TitanicEasy.ipynb

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  {
   "cell_type": "code",
   "execution_count": 39,
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   "source": [
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#%matplotlib inline\n",
    "\n",
    "#plt.rcParams['figure.figsize'] =(6,8)\n",
    "\n",
    "#plt.rcParams['font.size']=14\n",
    "\n",
    "train = pd.read_csv(\"train.csv\")\n",
    "\n",
    "test = pd.read_csv('test.csv')\n",
    "\n",
    "train['source']= 'train'\n",
    "\n",
    "test['source'] = 'test'\n",
    "\n",
    "titanic = pd.concat([train,test],ignore_index= True)\n",
    "\n",
    "#titanic.head()\n",
    "\n",
    "titanic['Age']=titanic['Age'].fillna(titanic.Age.median())\n",
    "\n",
    "titanic.Embarked = titanic.Embarked.fillna(titanic.Embarked.value_counts().index[0])\n",
    "\n",
    "titanic[\"Sex_\"] = np.where(titanic.Sex ==\"male\",1,0)\n",
    "\n",
    "titanic[\"Embarked_\"] = np.where(titanic.Embarked ==\"C\",1,np.where(titanic.Embarked ==\"Q\",2,3))\n",
    "\n",
    "#Divide into Original form\n",
    "train = titanic.loc[titanic['source'] == \"train\"]\n",
    "\n",
    "test = titanic.loc[titanic['source'] == \"test\"]\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import sklearn.ensemble as ske\n",
    "from sklearn import svm\n",
    "\n",
    "\n",
    "feature_cols = [\"Age\",\"Sex_\",\"Pclass\"]\n",
    "\n",
    "X_train = train[feature_cols]\n",
    "\n",
    "Y_train = train.Survived\n",
    "\n",
    "X_test = test[feature_cols]\n",
    "\n",
    "#randreg = ske.RandomForestClassifier(n_estimators=100)\n",
    "#randreg.fit(X_train, Y_train)\n",
    "\n",
    "#svmclf = svm.SVC(kernel='poly', gamma=3)\n",
    "#svmclf.fit(X_train, Y_train)\n",
    "\n",
    "\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "logreg.fit(X_train, Y_train)\n",
    "#Y_log_pred = randreg.predict(X_test)\n",
    "\n",
    "#Y_log_pred = svmclf.predict(X_test)\n",
    "\n",
    "Y_log_pred = logreg.predict(X_test)\n",
    "Y_log_pred_int = Y_log_pred.astype(int)\n",
    "\n",
    "\n",
    "#acc_log = round(svmclf.score(X_train,Y_train)*100,2)\n",
    "#acc_log = round(randreg.score(X_train,Y_train)*100,2)\n",
    "acc_log = round(logreg.score(X_train,Y_train)*100,2)\n",
    "\n",
    "\n",
    "\n",
    "submission = pd.DataFrame({ \"PassengerId\": test[\"PassengerId\"], \"Survived\": Y_log_pred_int })\n",
    "\n",
    "submission.to_csv('submission4.csv', index=False)"
   ]
  },
  {
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   "execution_count": 40,
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       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
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       "      <th>892</th>\n",
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       "      <th>895</th>\n",
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       "     PassengerId  Survived\n",
       "891          892         0\n",
       "892          893         0\n",
       "893          894         0\n",
       "894          895         0\n",
       "895          896         1"
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>source</th>\n",
       "      <th>Sex_</th>\n",
       "      <th>Embarked_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>train</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>train</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>STON/O2. 3101282</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
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       "      <td>train</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>train</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Age Cabin Embarked     Fare  \\\n",
       "0   22   NaN        S   7.2500   \n",
       "1   38   C85        C  71.2833   \n",
       "2   26   NaN        S   7.9250   \n",
       "3   35  C123        S  53.1000   \n",
       "4   35   NaN        S   8.0500   \n",
       "\n",
       "                                                Name  Parch  PassengerId  \\\n",
       "0                            Braund, Mr. Owen Harris      0            1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                             Heikkinen, Miss. Laina      0            3   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)      0            4   \n",
       "4                           Allen, Mr. William Henry      0            5   \n",
       "\n",
       "   Pclass     Sex  SibSp  Survived            Ticket source  Sex_  Embarked_  \n",
       "0       3    male      1         0         A/5 21171  train     1          3  \n",
       "1       1  female      1         1          PC 17599  train     0          1  \n",
       "2       3  female      0         1  STON/O2. 3101282  train     0          3  \n",
       "3       1  female      1         1            113803  train     0          3  \n",
       "4       3    male      0         0            373450  train     1          3  "
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     "execution_count": 45,
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   ],
   "source": [
    "train.head()"
   ]
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