titanic-eda-svm.ipynb

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     "text": [
      "/kaggle/input/titanic/train.csv\n",
      "/kaggle/input/titanic/test.csv\n",
      "/kaggle/input/titanic/gender_submission.csv\n"
     ]
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\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",
    "sns.set(font_scale=1.5)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import cross_validate, GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "sns.set_style(\"whitegrid\")\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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   "source": [
    "gender = pd.read_csv('../input/titanic/gender_submission.csv')\n",
    "train = pd.read_csv('../input/titanic/train.csv')\n",
    "test = pd.read_csv('../input/titanic/test.csv')"
   ]
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       "      <td>Montvila, Rev. Juozas</td>\n",
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       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
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       "      <td>23.4500</td>\n",
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       "      <td>Behr, Mr. Karl Howell</td>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
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       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
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       "     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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \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",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
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AAACDIiAGAAAAAAAAAIMiIAYAAAAAAAAAgyIgBgAAAAAAAACDIiAGAAAAAAAAAIMiIAYAAAAAAAAAgyIgBgAAAAAAAACDIiAGAAAAAAAAAIMiIAYAAAAAAAAAg/r/AUwX+4ox7wr9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(24, 6), sharey=True)\n",
    "sns.histplot(ax=axs[0], data=train, x='Pclass', hue='Survived', multiple=\"dodge\",palette='crest')\n",
    "sns.histplot(ax=axs[1],data=train, x='Embarked', hue='Survived', multiple=\"dodge\",palette='crest')\n",
    "sns.histplot(ax=axs[2],data=train, x='Sex', hue='Survived', multiple=\"dodge\",palette='crest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b791f7a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:21.776678Z",
     "iopub.status.busy": "2022-07-24T20:09:21.776317Z",
     "iopub.status.idle": "2022-07-24T20:09:22.160830Z",
     "shell.execute_reply": "2022-07-24T20:09:22.159427Z"
    },
    "papermill": {
     "duration": 0.396706,
     "end_time": "2022-07-24T20:09:22.163287",
     "exception": false,
     "start_time": "2022-07-24T20:09:21.766581",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Sex')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(24, 6), sharey=True)\n",
    "train.Pclass.value_counts().plot(ax=axs[0],kind='pie',autopct='%1.1f%%',colors = sns.color_palette('crest')).set(title=\"Pclass\")\n",
    "train.Embarked.value_counts().plot(ax=axs[1],kind='pie',autopct='%1.1f%%',colors = sns.color_palette('crest')).set(title=\"Embarked\")\n",
    "train.Sex.value_counts().plot(ax=axs[2],kind='pie',autopct='%1.1f%%',colors = sns.color_palette('crest')).set(title=\"Sex\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cc554474",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:22.185302Z",
     "iopub.status.busy": "2022-07-24T20:09:22.184858Z",
     "iopub.status.idle": "2022-07-24T20:09:23.002691Z",
     "shell.execute_reply": "2022-07-24T20:09:23.001562Z"
    },
    "papermill": {
     "duration": 0.832804,
     "end_time": "2022-07-24T20:09:23.005606",
     "exception": false,
     "start_time": "2022-07-24T20:09:22.172802",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(24, 12), sharey=True)\n",
    "sns.set(font_scale=1.5)\n",
    "sns.histplot(ax=axs[0],data=train,hue='Survived' ,x='Age',multiple=\"dodge\", kde=True,palette='crest')\n",
    "sns.histplot(ax=axs[1],data=train,x='Age',multiple=\"dodge\", kde=True, color='grey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0498c78c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:23.029609Z",
     "iopub.status.busy": "2022-07-24T20:09:23.028618Z",
     "iopub.status.idle": "2022-07-24T20:09:24.126434Z",
     "shell.execute_reply": "2022-07-24T20:09:24.125412Z"
    },
    "papermill": {
     "duration": 1.112777,
     "end_time": "2022-07-24T20:09:24.129408",
     "exception": false,
     "start_time": "2022-07-24T20:09:23.016631",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Parch', ylabel='Count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1728x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(24, 6), sharey=True)\n",
    "sns.histplot(ax=axs[0][0], data=train, x='SibSp', hue='Survived', multiple=\"dodge\",palette='crest')\n",
    "sns.histplot(ax=axs[0][1],data=train, x='Parch', hue='Survived', multiple=\"dodge\",palette='crest')\n",
    "sns.histplot(ax=axs[1][0], data=train, x='SibSp', multiple=\"dodge\", color='grey')\n",
    "sns.histplot(ax=axs[1][1],data=train, x='Parch', multiple=\"dodge\", color='grey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2167346f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:24.155469Z",
     "iopub.status.busy": "2022-07-24T20:09:24.155079Z",
     "iopub.status.idle": "2022-07-24T20:09:25.255581Z",
     "shell.execute_reply": "2022-07-24T20:09:25.254719Z"
    },
    "papermill": {
     "duration": 1.116558,
     "end_time": "2022-07-24T20:09:25.257580",
     "exception": false,
     "start_time": "2022-07-24T20:09:24.141022",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Fare', ylabel='Count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(24, 6), sharey=True)\n",
    "sns.set(font_scale=1.5)\n",
    "sns.histplot(ax=axs[0],data=train,x='Fare',hue='Survived',multiple=\"dodge\", kde=True,palette='crest')\n",
    "sns.histplot(ax=axs[1],data=train,x='Fare',multiple=\"dodge\", kde=True,color='grey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77df393a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:25.284603Z",
     "iopub.status.busy": "2022-07-24T20:09:25.283830Z",
     "iopub.status.idle": "2022-07-24T20:09:25.290781Z",
     "shell.execute_reply": "2022-07-24T20:09:25.290025Z"
    },
    "papermill": {
     "duration": 0.023099,
     "end_time": "2022-07-24T20:09:25.293052",
     "exception": false,
     "start_time": "2022-07-24T20:09:25.269953",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train_prep = train.drop([\"Ticket\", \"Cabin\"], axis=1)\n",
    "df_test_prep = test.drop([\"Ticket\", \"Cabin\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "842bbaad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:25.319674Z",
     "iopub.status.busy": "2022-07-24T20:09:25.318917Z",
     "iopub.status.idle": "2022-07-24T20:09:25.352894Z",
     "shell.execute_reply": "2022-07-24T20:09:25.351677Z"
    },
    "papermill": {
     "duration": 0.050437,
     "end_time": "2022-07-24T20:09:25.355624",
     "exception": false,
     "start_time": "2022-07-24T20:09:25.305187",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def substrings_in_string(big_string, substrings):\n",
    "    for substring in substrings:\n",
    "        if substring in big_string:\n",
    "            return substring\n",
    "    return np.nan\n",
    "\n",
    "title_list=['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',\n",
    "                    'Dr', 'Ms', 'Mlle','Col', 'Capt', 'Mme', 'Countess',\n",
    "                    'Don', 'Jonkheer']\n",
    "\n",
    "df_train_prep['Title']=df_train_prep['Name'].map(lambda x: substrings_in_string(x, title_list))\n",
    "df_test_prep['Title']=df_test_prep['Name'].map(lambda x: substrings_in_string(x, title_list))\n",
    " \n",
    "def replace_titles(x):\n",
    "    title=x['Title']\n",
    "    if title in ['Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col']:\n",
    "        return 'Mr'\n",
    "    elif title in ['Countess', 'Mme']:\n",
    "        return 'Mrs'\n",
    "    elif title in ['Mlle', 'Ms']:\n",
    "        return 'Miss'\n",
    "    elif title =='Dr':\n",
    "        if x['Sex']=='Male':\n",
    "            return 'Mr'\n",
    "        else:\n",
    "            return 'Mrs'\n",
    "    else:\n",
    "        return title\n",
    "df_train_prep['Title']=df_train_prep.apply(replace_titles, axis=1)\n",
    "df_test_prep['Title']=df_test_prep.apply(replace_titles, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b4ae8b5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:25.383025Z",
     "iopub.status.busy": "2022-07-24T20:09:25.382260Z",
     "iopub.status.idle": "2022-07-24T20:09:25.389086Z",
     "shell.execute_reply": "2022-07-24T20:09:25.388381Z"
    },
    "papermill": {
     "duration": 0.023641,
     "end_time": "2022-07-24T20:09:25.391451",
     "exception": false,
     "start_time": "2022-07-24T20:09:25.367810",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train_prep = df_train_prep.drop('Name', axis=1)\n",
    "df_test_prep = df_test_prep.drop('Name', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07e7d2aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:25.417484Z",
     "iopub.status.busy": "2022-07-24T20:09:25.417091Z",
     "iopub.status.idle": "2022-07-24T20:09:25.424087Z",
     "shell.execute_reply": "2022-07-24T20:09:25.423367Z"
    },
    "papermill": {
     "duration": 0.022703,
     "end_time": "2022-07-24T20:09:25.426356",
     "exception": false,
     "start_time": "2022-07-24T20:09:25.403653",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train_prep['Family_size']=df_train_prep['SibSp']+df_train_prep['Parch']\n",
    "df_test_prep['Family_size']=df_test_prep['SibSp']+df_test_prep['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3cb2eca0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:25.452825Z",
     "iopub.status.busy": "2022-07-24T20:09:25.452089Z",
     "iopub.status.idle": "2022-07-24T20:09:26.196352Z",
     "shell.execute_reply": "2022-07-24T20:09:26.195232Z"
    },
    "papermill": {
     "duration": 0.760236,
     "end_time": "2022-07-24T20:09:26.198641",
     "exception": false,
     "start_time": "2022-07-24T20:09:25.438405",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Family_size', ylabel='Count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1728x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(24, 6), sharey=True)\n",
    "sns.histplot(ax=axs[0], data=df_train_prep, x='Family_size', hue='Survived', multiple=\"dodge\",palette='crest')\n",
    "sns.histplot(ax=axs[1],data=df_train_prep, x='Family_size', multiple=\"dodge\",color='grey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9e0d4cf0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:26.226268Z",
     "iopub.status.busy": "2022-07-24T20:09:26.225177Z",
     "iopub.status.idle": "2022-07-24T20:09:26.242314Z",
     "shell.execute_reply": "2022-07-24T20:09:26.241509Z"
    },
    "papermill": {
     "duration": 0.032936,
     "end_time": "2022-07-24T20:09:26.244254",
     "exception": false,
     "start_time": "2022-07-24T20:09:26.211318",
     "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></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>Master</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>41.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2</th>\n",
       "      <th>Master</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">3</th>\n",
       "      <th>Master</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age\n",
       "Pclass Title       \n",
       "1      Master   4.0\n",
       "       Miss    30.0\n",
       "       Mr      42.0\n",
       "       Mrs     41.5\n",
       "2      Master   1.0\n",
       "       Miss    24.0\n",
       "       Mr      31.0\n",
       "       Mrs     31.0\n",
       "3      Master   4.0\n",
       "       Miss    18.0\n",
       "       Mr      26.0\n",
       "       Mrs     31.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac = pd.DataFrame(df_train_prep.groupby(['Pclass', 'Title'])['Age'].median())\n",
    "ac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "acbef6a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:26.272225Z",
     "iopub.status.busy": "2022-07-24T20:09:26.271330Z",
     "iopub.status.idle": "2022-07-24T20:09:26.938000Z",
     "shell.execute_reply": "2022-07-24T20:09:26.937027Z"
    },
    "papermill": {
     "duration": 0.68317,
     "end_time": "2022-07-24T20:09:26.940443",
     "exception": false,
     "start_time": "2022-07-24T20:09:26.257273",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:title={'center':'(Age, 1)'}, xlabel='Title'>,\n",
       "       <AxesSubplot:title={'center':'(Age, 2)'}, xlabel='Title'>,\n",
       "       <AxesSubplot:title={'center':'(Age, 3)'}, xlabel='Title'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(24, 6), sharey=True)\n",
    "ac.unstack(level=0).plot(ax=axs, kind='bar', subplots=True, color=\"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e64c5ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:26.970807Z",
     "iopub.status.busy": "2022-07-24T20:09:26.970442Z",
     "iopub.status.idle": "2022-07-24T20:09:27.025167Z",
     "shell.execute_reply": "2022-07-24T20:09:27.024225Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "papermill": {
     "duration": 0.073263,
     "end_time": "2022-07-24T20:09:27.027634",
     "exception": false,
     "start_time": "2022-07-24T20:09:26.954371",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train_prep.loc[(df_train_prep.Age.isnull()) & (df_train_prep[\"Title\"]=='Master') & (df_train_prep[\"Pclass\"]==1), 'Age'] = 4\n",
    "df_train_prep.loc[(df_train_prep.Age.isnull()) & (df_train_prep[\"Title\"]=='Miss') & (df_train_prep[\"Pclass\"]==1), 'Age'] = 30\n",
    "df_train_prep.loc[(df_train_prep.Age.isnull()) & (df_train_prep[\"Title\"]=='Mr') & (df_train_prep[\"Pclass\"]==1), 'Age'] = 42\n",
    "df_train_prep.loc[(df_train_prep.Age.isnull()) & (df_train_prep[\"Title\"]=='Mrs') & (df_train_prep[\"Pclass\"]==1), 'Age'] = 42\n",
    "\n",
    "df_train_prep.loc[(df_train_prep.Age.isnull()) & (df_train_prep[\"Title\"]=='Master') & (df_train_prep[\"Pclass\"]==2), 'Age'] = 1\n",
    "df_train_prep.loc[(df_train_prep.Age.isnull()) & (df_train_prep[\"Title\"]=='Miss') & (df_train_prep[\"Pclass\"]==2), 'Age'] = 24\n",
    "df_train_prep.loc[(df_train_prep.Age.isnull()) & (df_train_prep[\"Title\"]=='Mr') & (df_train_prep[\"Pclass\"]==2), 'Age'] = 31\n",
    "df_train_prep.loc[(df_train_prep.Age.isnull()) & (df_train_prep[\"Title\"]=='Mrs') & (df_train_prep[\"Pclass\"]==2), 'Age'] = 31\n",
    "\n",
    "df_train_prep.loc[(df_train_prep.Age.isnull()) & (df_train_prep[\"Title\"]=='Master') & (df_train_prep[\"Pclass\"]==3), 'Age'] = 4\n",
    "df_train_prep.loc[(df_train_prep.Age.isnull()) & (df_train_prep[\"Title\"]=='Miss') & (df_train_prep[\"Pclass\"]==3), 'Age'] = 18\n",
    "df_train_prep.loc[(df_train_prep.Age.isnull()) & (df_train_prep[\"Title\"]=='Mr') & (df_train_prep[\"Pclass\"]==3), 'Age'] = 26\n",
    "df_train_prep.loc[(df_train_prep.Age.isnull()) & (df_train_prep[\"Title\"]=='Mrs') & (df_train_prep[\"Pclass\"]==3), 'Age'] = 31\n",
    "\n",
    "\n",
    "df_test_prep.loc[(df_test_prep.Age.isnull()) & (df_test_prep[\"Title\"]=='Master') & (df_test_prep[\"Pclass\"]==1), 'Age'] = 4\n",
    "df_test_prep.loc[(df_test_prep.Age.isnull()) & (df_test_prep[\"Title\"]=='Miss') & (df_test_prep[\"Pclass\"]==1), 'Age'] = 30\n",
    "df_test_prep.loc[(df_test_prep.Age.isnull()) & (df_test_prep[\"Title\"]=='Mr') & (df_test_prep[\"Pclass\"]==1), 'Age'] =  42\n",
    "df_test_prep.loc[(df_test_prep.Age.isnull()) & (df_test_prep[\"Title\"]=='Mrs') & (df_test_prep[\"Pclass\"]==1), 'Age'] = 42\n",
    "\n",
    "df_test_prep.loc[(df_test_prep.Age.isnull()) & (df_test_prep[\"Title\"]=='Master') & (df_test_prep[\"Pclass\"]==2), 'Age'] = 1\n",
    "df_test_prep.loc[(df_test_prep.Age.isnull()) & (df_test_prep[\"Title\"]=='Miss') & (df_test_prep[\"Pclass\"]==2), 'Age'] = 24\n",
    "df_test_prep.loc[(df_test_prep.Age.isnull()) & (df_test_prep[\"Title\"]=='Mr') & (df_test_prep[\"Pclass\"]==2), 'Age'] = 31\n",
    "df_test_prep.loc[(df_test_prep.Age.isnull()) & (df_test_prep[\"Title\"]=='Mrs') & (df_test_prep[\"Pclass\"]==2), 'Age'] = 31\n",
    "\n",
    "df_test_prep.loc[(df_test_prep.Age.isnull()) & (df_test_prep[\"Title\"]=='Master') & (df_test_prep[\"Pclass\"]==3), 'Age'] = 4\n",
    "df_test_prep.loc[(df_test_prep.Age.isnull()) & (df_test_prep[\"Title\"]=='Miss') & (df_test_prep[\"Pclass\"]==3), 'Age'] = 18\n",
    "df_test_prep.loc[(df_test_prep.Age.isnull()) & (df_test_prep[\"Title\"]=='Mr') & (df_test_prep[\"Pclass\"]==3), 'Age'] = 26\n",
    "df_test_prep.loc[(df_test_prep.Age.isnull()) & (df_test_prep[\"Title\"]=='Mrs') & (df_test_prep[\"Pclass\"]==3), 'Age'] = 31\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "57ccd9a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:27.057419Z",
     "iopub.status.busy": "2022-07-24T20:09:27.056333Z",
     "iopub.status.idle": "2022-07-24T20:09:27.064084Z",
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     "exception": false,
     "start_time": "2022-07-24T20:09:27.041344",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_test_prep.Fare.fillna(train.groupby(\"Pclass\").median()[\"Fare\"][3], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "85868155",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:27.095469Z",
     "iopub.status.busy": "2022-07-24T20:09:27.094843Z",
     "iopub.status.idle": "2022-07-24T20:09:27.101840Z",
     "shell.execute_reply": "2022-07-24T20:09:27.101053Z"
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     "exception": false,
     "start_time": "2022-07-24T20:09:27.079789",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train_prep[\"Pclass\"].replace([1,2,3], [3,2,1], inplace=True)\n",
    "df_test_prep[\"Pclass\"].replace([1,2,3], [3,2,1], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "276fb144",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:27.133296Z",
     "iopub.status.busy": "2022-07-24T20:09:27.132468Z",
     "iopub.status.idle": "2022-07-24T20:09:27.148861Z",
     "shell.execute_reply": "2022-07-24T20:09:27.148114Z"
    },
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     "exception": false,
     "start_time": "2022-07-24T20:09:27.117296",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train_prep = pd.get_dummies(df_train_prep, prefix=[\"Sex\", \"Embarked\", \"Title\"])\n",
    "df_test_prep = pd.get_dummies(df_test_prep, prefix=[\"Sex\", \"Embarked\", \"Title\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "db363a99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:27.180156Z",
     "iopub.status.busy": "2022-07-24T20:09:27.179209Z",
     "iopub.status.idle": "2022-07-24T20:09:27.186068Z",
     "shell.execute_reply": "2022-07-24T20:09:27.185324Z"
    },
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     "exception": false,
     "start_time": "2022-07-24T20:09:27.164496",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train = df_train_prep.drop('Survived',axis=1)\n",
    "y_train = df_train_prep.Survived\n",
    "X_test = df_test_prep.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eda16cca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:27.218449Z",
     "iopub.status.busy": "2022-07-24T20:09:27.217775Z",
     "iopub.status.idle": "2022-07-24T20:09:27.230194Z",
     "shell.execute_reply": "2022-07-24T20:09:27.229393Z"
    },
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     "exception": false,
     "start_time": "2022-07-24T20:09:27.202347",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e9043113",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:27.262302Z",
     "iopub.status.busy": "2022-07-24T20:09:27.261614Z",
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     "shell.execute_reply": "2022-07-24T20:09:27.300603Z"
    },
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     "exception": false,
     "start_time": "2022-07-24T20:09:27.246126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "clf = SVC().fit(X_train_scaled, y_train)\n",
    "y_pred = clf.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90479451",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-24T20:09:27.333396Z",
     "iopub.status.busy": "2022-07-24T20:09:27.332995Z",
     "iopub.status.idle": "2022-07-24T20:09:27.342376Z",
     "shell.execute_reply": "2022-07-24T20:09:27.341600Z"
    },
    "papermill": {
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     "end_time": "2022-07-24T20:09:27.344818",
     "exception": false,
     "start_time": "2022-07-24T20:09:27.318079",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": X_test[\"PassengerId\"],\n",
    "        \"Survived\": y_pred\n",
    "    })\n",
    "submission.to_csv('./submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fc9c373",
   "metadata": {
    "papermill": {
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   "source": []
  }
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