EDA.ipynb

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   "source": [
    "\n",
    "# Logistic Regression with Python\n",
    "\n",
    "For this, 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 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": [
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <td>22.0</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>38.0</td>\n",
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       "      <td>PC 17599</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\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",
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     "execution_count": 3,
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   ],
   "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": [
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       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c8de7a910>"
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     "execution_count": 5,
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     "data": {
      "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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c8e5f32b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OGhsb5fP5pmMcAMBlXAvBggUL1NnZKUlatGiRDhw48KnnRCIRRSIRt0YAAGSAN5QBgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYjhAAgOEIAQAYLj/XA4yNjWnbtm06ffq0vF6vtm/fruLi4lyPBQDGyPkRwSuvvKKRkRH9+te/1o9//GO1tbXleiQAMErOQ9Df369ly5ZJksrLy/XHP/4xxxMBgFlyfmoolUopEAiM387Ly9OlS5eUn3/10WzbViKRmPLnPbDu/6a8D8wu2VhXWVHXmesJMMNkY23atj3hYzkPQSAQUDqdHr89NjY2YQSk/x41AACyJ+enhr7+9a+rt7dXknTixAmVlpbmeCIAMIvlOI6TywE+/quhd999V47j6Omnn9Ytt9ySy5EAwCg5DwEAILdyfmoIAJBbhAAADEcIAMBwhMBAY2Njam5u1qpVqxSLxXTmzJlcjwRc4eTJk4rFYrkewxg5fx8Bpt/ll/U4ceKE2tra9Oyzz+Z6LECStHfvXh05ckRz5szJ9SjG4IjAQFzWAzNZMBjUrl27cj2GUQiBgSa6rAcwE4TD4UmvLoDsIwQGut7LegCY3QiBgbisB4DL8WOgge68804dO3ZM9fX145f1AGAuLjEBAIbj1BAAGI4QAIDhCAEAGI4QAIDhCAEAGI4QwHjPPfec1q5dq3Xr1unBBx+c0iU3fvazn+ncuXOfefvGxkb19fV95u2Bz4L3EcBo7733nl599VW9+OKLsixLiURCTU1NOnLkyGfa35YtW7I8IeA+jghgtKKiIp07d04HDx7UhQsXVFZWpoMHDyoWi2loaEiS9OKLL2rXrl06e/asli9frlgspr179+qee+7Rx2/DefLJJ/Xyyy+Pb7dixQqdPXtWknT06FFt375dyWRSjzzyiGKxmGKxmE6fPi1Jev7553Xfffdp/fr1XBIcOUEIYLSioiI9++yz+sMf/qBVq1bp7rvvVk9Pz4TPHx4e1r59+7R+/Xp95Stf0e9//3uNjIzorbfeUm1t7fjzVq5cqZdeekmSdPjwYUUiEe3Zs0d33HGH4vG4fvrTn2rbtm1KJpPav3+/Ojs7tXv3bl28eNH11wz8L04NwWhnzpxRIBBQa2urJOntt9/W97//fc2bN2/8OZe/+X7BggXyer2SpEgkosOHD2t4eFjf/va3r7hw33e/+12tXr1adXV1SqVSKi0t1bvvvqs333xTR48elSR99NFH+vOf/6zFixeP7zMUCrn+moH/xREBjHb69Glt27ZNtm1LkhYtWqSCggLNnTtXw8PDkqR33nln/PkezydfMt/4xjeUSCTU1dWllStXXrHfQCCg2267Ta2trVqxYoUkqaSkRGvXrlU8Htczzzyj5cuXa+HChXrvvff0n//8R6Ojo0okEm6/ZOBTOCKA0e666y4NDQ2prq5ON910kxzH0aOPPqobbrhBTz31lL70pS9p/vz5V93WsiyFw2EdP35cxcXFn3q8rq5ODz300PhF/TZs2KAtW7aos7NTqVRKDz/8sIqKivTDH/5Q9fX1Kioq4r9yISe46BwAGI5TQwBgOEIAAIYjBABgOEIAAIYjBABgOEIAAIYjBABguP8HWtFFTEYXBJ4AAAAASUVORK5CYII=\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c8e652f70>"
      ]
     },
     "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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c8e6b6100>"
      ]
     },
     "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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c8e7182e0>"
      ]
     },
     "execution_count": 9,
     "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.distplot(train['Age'].dropna(),kde=False,color='darkred',bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c8e7dd190>"
      ]
     },
     "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=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c8e8bd670>"
      ]
     },
     "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c8e922130>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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avHix/H6/+vv7ZVmWIpGIhoeHVVBQ4HzGAABMM+M+Q963b5+am5s1Y8YMzZ49W83NzcrOzlYoFFIwGJRt26qtrZXX652I+QIAkJbGVMhz5sxRZ2enJGnx4sXq6Oh4ZEwgEFAgEEjt7AAAmCa4MQgAAAagkJOI34undBwAAN9n3N9Dnm6y3Flyve1KOs5usp/AbAAA6YozZAAADEAhAwBgAAoZAAADUMgAABiAQgYAwAAUMgAABqCQAQAwAIUMAIABKGQAAAxAIQMAYAAKGQAAA1DIAAAYgEIGAMAAFDIAAAagkAEAMACFDACAAcZUyOfPn1coFJIkXbp0SRs3blQwGFRTU5MePHggSers7NS6desUCATU09MzcTMGACANJS3kjz/+WA0NDbIsS5LU2tqqmpoa/eEPf5Bt2+ru7taNGzfU3t6ujo4OffLJJzp48KBGRkYmfPIAAKSLpIWcl5enQ4cOJR4PDg6qpKREklReXq4zZ85oYGBARUVF8ng8ysnJUV5enoaGhiZu1gAApBl3sgEVFRW6fPly4rFt23K5XJIkn8+nSCSiaDSqnJycxBifz6doNJr04JZlKRwOO5n39yosLEzZvpxI5VomQzwen/JrmCxk5xzZOUd2zpmYXdJC/v8yMv53Uh2LxZSbm6vs7GzFYrGHtn+3oB/H6/VOeomm0lRfSzgcnvJrmCxk5xzZOUd2zk1WdqO9CRj3p6wXLVqkvr4+SVJvb6+Ki4vl9/vV398vy7IUiUQ0PDysgoIC5zMGAGCaGfcZcl1dnRobG3Xw4EHl5+eroqJCmZmZCoVCCgaDsm1btbW18nq9EzFfAADS0pgKec6cOers7JQkzZs3T8eOHXtkTCAQUCAQSO3sAACYJrgxCAAABqCQAQAwAIUMAIABKGQAAAxAIQMAYAAKGQAAA1DIAAAYgEIGAMAAFDIAAAagkAEAMACFDACAAShkAAAMQCEDAGAAChkAAANQyAAAGIBCBgDAABQyAAAGoJABADCA2+kLX3vtNeXk5EiS5syZo6qqKu3evVsul0sLFixQU1OTMjLoewAAxsJRIVuWJUlqb29PbKuqqlJNTY1KS0u1d+9edXd3a+XKlamZJQAAac7RKezQ0JC+/fZbVVZWavPmzTp37pwGBwdVUlIiSSovL9eZM2dSOlEAANKZozPkrKwsbd26VRs2bNA///lPvfnmm7JtWy6XS5Lk8/kUiURSOlEAANKZo0KeN2+e5s6dK5fLpXnz5mnWrFkaHBxMPB+LxZSbm5t0P5ZlKRwOO5nC9yosLEzZvpxI5VomQzwen/JrmCxk5xzZOUd2zpmYnaNCPn78uL7++mvt27dP169fVzQaVVlZmfr6+lRaWqre3l69+OKLSffj9XonvURTaaqvJRwOT/k1TBayc47snCM75yYru9HeBDgq5PXr12vPnj3auHGjXC6X9u/fr6efflqNjY06ePCg8vPzVVFR4XjCAABMN44K2ePx6He/+90j248dO/aDJwQAwHTEDwoDAGAACvkJi9+Lp3QcACA9OL5TF5zJcmfJ9bYr6Ti7yX4CswEAmIIzZAAADEAhAwBgAAoZAAADUMgpMlkfwuJDYgCQHvhQV4pM1oe1+JAYAKQHzpABADAAhWwoLjEDwPTCJWtDcSkaAKYXzpABADAAhQwAgAEoZAAADEAhAwBgAAoZAAADUMgAABiAQsYjuB0nADx5/BzyNBG/F1eWO2vUMYWFhZIm72egxzLH8YwDgKkkpYX84MED7du3T3//+9/l8XjU0tKiuXPnpvIQcGisJStNXtFyMxQA01lKC/kvf/mLRkZG9Nlnn+ncuXN65513dPTo0VQeAlMQRft4k3VVgKsRgHlSWsj9/f1atmyZJGnp0qW6cOFCKncPw/Cf9eOZflWAN0mY7p6d9+yYxj3J/+dSWsjRaFTZ2dmJx5mZmbp3757cbr5VnY74XvPjmZ4NMN1lZ2Ub96bUZdt2yo7W2tqqn/3sZ1qzZo0kqby8XL29vY8df+7cOXm93lQdHgAAo1mWpaVLl37vcyk9dX3hhRfU09OjNWvW6Ny5cyooKBh1/OMmBQDAdJPSM+T/fsr666+/lm3b2r9/v+bPn5+q3QMAkLZSWsgAAMAZ7tQFAIABKGQAAAxAIQMAYIC0+AFhbtk5dufPn9dvf/tbtbe369KlS9q9e7dcLpcWLFigpqYmZWRkqLOzUx0dHXK73aqurtby5csne9qT7u7du6qvr9eVK1c0MjKi6upqPf/88+Q3Bvfv31dDQ4MuXryozMxMtba2yrZtshuHW7duad26dfr9738vt9tNdmP02muvKScnR5I0Z84cVVVVmZ2dnQa++OILu66uzrZt2/7rX/9qV1VVTfKMzPTRRx/Zr7zyir1hwwbbtm1727Zt9ldffWXbtm03Njbaf/7zn+1//etf9iuvvGJblmXfuXMn8fV0d/z4cbulpcW2bdu+ffu2/dJLL5HfGJ06dcrevXu3bdu2/dVXX9lVVVVkNw4jIyP2L3/5S3vVqlX2P/7xD7Ibo3g8br/66qsPbTM9u7S4ZM0tO8cmLy9Phw4dSjweHBxUSUmJpP/cxOXMmTMaGBhQUVGRPB6PcnJylJeXp6GhocmasjFWr16tX//614nHmZmZ5DdGK1asUHNzsyTp6tWrmj17NtmNQ1tbm9544w396Ec/ksS/27EaGhrSt99+q8rKSm3evFnnzp0zPru0KOTH3bITD6uoqHjoNqa2bcvl+s+t43w+nyKRiKLRaOISz3+3R6PRJz5X0/h8PmVnZysajWrHjh2qqakhv3Fwu92qq6tTc3OzKioqyG6MPv/8cz3zzDOJEw6Jf7djlZWVpa1bt+qTTz7R22+/rZ07dxqfXVoUcnZ2tmKxWOLxgwcPuH/2GGRk/O+PPxaLKTc395EsY7HYQ39Zp7Nr165p8+bNevXVV7V27VryG6e2tjZ98cUXamxslGVZie1k93hdXV06c+aMQqGQwuGw6urqdPv27cTzZPd48+bN0y9+8Qu5XC7NmzdPs2bN0q1btxLPm5hdWhTyCy+8kLhn9lhu2Yn/WLRokfr6+iRJvb29Ki4ult/vV39/vyzLUiQS0fDwMHlKunnzpiorK7Vr1y6tX79eEvmN1YkTJ/Thhx9KkmbOnCmXy6UlS5aQ3Rh8+umnOnbsmNrb21VYWKi2tjaVl5eT3RgcP35c77zzjiTp+vXrikajKisrMzq7tLhTF7fsHLvLly/rN7/5jTo7O3Xx4kU1Njbq7t27ys/PV0tLizIzM9XZ2anPPvtMtm1r27ZtqqiomOxpT7qWlhb96U9/Un5+fmLbW2+9pZaWFvJL4t///rf27Nmjmzdv6t69e3rzzTc1f/58/u6NUygU0r59+5SRkUF2YzAyMqI9e/bo6tWrcrlc2rlzp55++mmjs0uLQgYAYKpLi0vWAABMdRQyAAAGoJABADAAhQwAgAEoZAAADEAhAwBgAAoZAAADUMgAABjg/wDAFlyrgNd96AAAAABJRU5ErkJggg==\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",
    "### 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) {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-latest.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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          30.0708,
          16.7,
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          16,
          29.125,
          13,
          18,
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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": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c8f38c580>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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": 16,
   "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": 17,
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c90a7f0d0>"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   Q  S\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  1\n",
       "4  0  1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(train['Embarked'],drop_first=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
       "0            1         0       3  22.0      1      0   7.2500\n",
       "1            2         1       1  38.0      1      0  71.2833\n",
       "2            3         1       3  26.0      0      0   7.9250\n",
       "3            4         1       1  35.0      1      0  53.1000\n",
       "4            5         0       3  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "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": [
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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>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",
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       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <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": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0            1       3  22.0      1      0   7.2500     1  0  1\n",
       "1            2       1  38.0      1      0  71.2833     0  0  0\n",
       "2            3       3  26.0      0      0   7.9250     0  0  1\n",
       "3            4       1  35.0      1      0  53.1000     0  0  1\n",
       "4            5       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop('Survived',axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Survived'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alina Sahoo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: 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": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy=confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[148,  15],\n",
       "       [ 39,  65]], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.797752808988764"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy=accuracy_score(y_test,predictions)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "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": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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))"
   ]
  },
  {
   "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"
   ]
  }
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