Kaggle_Titanic2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing The Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading The Dataset to a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n",
      "Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
      "       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "data=pd.read_csv(r\"C:\\Users\\ANONYMOUS\\Downloads\\train.csv\", delimiter=\",\")\n",
    "te=pd.read_csv(r\"C:\\Users\\ANONYMOUS\\Downloads\\test.csv\", delimiter=\",\")\n",
    "print(data.keys())\n",
    "print(te.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting The Features And Combining The Traing Set And test set For Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.drop(['Survived'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xf=pd.concat([X, te],axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Function Replaces The Names Of Passengers With Thier Initials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Initials(X):\n",
    "    lmao=[]\n",
    "    name=X[['Name']]\n",
    "    dct=name.values\n",
    "    for j in dct :\n",
    "        i=j[0][:]\n",
    "        tials=i[i.find(',')+1:i.find('.')]\n",
    "        lmao.append(tials)\n",
    "    X=X.drop(['Name'],axis=1)\n",
    "    X.insert(0, 'Initials', lmao, True)\n",
    "\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xf=Initials(Xf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Function Replaces The Age Of The Passengers With 1(adult) and 0(child)\n",
    "(The Function was later not used because it is related to the initials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Adult(X):\n",
    "    age=X['Age']\n",
    "    age=age.values\n",
    "    age=np.where(age>=18, 1, 0)\n",
    "    X.insert(2, 'Adult', age, True)\n",
    "    age=X.drop(['Age'],axis=1)\n",
    "    return age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xf=Xf.drop(['Cabin', 'PassengerId', 'Ticket','Embarked','Age', 'SibSp', 'Parch'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Initials</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mr</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miss</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mr</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Mr</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>Dona</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>108.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Mr</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>Mr</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Master</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.3583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Initials  Pclass     Sex      Fare\n",
       "0         Mr       3    male    7.2500\n",
       "1        Mrs       1  female   71.2833\n",
       "2       Miss       3  female    7.9250\n",
       "3        Mrs       1  female   53.1000\n",
       "4         Mr       3    male    8.0500\n",
       "..       ...     ...     ...       ...\n",
       "413       Mr       3    male    8.0500\n",
       "414     Dona       1  female  108.9000\n",
       "415       Mr       3    male    7.2500\n",
       "416       Mr       3    male    8.0500\n",
       "417   Master       3    male   22.3583\n",
       "\n",
       "[1309 rows x 4 columns]"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Label Encoding Initials And Sex Of Passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "def enc(dtn):\n",
    "    lb=LabelEncoder()\n",
    "    #le=LabelEncoder()\n",
    "    ini=lb.fit_transform(dtn[['Initials']])\n",
    "    sex=lb.fit_transform(dtn[['Sex']])\n",
    "    dtn=dtn.drop(['Initials'], axis=1)\n",
    "    dtn=dtn.drop(['Sex'], axis=1)\n",
    "    dtn.insert(1,'Initials',ini,True)    \n",
    "    dtn.insert(1,'Sex',sex,True)\n",
    "    return dtn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ANONYMOUS\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "Xf=enc(Xf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Initials</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>108.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>22.3583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "     Pclass  Sex  Initials      Fare\n",
       "0         3    1        12    7.2500\n",
       "1         1    0        13   71.2833\n",
       "2         3    0         9    7.9250\n",
       "3         1    0        13   53.1000\n",
       "4         3    1        12    8.0500\n",
       "..      ...  ...       ...       ...\n",
       "413       3    1        12    8.0500\n",
       "414       1    0         3  108.9000\n",
       "415       3    1        12    7.2500\n",
       "416       3    1        12    8.0500\n",
       "417       3    1         8   22.3583\n",
       "\n",
       "[1309 rows x 4 columns]"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 411.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.catplot(x='SibSp',y='Parch', hue='Survived', data=data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x75a51e7fa0>"
      ]
     },
     "execution_count": 371,
     "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": [
    "plt.scatter(data[['SibSp']], data[['Parch']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seperate The Training Set After Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=Xf.iloc[0:891,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression\n",
    "coding Logistic regression without a logistic regression library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def parameter_initialize(X):\n",
    "    a=np.zeros((1,X.shape[1]))*0.01\n",
    "    b=np.zeros((1,1))\n",
    "    return a,b\n",
    "\n",
    "def Z(X,a,b):\n",
    "    z=np.dot(X,a.T).T+b\n",
    "    #return z.T\n",
    "    return z\n",
    "def sigmoid(z):\n",
    "    a=1/(1+np.exp(-z))\n",
    "    return a\n",
    "def foward(X,theta,b):\n",
    "    z=Z(X,theta,b)\n",
    "    a=sigmoid(z)\n",
    "    return a.T\n",
    "def cost(y_pred, y_ref):\n",
    "    error=-(y_ref*np.log(y_pred)+(1-y_ref)*np.log(1-y_pred))\n",
    "    \n",
    "    return np.sum(error)/y_ref.shape[0]\n",
    "def Dee_Z(y_pred,Y):\n",
    "    dee_z=y_pred-Y\n",
    "    return dee_z\n",
    "def Dee_parameter(X,dz):\n",
    "    dee_theta=np.dot(dz.T,X)/X.shape[0]\n",
    "    dee_b=np.sum(dz)/X.shape[0]\n",
    "    dee_b=np.reshape(dee_b,(1,1))\n",
    "    return dee_theta.T, dee_b\n",
    "    \n",
    "def back(X,Y,y_pred):\n",
    "    dz=Dee_Z(y_pred,Y)\n",
    "    dee_theta, dee_b=Dee_parameter(X,dz)\n",
    "    return dee_theta, dee_b\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss=StandardScaler()\n",
    "\n",
    "def update(theta, b,dee_theta, dee_b, learning_rate):\n",
    "    m=X.shape[0]\n",
    "    #print(theta.shape, dee_theta.shape)\n",
    "    #print(dee_theta.shape, dee_b.shape)\n",
    "    theta=theta-learning_rate*dee_theta.T\n",
    "    b=b-learning_rate*dee_b\n",
    "    return theta,b\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "def Logistic(X,Y):\n",
    "    X=ss.fit_transform(X)\n",
    "    #x_test=ss.fit_transform(x_test)\n",
    "    #X=X.values\n",
    "    Y=Y.values\n",
    "    Y=np.reshape(Y,(len(Y),1))\n",
    "    J,x=[],[]\n",
    "    theta, b=parameter_initialize(X)\n",
    "    for i in range(300):\n",
    "        y_pred=foward(X,theta,b)\n",
    "        error=cost(y_pred,Y)\n",
    "        x.append(i)\n",
    "        J.append(error)\n",
    "        dee_theta, dee_b=back(X,Y,y_pred)\n",
    "        theta,b=update(theta, b,dee_theta, dee_b, 0.1)\n",
    "        #print(error)\n",
    "    #plt.plot(x,J)\n",
    "    return theta,b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=StandardScaler()\n",
    "def predict(X,theta,b):\n",
    "    m=X.shape[0]\n",
    "    boun=(342/891)\n",
    "    X=sc.fit_transform(X)\n",
    "    y_pred=foward(X,theta,b)\n",
    "    #print(y_pred)\n",
    "    y_pred=np.where(y_pred>=0.45, 1, 0)\n",
    "    return y_pred.squeeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting The Training Set into train/test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test=train_test_split(X_train,data[['Survived']], test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta,b=Logistic(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting With The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=predict(x_test,theta,b)\n",
    "predt=predict(x_train,theta,b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7704654895666132\n",
      "[[318  67]\n",
      " [ 76 162]]\n",
      "0.8022388059701493\n",
      "[[135  29]\n",
      " [ 24  80]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_train, predt))\n",
    "print(confusion_matrix(y_train, predt))\n",
    "print(accuracy_score(y_test, pred))\n",
    "print(confusion_matrix(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Kaggle Test Set And Saving Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test=Xf.iloc[891:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-381-03fe1e434b30>:8: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  y_pred=np.where(y_pred>=0.45, 1, 0)\n"
     ]
    }
   ],
   "source": [
    "pred_test=predict(X_test,theta,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
       "       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0,\n",
       "       1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "te.insert(2, 'Survived', pred_test, True)\n",
    "file=te[['PassengerId', 'Survived']]\n",
    "file.to_csv('mysolution.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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