01-Logistic Regression with Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 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": 2,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>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",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
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     "execution_count": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjYAAAGsCAYAAADOo+2NAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgkUlEQVR4nO3dfVCVdf7/8RccQo5kAmnm7rrbLw/g4g8EodJvWoadbpYU4kZnR92lm3UXa5qaxMosnRRlt13HdRt3NwmZNnZ0YSPFsWK33J20QCxFvhUEzpaVpYlKcnPicDi/P/rJdNZUjnK44NPzMeNMXHfnfTlzxZPrHLyCvF6vVwAAAAYItnoAAACA/kLYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYIVYPMNB6enrU3d2t4OBgBQUFWT0OAADoA6/Xq56eHoWEhCg4+Oz3Zb5zYdPd3a36+nqrxwAAABcgPj5eoaGhZ13/nQub05UXHx8vm81m8TQAAKAvPB6P6uvrz3m3RvoOhs3pt59sNhthAwDAEHO+j5Hw4WEAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbALA6/VaPQIwKHFtAAi0EKsHMFFQUJD2/ueITrm6rB4FGDRGhIUq5f+MsXoMAIYjbALklKtLrZ2EDQAAA4m3ogAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEsCZsdO3YoLi5OSUlJvX/y8/MlSXV1dcrJyVFSUpJSU1NVVlbms29FRYWcTqcSExOVmZmpffv2WXEKAABgEAqx4kXr6+uVnp6uNWvW+CxvbW3VwoUL9cADD2ju3Lmqra3Vfffdp9jYWCUkJKimpkYrV67Uxo0blZCQoNLSUuXl5Wnnzp2y2+1WnAoAABhELAub22+//YzlVVVVioiI0Lx58yRJU6dO1axZs1RaWqqEhASVlZUpLS1NycnJkqTc3Fxt2bJFO3bsUFZWll8zeDyeiz+Rs7DZbAE7NjDUBfLaA2Cuvv6/Y8DDpqenR++++67sdruKiork8Xh04403avHixWpqalJMTIzP9g6HQ+Xl5ZKk5ubmMwLG4XCooaHB7znq6+sv/CTOwW63Ky4uLiDHBkzQ2Niozs5Oq8cAYKgBD5vjx48rLi5Ot956q9avX68TJ07okUceUX5+vkaPHn3GW0phYWHq6OiQJLW3t59zvT/i4+O5swJYIDY21uoRAAxBHo+nTzclBjxsRo0apdLS0t6v7Xa78vPzNWfOHGVmZsrlcvls73K5FB4e3rvtt62PjIz0ew6bzUbYABbgugMQSAP+W1ENDQ367W9/K6/X27usq6tLwcHBSkhIUFNTk8/2zc3Nio6OliRFR0efcz0AAPhuG/CwiYiIUGlpqYqKitTd3a3Dhw/r6aef1p133qlbb71Vx44dU0lJidxut6qrq1VZWdn7uZrs7GxVVlaqurpabrdbJSUlamlpkdPpHOjTAAAAg1CQ95u3TgbInj17tHbtWn3wwQcaNmyY0tLSlJ+fr2HDhqm+vl4FBQX64IMPFBUVpUWLFikzM7N3361bt+qPf/yjjhw5IofDoWXLlmnSpEl9fm2Px6P9+/crMTExoLfEd77/sVo7uwJ2fGCoGWkP1U0/Hmf1GACGqL5+/7YkbKxE2ADWIGwAXIy+fv/mkQoAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMIalYePxeLRgwQI9+uijvcvq6uqUk5OjpKQkpaamqqyszGefiooKOZ1OJSYmKjMzU/v27RvosQEAwCBladg888wz2rt3b+/Xra2tWrhwoTIyMlRbW6uCggKtWbNGBw4ckCTV1NRo5cqVKiwsVG1trWbPnq28vDx1dnZadQoAAGAQCbHqhd966y1VVVXplltu6V1WVVWliIgIzZs3T5I0depUzZo1S6WlpUpISFBZWZnS0tKUnJwsScrNzdWWLVu0Y8cOZWVl+fX6Ho+n/07mv9hstoAdGxjqAnntATBXX//fYUnYtLS06PHHH9eGDRtUUlLSu7ypqUkxMTE+2zocDpWXl0uSmpubzwgYh8OhhoYGv2eor6/3f/A+sNvtiouLC8ixARM0NjZylxVAwAx42PT09Cg/P1933XWXJkyY4LOuvb1ddrvdZ1lYWJg6Ojr6tN4f8fHx3FkBLBAbG2v1CACGII/H06ebEgMeNn/+858VGhqqBQsWnLHObrfr1KlTPstcLpfCw8N717tcrjPWR0ZG+j2HzWYjbAALcN0BCKQBD5utW7fq6NGjSklJkaTeUPnnP/+pJUuWaPfu3T7bNzc3Kzo6WpIUHR2tpqamM9bfcMMNAzA5AAAY7Ab8t6JeeeUVvfPOO9q7d6/27t2rO+64Q3fccYf27t0rp9OpY8eOqaSkRG63W9XV1aqsrOz9XE12drYqKytVXV0tt9utkpIStbS0yOl0DvRpAACAQciy34r6NpGRkSouLlZBQYHWr1+vqKgoLVu2TFOmTJH09W9JLV++XCtWrNCRI0fkcDi0ceNGRUREWDs4AAAYFIK8Xq/X6iEGksfj0f79+5WYmBjQ9/p3vv+xWju7AnZ8YKgZaQ/VTT8eZ/UYAIaovn7/5pEKAADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAfvB6vVaPAAxKg+XaCLF6AAAYSoKCgnTqf99Ud3ur1aMAg0ZI+EiN+L//Y/UYkggbAPBbd3urPKdOWD0GgG/BW1EAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMIbfYZOXl/ety+fPn3/RwwAAAFyMkL5s9Mknn+ill16SJO3atUvPPPOMz/q2tjY1Njb2+3AAAAD+6FPYfO9731NTU5OOHz8uj8ejmpoan/XDhg3T8uXLAzIgAABAX/UpbIKDg/X73/9ekrRs2TKtWrXqol70rbfe0tq1a3Xw4EHZ7Xbddtttys/PV1hYmOrq6rRq1So1NzcrMjJSeXl5ysnJ6d23oqJCGzZs0BdffKGrr75aTzzxhJKSki5qHgAAYAa/P2OzatUqdXV16fPPP9fhw4d9/vTF8ePH9ctf/lI//elPtXfvXlVUVGjPnj169tln1draqoULFyojI0O1tbUqKCjQmjVrdODAAUlSTU2NVq5cqcLCQtXW1mr27NnKy8tTZ2env6cBAAAM1Kc7Nt/0yiuv6IknnlBbW1vvMq/Xq6CgIL3//vvn3T8qKkpvvvmmLr30Unm9Xp08eVJfffWVoqKiVFVVpYiICM2bN0+SNHXqVM2aNUulpaVKSEhQWVmZ0tLSlJycLEnKzc3Vli1btGPHDmVlZfl1Hh6Px6/t/WGz2QJ2bGCoC+S1NxC4voGzC+T13ddj+x0269ev17x583TnnXcqJMTv3SVJl156qSTpxhtv1JEjR5SSkqLMzEytW7dOMTExPts6HA6Vl5dLkpqbm88IGIfDoYaGBr9nqK+vv6DZz8dutysuLi4gxwZM0NjYOGTvsnJ9A+c2GK5vv8vks88+0/3333/BUfNNVVVVam1t1eLFi/XAAw9ozJgxstvtPtuEhYWpo6NDktTe3n7O9f6Ij4/nJy/AArGxsVaPACBAAnl9ezyePt2U8LtOJk6cqObmZk2YMOGCBvumsLAwhYWFKT8/Xzk5OVqwYIFOnTrls43L5VJ4eLikr39acrlcZ6yPjIz0+7VtNhthA1iA6w4w12C4vv0Om8mTJys3N1e33XabRo0a5bPu/vvvP+/+77zzjpYuXapt27YpNDRUktTV1aVLLrlEDodDu3fv9tm+ublZ0dHRkqTo6Gg1NTWdsf6GG27w9zQAAICB/P6tqH379ik6OloHDx5UTU1N7589e/b0af/Y2Fi5XC797ne/U1dXlz799FP9+te/VnZ2tm699VYdO3ZMJSUlcrvdqq6uVmVlZe/narKzs1VZWanq6mq53W6VlJSopaVFTqfT39MAAAAG8vuOzV/+8peLesHw8HAVFRVp9erVuv766zVixAjNmjVL9913n0JDQ1VcXKyCggKtX79eUVFRWrZsmaZMmSLp69+SWr58uVasWKEjR47I4XBo48aNioiIuKiZAACAGfwOm9OPVvg2GRkZfTqGw+FQcXHxt66Lj4/X5s2bz7pvenq60tPT+/Q6AADgu+WCft37m1pbW9XZ2ank5OQ+hw0AAEAg+B02r7/+us/XXq9XGzdu1MmTJ/trJgAAgAvi94eH/1tQUJDuuecebd26tT/mAQAAuGAXHTaS9J///EdBQUH9cSgAAIAL5vdbUQsWLPCJGLfbrcbGRs2ePbtfBwMAAPCX32Fz3XXX+XwdHBys3Nxc3Xzzzf02FAAAwIXwO2y++a8Lt7S0aOTIkf3y3CgAAICL5fdnbNxut1avXq2kpCRNmzZNycnJeuKJJ9TV1RWI+QAAAPrM77DZsGGDampqtG7dOm3fvl3r1q1TXV2d1q1bF4DxAAAA+s7v95AqKyu1adMmjRs3TpI0fvx4jR8/XvPmzdOSJUv6fUAAAIC+8vuOTWtrq8aOHeuzbOzYsXK5XP02FAAAwIXwO2xiY2PPeJbT5s2bFRMT029DAQAAXAi/34p68MEHdffdd2vbtm0aN26cDh06pObmZj333HOBmA8AAKDP/A6blJQUPf7446qrq1NISIhuuukmzZkzR5MnTw7EfAAAAH12QU/3rqio0KZNm3TVVVfptdde0+rVq9Xa2qp77703EDMCAAD0id+fsSkvL9fzzz+vq666SpI0c+ZMbdq0SaWlpf09GwAAgF/8Dpu2trZv/a2ojo6OfhsKAADgQvgdNhMnTtSzzz7rs6y4uFgTJkzot6EAAAAuhN+fsXn00Ud19913629/+5uuvPJKff755+ru7lZRUVEg5gMAAOgzv8Nm4sSJqqqq0s6dO3X06FGNHTtWM2bM0IgRIwIxHwAAQJ9d0GO5R44cqYyMjH4eBQAA4OL4/RkbAACAwYqwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABjDkrBpaGjQXXfdpWuvvVbXX3+9lixZouPHj0uS6urqlJOTo6SkJKWmpqqsrMxn34qKCjmdTiUmJiozM1P79u2z4hQAAMAgNOBh43K5dO+99yopKUm7du3S9u3bdfLkSS1dulStra1auHChMjIyVFtbq4KCAq1Zs0YHDhyQJNXU1GjlypUqLCxUbW2tZs+erby8PHV2dg70aQAAgEEoZKBf8PDhw5owYYLuu+8+2Ww2hYaGau7cuVqyZImqqqoUERGhefPmSZKmTp2qWbNmqbS0VAkJCSorK1NaWpqSk5MlSbm5udqyZYt27NihrKwsv+bweDz9fm6n2Wy2gB0bGOoCee0NBK5v4OwCeX339dgDHjZXX321ioqKfJa9+uqrmjhxopqamhQTE+OzzuFwqLy8XJLU3Nx8RsA4HA41NDT4PUd9fb3f+/SF3W5XXFxcQI4NmKCxsXHI3mXl+gbObTBc3wMeNt/k9Xq1bt067dy5Uy+88IKef/552e12n23CwsLU0dEhSWpvbz/nen/Ex8fzkxdggdjYWKtHABAggby+PR5Pn25KWBY2bW1teuyxx/Tuu+/qhRdeUGxsrOx2u06dOuWzncvlUnh4uKSvf1pyuVxnrI+MjPT79W02G2EDWIDrDjDXYLi+LfmtqEOHDikrK0ttbW0qLy/vLbyYmBg1NTX5bNvc3Kzo6GhJUnR09DnXAwCA77YBD5vW1lb9/Oc/1+TJk/Xcc88pKiqqd53T6dSxY8dUUlIit9ut6upqVVZW9n6uJjs7W5WVlaqurpbb7VZJSYlaWlrkdDoH+jQAAMAgNOBvRb344os6fPiwXn75Zb3yyis+6/bt26fi4mIVFBRo/fr1ioqK0rJlyzRlyhRJX/+W1PLly7VixQodOXJEDodDGzduVERExECfBgAAGISCvF6v1+ohBpLH49H+/fuVmJgY0PcCd77/sVo7uwJ2fGCoGWkP1U0/Hmf1GP3iRM3L8pw6YfUYwKBhGxGpyOtuD+hr9PX7N49UAAAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxLA2b48ePy+l0qqampndZXV2dcnJylJSUpNTUVJWVlfnsU1FRIafTqcTERGVmZmrfvn0DPTYAABikLAubt99+W3PnztWhQ4d6l7W2tmrhwoXKyMhQbW2tCgoKtGbNGh04cECSVFNTo5UrV6qwsFC1tbWaPXu28vLy1NnZadVpAACAQcSSsKmoqNDixYv10EMP+SyvqqpSRESE5s2bp5CQEE2dOlWzZs1SaWmpJKmsrExpaWlKTk7WJZdcotzcXEVGRmrHjh1WnAYAABhkQqx40WnTpmnWrFkKCQnxiZumpibFxMT4bOtwOFReXi5Jam5uVlZW1hnrGxoa/J7B4/FcwOR9Y7PZAnZsYKgL5LU3ELi+gbML5PXd12NbEjajR4/+1uXt7e2y2+0+y8LCwtTR0dGn9f6or6/3e5++sNvtiouLC8ixARM0NjYO2bePub6BcxsM17clYXM2drtdp06d8lnmcrkUHh7eu97lcp2xPjIy0u/Xio+P5ycvwAKxsbFWjwAgQAJ5fXs8nj7dlBhUYRMTE6Pdu3f7LGtublZ0dLQkKTo6Wk1NTWesv+GGG/x+LZvNRtgAFuC6A8w1GK7vQfXv2DidTh07dkwlJSVyu92qrq5WZWVl7+dqsrOzVVlZqerqarndbpWUlKilpUVOp9PiyQEAwGAwqO7YREZGqri4WAUFBVq/fr2ioqK0bNkyTZkyRZI0depULV++XCtWrNCRI0fkcDi0ceNGRUREWDs4AAAYFCwPm8bGRp+v4+PjtXnz5rNun56ervT09ECPBQAAhqBB9VYUAADAxSBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxhmTYtLS0aNGiRUpJSdF1112ngoICdXd3Wz0WAACw2JAMmwcffFDDhw/XG2+8ofLycr311lsqKSmxeiwAAGCxIRc2H330kfbs2aP8/HzZ7XaNGzdOixYtUmlpqdWjAQAAi4VYPYC/mpqaFBERoTFjxvQuGz9+vA4fPqwvv/xSl1122Tn393q9kqSuri7ZbLaAzGiz2TRiWIjk7QnI8YGhaMSwEHk8Hnk8HqtHuSg2m01Bw0cqSEFWjwIMGkHDLwv49X362Ke/j5/NkAub9vZ22e12n2Wnv+7o6Dhv2PT0fB0b7733XmAG/P+CJJ17EuA7plPaf+Kw1VP0k2FSyGirhwAGjy5J+/cPyEud/j5+NkMubIYPH67Ozk6fZae/Dg8PP+/+ISEhio+PV3BwsIKC+IkLAIChwOv1qqenRyEh506XIRc20dHROnnypI4dO6ZRo0ZJkg4ePKgrr7xSI0aMOO/+wcHBCg0NDfSYAADAAkPuw8NXXXWVkpOTtXr1arW1tenjjz/Whg0blJ2dbfVoAADAYkHe830KZxA6duyYnnrqKdXU1Cg4OFgZGRlavHhxwD4MDAAAhoYhGTYAAADfZsi9FQUAAHA2hA0AADAGYQMAAIxB2AAAAGMQNjAST4AHzHf8+HE5nU7V1NRYPQoGEcIGRuIJ8IDZ3n77bc2dO1eHDh2yehQMMoQNjMMT4AGzVVRUaPHixXrooYesHgWDEGED45zvCfAAhrZp06bpH//4h37yk59YPQoGIcIGxjnfE+ABDG2jR48+74MQ8d1F2MA4F/sEeADA0EXYwDjffAL8af48AR4AMHQRNjAOT4AHgO8uwgZGWr9+vbq7uzVz5kzNmTNH06dP16JFi6weCwAQYDzdGwAAGIM7NgAAwBiEDQAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYABkRra6tWrFihG2+8UYmJiZo2bZoeeeQRff755/3+Wn/6059077339vtxJSk2NlY1NTUBOTaAi0fYABgQDz30kE6cOKHy8nLt379fL730krq6unTXXXepu7u7X1/rV7/6lYqKivr1mACGBsIGwIB4++235XQ6NXr0aEnSqFGjtHTpUk2aNElffvmlUlNT9eKLL/ZuX1NTo9jYWEnSJ598otjYWBUWFuqaa67R0qVLlZSUpF27dvVu/+WXXyohIUEHDhzQH/7wBy1YsEA9PT1KTU3Vli1berfzeDyaPn26Xn75ZUnSm2++qezsbKWkpCgtLU3btm3r3dbtdmvNmjW67rrrNGXKFGIJGAJCrB4AwHdDWlqali9frr179+raa6/VpEmT9P3vf1+FhYV9PkZ7e7t2794tl8slSaqoqNC0adMkSdu3b9ePfvQjJSQk6N///rckKTg4WFlZWaqoqNDcuXMlSbt27VJXV5dmzpyphoYG5eXl6emnn9bMmTNVV1enRYsWKTIyUtOnT9eGDRv0r3/9S+Xl5br88su1YsWK/v1LAdDvuGMDYECsWrVKTz75pD777DM9+eSTSk1NldPp9LlDcj4ZGRkKDQ3VZZddppycHL322mtqa2uT9HXkZGdnn7FPdna2Dhw4oEOHDvVul56ertDQUG3evFkzZ87ULbfcIpvNpsmTJ2vOnDkqLS2VJG3dulX33HOPxo0bp+HDh2vZsmUKCgrqh78NAIHCHRsAAyI4OFjp6elKT0+X1+vVwYMHtXXrVi1ZsqT37anzueKKK3r/OykpST/4wQ/06quvKjExUQ0NDdq4ceMZ+4wZM0bTp0/XSy+9pNzcXL3++uv6+9//Lkn69NNPVV1drZSUlN7tPR6PfvjDH0qSjh49qrFjx/auu+yyyzRy5MgLOn8AA4OwARBwb7zxhh544AHt3LlTERERCgoKksPh0MMPP6zdu3frvffeU3BwsNxud+8+J06cOOM4/323JDs7W9u3b9dHH32km2++WREREd/6+jk5OfrNb36jK664QhMmTFB0dLQk6corr9Sdd96pp556qnfbo0ePyuv19q7/+OOPe9d1dHTo1KlTF/z3ACDweCsKQMBdc801uvzyy/XYY4+psbFRbrdbbW1t2rZtmz788EPNmDFD48eP12uvvSaXy6UvvvhCzz///HmPm5GR0fsbVjk5OWfdbsaMGero6NCzzz7rs93pMNq1a5d6enr04Ycfav78+SouLpb0dRAVFRXp4MGD+uqrr1RYWCiPx3PxfyEAAoawARBwYWFh+utf/6rRo0crLy9PKSkpmjFjhrZt26ZNmzZp/PjxWrx4sdrb23X99dfrZz/7mWbPnn3e40ZERCg1NVUhISGaOnXqWbcLCQlRZmamTpw4odtvv713+aRJk7R27VqtXbtW11xzjebPn6/U1FQ9/PDDkqRf/OIXmj17tubPn69p06ZpxIgRZ70rBGBwCPKevucKAAAwxHHHBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDH+H0W4xoW6DJuYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 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": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 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": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 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 0x25d65f8e050>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 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": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 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": [
       "<Axes: xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 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",
    "### Cufflinks for plots\n",
    "___\n",
    " Let's take a quick moment to show an example of cufflinks!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cufflinks as cf\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "histfunc": "count",
         "histnorm": "",
         "marker": {
          "color": "rgba(0, 128, 0, 1.0)",
          "line": {
           "color": "#4D5663",
           "width": 1.3
          }
         },
         "name": "Fare",
         "nbinsx": 30,
         "opacity": 0.8,
         "orientation": "v",
         "type": "histogram",
         "x": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].iplot(kind='hist',bins=30,color='green')"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38.233440860215055\n",
      "29.87763005780347\n",
      "25.14061971830986\n"
     ]
    }
   ],
   "source": [
    "mean_1 = train.loc[train[\"Pclass\"]==1,'Age'].mean()\n",
    "mean_2 = train.loc[train[\"Pclass\"]==2,'Age'].mean()\n",
    "mean_3 = train.loc[train[\"Pclass\"]==3,'Age'].mean()\n",
    "print(mean_1)\n",
    "print(mean_2)\n",
    "print(mean_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x700 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": 17,
   "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>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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "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: 83.3+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(train['Sex'],drop_first=True)\n",
    "embark = pd.get_dummies(train['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train,sex,embark],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
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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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>22.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</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>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0            1         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1            2         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2            3         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3            4         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4            5         0       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dev2005\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's move on to evaluate our model!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check precision,recall,f1-score using classification report!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.91      0.85       163\n",
      "           1       0.81      0.62      0.71       104\n",
      "\n",
      "    accuracy                           0.80       267\n",
      "   macro avg       0.80      0.77      0.78       267\n",
      "weighted avg       0.80      0.80      0.79       267\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  }
 ],
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