Titanic logistic regression Tensorflow_PL.ipynb

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   "source": [
    "# **Introduction**"
   ]
  },
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    "tags": []
   },
   "source": [
    "***Howdy, Welcome to the Titanic***"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "**To whom does this notebook appeal to?**<br><br>\n",
    "If you are just starting with WNNC's HELLO FOSS and want to do beginner task in ML, you can try this project!\n",
    "\n",
    "Data reading, visualization and pre-processing has been done for you. We need you to use logistic regression using sklearn library to predict whether or not they survived the sinking of the Titanic. We have also included `gender_submission.csv`, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.\n",
    "\n",
    "Head over to [this cell](#main) to implement logistic regression and then add code for writing the output to file `predictions-ann.csv` [here](#main2). "
   ]
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    "tags": []
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   "source": [
    "![](https://faithmag.com/sites/default/files/styles/article_full/public/2018-09/titanic2.jpg?h=6521bd5e&itok=H8td6QVv)"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "**Basic Imports**"
   ]
  },
  {
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "ouput.csv\n",
      "gender_submission.csv\n",
      "train.csv\n",
      "test.csv\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set()\n",
    "\n",
    "import os\n",
    "files = [f for f in os.listdir('./titanic/')]\n",
    "for f in files:\n",
    "        print(f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "# Reading In the Data"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "We will be first importing the data, and creating copies. I recommend this because it give you access to a clean untouched base file.\n",
    "Next, we will be dropping some things that we don't need such as passenger Id and Ticket price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "source": [
    "train_data = pd.read_csv('./titanic/train.csv')\n",
    "test_data = pd.read_csv('./titanic/test.csv')\n",
    "#make a copy so your original data is not touched\n",
    "train = train_data.copy()\n",
    "test = test_data.copy()\n",
    "train.shape\n",
    "y_train = train['Survived']\n",
    "\n",
    "#We won't need passenger ID or ticket price for the model! They do not provide much insight on the training.\n",
    "Id = pd.DataFrame(test['PassengerId'])\n",
    "train.drop(['PassengerId'], axis = 1, inplace=True)\n",
    "test.drop(['PassengerId'], axis = 1, inplace=True)\n",
    "train.drop(['Survived'], axis = 1, inplace=True)\n",
    "train.drop(['Ticket'], axis = 1, inplace=True)\n",
    "test.drop(['Ticket'], axis = 1, inplace=True)"
   ]
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    "tags": []
   },
   "source": [
    "**Let's take a look at our data!**"
   ]
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  {
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       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2       3                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4       3                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "   SibSp  Parch     Fare Cabin Embarked  \n",
       "0      1      0   7.2500   NaN        S  \n",
       "1      1      0  71.2833   C85        C  \n",
       "2      0      0   7.9250   NaN        S  \n",
       "3      1      0  53.1000  C123        S  \n",
       "4      0      0   8.0500   NaN        S  "
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train.head()"
   ]
  },
  {
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     "start_time": "2020-08-16T00:03:04.355025",
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    "tags": []
   },
   "source": [
    "**Lets see how many null values there are! We need to fill out these values later.**"
   ]
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  {
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   "source": [
    "**We see that we have some midding values from age, and a ton missing from cabin**"
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       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa15e7fb310>"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar='BuPu')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "papermill": {
     "duration": 0.027205,
     "end_time": "2020-08-16T00:03:04.799633",
     "exception": false,
     "start_time": "2020-08-16T00:03:04.772428",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin       687\n",
       "Age         177\n",
       "Embarked      2\n",
       "Fare          0\n",
       "Parch         0\n",
       "SibSp         0\n",
       "Sex           0\n",
       "Name          0\n",
       "Pclass        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum().sort_values(ascending=False)[0:20]\n",
    "# we can see that cabin is midding a lot of values, and age is tooi!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.011933,
     "end_time": "2020-08-16T00:03:04.823720",
     "exception": false,
     "start_time": "2020-08-16T00:03:04.811787",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Data Cleaning**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.011829,
     "end_time": "2020-08-16T00:03:04.847591",
     "exception": false,
     "start_time": "2020-08-16T00:03:04.835762",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**This is an awesome function I created that preprocesses the data. It does thes following**\n",
    "\n",
    "* Fills in null values based on mean or mode\n",
    "* Drops columns that are missing 50 percent of the data\n",
    "\n",
    "*You guys are free to copy this for loop for your own projects!*[](http://)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "papermill": {
     "duration": 0.047502,
     "end_time": "2020-08-16T00:03:04.907233",
     "exception": false,
     "start_time": "2020-08-16T00:03:04.859731",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#clean the train data\n",
    "for i in list(train.columns):\n",
    "    dtype = train[i].dtype\n",
    "    values = 0\n",
    "    if(dtype == float or dtype == int):\n",
    "        method = 'mean'\n",
    "    else:\n",
    "        method = 'mode'\n",
    "    if(train[i].notnull().sum() / 891 <= .5):\n",
    "        train.drop(i, axis = 1, inplace=True)\n",
    "    elif method == 'mean':\n",
    "        train[i]=train[i].fillna(train[i].mean())\n",
    "\n",
    "    else:\n",
    "        train[i]=train[i].fillna(train[i].mode()[0])\n",
    "\n",
    "# WE CAN DO THIS FOR THE TEST SET TOO!\n",
    "\n",
    "#clean the test data\n",
    "for i in list(test.columns):\n",
    "    dtype = test[i].dtype\n",
    "    values = 0\n",
    "    if(dtype == float or dtype == int):\n",
    "        method = 'mean'\n",
    "    else:\n",
    "        method = 'mode'\n",
    "    if(test[i].notnull().sum() / 418 <= .5):\n",
    "        test.drop(i, axis = 1, inplace=True)\n",
    "    elif method == 'mean':\n",
    "        test[i]=test[i].fillna(test[i].mean())\n",
    "\n",
    "    else:\n",
    "        test[i]=test[i].fillna(test[i].mode()[0])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.011993,
     "end_time": "2020-08-16T00:03:04.931751",
     "exception": false,
     "start_time": "2020-08-16T00:03:04.919758",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**As we can see, all of the missing values are gone!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_kg_hide-input": true,
    "papermill": {
     "duration": 0.275108,
     "end_time": "2020-08-16T00:03:05.219105",
     "exception": false,
     "start_time": "2020-08-16T00:03:04.943997",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa156699810>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar='BuPu')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.013345,
     "end_time": "2020-08-16T00:03:05.246558",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.233213",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Feature Engineering**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014138,
     "end_time": "2020-08-16T00:03:05.274477",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.260339",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Title**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.012818,
     "end_time": "2020-08-16T00:03:05.300776",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.287958",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Lets take out the Mr, Miss, etc from the name section, and create a new column names title!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "papermill": {
     "duration": 0.035659,
     "end_time": "2020-08-16T00:03:05.350024",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.314365",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#TITLE\n",
    "\n",
    "train_test_data = [train, test] # combining train and test dataset\n",
    "\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "\n",
    "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 1, \n",
    "                 \"Master\": 0, \"Dr\": 1, \"Rev\": 0, \"Col\": 0, \"Major\": 0, \"Mlle\": 1,\"Countess\": 1,\n",
    "                 \"Ms\": 1, \"Lady\": 1, \"Jonkheer\": 1, \"Don\": 0, \"Dona\" : 1, \"Mme\": 0,\"Capt\": 0,\"Sir\": 0 }\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.013586,
     "end_time": "2020-08-16T00:03:05.377981",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.364395",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Sex**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.013191,
     "end_time": "2020-08-16T00:03:05.405476",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.392285",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can make male and female into catagorical variables such as 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "papermill": {
     "duration": 0.025484,
     "end_time": "2020-08-16T00:03:05.444947",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.419463",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "    \n",
    "sex_mapping = {\"male\": 0, \"female\":1}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Sex'] = dataset['Sex'].map(sex_mapping)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.013681,
     "end_time": "2020-08-16T00:03:05.472676",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.458995",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Embarked**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014216,
     "end_time": "2020-08-16T00:03:05.500391",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.486175",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Lets test to see if there is any correlation with Pclass and Embarked "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "papermill": {
     "duration": 0.249537,
     "end_time": "2020-08-16T00:03:05.764169",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.514632",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa15663b310>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pclass1 = train_data[train_data['Pclass'] == 1]['Embarked'].value_counts()\n",
    "Pclass2 = train_data[train_data['Pclass'] == 2]['Embarked'].value_counts()\n",
    "Pclass3 = train_data[train_data['Pclass'] == 3]['Embarked'].value_counts()\n",
    "\n",
    "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n",
    "df.index = ['1st class','2nd class', '3rd class']\n",
    "df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014366,
     "end_time": "2020-08-16T00:03:05.793175",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.778809",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can assign each embarked value to a numberical value for training later!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "papermill": {
     "duration": 0.027899,
     "end_time": "2020-08-16T00:03:05.835744",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.807845",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for data in train_test_data:\n",
    "    data['Embarked'] = data['Embarked'].fillna(\"S\")\n",
    "    \n",
    "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.013917,
     "end_time": "2020-08-16T00:03:05.863782",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.849865",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Family Size**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014221,
     "end_time": "2020-08-16T00:03:05.892815",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.878594",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Parch ( Parent & child ) and Sibsp( Sibling & Spouse ) are both contributing factors to family size, so lets make a new column called family size, and drop the other ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "papermill": {
     "duration": 0.025993,
     "end_time": "2020-08-16T00:03:05.933872",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.907879",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train[\"FamilySize\"] = train['SibSp'] + train['Parch'] + 1\n",
    "test[\"FamilySize\"] = test['SibSp'] + test['Parch'] + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014325,
     "end_time": "2020-08-16T00:03:05.962742",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.948417",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Correlations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "papermill": {
     "duration": 0.334816,
     "end_time": "2020-08-16T00:03:06.312796",
     "exception": false,
     "start_time": "2020-08-16T00:03:05.977980",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1565d1c50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.corr(),cbar='plasma')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "papermill": {
     "duration": 0.027178,
     "end_time": "2020-08-16T00:03:06.355462",
     "exception": false,
     "start_time": "2020-08-16T00:03:06.328284",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train.drop(['Name'], axis = 1, inplace=True)\n",
    "test.drop(['Name'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "papermill": {
     "duration": 0.031322,
     "end_time": "2020-08-16T00:03:06.401771",
     "exception": false,
     "start_time": "2020-08-16T00:03:06.370449",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked  Title  FamilySize\n",
       "0       3    0  22.0      1      0   7.2500         0      0           2\n",
       "1       1    1  38.0      1      0  71.2833         1      1           2\n",
       "2       3    1  26.0      0      0   7.9250         0      1           1\n",
       "3       1    1  35.0      1      0  53.1000         0      1           2\n",
       "4       3    0  35.0      0      0   8.0500         0      0           1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "papermill": {
     "duration": 0.030995,
     "end_time": "2020-08-16T00:03:06.447465",
     "exception": false,
     "start_time": "2020-08-16T00:03:06.416470",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <td>3</td>\n",
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      ],
      "text/plain": [
       "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked  Title  FamilySize\n",
       "0       3    0  34.5      0      0   7.8292         2      0           1\n",
       "1       3    1  47.0      1      0   7.0000         0      1           2\n",
       "2       2    0  62.0      0      0   9.6875         2      0           1\n",
       "3       3    0  27.0      0      0   8.6625         0      0           1\n",
       "4       3    1  22.0      1      1  12.2875         0      1           3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01354,
     "end_time": "2020-08-16T00:03:06.476026",
     "exception": false,
     "start_time": "2020-08-16T00:03:06.462486",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **PreProcessing**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014099,
     "end_time": "2020-08-16T00:03:06.504426",
     "exception": false,
     "start_time": "2020-08-16T00:03:06.490327",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Now that our data looks good, lets get ready to build our models!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "papermill": {
     "duration": 5.585812,
     "end_time": "2020-08-16T00:03:12.103987",
     "exception": false,
     "start_time": "2020-08-16T00:03:06.518175",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#imports\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01503,
     "end_time": "2020-08-16T00:03:12.134200",
     "exception": false,
     "start_time": "2020-08-16T00:03:12.119170",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We should scale the values in the data, so that the neural network can train better!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "papermill": {
     "duration": 0.038245,
     "end_time": "2020-08-16T00:03:12.188266",
     "exception": false,
     "start_time": "2020-08-16T00:03:12.150021",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "continuous = ['Age', 'Fare', 'Parch', 'Pclass', 'SibSp', 'FamilySize']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "for var in continuous:\n",
    "    train[var] = train[var].astype('float64')\n",
    "    train[var] = scaler.fit_transform(train[var].values.reshape(-1, 1))\n",
    "for var in continuous:\n",
    "    test[var] = test[var].astype('float64')\n",
    "    test[var] = scaler.fit_transform(test[var].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "papermill": {
     "duration": 0.057722,
     "end_time": "2020-08-16T00:03:12.260637",
     "exception": false,
     "start_time": "2020-08-16T00:03:12.202915",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
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       "      <th>Pclass</th>\n",
       "      <td>891.0</td>\n",
       "      <td>-8.772133e-17</td>\n",
       "      <td>1.000562</td>\n",
       "      <td>-1.566107</td>\n",
       "      <td>-0.369365</td>\n",
       "      <td>0.827377</td>\n",
       "      <td>0.827377</td>\n",
       "      <td>0.827377</td>\n",
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       "      <th>Sex</th>\n",
       "      <td>891.0</td>\n",
       "      <td>3.524130e-01</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>891.0</td>\n",
       "      <td>2.232906e-16</td>\n",
       "      <td>1.000562</td>\n",
       "      <td>-2.253155</td>\n",
       "      <td>-0.592481</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.407926</td>\n",
       "      <td>3.870872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
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       "      <td>4.386066e-17</td>\n",
       "      <td>1.000562</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>0.432793</td>\n",
       "      <td>6.784163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>891.0</td>\n",
       "      <td>5.382900e-17</td>\n",
       "      <td>1.000562</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>6.974147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>891.0</td>\n",
       "      <td>3.987333e-18</td>\n",
       "      <td>1.000562</td>\n",
       "      <td>-0.648422</td>\n",
       "      <td>-0.489148</td>\n",
       "      <td>-0.357391</td>\n",
       "      <td>-0.024246</td>\n",
       "      <td>9.667167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>891.0</td>\n",
       "      <td>3.613917e-01</td>\n",
       "      <td>0.635673</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <td>891.0</td>\n",
       "      <td>3.591470e-01</td>\n",
       "      <td>0.480020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FamilySize</th>\n",
       "      <td>891.0</td>\n",
       "      <td>-2.392400e-17</td>\n",
       "      <td>1.000562</td>\n",
       "      <td>-0.560975</td>\n",
       "      <td>-0.560975</td>\n",
       "      <td>-0.560975</td>\n",
       "      <td>0.059160</td>\n",
       "      <td>5.640372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count          mean       std       min       25%       50%  \\\n",
       "Pclass      891.0 -8.772133e-17  1.000562 -1.566107 -0.369365  0.827377   \n",
       "Sex         891.0  3.524130e-01  0.477990  0.000000  0.000000  0.000000   \n",
       "Age         891.0  2.232906e-16  1.000562 -2.253155 -0.592481  0.000000   \n",
       "SibSp       891.0  4.386066e-17  1.000562 -0.474545 -0.474545 -0.474545   \n",
       "Parch       891.0  5.382900e-17  1.000562 -0.473674 -0.473674 -0.473674   \n",
       "Fare        891.0  3.987333e-18  1.000562 -0.648422 -0.489148 -0.357391   \n",
       "Embarked    891.0  3.613917e-01  0.635673  0.000000  0.000000  0.000000   \n",
       "Title       891.0  3.591470e-01  0.480020  0.000000  0.000000  0.000000   \n",
       "FamilySize  891.0 -2.392400e-17  1.000562 -0.560975 -0.560975 -0.560975   \n",
       "\n",
       "                 75%       max  \n",
       "Pclass      0.827377  0.827377  \n",
       "Sex         1.000000  1.000000  \n",
       "Age         0.407926  3.870872  \n",
       "SibSp       0.432793  6.784163  \n",
       "Parch      -0.473674  6.974147  \n",
       "Fare       -0.024246  9.667167  \n",
       "Embarked    1.000000  2.000000  \n",
       "Title       1.000000  1.000000  \n",
       "FamilySize  0.059160  5.640372  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe(include='all').T\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014497,
     "end_time": "2020-08-16T00:03:12.290830",
     "exception": false,
     "start_time": "2020-08-16T00:03:12.276333",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Logistic Regression**\n",
    "\n",
    "* `train` - stores training set data\n",
    "* `y_train` - stores training set results\n",
    "* `test` - stores test set data on which predictions have to be made\n",
    "\n",
    "First, fit the model using `train` and `y_train` and then make prediction on `test`\n",
    "<a id='main'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "papermill": {
     "duration": 2.812936,
     "end_time": "2020-08-16T00:03:15.147176",
     "exception": false,
     "start_time": "2020-08-16T00:03:12.334240",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n"
     ]
    },
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       "       0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "w=LogisticRegression(solver='lbfgs',verbose=1).fit(train,y_train)\n",
    "# w.predict_proba(train)\n",
    "# w.predict(train)\n",
    "# w.score(train,y_train)\n",
    "w.predict(test)\n",
    "#Write code for logistic regression here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.026701,
     "end_time": "2020-08-16T00:03:18.692199",
     "exception": false,
     "start_time": "2020-08-16T00:03:18.665498",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Create Predictions**\n",
    "<a id='main2'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "papermill": {
     "duration": 0.350469,
     "end_time": "2020-08-16T00:03:19.062253",
     "exception": false,
     "start_time": "2020-08-16T00:03:18.711784",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-d7d653938209>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# writer=\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0moutput_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./titanic/ouput.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Survived'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "#Write code to generate predictions file\n",
    "# writer=\n",
    "import csv\n",
    "output_data=pd.read_csv(\"./titanic/ouput.csv\")\n",
    "y_test=output_data.copy()\n",
    "y_test=y_test['Survived']\n",
    "# print(y_test)\n",
    "# f=w.predict(test)\n",
    "# print(f)\n",
    "\n",
    "with open(\"./titanic/output_pred.csv\",\"w\") as writer:\n",
    "    write_pred=csv.writer(writer)\n",
    "    f=w.predict(test)    \n",
    "#     transform_row(row,f)\n",
    "    write_pred.writerow(f)\n",
    "\n",
    "w.score(test,y_test)\n",
    "#     for val in f:\n",
    "#         write_pred.writerow(val)"
   ]
  }
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