Logistic Regression(1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "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",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df = sns.load_dataset('titanic')\n",
    "titanic_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's spilt the data between trainning (train_df) and testing dataset (test_df) first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(712, 15)\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
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       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>853</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.4000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>D</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>663</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.4958</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>D</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>383</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>525</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "853         1       1  female  16.0      0      1  39.4000        S   First   \n",
       "663         0       3    male  36.0      0      0   7.4958        S   Third   \n",
       "21          1       2    male  34.0      0      0  13.0000        S  Second   \n",
       "383         1       1  female  35.0      1      0  52.0000        S   First   \n",
       "525         0       3    male  40.5      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "853  woman       False    D  Southampton   yes  False  \n",
       "663    man        True  NaN  Southampton    no   True  \n",
       "21     man        True    D  Southampton   yes   True  \n",
       "383  woman       False  NaN  Southampton   yes  False  \n",
       "525    man        True  NaN   Queenstown    no   True  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = titanic_df.sample(int(titanic_df.shape[0] * 0.8))\n",
    "print(train_df.shape)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember the row that are in the full df, but not in the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_rows = [idx for idx in titanic_df.index if idx not in train_df.index]\n",
    "len(testing_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(179, 15)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>E</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>A</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "6          0       1    male  54.0      0      0  51.8625        S  First   \n",
       "8          1       3  female  27.0      0      2  11.1333        S  Third   \n",
       "12         0       3    male  20.0      0      0   8.0500        S  Third   \n",
       "23         1       1    male  28.0      0      0  35.5000        S  First   \n",
       "26         0       3    male   NaN      0      0   7.2250        C  Third   \n",
       "\n",
       "      who  adult_male deck  embark_town alive  alone  \n",
       "6     man        True    E  Southampton    no   True  \n",
       "8   woman       False  NaN  Southampton   yes  False  \n",
       "12    man        True  NaN  Southampton    no   True  \n",
       "23    man        True    A  Southampton   yes   True  \n",
       "26    man        True  NaN    Cherbourg    no   True  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = titanic_df.iloc[testing_rows, :]\n",
    "print(test_df.shape)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A new way for checking Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ed8b210>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train_df.isnull(), yticklabels=False, cbar=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So Age is missing about 20% which can be fixed by imputation. But deck seems too much"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[D, NaN, E, C, B, F, A, G]\n",
       "Categories (7, object): [D, E, C, B, F, A, G]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.deck.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1eea2310>"
      ]
     },
     "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.set_style('whitegrid')\n",
    "sns.countplot(x='survived',data= train_df, palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1eefbcd0>"
      ]
     },
     "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": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='survived',hue='pclass',data=train_df,palette='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to fill in the missing value for Age. However, instead of just using the mean for all passengers, we fill it by the mean by pclass because wealthier passengers tend to be older"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ef35f10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 7))\n",
    "sns.boxplot(x='pclass',y='age',data=train_df,palette='winter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    age = cols[0]\n",
    "    pclass = cols[1]\n",
    "    if pd.isnull(age):\n",
    "        if pclass == 1:\n",
    "            return 37\n",
    "        elif pclass == 2:\n",
    "            return 29\n",
    "        else:\n",
    "            return 24\n",
    "    else:\n",
    "        return age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now apply this function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['age'] = train_df[['age','pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f24a1d0>"
      ]
     },
     "execution_count": 14,
     "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_df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now deck has too much missin value and so we just drop that column. Also we will simply drop the row that has missing embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop('deck',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(711, 14)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>853</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.4000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>663</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.4958</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>383</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>525</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "853         1       1  female  16.0      0      1  39.4000        S   First   \n",
       "663         0       3    male  36.0      0      0   7.4958        S   Third   \n",
       "21          1       2    male  34.0      0      0  13.0000        S  Second   \n",
       "383         1       1  female  35.0      1      0  52.0000        S   First   \n",
       "525         0       3    male  40.5      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male  embark_town alive  alone  \n",
       "853  woman       False  Southampton   yes  False  \n",
       "663    man        True  Southampton    no   True  \n",
       "21     man        True  Southampton   yes   True  \n",
       "383  woman       False  Southampton   yes  False  \n",
       "525    man        True   Queenstown    no   True  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f2c1590>"
      ]
     },
     "execution_count": 19,
     "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_df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's use just the numerical variables (will come back to revisit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>853</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>663</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.4958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>383</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>525</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass   age  sibsp  parch     fare\n",
       "853         1       1  16.0      0      1  39.4000\n",
       "663         0       3  36.0      0      0   7.4958\n",
       "21          1       2  34.0      0      0  13.0000\n",
       "383         1       1  35.0      1      0  52.0000\n",
       "525         0       3  40.5      0      0   7.7500"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train_df[['survived','pclass', 'age', 'sibsp', 'parch', 'fare']]\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a Logistic Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train.drop('survived',axis=1), \n",
    "                                                    train['survived'], test_size=0.20, \n",
    "                                                    random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.65      0.87      0.74        85\n",
      "           1       0.62      0.31      0.41        58\n",
      "\n",
      "    accuracy                           0.64       143\n",
      "   macro avg       0.63      0.59      0.58       143\n",
      "weighted avg       0.64      0.64      0.61       143\n",
      "\n",
      "0.6433566433566433\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 70% on average, not bad!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NOW, let's talk about how to include the categorical variables (such as Sex, and Embarked etc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2006f190>"
      ]
     },
     "execution_count": 28,
     "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_df,palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20189b10>"
      ]
     },
     "execution_count": 29,
     "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='embarked',data=train_df,palette='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clearly Sex and Embarked makes a difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(train_df['sex'],drop_first=True)\n",
    "embark = pd.get_dummies(train_df['embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>853</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>663</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>383</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>525</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>469</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>474</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>316</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>711 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     male\n",
       "853     0\n",
       "663     1\n",
       "21      1\n",
       "383     0\n",
       "525     1\n",
       "..    ...\n",
       "103     1\n",
       "469     0\n",
       "474     0\n",
       "158     1\n",
       "316     0\n",
       "\n",
       "[711 rows x 1 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>711 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Q  S\n",
       "853  0  1\n",
       "663  0  1\n",
       "21   0  1\n",
       "383  0  1\n",
       "525  1  0\n",
       "..  .. ..\n",
       "103  0  1\n",
       "469  0  0\n",
       "474  0  1\n",
       "158  0  1\n",
       "316  0  1\n",
       "\n",
       "[711 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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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       "  <tbody>\n",
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       "      <td>853</td>\n",
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       "      <td>663</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <td>21</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>525</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass   age  sibsp  parch     fare\n",
       "853         1       1  16.0      0      1  39.4000\n",
       "663         0       3  36.0      0      0   7.4958\n",
       "21          1       2  34.0      0      0  13.0000\n",
       "383         1       1  35.0      1      0  52.0000\n",
       "525         0       3  40.5      0      0   7.7500"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>survived</th>\n",
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       "      <th>sibsp</th>\n",
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       "      <th>S</th>\n",
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       "      <td>853</td>\n",
       "      <td>1</td>\n",
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       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>383</td>\n",
       "      <td>1</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>525</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass   age  sibsp  parch     fare  male  Q  S\n",
       "853         1       1  16.0      0      1  39.4000     0  0  1\n",
       "663         0       3  36.0      0      0   7.4958     1  0  1\n",
       "21          1       2  34.0      0      0  13.0000     1  0  1\n",
       "383         1       1  35.0      1      0  52.0000     0  0  1\n",
       "525         0       3  40.5      0      0   7.7500     1  1  0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.concat([train, sex, embark], axis=1)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## So we have 3 more features (male, Q, S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.88      0.85       127\n",
      "           1       0.80      0.70      0.75        87\n",
      "\n",
      "    accuracy                           0.81       214\n",
      "   macro avg       0.81      0.79      0.80       214\n",
      "weighted avg       0.81      0.81      0.81       214\n",
      "\n",
      "0.8084112149532711\n"
     ]
    }
   ],
   "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)\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(y_test,predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wonderful, we have increased our F1-score from 70% to 81%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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