titanic_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/Users/DELL/Desktop/titanic data\\gender_submission.csv\n",
      "C:/Users/DELL/Desktop/titanic data\\test.csv\n",
      "C:/Users/DELL/Desktop/titanic data\\train.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy  as np \n",
    "import os\n",
    "for dirname, _, filenames in os.walk('C:/Users/DELL/Desktop/titanic data'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch     Fare Embarked  \n",
       "0      0   7.2500        S  \n",
       "1      0  71.2833        C  \n",
       "2      0   7.9250        S  \n",
       "3      0  53.1000        S  \n",
       "4      0   8.0500        S  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf = pd.read_csv(\"C:/Users/DELL/Desktop/titanic data/train.csv\")\n",
    "tf.drop(['Ticket','Cabin'],axis=1,inplace=True)\n",
    "tf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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>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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.362500</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.564165</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.396250</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.655000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.402500</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex  Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0  2.2      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  3.8      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1  2.6      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  3.5      1      0   \n",
       "4                           Allen, Mr. William Henry    0  3.5      0      0   \n",
       "\n",
       "       Fare  Embarked  \n",
       "0  0.362500       1.0  \n",
       "1  3.564165       0.0  \n",
       "2  0.396250       1.0  \n",
       "3  2.655000       1.0  \n",
       "4  0.402500       1.0  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_mapping={'male':0,'female':1}\n",
    "class_map = {'C':0,'S':1,'Q':2}\n",
    "tf['Embarked'] = tf['Embarked'].map(class_map)\n",
    "tf['Sex']  = tf['Sex'].map(gender_mapping)\n",
    "tf['Age'] = tf['Age'].fillna(tf['Age'].median())\n",
    "tf['Embarked'].fillna(0,inplace=True)\n",
    "tf['Fare']=tf['Fare']/20\n",
    "tf['Age'] = tf['Age']/10\n",
    "tf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5    517\n",
       "1.5    182\n",
       "1.0    125\n",
       "0.0     40\n",
       "4.5     13\n",
       "3.5      7\n",
       "2.0      6\n",
       "4.0      1\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(tf.shape[0]):\n",
    "    if(\"Master\" in str(tf.loc[i,\"Name\"])):\n",
    "        tf.loc[i,\"Name\"] = 0\n",
    "    elif(\"Mr.\" in str(tf.loc[i,\"Name\"])):\n",
    "        tf.loc[i,\"Name\"] = 0.5\n",
    "    elif(\"Mrs.\" in str(tf.loc[i,\"Name\"])):\n",
    "        tf.loc[i,\"Name\"] = 1\n",
    "    elif(\"Miss.\" in str(tf.loc[i,\"Name\"])):\n",
    "        tf.loc[i,\"Name\"] = 1.5\n",
    "    elif(\"Rev.\" in str(tf.loc[i,\"Name\"])):\n",
    "        tf.loc[i,\"Name\"] = 2\n",
    "    elif(\"Dr.\" in str(tf.loc[i,\"Name\"])):\n",
    "        tf.loc[i,\"Name\"] = 3.5\n",
    "    elif(\"Sir\" in str(tf.loc[i,\"Name\"])):\n",
    "        tf.loc[i,\"Name\"] = 4\n",
    "    else:\n",
    "        tf.loc[i,\"Name\"] = 4.5\n",
    "tf[\"Name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>p3</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.564165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.396250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.655000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.402500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass Name  Sex  Age  SibSp  Parch      Fare  \\\n",
       "0            1         0       3  0.5    0  2.2      1      0  0.362500   \n",
       "1            2         1       1    1    1  3.8      1      0  3.564165   \n",
       "2            3         1       3  1.5    1  2.6      0      0  0.396250   \n",
       "3            4         1       1    1    1  3.5      1      0  2.655000   \n",
       "4            5         0       3  0.5    0  3.5      0      0  0.402500   \n",
       "\n",
       "   Embarked  p1  p2  p3  male  female  \n",
       "0       1.0   0   0   1     0       1  \n",
       "1       0.0   1   0   0     1       0  \n",
       "2       1.0   0   0   1     1       0  \n",
       "3       1.0   1   0   0     1       0  \n",
       "4       1.0   0   0   1     0       1  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m= [[],[]]\n",
    "l=[[],[],[]]\n",
    "for i in range(tf.shape[0]):\n",
    "    if(tf.loc[i,\"Pclass\"]==1):\n",
    "        l[0].append(1)\n",
    "        l[1].append(0)\n",
    "        l[2].append(0)\n",
    "    elif(tf.loc[i,\"Pclass\"]==2):\n",
    "        l[0].append(0)\n",
    "        l[1].append(1)\n",
    "        l[2].append(0)\n",
    "    else:\n",
    "        l[0].append(0)\n",
    "        l[1].append(0)\n",
    "        l[2].append(1)    \n",
    "    if(tf.loc[i,\"Sex\"]==1):\n",
    "        m[0].append(1)\n",
    "        m[1].append(0)\n",
    "    else:\n",
    "        m[0].append(0)\n",
    "        m[1].append(1)\n",
    "tf[\"p1\"]=l[0]\n",
    "tf[\"p2\"]=l[1]\n",
    "tf[\"p3\"]=l[2]\n",
    "tf[\"male\"]=m[0]\n",
    "tf[\"female\"]=m[1]\n",
    "tf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-5e2ff45dbecf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLogisticRegression\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdre\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#from sklearn.ensemble import RandomForestClassifier as dre\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Survived\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"male\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"female\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Parch\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Age\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"p1\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"p2\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"p3\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"SibSp\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Embarked\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Fare\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Name\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'tf' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn import utils\n",
    "from sklearn.linear_model import LogisticRegression as dre\n",
    "#from sklearn.ensemble import RandomForestClassifier as dre\n",
    "y = tf[\"Survived\"]\n",
    "features = [\"male\",\"female\",\"Parch\",\"Age\",\"p1\",\"p2\",\"p3\",\"SibSp\",\"Embarked\",\"Fare\",\"Name\"]\n",
    "X = pd.get_dummies(tf[features])\n",
    "#model = dre(n_estimators=200,max_depth=40,n_jobs=-1)\n",
    "model = dre(n_jobs=-1,max_iter=10000)\n",
    "model.fit(X, y)\n",
    "predictions = model.predict(X)\n",
    "#output = pd.DataFrame({'PassengerId': train_data.PassengerId, 'Survived': predictions})\n",
    "#output.to_csv('my_submission.csv', index=False)\n",
    "#print(\"Your submission was successfully saved!\")\n",
    "from sklearn.metrics import f1_score\n",
    "print((f1_score(tf['Survived'],predictions))*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.63313609467455\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print((f1_score(tf['Survived'],predictions))*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 14 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null int64\n",
      "Age            418 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Fare           418 non-null float64\n",
      "Embarked       418 non-null int64\n",
      "p1             418 non-null int64\n",
      "p2             418 non-null int64\n",
      "p3             418 non-null int64\n",
      "male           418 non-null int64\n",
      "female         418 non-null int64\n",
      "dtypes: float64(2), int64(11), object(1)\n",
      "memory usage: 45.8+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "td = pd.read_csv(\"C:/Users/DELL/Desktop/titanic data/test.csv\")\n",
    "td.drop(['Ticket','Cabin'],axis=1,inplace=True)\n",
    "gender_mapping={'male':0,'female':1}\n",
    "class_map = {'C':0,'S':1,'Q':2}\n",
    "td['Embarked'] = td['Embarked'].map(class_map)\n",
    "td['Sex']  = td['Sex'].map(gender_mapping)\n",
    "td['Age'] = td['Age'].fillna(td['Age'].median())\n",
    "td['Embarked'].fillna(0,inplace=True)\n",
    "td['Fare'].fillna(10,inplace=True)\n",
    "td['Fare']=td['Fare']/20\n",
    "td['Age'] = td['Age']/10\n",
    "male= []\n",
    "female=[]\n",
    "p1=[]\n",
    "p2=[]\n",
    "p3=[]\n",
    "f1=[]\n",
    "f2=[]\n",
    "f3=[]\n",
    "for i in range(td.shape[0]):\n",
    "    if(td.loc[i,\"Pclass\"]==1):\n",
    "        p1.append(1)\n",
    "        p2.append(0)\n",
    "        p3.append(0)\n",
    "    elif(td.loc[i,\"Pclass\"]==2):\n",
    "        p1.append(0)\n",
    "        p2.append(1)\n",
    "        p3.append(0)\n",
    "    else:\n",
    "        p1.append(0)\n",
    "        p2.append(0)\n",
    "        p3.append(1)    \n",
    "    if(td.loc[i,\"Sex\"]==1):\n",
    "        female.append(1)\n",
    "        male.append(0)\n",
    "    else:\n",
    "        female.append(0)\n",
    "        male.append(1)\n",
    "td[\"p1\"]=p1\n",
    "td[\"p2\"]=p2\n",
    "td[\"p3\"]=p3\n",
    "td[\"male\"]=male\n",
    "td[\"female\"]=female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your submission was successfully saved!\n"
     ]
    }
   ],
   "source": [
    "X_test = pd.get_dummies(td[features]) \n",
    "predictions = model.predict(X_test)\n",
    "output = pd.DataFrame({'PassengerId': td.PassengerId, 'Survived': predictions})\n",
    "output.to_csv('my_submission33.csv', index=False)\n",
    "print(\"Your submission was successfully saved!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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