keggle titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "test= pd.read_csv('test.csv')\n",
    "train= pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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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>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",
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       "      <td>0</td>\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",
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       "      <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",
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       "    <tr>\n",
       "      <td>2</td>\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",
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       "      <td>3</td>\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",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
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       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "  </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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
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       "      <td>Kelly, Mr. James</td>\n",
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       "      <td>895</td>\n",
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       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
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      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sex_pivot= train.pivot_table(index='Sex',values='Survived')\n",
    "sex_pivot.plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "class_pivot= train.pivot_table(index='Pclass',values='Survived')\n",
    "class_pivot.plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    714.000000\n",
       "mean      29.699118\n",
       "std       14.526497\n",
       "min        0.420000\n",
       "25%       20.125000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "survived= train[train['Survived']==1]\n",
    "died= train[train['Survived']==0]\n",
    "survived[\"Age\"].plot.hist(alpha=0.5,color='red',bins=50)\n",
    "died[\"Age\"].plot.hist(alpha=0.5,color='blue',bins=50)\n",
    "plt.legend(['Survived','Died'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "def process_age(df,cut_points,label_names) :\n",
    "    df[\"Age\"]= df[\"Age\"].fillna(-0.5)\n",
    "    df[\"age_categories\"]=pd.cut(df[\"Age\"],cut_points,labels=label_names)\n",
    "    return df\n",
    "\n",
    "cut_points=[-1,0,5,12,18,35,60,100]\n",
    "label_names=[\"Missing\",\"Infant\",\"Child\",\"Teenager\",\"young adult\",\"adult\",\"senior\"]\n",
    "train= process_age(train,cut_points,label_names)\n",
    "test= process_age(test,cut_points,label_names)\n",
    "\n",
    "pivot= train.pivot_table(index=\"age_categories\",values='Survived')\n",
    "pivot.plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dummies(df,column_name) :\n",
    "    dummies= pd.get_dummies(df[column_name],prefix=column_name)\n",
    "    df= pd.concat([df,dummies],axis=1)\n",
    "    return df\n",
    "for column in [\"Pclass\",\"Sex\",\"age_categories\"]:\n",
    "               train= create_dummies(train,column)\n",
    "               test= create_dummies(test,column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr= LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['Pclass_2', 'Pclass_3', 'Sex_male']\n",
    "lr.fit(train[columns], train['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "columns = ['Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female', 'Sex_male',\n",
    "       'age_categories_Missing','age_categories_Infant',\n",
    "       'age_categories_Child', 'age_categories_Teenager',\n",
    "       'age_categories_young adult', 'age_categories_adult',\n",
    "      'age_categories_senior']\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(train[columns], train[\"Survived\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "holdout=test\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "all_X = train[columns]\n",
    "all_y = train['Survived']\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(\n",
    "    all_X, all_y, test_size=0.20,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "lr.fit(train_x,train_y)\n",
    "predictions= lr.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy= accuracy_score(test_y,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8100558659217877\n"
     ]
    }
   ],
   "source": [
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.76404494 0.76404494 0.7752809  0.78651685 0.8        0.80681818\n",
      " 0.80898876 0.81111111 0.83146067 0.87640449]\n",
      "0.8024670865963002\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "lr= LogisticRegression()\n",
    "scores = cross_val_score(lr,all_X,all_y,cv=10)\n",
    "scores.sort()\n",
    "accuracy= scores.mean()\n",
    "print(scores)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "lr= LogisticRegression()\n",
    "lr.fit(all_X,all_y)\n",
    "holdout_predictions= lr.predict(holdout[columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "holdout_ids= holdout['PassengerId']\n",
    "submission_df= {\"PassengerId\" : holdout_ids,\n",
    "               \"Survived\":holdout_predictions}\n",
    "submission= pd.DataFrame(submission_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"submission.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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