  \\\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "Let's begin some exploratory data analysis! We'll start by checking out missing data!\n",
    "\n",
    "## Missing Data\n",
    "\n",
    "We can use seaborn to create a simple heatmap to see where we are missing data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Roughly 20 percent of the Age data is missing. The proportion of Age missing is likely small enough for reasonable replacement with some form of imputation. Looking at the Cabin column, it looks like we are just missing too much of that data to do something useful with at a basic level. We'll probably drop this later, or change it to another feature like \"Cabin Known: 1 or 0\"\n",
    "\n",
    "Let's continue on by visualizing some more of the data! Check out the video for full explanations over these plots, this code is just to serve as reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',data=train,palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',hue='Sex',data=train,palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',hue='Pclass',data=train,palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x13af9e50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(train['Age'].dropna(),kde=False,color='darkred',bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Age'].hist(bins=30,color='darkred',alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].hist(color='green',bins=40,figsize=(8,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Data Cleaning\n",
    "We want to fill in missing age data instead of just dropping the missing age data rows. One way to do this is by filling in the mean age of all the passengers (imputation).\n",
    "However we can be smarter about this and check the average age by passenger class. For example:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 7))\n",
    "sns.boxplot(x='Pclass',y='Age',data=train,palette='winter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the wealthier passengers in the higher classes tend to be older, which makes sense. We'll use these average age values to impute based on Pclass for Age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "\n",
    "        if Pclass == 1:\n",
    "            return 37\n",
    "\n",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "\n",
    "        else:\n",
    "            return 24\n",
    "\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now apply that function!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age'] = train[['Age','Pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's check that heat map again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Let's go ahead and drop the Cabin column and the row in Embarked that is NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>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>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>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>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>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>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>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting Categorical Features \n",
    "\n",
    "We'll need to convert categorical features to dummy variables using pandas! Otherwise our machine learning algorithm won't be able to directly take in those features as inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  889 non-null    int64  \n",
      " 1   Survived     889 non-null    int64  \n",
      " 2   Pclass       889 non-null    int64  \n",
      " 3   Name         889 non-null    object \n",
      " 4   Sex          889 non-null    object \n",
      " 5   Age          889 non-null    float64\n",
      " 6   SibSp        889 non-null    int64  \n",
      " 7   Parch        889 non-null    int64  \n",
      " 8   Ticket       889 non-null    object \n",
      " 9   Fare         889 non-null    float64\n",
      " 10  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 69.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(train['Sex'],drop_first=False)\n",
    "embark = pd.get_dummies(train['Embarked'],drop_first=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train,sex,embark],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>0</th>\n",
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       "      <td>26.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  female  male  \\\n",
       "0            1         0       3  22.0      1      0   7.2500       0     1   \n",
       "1            2         1       1  38.0      1      0  71.2833       1     0   \n",
       "2            3         1       3  26.0      0      0   7.9250       1     0   \n",
       "3            4         1       1  35.0      1      0  53.1000       1     0   \n",
       "4            5         0       3  35.0      0      0   8.0500       0     1   \n",
       "\n",
       "   C  Q  S  \n",
       "0  0  0  1  \n",
       "1  1  0  0  \n",
       "2  0  0  1  \n",
       "3  0  0  1  \n",
       "4  0  0  1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     female  male\n",
       "0         0     1\n",
       "1         1     0\n",
       "2         1     0\n",
       "3         1     0\n",
       "4         0     1\n",
       "..      ...   ...\n",
       "886       0     1\n",
       "887       1     0\n",
       "888       1     0\n",
       "889       0     1\n",
       "890       0     1\n",
       "\n",
       "[889 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>887</th>\n",
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       "      <td>1</td>\n",
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       "      <th>888</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>889</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     C  Q  S\n",
       "0    0  0  1\n",
       "1    1  0  0\n",
       "2    0  0  1\n",
       "3    0  0  1\n",
       "4    0  0  1\n",
       "..  .. .. ..\n",
       "886  0  0  1\n",
       "887  0  0  1\n",
       "888  0  0  1\n",
       "889  1  0  0\n",
       "890  0  1  0\n",
       "\n",
       "[889 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.corr(),annot=True,cmap='RdYlGn',linewidths=0.2) #data.corr()-->correlation matrix\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(16,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Our data is ready for our model!\n",
    "\n",
    "# Building a Logistic Regression model\n",
    "\n",
    "Let's start by splitting our data into a training set and test set (there is another test.csv file that you can play around with in case you want to use all this data for training).\n",
    "\n",
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train.drop('Survived',axis=1), \n",
    "                                                    train['Survived'], test_size=0.30, \n",
    "                                                    random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=5000)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel = LogisticRegression(max_iter= 5000)\n",
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's move on to evaluate our model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8277153558052435"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not so bad! You might want to explore other feature engineering and the other titanic_text.csv file, some suggestions for feature engineering:\n",
    "\n",
    "* Try grabbing the Title (Dr.,Mr.,Mrs,etc..) from the name as a feature\n",
    "* Maybe the Cabin letter could be a feature\n",
    "* Is there any info you can get from the ticket?\n",
    "\n",
    "## Great Job!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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