TitanicCompetition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "34a7455a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "64668a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=pd.read_csv(\"C:\\\\Users\\\\aarus\\\\Downloads\\\\titanic\\\\train.csv\", usecols=['Age','Fare','Survived'])\n",
    "test_data=pd.read_csv(\"C:\\\\Users\\\\aarus\\\\Downloads\\\\titanic\\\\test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b069c977",
   "metadata": {},
   "source": [
    "# Analysis on the Training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2737834a",
   "metadata": {},
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c2062d65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0.000000\n",
       "Age         0.198653\n",
       "Fare        0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "34dd6372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1f9335b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Age       714 non-null    float64\n",
      " 2   Fare      891 non-null    float64\n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 21.0 KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5c3f153e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e6f1fef6",
   "metadata": {},
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "22cb1393",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(columns=['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bc60fe34",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8270a153",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "056bb447",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e19aef0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_imputed=X_train['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "182eed94",
   "metadata": {},
   "outputs": [],
   "source": [
    "null_mask = X_train['Age'].isnull()\n",
    "\n",
    "# Generate a list of non-null age values\n",
    "non_null_ages = X_train['Age'].dropna()\n",
    "\n",
    "# Use np.random.choice to fill null values with random values from non-null ages\n",
    "X_train.loc[null_mask, 'Age'] = np.random.choice(non_null_ages, null_mask.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9b926d62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age     0\n",
       "Fare    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6c6c34dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Age         177\n",
       "Fare          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "622dcc4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aarus\\AppData\\Local\\Temp\\ipykernel_13448\\468893779.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(X_train['Age'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Density'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(X_train['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f3b6c1ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aarus\\AppData\\Local\\Temp\\ipykernel_13448\\4243102321.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(X_train['Age'], ax=ax,hist=False)\n",
      "C:\\Users\\aarus\\AppData\\Local\\Temp\\ipykernel_13448\\4243102321.py:6: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df['Age'], ax=ax, color='yellow',hist=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "\n",
    "sns.distplot(X_train['Age'], ax=ax,hist=False)\n",
    "\n",
    "# Create a new distplot just above the first one with a different color\n",
    "sns.distplot(df['Age'], ax=ax, color='yellow',hist=False)\n",
    "\n",
    "# Optionally, add labels or other customizations\n",
    "plt.xlabel('X-axis Label')\n",
    "plt.ylabel('Y-axis Label')\n",
    "plt.title('Distribution Plot')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e28daf45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e514b6d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b37aaef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ed0b8e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cc85d2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d18a5cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b18b7352",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c09adba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6708ffaf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b95ead1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "090a4789",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3f6cfc0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0016ddd9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "69fc15bf",
   "metadata": {},
   "source": [
    "# Data Visualisation Part\n",
    "\n",
    "**1.countplot**: This plot gives the 'count' of a particular column. The value given in the hue property gives the count of that property"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "643f6237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Survived VS Dead according to Pclass')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y5Ur16NFDCxYsUO3atdWwYUN7m/z8fNWvX/+yj2JXqlTpqscpLg8PDz344IOaPn260tPTdfjwYe3fv1+vvfbaFbcrV66cunXrpm7duqlVq1ZKSkrSzz//fF2PLe/atUvu7u72gFncz+HgwYNq27atateurYkTJ6pSpUry9PTUihUrNGnSpFL7hfHll1+qW7duatGihaZMmaLQ0FB5eHho5syZV3yE/2axWq2FepJKw+We0DJKOED4ZnN3d1e7du1Kfb+nT59Wy5YtZbPZNG7cOFWrVk1eXl76/vvvNXLkSIfr7t///rcGDBigJUuWaM2aNXrqqaeUmJioLVu2qGLFivL29lZycrI2bNigzz//XKtWrdL8+fPVpk0brVmzxpRPyd3KCDtwKY0bN5YkHTt2TJLsf72fPn3aoV1x/jr8sz59+ujJJ5/Uvn37NH/+fPn4+Khr165X3a5FixYKDQ3V/Pnz1bx5c61fv77QEx/VqlXTjh071LZt2yt2o4eHhys/P18HDx50+Ev9Wt/tExMTo2nTpmn+/PlKTU2VxWJRv379ir1948aNlZSUpGPHjpU47Bw+fFhJSUmKioqy/xVf3M9h2bJlysnJ0dKlSx16If54m0v6v0HW+/fvt/9VL/1+G+OPvRSX89///ldeXl5avXq1w8DbmTNnOrSrVq2a8vPztWfPHofbOn9uI/0e8C7X+1FQ7759++y37grs27evWJ91aV0jxTnOgQMHCi0vallRLvfvGx4eri+++EJnzpxx6N0puEVUGu8EKrgWdu3adU3bbdy4USdOnNBnn32mFi1a2JenpqYW2b5+/fqqX7++XnjhBW3evFn33HOPpk2bppdeeknS7z2nbdu2Vdu2bTVx4kS98sor+te//qUNGzbckLCGkuM2Fpxiw4YNRf6VWTBeoeB/8jabTeXLl1dycrJDuylTplzzMXv16iV3d3d9/PHHWrhwoe67775ivdPEzc1NvXv31rJlyzR79mxdunTJ4RaWJD344IP69ddfNX369ELbnz9/XtnZ2ZJkf9nf22+/7dDmzTffvKZzueeee1SlShXNmTNH8+fPV8uWLQvdDklLSyvydmBubq7WrVsnNze3Et+yOHnypPr166e8vDyH4Ffcz6Hgr94/XgOZmZmFQki7du3k4eGhd955x6FtcT8vd3d3WSwWh57AQ4cOOTwVJkk9evSQm5ubxo0bV6hXqeC4HTp0kJ+fnxITE3XhwoUi2zRu3FjBwcGaNm2aw6P2K1eu1I8//lisJ6pK6xq5mujoaKWkpGj79u32ZSdPntTcuXOLtX3Bfzt//kOkc+fOysvL07vvvuuwfNKkSbJYLPbzux5BQUFq0aKFZsyYocOHDzusu1LvVVHXXW5ubqH/n2RlZenSpUsOy+rXry83Nzf7v+vJkycL7b8gKPOmZ9dDzw6cYtiwYTp37pzuv/9+1a5dW7m5udq8ebPmz5+vKlWqOLzb5vHHH9eECRP0+OOPq3HjxkpOTtZPP/10zccMDg5W69atNXHiRJ05c6ZQYLmSPn366J133tGYMWNUv359+/iDAg8//LAWLFigJ554Qhs2bNA999yjvLw87d27VwsWLNDq1avVuHFjNWrUSP369dOUKVOUmZmpu+++W+vWrSv2X9MFLBaL+vfvr1deeUWSNG7cuEJtfvnlFzVt2lRt2rRR27ZtFRISooyMDH388cfasWOHhg8frvLly1/1WD/99JPmzJkjwzCUlZWlHTt2aOHChTp79qwmTpyojh07XvPn0KFDB3l6eqpr1676+9//rrNnz2r69OkKDg629+pJv/9Se/bZZ5WYmKj77rtPnTt31rZt27Ry5cpi1d6lSxd7jf3791dGRoYmT56s6tWr64cffrC3q169uv71r39p/Pjxuvfee9WzZ09ZrVZ9++23CgsLU2Jiomw2myZNmqTHH39cTZo0sb/LaceOHTp37pw++ugjeXh46NVXX9Wjjz6qli1bql+/fvZHz6tUqaKnn376qjWX1jVyNSNGjNCcOXPUvn17DRs2zP7oeeXKlXXy5MmrDvSNjIyU9PtA6ujoaLm7u6tv377q2rWrWrdurX/96186dOiQGjZsqDVr1mjJkiUaPnx4obFIJfX222+refPmuvPOOzV48GBVrVpVhw4d0ueff+4Q4P7o7rvv1m233abY2Fg99dRTslgsmj17dqGAtH79eg0dOlQPPPCAatasqUuXLmn27Nlyd3dXr169JP3+31xycrK6dOmi8PBwZWRkaMqUKapYsaKaN29eKueIUuSch8DwV7dy5UrjscceM2rXrm34+voanp6eRvXq1Y1hw4YZ6enpDm3PnTtnDBw40PD39zf8/PyMBx980MjIyLjso+dXehR0+vTphiTDz8+v0OPDhlH40fMC+fn5RqVKlYp8pLZAbm6u8eqrrxp169Y1rFarcdtttxmRkZHG2LFjHR4TP3/+vPHUU08Z5cqVM8qWLWt07drVOHLkSLEfPS+we/duQ5JhtVqNU6dOFVqflZVlvPXWW0Z0dLRRsWJFw8PDw/Dz8zOioqKM6dOnOzyiezmS7JObm5sREBBg3HHHHcY//vEPY/fu3df1OSxdutRo0KCB4eXlZVSpUsV49dVXjRkzZhR6VDgvL88YO3asERoaanh7exutWrUydu3aZYSHhxfr0fMPP/zQqFGjhmG1Wo3atWsbM2fOtF8rfzZjxgzjjjvusNfdsmVLY+3atQ5tli5datx9992Gt7e3YbPZjKZNmxoff/yxQ5v58+fb9xMYGGjExMQYv/zyi0Obgkesi1Lca+Ryj5536dKl0D5btmxptGzZ0mHZtm3bjHvvvdewWq1GxYoVjcTEROPtt982JBlpaWlF1lbg0qVLxrBhw4ygoCDDYrE4fJ5nzpwxnn76aSMsLMzw8PAwatSoYbz++uvFuuau9Ln82a5du4z777/fCAgIMLy8vIxatWoZo0aNsq8v6vPZtGmTcddddxne3t5GWFiY/bUXkowNGzYYhmEY//vf/4zHHnvMqFatmuHl5WUEBgYarVu3Nr744gv7ftatW2d0797dCAsLMzw9PY2wsDCjX79+xk8//VSs2nFzWQzjFhmxBgC44YYPH6733ntPZ8+eZZAtTIMxOwDwF3X+/HmH+RMnTmj27Nlq3rw5QQemwpgdAPiLioqKUqtWrVSnTh2lp6frww8/VFZWlkaNGuXs0oBSRdgBgL+ozp0769NPP9X7778vi8WiO++8Ux9++KHDY9mAGTBmBwAAmBpjdgAAgKkRdgAAgKkxZke/f5/P0aNH5efnd9UXaQEAANdgGIbOnDmjsLCwK37PHGFH0tGjR0v1ixoBAMDNc+TIkUJfmfNHhB3J/mV1R44ckc1mc3I1AACgOLKyslSpUiWHL50tCmFH//ftvTabjbADAMAt5mpDUBigDAAATI2wAwAATI2wAwAATI0xOwAAuID8/Hzl5uY6uwyX4uHhUSpfSkvYAQDAyXJzc5Wamqr8/Hxnl+JyAgICFBIScl3vwSPsAADgRIZh6NixY3J3d1elSpWu+HK8vxLDMHTu3DllZGRIkkJDQ0u8L8IOAABOdOnSJZ07d05hYWHy8fFxdjkuxdvbW5KUkZGh4ODgEt/SIj4CAOBEeXl5kiRPT08nV+KaCgLgxYsXS7wPwg4AAC6A72YsWml8LoQdAABgaoQdAABQyMaNG2WxWHT69OkbepwBAwaoR48eN/QYhB0AAFzY8ePHNWTIEFWuXFlWq1UhISGKjo7Wpk2bbuhx7777bh07dkz+/v439Dg3A09jAQDgwnr16qXc3Fx99NFHuv3225Wenq5169bpxIkTJdqfYRjKy8tTmTJXjgCenp4KCQkp0TFcDT07AAC4qNOnT+vLL7/Uq6++qtatWys8PFxNmzZVQkKCunXrpkOHDslisWj79u0O21gsFm3cuFHS/92OWrlypSIjI2W1WjVjxgxZLBbt3bvX4XiTJk1StWrVHLY7ffq0srKy5O3trZUrVzq0X7Rokfz8/HTu3DlJ0pEjR/Tggw8qICBAgYGB6t69uw4dOmRvn5eXp/j4eAUEBKhcuXIaMWKEDMMo/Q/uTwg7AAC4KF9fX/n6+mrx4sXKycm5rn3985//1IQJE/Tjjz+qd+/eaty4sebOnevQZu7cuerfv3+hbW02m+677z7NmzevUPsePXrIx8dHFy9eVHR0tPz8/PTll19q06ZN8vX1VceOHe1fg/Hvf/9bs2bN0owZM/TVV1/p5MmTWrRo0XWdV3FwGwsAYCqP3/++s0u4qg8WDS5WuzJlymjWrFkaNGiQpk2bpjvvvFMtW7ZU37591aBBg2s65rhx49S+fXv7fExMjN59912NHz9ekvTTTz9p69atmjNnTpHbx8TE6OGHH9a5c+fk4+OjrKwsff755/awMn/+fOXn5+uDDz6wPy4+c+ZMBQQEaOPGjerQoYPefPNNJSQkqGfPnpKkadOmafXq1dd0HiVBzw4AAC6sV69eOnr0qJYuXaqOHTtq48aNuvPOOzVr1qxr2k/jxo0d5vv27atDhw5py5Ytkn7vpbnzzjtVu3btIrfv3LmzPDw8tHTpUknSf//7X9lsNrVr106StGPHDh04cEB+fn72HqnAwEBduHBBBw8eVGZmpo4dO6ZmzZrZ91mmTJlCdd0IhB0AAFycl5eX2rdvr1GjRmnz5s0aMGCAxowZY/8erT+Oe7ncm4bLli3rMB8SEqI2bdrYb03NmzdPMTExl63B09NTvXv3dmjfp08f+0Dns2fPKjIyUtu3b3eYfvrppyJvjd1MhB0AAG4xERERys7OVlBQkCTp2LFj9nV/HKx8NTExMZo/f75SUlL0v//9T3379r1q+1WrVmn37t1av369Qzi68847tX//fgUHB6t69eoOk7+/v/z9/RUaGqqvv/7avs2lS5e0devWYtdbUoQdAABc1IkTJ9SmTRvNmTNHP/zwg1JTU7Vw4UK99tpr6t69u7y9vXXXXXfZBx4nJSXphRdeKPb+e/bsqTNnzmjIkCFq3bq1wsLCrti+RYsWCgkJUUxMjKpWrepwSyomJkbly5dX9+7d9eWXXyo1NVUbN27UU089pV9++UWS9I9//EMTJkzQ4sWLtXfvXj355JM3/KWFEmEHAACX5evrq2bNmmnSpElq0aKF6tWrp1GjRmnQoEF69913JUkzZszQpUuXFBkZqeHDh+ull14q9v79/PzUtWtX7dix44q3sApYLBb169evyPY+Pj5KTk5W5cqV1bNnT9WpU0cDBw7UhQsXZLPZJEnPPPOMHn74YcXGxioqKkp+fn66//77r+ETKRmLcTMecHdxWVlZ8vf3V2Zmpv0fBABwa7rVnsa6cOGCUlNTVbVqVXl5eTmxKtd0pc+nuL+/6dkBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACm5tSwM3XqVDVo0EA2m002m01RUVFauXKlfX2rVq1ksVgcpieeeMJhH4cPH1aXLl3k4+Oj4OBgPffcc7p06dLNPhUAAOCiyjjz4BUrVtSECRNUo0YNGYahjz76SN27d9e2bdtUt25dSdKgQYM0btw4+zY+Pj72n/Py8tSlSxeFhIRo8+bNOnbsmB555BF5eHjolVdeuennAwDAzXCzvxLjj19vcS0mT56s119/XWlpaWrYsKHeeecdNW3atJSruzqn9ux07dpVnTt3Vo0aNVSzZk29/PLL8vX11ZYtW+xtfHx8FBISYp/++N0Xa9as0Z49ezRnzhw1atRInTp10vjx4zV58mTl5uY645QAAICk+fPnKz4+XmPGjNH333+vhg0bKjo6WhkZGTe9FpcZs5OXl6dPPvlE2dnZioqKsi+fO3euypcvr3r16ikhIUHnzp2zr0tJSVH9+vVVoUIF+7Lo6GhlZWVp9+7dN7V+AADwfyZOnKhBgwbp0UcfVUREhKZNmyYfHx/NmDHjptfi1NtYkrRz505FRUXpwoUL8vX11aJFixQRESFJ6t+/v8LDwxUWFqYffvhBI0eO1L59+/TZZ59JktLS0hyCjiT7fFpa2mWPmZOTo5ycHPt8VlZWaZ8WAAB/Wbm5udq6dasSEhLsy9zc3NSuXTulpKTc9HqcHnZq1aql7du3KzMzU59++qliY2OVlJSkiIgIDR78f/cI69evr9DQULVt21YHDx5UtWrVSnzMxMREjR07tjTKBwAAf/Lbb78pLy+vyA6JvXv33vR6nH4by9PTU9WrV1dkZKQSExPVsGFDvfXWW0W2bdasmSTpwIEDkqSQkBClp6c7tCmYDwkJuewxExISlJmZaZ+OHDlSGqcCAABckNPDzp/l5+c73GL6o+3bt0uSQkNDJUlRUVHauXOnw2CntWvXymaz2W+FFcVqtdofdy+YAABA6Shfvrzc3d2L7JC4UmfEjeLUsJOQkKDk5GQdOnRIO3fuVEJCgjZu3KiYmBgdPHhQ48eP19atW3Xo0CEtXbpUjzzyiFq0aKEGDRpIkjp06KCIiAg9/PDD2rFjh1avXq0XXnhBcXFxslqtzjw1AAD+sjw9PRUZGal169bZl+Xn52vdunUODyHdLE4ds5ORkaFHHnlEx44dk7+/vxo0aKDVq1erffv2OnLkiL744gu9+eabys7OVqVKldSrVy+98MIL9u3d3d21fPlyDRkyRFFRUSpbtqxiY2Md3ssDAABuvvj4eMXGxqpx48Zq2rSp/ff5o48+etNrcWrY+fDDDy+7rlKlSkpKSrrqPsLDw7VixYrSLAsAAFynPn366Pjx4xo9erTS0tLUqFEjrVq1qtCg5ZvB6U9jAQCAa1PSNxrfbEOHDtXQoUOdXYbrDVAGAAAoTYQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABganxdBAAAt5jxUb1v6vFGpXx6Te2Tk5P1+uuva+vWrTp27JgWLVqkHj163JjiioGeHQAAUKqys7PVsGFDTZ482dmlSKJnBwAAlLJOnTqpU6dOzi7Djp4dAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgajyNBQAAStXZs2d14MAB+3xqaqq2b9+uwMBAVa5c+abXQ9gBAACl6rvvvlPr1q3t8/Hx8ZKk2NhYzZo166bXQ9gBAOAWc61vNL7ZWrVqJcMwnF2GHWN2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AABwAa40oNeVlMbnQtgBAMCJ3N3dJUm5ublOrsQ1nTt3TpLk4eFR4n3w6DkAAE5UpkwZ+fj46Pjx4/Lw8JCbG/0Q0u89OufOnVNGRoYCAgLsobAkCDsAADiRxWJRaGioUlNT9fPPPzu7HJcTEBCgkJCQ69oHYQcAACfz9PRUjRo1uJX1Jx4eHtfVo1OAsAMAgAtwc3OTl5eXs8swJW4MAgAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAU3Nq2Jk6daoaNGggm80mm82mqKgorVy50r7+woULiouLU7ly5eTr66tevXopPT3dYR+HDx9Wly5d5OPjo+DgYD333HO6dOnSzT4VAADgopwadipWrKgJEyZo69at+u6779SmTRt1795du3fvliQ9/fTTWrZsmRYuXKikpCQdPXpUPXv2tG+fl5enLl26KDc3V5s3b9ZHH32kWbNmafTo0c46JQAA4GIshot981hgYKBef/119e7dW0FBQZo3b5569+4tSdq7d6/q1KmjlJQU3XXXXVq5cqXuu+8+HT16VBUqVJAkTZs2TSNHjtTx48fl6elZrGNmZWXJ399fmZmZstlsN+zcAAA33uP3v+/sEq7qg0WDnV2CKRT397fLjNnJy8vTJ598ouzsbEVFRWnr1q26ePGi2rVrZ29Tu3ZtVa5cWSkpKZKklJQU1a9f3x50JCk6OlpZWVn23qGi5OTkKCsry2ECAADm5PSws3PnTvn6+spqteqJJ57QokWLFBERobS0NHl6eiogIMChfYUKFZSWliZJSktLcwg6BesL1l1OYmKi/P397VOlSpVK96QAAIDLcHrYqVWrlrZv366vv/5aQ4YMUWxsrPbs2XNDj5mQkKDMzEz7dOTIkRt6PAAA4DxO/24sT09PVa9eXZIUGRmpb7/9Vm+99Zb69Omj3NxcnT592qF3Jz093f7tpyEhIfrmm28c9lfwtNaVviHVarXKarWW8pkAAABX5PSenT/Lz89XTk6OIiMj5eHhoXXr1tnX7du3T4cPH1ZUVJQkKSoqSjt37lRGRoa9zdq1a2Wz2RQREXHTawcAAK7HqT07CQkJ6tSpkypXrqwzZ85o3rx52rhxo1avXi1/f38NHDhQ8fHxCgwMlM1m07BhwxQVFaW77rpLktShQwdFRETo4Ycf1muvvaa0tDS98MILiouLo+cGAABIcnLYycjI0COPPKJjx47J399fDRo00OrVq9W+fXtJ0qRJk+Tm5qZevXopJydH0dHRmjJlin17d3d3LV++XEOGDFFUVJTKli2r2NhYjRs3zlmnBAAAXIzLvWfHGXjPDgCYB+/Z+eu45d6zAwAAcCMQdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKk5NewkJiaqSZMm8vPzU3BwsHr06KF9+/Y5tGnVqpUsFovD9MQTTzi0OXz4sLp06SIfHx8FBwfrueee06VLl27mqQAAABdVxpkHT0pKUlxcnJo0aaJLly7p+eefV4cOHbRnzx6VLVvW3m7QoEEaN26cfd7Hx8f+c15enrp06aKQkBBt3rxZx44d0yOPPCIPDw+98sorN/V8AACA63Fq2Fm1apXD/KxZsxQcHKytW7eqRYsW9uU+Pj4KCQkpch9r1qzRnj179MUXX6hChQpq1KiRxo8fr5EjR+rFF1+Up6fnDT0HAADg2lxqzE5mZqYkKTAw0GH53LlzVb58edWrV08JCQk6d+6cfV1KSorq16+vChUq2JdFR0crKytLu3fvLvI4OTk5ysrKcpgAAIA5ObVn54/y8/M1fPhw3XPPPapXr559ef/+/RUeHq6wsDD98MMPGjlypPbt26fPPvtMkpSWluYQdCTZ59PS0oo8VmJiosaOHXuDzgQAALgSlwk7cXFx2rVrl7766iuH5YMHD7b/XL9+fYWGhqpt27Y6ePCgqlWrVqJjJSQkKD4+3j6flZWlSpUqlaxwAADg0lziNtbQoUO1fPlybdiwQRUrVrxi22bNmkmSDhw4IEkKCQlRenq6Q5uC+cuN87FarbLZbA4TAAAwJ6eGHcMwNHToUC1atEjr169X1apVr7rN9u3bJUmhoaGSpKioKO3cuVMZGRn2NmvXrpXNZlNERMQNqRsAANw6nHobKy4uTvPmzdOSJUvk5+dnH2Pj7+8vb29vHTx4UPPmzVPnzp1Vrlw5/fDDD3r66afVokULNWjQQJLUoUMHRURE6OGHH9Zrr72mtLQ0vfDCC4qLi5PVanXm6QEAABfg1J6dqVOnKjMzU61atVJoaKh9mj9/viTJ09NTX3zxhTp06KDatWvrmWeeUa9evbRs2TL7Ptzd3bV8+XK5u7srKipKDz30kB555BGH9/IAAIC/Lqf27BiGccX1lSpVUlJS0lX3Ex4erhUrVpRWWQAAwERcYoAyAADAjULYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAAplaisNOmTRudPn260PKsrCy1adPmemsCAAAoNSUKOxs3blRubm6h5RcuXNCXX3553UUBAACUljLX0viHH36w/7xnzx6lpaXZ5/Py8rRq1Sr97W9/K73qAAAArtM1hZ1GjRrJYrHIYrEUebvK29tb77zzTqkVBwAAcL2uKeykpqbKMAzdfvvt+uabbxQUFGRf5+npqeDgYLm7u5d6kQAAACV1TWEnPDxckpSfn39DigEAACht1xR2/mj//v3asGGDMjIyCoWf0aNHX3dhAAAApaFEYWf69OkaMmSIypcvr5CQEFksFvs6i8VC2AEAAC6jRI+ev/TSS3r55ZeVlpam7du3a9u2bfbp+++/L/Z+EhMT1aRJE/n5+Sk4OFg9evTQvn37HNpcuHBBcXFxKleunHx9fdWrVy+lp6c7tDl8+LC6dOkiHx8fBQcH67nnntOlS5dKcmoAAMBkShR2Tp06pQceeOC6D56UlKS4uDht2bJFa9eu1cWLF9WhQwdlZ2fb2zz99NNatmyZFi5cqKSkJB09elQ9e/a0r8/Ly1OXLl2Um5urzZs366OPPtKsWbPoXQIAAJIki2EYxrVuNHDgQDVp0kRPPPFEqRZz/PhxBQcHKykpSS1atFBmZqaCgoI0b9489e7dW5K0d+9e1alTRykpKbrrrru0cuVK3XfffTp69KgqVKggSZo2bZpGjhyp48ePy9PT86rHzcrKkr+/vzIzM2Wz2Ur1nAAAN9fj97/v7BKu6oNFg51dgikU9/d3icbsVK9eXaNGjdKWLVtUv359eXh4OKx/6qmnSrJbZWZmSpICAwMlSVu3btXFixfVrl07e5vatWurcuXK9rCTkpKi+vXr24OOJEVHR2vIkCHavXu37rjjjkLHycnJUU5Ojn0+KyurRPUCAADXV6Kw8/7778vX11dJSUlKSkpyWGexWEoUdvLz8zV8+HDdc889qlevniQpLS1Nnp6eCggIcGhboUIF+9ub09LSHIJOwfqCdUVJTEzU2LFjr7lGAABw6ylR2ElNTS3tOhQXF6ddu3bpq6++KvV9/1lCQoLi4+Pt81lZWapUqdINPy4AALj5SvyendI0dOhQLV++XMnJyapYsaJ9eUhIiHJzc3X69GmH3p309HSFhITY23zzzTcO+yt4WqugzZ9ZrVZZrdZSPgsAAOCKShR2HnvssSuunzFjRrH2YxiGhg0bpkWLFmnjxo2qWrWqw/rIyEh5eHho3bp16tWrlyRp3759Onz4sKKioiRJUVFRevnll5WRkaHg4GBJ0tq1a2Wz2RQREXGtpwYAAEymRGHn1KlTDvMXL17Url27dPr06SK/IPRy4uLiNG/ePC1ZskR+fn72MTb+/v7y9vaWv7+/Bg4cqPj4eAUGBspms2nYsGGKiorSXXfdJUnq0KGDIiIi9PDDD+u1115TWlqaXnjhBcXFxdF7AwAAShZ2Fi1aVGhZfn6+hgwZomrVqhV7P1OnTpUktWrVymH5zJkzNWDAAEnSpEmT5Obmpl69eiknJ0fR0dGaMmWKva27u7uWL1+uIUOGKCoqSmXLllVsbKzGjRt37ScGAABMp0Tv2bmcffv2qVWrVjp27Fhp7fKm4D07AGAevGfnr6O4v79L9Ablyzl48CBf0wAAAFxKiW5j/fGxben3gcbHjh3T559/rtjY2FIpDAAAoDSUKOxs27bNYd7NzU1BQUH697//fdUntQAAAG6mEoWdDRs2lHYdAAAAN8R1vVTw+PHj2rdvnySpVq1aCgoKKpWiAAAASkuJBihnZ2frscceU2hoqFq0aKEWLVooLCxMAwcO1Llz50q7RgAAgBIrUdiJj49XUlKSli1bptOnT+v06dNasmSJkpKS9Mwzz5R2jQAAACVWottY//3vf/Xpp586vAywc+fO8vb21oMPPmh/WSAAAICzlahn59y5c6pQoUKh5cHBwdzGAgAALqVEYScqKkpjxozRhQsX7MvOnz+vsWPH2r+gEwAAwBWU6DbWm2++qY4dO6pixYpq2LChJGnHjh2yWq1as2ZNqRYIAABwPUoUdurXr6/9+/dr7ty52rt3rySpX79+iomJkbe3d6kWCAAAcD1KFHYSExNVoUIFDRo0yGH5jBkzdPz4cY0cObJUigMAALheJRqz895776l27dqFltetW1fTpk277qIAAABKS4nCTlpamkJDQwstDwoK0rFjx667KAAAgNJSorBTqVIlbdq0qdDyTZs2KSws7LqLAgAAKC0lGrMzaNAgDR8+XBcvXlSbNm0kSevWrdOIESN4gzIAAHApJQo7zz33nE6cOKEnn3xSubm5kiQvLy+NHDlSCQkJpVogAADA9ShR2LFYLHr11Vc1atQo/fjjj/L29laNGjVktVpLuz4AAIDrUqKwU8DX11dNmjQprVoAAABKXYkGKAMAANwqCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUnBp2kpOT1bVrV4WFhclisWjx4sUO6wcMGCCLxeIwdezY0aHNyZMnFRMTI5vNpoCAAA0cOFBnz569iWcBAABcmVPDTnZ2tho2bKjJkydftk3Hjh117Ngx+/Txxx87rI+JidHu3bu1du1aLV++XMnJyRo8ePCNLh0AANwiyjjz4J06dVKnTp2u2MZqtSokJKTIdT/++KNWrVqlb7/9Vo0bN5YkvfPOO+rcubPeeOMNhYWFlXrNAADg1uLyY3Y2btyo4OBg1apVS0OGDNGJEyfs61JSUhQQEGAPOpLUrl07ubm56euvv3ZGuQAAwMU4tWfnajp27KiePXuqatWqOnjwoJ5//nl16tRJKSkpcnd3V1pamoKDgx22KVOmjAIDA5WWlnbZ/ebk5CgnJ8c+n5WVdcPOAQAAOJdLh52+ffvaf65fv74aNGigatWqaePGjWrbtm2J95uYmKixY8eWRokAAMDFufxtrD+6/fbbVb58eR04cECSFBISooyMDIc2ly5d0smTJy87zkeSEhISlJmZaZ+OHDlyQ+sGAADOc0uFnV9++UUnTpxQaGioJCkqKkqnT5/W1q1b7W3Wr1+v/Px8NWvW7LL7sVqtstlsDhMAADAnp97GOnv2rL2XRpJSU1O1fft2BQYGKjAwUGPHjlWvXr0UEhKigwcPasSIEapevbqio6MlSXXq1FHHjh01aNAgTZs2TRcvXtTQoUPVt29fnsQCAACSnNyz89133+mOO+7QHXfcIUmKj4/XHXfcodGjR8vd3V0//PCDunXrppo1a2rgwIGKjIzUl19+KavVat/H3LlzVbt2bbVt21adO3dW8+bN9f777zvrlAAAgItxas9Oq1atZBjGZdevXr36qvsIDAzUvHnzSrMsAABgIrfUmB0AAIBrRdgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACm5tJfBIpbz/io3s4uoVhGpXzq7BIAADcJPTsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUyji7AAAA/mrGR/V2dglXNSrlU2eXUGro2QEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKbm1LCTnJysrl27KiwsTBaLRYsXL3ZYbxiGRo8erdDQUHl7e6tdu3bav3+/Q5uTJ08qJiZGNptNAQEBGjhwoM6ePXsTzwIAALgyp4ad7OxsNWzYUJMnTy5y/Wuvvaa3335b06ZN09dff62yZcsqOjpaFy5csLeJiYnR7t27tXbtWi1fvlzJyckaPHjwzToFAADg4so48+CdOnVSp06dilxnGIbefPNNvfDCC+revbsk6T//+Y8qVKigxYsXq2/fvvrxxx+1atUqffvtt2rcuLEk6Z133lHnzp31xhtvKCws7KadCwAAcE0uO2YnNTVVaWlpateunX2Zv7+/mjVrppSUFElSSkqKAgIC7EFHktq1ayc3Nzd9/fXXl913Tk6OsrKyHCYAAGBOLht20tLSJEkVKlRwWF6hQgX7urS0NAUHBzusL1OmjAIDA+1tipKYmCh/f3/7VKlSpVKuHgAAuAqXDTs3UkJCgjIzM+3TkSNHnF0SAAC4QVw27ISEhEiS0tPTHZanp6fb14WEhCgjI8Nh/aVLl3Ty5El7m6JYrVbZbDaHCQAAmJNTByhfSdWqVRUSEqJ169apUaNGkqSsrCx9/fXXGjJkiCQpKipKp0+f1tatWxUZGSlJWr9+vfLz89WsWTNnlQ78ZTx+//vOLqFYPljEE5rAX5lTw87Zs2d14MAB+3xqaqq2b9+uwMBAVa5cWcOHD9dLL72kGjVqqGrVqho1apTCwsLUo0cPSVKdOnXUsWNHDRo0SNOmTdPFixc1dOhQ9e3blyexAACAJCeHne+++06tW7e2z8fHx0uSYmNjNWvWLI0YMULZ2dkaPHiwTp8+rebNm2vVqlXy8vKybzN37lwNHTpUbdu2lZubm3r16qW33377pp8LAABwTU4NO61atZJhGJddb7FYNG7cOI0bN+6ybQIDAzVv3rwbUR4AADABlx2gDAAAUBoIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNRc9qWCAFBaxkf1dnYJVzUq5VNnlwCYFj07AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Hj0/Bby+P3vO7uEqwp3dgEAAPwJPTsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUXDrsvPjii7JYLA5T7dq17esvXLiguLg4lStXTr6+vurVq5fS09OdWDEAAHA1Lh12JKlu3bo6duyYffrqq6/s655++mktW7ZMCxcuVFJSko4ePaqePXs6sVoAAOBqyji7gKspU6aMQkJCCi3PzMzUhx9+qHnz5qlNmzaSpJkzZ6pOnTrasmWL7rrrrptdKgAAcEEu37Ozf/9+hYWF6fbbb1dMTIwOHz4sSdq6dasuXryodu3a2dvWrl1blStXVkpKyhX3mZOTo6ysLIcJAACYk0uHnWbNmmnWrFlatWqVpk6dqtTUVN177706c+aM0tLS5OnpqYCAAIdtKlSooLS0tCvuNzExUf7+/vapUqVKN/AsAACAM7n0baxOnTrZf27QoIGaNWum8PBwLViwQN7e3iXeb0JCguLj4+3zWVlZBB4AAEzKpXt2/iwgIEA1a9bUgQMHFBISotzcXJ0+fdqhTXp6epFjfP7IarXKZrM5TAAAwJxuqbBz9uxZHTx4UKGhoYqMjJSHh4fWrVtnX79v3z4dPnxYUVFRTqwSAAC4Epe+jfXss8+qa9euCg8P19GjRzVmzBi5u7urX79+8vf318CBAxUfH6/AwEDZbDYNGzZMUVFRPIkFAADsXDrs/PLLL+rXr59OnDihoKAgNW/eXFu2bFFQUJAkadKkSXJzc1OvXr2Uk5Oj6OhoTZkyxclVAwAAV+LSYeeTTz654novLy9NnjxZkydPvkkVAQCAW80tNWYHAADgWhF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqZkm7EyePFlVqlSRl5eXmjVrpm+++cbZJQEAABdgirAzf/58xcfHa8yYMfr+++/VsGFDRUdHKyMjw9mlAQAAJzNF2Jk4caIGDRqkRx99VBEREZo2bZp8fHw0Y8YMZ5cGAACc7JYPO7m5udq6davatWtnX+bm5qZ27dopJSXFiZUBAABXUMbZBVyv3377TXl5eapQoYLD8goVKmjv3r1FbpOTk6OcnBz7fGZmpiQpKyvrxhVaCnIvnnd2CVd14dJFZ5dQLK7+b32ruBWuSenWuC65JkvPrXBdck2WjoIaDcO4YrtbPuyURGJiosaOHVtoeaVKlZxQDZzhFX9/Z5cAOOCahKu5la7JM2fOyP8K9d7yYad8+fJyd3dXenq6w/L09HSFhIQUuU1CQoLi4+Pt8/n5+Tp58qTKlSsni8VyQ+s1s6ysLFWqVElHjhyRzWZzdjmAJK5LuB6uydJjGIbOnDmjsLCwK7a75cOOp6enIiMjtW7dOvXo0UPS7+Fl3bp1Gjp0aJHbWK1WWa1Wh2UBAQE3uNK/DpvNxn/AcDlcl3A1XJOl40o9OgVu+bAjSfHx8YqNjVXjxo3VtGlTvfnmm8rOztajjz7q7NIAAICTmSLs9OnTR8ePH9fo0aOVlpamRo0aadWqVYUGLQMAgL8eU4QdSRo6dOhlb1vh5rBarRozZkyhW4SAM3FdwtVwTd58FuNqz2sBAADcwm75lwoCAABcCWEHAACYGmEHAACYGmEHAACYGmEH1y05OVldu3ZVWFiYLBaLFi9e7OyS8BeXmJioJk2ayM/PT8HBwerRo4f27dvn7LLwFzd16lQ1aNDA/jLBqKgorVy50tll/SUQdnDdsrOz1bBhQ02ePNnZpQCSpKSkJMXFxWnLli1au3atLl68qA4dOig7O9vZpeEvrGLFipowYYK2bt2q7777Tm3atFH37t21e/duZ5dmejx6jlJlsVi0aNEi+1d3AK7g+PHjCg4OVlJSklq0aOHscgC7wMBAvf766xo4cKCzSzE107xUEAAuJzMzU9Lvv1gAV5CXl6eFCxcqOztbUVFRzi7H9Ag7AEwtPz9fw4cP1z333KN69eo5uxz8xe3cuVNRUVG6cOGCfH19tWjRIkVERDi7LNMj7AAwtbi4OO3atUtfffWVs0sBVKtWLW3fvl2ZmZn69NNPFRsbq6SkJALPDUbYAWBaQ4cO1fLly5WcnKyKFSs6uxxAnp6eql69uiQpMjJS3377rd566y299957Tq7M3Ag7AEzHMAwNGzZMixYt0saNG1W1alVnlwQUKT8/Xzk5Oc4uw/QIO7huZ8+e1YEDB+zzqamp2r59uwIDA1W5cmUnVoa/qri4OM2bN09LliyRn5+f0tLSJEn+/v7y9vZ2cnX4q0pISFCnTp1UuXJlnTlzRvPmzdPGjRu1evVqZ5dmejx6juu2ceNGtW7dutDy2NhYzZo16+YXhL88i8VS5PKZM2dqwIABN7cY4P8bOHCg1q1bp2PHjsnf318NGjTQyJEj1b59e2eXZnqEHQAAYGq8QRkAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQeAqbRq1UrDhw93dhkAXAhhB4DLGTBggCwWiywWi/2LE8eNG6dLly45uzQAtyC+GwuAS+rYsaNmzpypnJwcrVixQnFxcfLw8FBCQoKzSwNwi6FnB4BLslqtCgkJUXh4uIYMGaJ27dpp6dKlkqRNmzapVatW8vHx0W233abo6GidOnWqyP3Mnj1bjRs3lp+fn0JCQtS/f39lZGTY1586dUoxMTEKCgqSt7e3atSooZkzZ0qScnNzNXToUIWGhsrLy0vh4eFKTEy88ScPoFTRswPgluDt7a0TJ05o+/btatu2rR577DG99dZbKlOmjDZs2KC8vLwit7t48aLGjx+vWrVqKSMjQ/Hx8RowYIBWrFghSRo1apT27NmjlStXqnz58jpw4IDOnz8vSXr77be1dOlSLViwQJUrV9aRI0d05MiRm3bOAEoHYQeASzMMQ+vWrdPq1as1bNgwvfbaa2rcuLGmTJlib1O3bt3Lbv/YY4/Zf7799tv19ttvq0mTJjp79qx8fX11+PBh3XHHHWrcuLEkqUqVKvb2hw8fVo0aNdS8eXNZLBaFh4eX/gkCuOG4jQXAJS1fvly+vr7y8vJSp06d1KdPH7344ov2np3i2rp1q7p27arKlSvLz89PLVu2lPR7kJGkIUOG6JNPPlGjRo00YsQIbd682b7tgAEDtH37dtWqVUtPPfWU1qxZU7onCeCmIOwAcEmtW7fW9u3btX//fp0/f14fffSRypYtK29v72LvIzs7W9HR0bLZbJo7d66+/fZbLVq0SNLv43EkqVOnTvr555/19NNP6+jRo2rbtq2effZZSdKdd96p1NRUjR8/XufPn9eDDz6o3r17l/7JArihCDsAXFLZsmVVvXp1Va5cWWXK/N8d9wYNGmjdunXF2sfevXt14sQJTZgwQffee69q167tMDi5QFBQkGJjYzVnzhy9+eabev/99+3rbDab+vTpo+nTp2v+/Pn673//q5MnT17/CQK4aRizA+CWkpCQoPr16+vJJ5/UE088IU9PT23YsEEPPPCAypcv79C2cuXK8vT01DvvvKMnnnhCu3bt0vjx4x3ajB49WpGRkapbt65ycnK0fPly1alTR5I0ceJEhYaG6o477pCbm5sWLlyokJAQBQQE3KzTBVAK6NkBcEupWbOm1qxZox07dqhp06aKiorSkiVLHHp/CgQFBWnWrFlauHChIiIiNGHCBL3xxhsObTw9PZWQkKAGDRqoRYsWcnd31yeffCJJ8vPzsw+IbtKkiQ4dOqQVK1bIzY3/dQK3EothGIaziwAAALhR+PMEAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACY2v8Db06IpCHt4DQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['Pclass'],data=df,width=0.5,hue=df['Survived'], palette='twilight')\n",
    "plt.title(\"Survived VS Dead according to Pclass\") #hue_order: If you only want specific columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3ffb891b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Parch'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df['Age'],y=df['Parch'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "83e25aef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x=df['Age'],bins=20,kde=True,stat='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eca8eff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XVlYWj6kAADDIRx99pBtvvNG9fnaebGZmptasWdPh9RgadqqrqzVjxgxVVlYqNDRUiYmJ2rp1q8aOHasjR47orbfe0vLly1VXV6eYmBilp6dr/vz57uN9fX21adMmzZ49W3a7XV26dFFmZqbHe3kAADCby32jcUcbNWqUXC6X0WW4GRp2nn/++Rb3xcTEqLi4+KJ9xMXF6c0332zLsgAAgIl43ZwdAACAtkTYAQAApkbYAQAApkbYAQDAC3jThF5v0hb3hbADAICBfH19JUkNDQ0GV+Kdzv4mpp+fX6v7MPznIgAA+C7r1KmTgoKCdOzYMfn5+cnHh3EI6ZsRnVOnTqm6ulphYWHuUNgahB0AAAxksVgUFRWl8vJy/f3vfze6HK8TFhZ2xT8BRdgBAMBg/v7+uvbaa3mU9W/8/PyuaETnLMIOAABewMfHR507dza6DFPiwSAAADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1Q8POypUrlZiYKKvVKqvVKrvdrs2bN7v3nz59WllZWeratauCg4OVnp6uqqoqjz4qKio0ceJEBQUFKSIiQnPnztWZM2c6+lIAAICXMjTs9OjRQ0uWLFFpaak++ugjjR49WpMnT9b+/fslSffcc49ef/11vfzyyyouLtbRo0c1depU9/FNTU2aOHGiGhoatGvXLq1du1Zr1qzRwoULjbokAADgZSwul8tldBHfFh4erscff1w333yzunfvroKCAt18882SpC+++EL9+/dXSUmJRowYoc2bN+umm27S0aNHFRkZKUlatWqV5s2bp2PHjsnf3/+856ivr1d9fb173el0KiYmRrW1tbJaree0T5r7YjtcactKH5/RoecDAOBq5HQ6FRoa2uLn91leM2enqalJ69evV11dnex2u0pLS9XY2KiUlBR3m379+ik2NlYlJSWSpJKSEiUkJLiDjiSlpqbK6XS6R4fOJy8vT6Ghoe4lJiam/S4MAAAYyvCws2/fPgUHBysgIEC//OUvVVhYqPj4eDkcDvn7+yssLMyjfWRkpBwOhyTJ4XB4BJ2z+8/ua0lubq5qa2vdy5EjR9r2ogAAgNfoZHQBffv2VVlZmWpra/XKK68oMzNTxcXF7XrOgIAABQQEtOs5AACAdzA87Pj7+6tPnz6SpKSkJO3Zs0e///3vNW3aNDU0NKimpsZjdKeqqko2m02SZLPZ9OGHH3r0d/bbWmfbAACA7zbDH2P9u+bmZtXX1yspKUl+fn4qKipy7zt48KAqKipkt9slSXa7Xfv27VN1dbW7zfbt22W1WhUfH9/htQMAAO9j6MhObm6u0tLSFBsbqxMnTqigoEA7duzQ1q1bFRoaqpkzZyonJ0fh4eGyWq266667ZLfbNWLECEnSuHHjFB8fr1tvvVVLly6Vw+HQ/PnzlZWVxWMqAAAgyeCwU11drRkzZqiyslKhoaFKTEzU1q1bNXbsWEnSsmXL5OPjo/T0dNXX1ys1NVXPPPOM+3hfX19t2rRJs2fPlt1uV5cuXZSZmanFixcbdUkAAMDLeN17doxwse/p854dAAC8z1X3nh0AAID2QNgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmZmjYycvL07BhwxQSEqKIiAhNmTJFBw8e9GgzatQoWSwWj+WXv/ylR5uKigpNnDhRQUFBioiI0Ny5c3XmzJmOvBQAAOClOhl58uLiYmVlZWnYsGE6c+aMHnjgAY0bN04HDhxQly5d3O1mzZqlxYsXu9eDgoLcfzc1NWnixImy2WzatWuXKisrNWPGDPn5+em3v/1th14PAADwPoaGnS1btnisr1mzRhERESotLdXIkSPd24OCgmSz2c7bx7Zt23TgwAG99dZbioyM1ODBg/Xwww9r3rx5evDBB+Xv79+u1wAAALybV83Zqa2tlSSFh4d7bF+3bp26deumgQMHKjc3V6dOnXLvKykpUUJCgiIjI93bUlNT5XQ6tX///vOep76+Xk6n02MBAADmZOjIzrc1Nzdrzpw5uv766zVw4ED39p///OeKi4tTdHS09u7dq3nz5ungwYP661//KklyOBweQUeSe93hcJz3XHl5eXrooYfa6UoAAIA38Zqwk5WVpc8++0zvvfeex/Y777zT/XdCQoKioqI0ZswYHT58WL17927VuXJzc5WTk+NedzqdiomJaV3hAADAq3nFY6zs7Gxt2rRJ77zzjnr06HHBtsnJyZKkQ4cOSZJsNpuqqqo82pxdb2meT0BAgKxWq8cCAADMydCw43K5lJ2drcLCQr399tvq2bPnRY8pKyuTJEVFRUmS7Ha79u3bp+rqaneb7du3y2q1Kj4+vl3qBgAAVw9DH2NlZWWpoKBAr776qkJCQtxzbEJDQxUYGKjDhw+roKBAEyZMUNeuXbV3717dc889GjlypBITEyVJ48aNU3x8vG699VYtXbpUDodD8+fPV1ZWlgICAoy8PAAA4AUMHdlZuXKlamtrNWrUKEVFRbmXDRs2SJL8/f311ltvady4cerXr5/uvfdepaen6/XXX3f34evrq02bNsnX11d2u13/+Z//qRkzZni8lwcAAHx3GTqy43K5Lrg/JiZGxcXFF+0nLi5Ob775ZluVBQAATMQrJigDAAC0F8IOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwtVaFndGjR6umpuac7U6nU6NHj77SmgAAANpMq8LOjh071NDQcM7206dP6913373iogAAANpKp8tpvHfvXvffBw4ckMPhcK83NTVpy5Yt+t73vtd21QEAAFyhywo7gwcPlsVikcViOe/jqsDAQK1YsaLNigMAALhSlxV2ysvL5XK51KtXL3344Yfq3r27e5+/v78iIiLk6+vb5kUCAAC01mWFnbi4OElSc3NzuxQDAADQ1i4r7Hzbl19+qXfeeUfV1dXnhJ+FCxdecWEAAABtoVVh57nnntPs2bPVrVs32Ww2WSwW9z6LxULYAQAAXqNVYeeRRx7Ro48+qnnz5rV1PQAAAG2qVe/Z+de//qWf/vSnbV0LAABAm2tV2PnpT3+qbdu2tXUtAAAAba5Vj7H69OmjBQsWaPfu3UpISJCfn5/H/l//+tdtUhwAAMCValXY+cMf/qDg4GAVFxeruLjYY5/FYiHsAAAAr9GqsFNeXt7WdQAAALSLVs3ZAQAAuFq0amTnjjvuuOD+1atXt6oYAACAttaqsPOvf/3LY72xsVGfffaZampqzvsDobg8FYsTOvR8sQv3dej5AADoSK0KO4WFhedsa25u1uzZs9W7d+8rLgoAAKCttNmcHR8fH+Xk5GjZsmVt1SUAAMAVa9MJyocPH9aZM2fasksAAIAr0qrHWDk5OR7rLpdLlZWVeuONN5SZmdkmhQEAALSFVo3sfPLJJx7L3r17JUm/+93vtHz58kvuJy8vT8OGDVNISIgiIiI0ZcoUHTx40KPN6dOnlZWVpa5duyo4OFjp6emqqqryaFNRUaGJEycqKChIERERmjt3LiNMAABAUitHdt555502OXlxcbGysrI0bNgwnTlzRg888IDGjRunAwcOqEuXLpKke+65R2+88YZefvllhYaGKjs7W1OnTtX7778vSWpqatLEiRNls9m0a9cuVVZWasaMGfLz89Nvf/vbNqkTAABcvSwul8vV2oOPHTvmHonp27evunfvfkXFHDt2TBERESouLtbIkSNVW1ur7t27q6CgQDfffLMk6YsvvlD//v1VUlKiESNGaPPmzbrpppt09OhRRUZGSpJWrVqlefPm6dixY/L397/oeZ1Op0JDQ1VbWyur1XrO/qS5L17RdV2uwpDHO/R8fPUcAHA1utjn91mteoxVV1enO+64Q1FRURo5cqRGjhyp6OhozZw5U6dOnWp10bW1tZKk8PBwSVJpaakaGxuVkpLibtOvXz/FxsaqpKREklRSUqKEhAR30JGk1NRUOZ1O7d+//7znqa+vl9Pp9FgAAIA5tSrs5OTkqLi4WK+//rpqampUU1OjV199VcXFxbr33ntbVUhzc7PmzJmj66+/XgMHDpQkORwO+fv7KywszKNtZGSkHA6Hu823g87Z/Wf3nU9eXp5CQ0PdS0xMTKtqBgAA3q9VYecvf/mLnn/+eaWlpclqtcpqtWrChAl67rnn9Morr7SqkKysLH322Wdav359q46/HLm5uaqtrXUvR44cafdzAgAAY7RqgvKpU6fOGU2RpIiIiFY9xsrOztamTZu0c+dO9ejRw73dZrOpoaFBNTU1HqM7VVVVstls7jYffvihR39nv611ts2/CwgIUEBAwGXXCQAArj6tGtmx2+1atGiRTp8+7d72v//7v3rooYdkt9svuR+Xy6Xs7GwVFhbq7bffVs+ePT32JyUlyc/PT0VFRe5tBw8eVEVFhfs8drtd+/btU3V1tbvN9u3bZbVaFR8f35rLAwAAJtKqkZ3ly5dr/Pjx6tGjhwYNGiRJ+vTTTxUQEKBt27Zdcj9ZWVkqKCjQq6++qpCQEPccm9DQUAUGBio0NFQzZ85UTk6OwsPDZbVaddddd8lut2vEiBGSpHHjxik+Pl633nqrli5dKofDofnz5ysrK4vRGwAA0Lqwk5CQoC+//FLr1q3TF198IUm65ZZblJGRocDAwEvuZ+XKlZKkUaNGeWx/4YUXdNttt0mSli1bJh8fH6Wnp6u+vl6pqal65pln3G19fX21adMmzZ49W3a7XV26dFFmZqYWL17cmksDAAAm06qwk5eXp8jISM2aNctj++rVq3Xs2DHNmzfvkvq5lFf8dO7cWfn5+crPz2+xTVxcnN58881LOicAAPhuadWcnWeffVb9+vU7Z/uAAQO0atWqKy4KAACgrbQq7DgcDkVFRZ2zvXv37qqsrLziogAAANpKq8JOTEyM+7epvu39999XdHT0FRcFAADQVlo1Z2fWrFmaM2eOGhsbNXr0aElSUVGR7rvvvla/QRkAAKA9tCrszJ07V8ePH9evfvUrNTQ0SPpmIvG8efOUm5vbpgUCAABciVaFHYvFoscee0wLFizQ559/rsDAQF177bW81wYAAHidVoWds4KDgzVs2LC2qgUAAKDNtWqCMgAAwNWCsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEzN0LCzc+dOTZo0SdHR0bJYLNq4caPH/ttuu00Wi8VjGT9+vEebr7/+WhkZGbJarQoLC9PMmTN18uTJDrwKAADgzQwNO3V1dRo0aJDy8/NbbDN+/HhVVla6lz//+c8e+zMyMrR//35t375dmzZt0s6dO3XnnXe2d+kAAOAq0cnIk6elpSktLe2CbQICAmSz2c677/PPP9eWLVu0Z88eDR06VJK0YsUKTZgwQU888YSio6PbvGYAAHB18fo5Ozt27FBERIT69u2r2bNn6/jx4+59JSUlCgsLcwcdSUpJSZGPj48++OCDFvusr6+X0+n0WAAAgDl5ddgZP368XnzxRRUVFemxxx5TcXGx0tLS1NTUJElyOByKiIjwOKZTp04KDw+Xw+Fosd+8vDyFhoa6l5iYmHa9DgAAYBxDH2NdzPTp091/JyQkKDExUb1799aOHTs0ZsyYVvebm5urnJwc97rT6STwAABgUl49svPvevXqpW7duunQoUOSJJvNpurqao82Z86c0ddff93iPB/pm3lAVqvVYwEAAOZ0VYWdf/zjHzp+/LiioqIkSXa7XTU1NSotLXW3efvtt9Xc3Kzk5GSjygQAAF7E0MdYJ0+edI/SSFJ5ebnKysoUHh6u8PBwPfTQQ0pPT5fNZtPhw4d13333qU+fPkpNTZUk9e/fX+PHj9esWbO0atUqNTY2Kjs7W9OnT+ebWAAAQJLBIzsfffSRrrvuOl133XWSpJycHF133XVauHChfH19tXfvXv3kJz/RD37wA82cOVNJSUl69913FRAQ4O5j3bp16tevn8aMGaMJEyboRz/6kf7whz8YdUkAAMDLGDqyM2rUKLlcrhb3b9269aJ9hIeHq6CgoC3LAgAAJnJVzdkBAAC4XIQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgap2MLgC4HBWLEzr0fLEL93Xo+QAAbY+RHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqGhp2dO3dq0qRJio6OlsVi0caNGz32u1wuLVy4UFFRUQoMDFRKSoq+/PJLjzZff/21MjIyZLVaFRYWppkzZ+rkyZMdeBUAAMCbGRp26urqNGjQIOXn5593/9KlS/XUU09p1apV+uCDD9SlSxelpqbq9OnT7jYZGRnav3+/tm/frk2bNmnnzp268847O+oSAACAlzP0pYJpaWlKS0s77z6Xy6Xly5dr/vz5mjx5siTpxRdfVGRkpDZu3Kjp06fr888/15YtW7Rnzx4NHTpUkrRixQpNmDBBTzzxhKKjo8/bd319verr693rTqezja8MAAB4C6+ds1NeXi6Hw6GUlBT3ttDQUCUnJ6ukpESSVFJSorCwMHfQkaSUlBT5+Pjogw8+aLHvvLw8hYaGupeYmJj2uxAAAGAorw07DodDkhQZGemxPTIy0r3P4XAoIiLCY3+nTp0UHh7ubnM+ubm5qq2tdS9Hjhxp4+oBAIC3+E7+NlZAQIACAgKMLgMAAHQArw07NptNklRVVaWoqCj39qqqKg0ePNjdprq62uO4M2fO6Ouvv3Yfj/aVNPfFDj1fYUiHng4AYAJe+xirZ8+estlsKioqcm9zOp364IMPZLfbJUl2u101NTUqLS11t3n77bfV3Nys5OTkDq8ZAAB4H0NHdk6ePKlDhw6518vLy1VWVqbw8HDFxsZqzpw5euSRR3TttdeqZ8+eWrBggaKjozVlyhRJUv/+/TV+/HjNmjVLq1atUmNjo7KzszV9+vQWv4kFAAC+WwwNOx999JFuvPFG93pOTo4kKTMzU2vWrNF9992nuro63XnnnaqpqdGPfvQjbdmyRZ07d3Yfs27dOmVnZ2vMmDHy8fFRenq6nnrqqQ6/FgAA4J0MDTujRo2Sy+Vqcb/FYtHixYu1ePHiFtuEh4eroKCgPcoDAAAm4LVzdgAAANoCYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJiaV4edBx98UBaLxWPp16+fe//p06eVlZWlrl27Kjg4WOnp6aqqqjKwYgAA4G28OuxI0oABA1RZWele3nvvPfe+e+65R6+//rpefvllFRcX6+jRo5o6daqB1QIAAG/TyegCLqZTp06y2WznbK+trdXzzz+vgoICjR49WpL0wgsvqH///tq9e7dGjBjR0aUCAAAv5PUjO19++aWio6PVq1cvZWRkqKKiQpJUWlqqxsZGpaSkuNv269dPsbGxKikpuWCf9fX1cjqdHgsAADAnrw47ycnJWrNmjbZs2aKVK1eqvLxcP/7xj3XixAk5HA75+/srLCzM45jIyEg5HI4L9puXl6fQ0FD3EhMT045XAQAAjOTVj7HS0tLcfycmJio5OVlxcXF66aWXFBgY2Op+c3NzlZOT4153Op0EHgAATMqrR3b+XVhYmH7wgx/o0KFDstlsamhoUE1NjUebqqqq887x+baAgABZrVaPBQAAmNNVFXZOnjypw4cPKyoqSklJSfLz81NRUZF7/8GDB1VRUSG73W5glQAAwJt49WOs3/zmN5o0aZLi4uJ09OhRLVq0SL6+vrrlllsUGhqqmTNnKicnR+Hh4bJarbrrrrtkt9v5JhYAAHDz6rDzj3/8Q7fccouOHz+u7t2760c/+pF2796t7t27S5KWLVsmHx8fpaenq76+XqmpqXrmmWcMrhoAAHgTrw4769evv+D+zp07Kz8/X/n5+R1UEQAAuNpcVXN2AAAALhdhBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmFonowsAzChp7osdfs7Sx2d0+DnRtvh3A7QPRnYAAICpMbIDmETF4oQOPV/swn0dej4AaC3CDoAOxaMaAB2Nx1gAAMDUCDsAAMDUCDsAAMDUCDsAAMDUmKAMAMBVrKMn/V+NE/4JOwAAr8cHOq6EacJOfn6+Hn/8cTkcDg0aNEgrVqzQ8OHDjS4LgBfgHUTAd5spws6GDRuUk5OjVatWKTk5WcuXL1dqaqoOHjyoiIgIo8sDAK9FEMR3gSkmKD/55JOaNWuWbr/9dsXHx2vVqlUKCgrS6tWrjS4NAAAY7Kof2WloaFBpaalyc3Pd23x8fJSSkqKSkpLzHlNfX6/6+nr3em1trSTJ6XSet31T/f+2YcUXd8KvqUPP19J1Xwruzfl19H2RuDcXwr1pGffm/K7kv4sd7bt8b87W4nK5LtzQdZX7n//5H5ck165duzy2z5071zV8+PDzHrNo0SKXJBYWFhYWFhYTLEeOHLlgVrjqR3ZaIzc3Vzk5Oe715uZmff311+ratassFouBlX2TUmNiYnTkyBFZrVZDa/E23JuWcW9axr1pGffm/LgvLfO2e+NyuXTixAlFR0dfsN1VH3a6desmX19fVVVVeWyvqqqSzWY77zEBAQEKCAjw2BYWFtZeJbaK1Wr1in9I3oh70zLuTcu4Ny3j3pwf96Vl3nRvQkNDL9rmqp+g7O/vr6SkJBUVFbm3NTc3q6ioSHa73cDKAACAN7jqR3YkKScnR5mZmRo6dKiGDx+u5cuXq66uTrfffrvRpQEAAIOZIuxMmzZNx44d08KFC+VwODR48GBt2bJFkZGRRpd22QICArRo0aJzHrOBe3Mh3JuWcW9axr05P+5Ly67We2NxuS72fS0AAICr11U/ZwcAAOBCCDsAAMDUCDsAAMDUCDsAAMDUCDteJj8/X9///vfVuXNnJScn68MPPzS6JMPt3LlTkyZNUnR0tCwWizZu3Gh0SV4hLy9Pw4YNU0hIiCIiIjRlyhQdPHjQ6LK8wsqVK5WYmOh+8ZndbtfmzZuNLssrLVmyRBaLRXPmzDG6FMM9+OCDslgsHku/fv2MLssrNDU1acGCBerZs6cCAwPVu3dvPfzwwxf/TSovQdjxIhs2bFBOTo4WLVqkjz/+WIMGDVJqaqqqq6uNLs1QdXV1GjRokPLz840uxasUFxcrKytLu3fv1vbt29XY2Khx48aprq7O6NIM16NHDy1ZskSlpaX66KOPNHr0aE2ePFn79+83ujSvsmfPHj377LNKTEw0uhSvMWDAAFVWVrqX9957z+iSvMJjjz2mlStX6umnn9bnn3+uxx57TEuXLtWKFSuMLu2S8NVzL5KcnKxhw4bp6aeflvTNm6BjYmJ011136f777ze4Ou9gsVhUWFioKVOmGF2K1zl27JgiIiJUXFyskSNHGl2O1wkPD9fjjz+umTNnGl2KVzh58qSGDBmiZ555Ro888ogGDx6s5cuXG12WoR588EFt3LhRZWVlRpfidW666SZFRkbq+eefd29LT09XYGCg/vSnPxlY2aVhZMdLNDQ0qLS0VCkpKe5tPj4+SklJUUlJiYGV4WpRW1sr6ZsPdfyfpqYmrV+/XnV1dfyEzLdkZWVp4sSJHv/NgfTll18qOjpavXr1UkZGhioqKowuySv88Ic/VFFRkf72t79Jkj799FO99957SktLM7iyS2OKNyibwT//+U81NTWd89bnyMhIffHFFwZVhatFc3Oz5syZo+uvv14DBw40uhyvsG/fPtntdp0+fVrBwcEqLCxUfHy80WV5hfXr1+vjjz/Wnj17jC7FqyQnJ2vNmjXq27evKisr9dBDD+nHP/6xPvvsM4WEhBhdnqHuv/9+OZ1O9evXT76+vmpqatKjjz6qjIwMo0u7JIQdwASysrL02WefMb/gW/r27auysjLV1tbqlVdeUWZmpoqLi7/zgefIkSO6++67tX37dnXu3NnocrzKt0cpEhMTlZycrLi4OL300kvf+cefL730ktatW6eCggINGDBAZWVlmjNnjqKjo5WZmWl0eRdF2PES3bp1k6+vr6qqqjy2V1VVyWazGVQVrgbZ2dnatGmTdu7cqR49ehhdjtfw9/dXnz59JElJSUnas2ePfv/73+vZZ581uDJjlZaWqrq6WkOGDHFva2pq0s6dO/X000+rvr5evr6+BlboPcLCwvSDH/xAhw4dMroUw82dO1f333+/pk+fLklKSEjQ3//+d+Xl5V0VYYc5O17C399fSUlJKioqcm9rbm5WUVER8wxwXi6XS9nZ2SosLNTbb7+tnj17Gl2SV2tublZ9fb3RZRhuzJgx2rdvn8rKytzL0KFDlZGRobKyMoLOt5w8eVKHDx9WVFSU0aUY7tSpU/Lx8YwMvr6+am5uNqiiy8PIjhfJyclRZmamhg4dquHDh2v58uWqq6vT7bffbnRphjp58qTH/1mVl5errKxM4eHhio2NNbAyY2VlZamgoECvvvqqQkJC5HA4JEmhoaEKDAw0uDpj5ebmKi0tTbGxsTpx4oQKCgq0Y8cObd261ejSDBcSEnLOvK4uXbqoa9eu3/n5Xr/5zW80adIkxcXF6ejRo1q0aJF8fX11yy23GF2a4SZNmqRHH31UsbGxGjBggD755BM9+eSTuuOOO4wu7dK44FVWrFjhio2Ndfn7+7uGDx/u2r17t9ElGe6dd95xSTpnyczMNLo0Q53vnkhyvfDCC0aXZrg77rjDFRcX5/L393d1797dNWbMGNe2bduMLstr3XDDDa67777b6DIMN23aNFdUVJTL39/f9b3vfc81bdo016FDh4wuyys4nU7X3Xff7YqNjXV17tzZ1atXL9d//dd/uerr640u7ZLwnh0AAGBqzNkBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBcNWyWCzauHGjJOmrr76SxWJRWVmZoTUB8D6EHQBe69ixY5o9e7ZiY2MVEBAgm82m1NRUvf/++5KkyspKpaWlXVafhYWFGjFihEJDQxUSEqIBAwZozpw57VA9AG/BD4EC8Frp6elqaGjQ2rVr1atXL1VVVamoqEjHjx+XJNlstsvqr6ioSNOmTdOjjz6qn/zkJ7JYLDpw4IC2b9/eHuUD8BL8NhYAr1RTU6NrrrlGO3bs0A033HDeNhaLRYWFhZoyZYq++uor9ezZU3/+85/11FNP6eOPP1afPn2Un5/vPn7OnDn69NNP9c4777R43gcffFAbN27U7Nmz9cgjj+j48eO66aab9Nxzzyk0NLRdrhVA++IxFgCvFBwcrODgYG3cuFH19fWXfNzcuXN177336pNPPpHdbtekSZM8RoL279+vzz777IJ9HDp0SC+99JJef/11bdmyRZ988ol+9atfXdH1ADAOYQeAV+rUqZPWrFmjtWvXKiwsTNdff70eeOAB7d2794LHZWdnKz09Xf3799fKlSsVGhqq559/XpJ01113adiwYUpISND3v/99TZ8+XatXrz4nTJ0+fVovvviiBg8erJEjR2rFihVav369HA5Hu10vgPZD2AHgtdLT03X06FG99tprGj9+vHbs2KEhQ4ZozZo1LR5jt9vdf3fq1ElDhw7V559/Lknq0qWL3njjDR06dEjz589XcHCw7r33Xg0fPlynTp1yHxcbG6vvfe97Hn02Nzfr4MGDbX+RANodYQeAV+vcubPGjh2rBQsWaNeuXbrtttu0aNGiK+qzd+/e+sUvfqE//vGP+vjjj3XgwAFt2LChjSoG4G0IOwCuKvHx8aqrq2tx/+7du91/nzlzRqWlperfv3+L7b///e8rKCjIo8+KigodPXrUo08fHx/17dv3CqsHYAS+eg7AKx0/flw//elPdccddygxMVEhISH66KOPtHTpUk2ePLnF4/Lz83Xttdeqf//+WrZsmf71r3/pjjvukPTNN61OnTqlCRMmKC4uTjU1NXrqqafU2NiosWPHuvvo3LmzMjMz9cQTT8jpdOrXv/61fvazn132V90BeAfCDgCvFBwcrOTkZC1btkyHDx9WY2OjYmJiNGvWLD3wwAMtHrdkyRItWbJEZWVl6tOnj1577TV169ZNknTDDTcoPz9fM2bMUFVVla655hpdd9112rZtm8eoTZ8+fTR16lRNmDBBX3/9tW666SY988wz7X7NANoH79kBgG85+54dfnYCMA/m7AAAAFMj7AAAAFPjMRYAADA1RnYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICp/T8w++LKadnJ7AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp',hue='Survived',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4022c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Embarked', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['Embarked'],palette='rainbow',width=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4bd97963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Embarked', ylabel='Survived'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y=df['Survived'],x=df['Embarked'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60f5650b",
   "metadata": {},
   "source": [
    "#### From the above visualisation it's clear that\n",
    "- The chances of survival for people who embarked at Cherbough are more\n",
    "- SouthHampton and Queenstown has closer relations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6207644f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Fare', ylabel='Count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x=df['Fare'],bins=20,color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4547fb55",
   "metadata": {},
   "source": [
    "2.**Heatmap**: To find relation among different values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f02577",
   "metadata": {},
   "source": [
    "# Data Cleaning \n",
    "- Heatmap depicting null values in training dataset\n",
    "- Boxplot for depicting outliers in the age column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "62682c5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),cbar=False,yticklabels=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1ccdc8",
   "metadata": {},
   "source": [
    "**3.Boxplot:** Helps in finding the outliers in the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3d3e6bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['Pclass'],y=df['Age'])\n",
    "plt.title(\"Boxplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b04c87b2",
   "metadata": {},
   "source": [
    "#### Filling in the empty age values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5f646b4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df['Pclass']: \n",
    "    for j in df['Age']:\n",
    "        if(i==1 and pd.isna(j)):\n",
    "            df['Age']=df['Age'].replace(np.nan,38)\n",
    "        if(i==2 and pd.isna(j)):\n",
    "            df['Age']=df['Age'].replace(np.nan\n",
    "                                        ,29)\n",
    "        if(i==3 and pd.isna(j)):\n",
    "            df['Age']=df['Age'].replace(np.nan,24)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c31e4ba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7710437710437711"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_null=(df['Cabin'].isnull().sum())/891\n",
    "per_null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e83910a2",
   "metadata": {},
   "source": [
    "**Since around 77% data is missing in this column, it is difficult to fill the empty values**  so we should drop this column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08eb7a6c",
   "metadata": {},
   "source": [
    "**Convert the categorical features into numerical features in both test and train data**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "347fa78a",
   "metadata": {},
   "source": [
    "## 1. Using dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9a02cf1b",
   "metadata": {},
   "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>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Q  S\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  1\n",
       "4  0  1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked=pd.get_dummies(df['Embarked'],dtype=int,drop_first=True) #We drop the first column to prevent multicollinearity\n",
    "embarked.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7b8acb88",
   "metadata": {},
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1  2  3\n",
       "0  0  0  1\n",
       "1  1  0  0\n",
       "2  0  0  1\n",
       "3  1  0  0\n",
       "4  0  0  1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass=pd.get_dummies(df['Pclass'],dtype=int)\n",
    "pclass.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99abf81e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df['Sex']:\n",
    "    if(i==\"male\"):\n",
    "        df['Sex']=df['Sex'].replace(\"male\",1)\n",
    "    if(i==\"female\"):\n",
    "        df['Sex']=df['Sex'].replace(\"female\",0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "745a272f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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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       "<p>891 rows × 17 columns</p>\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",
       "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    1  22.0      1  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina    0  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1   \n",
       "4                             Allen, Mr. William Henry    1  35.0      0   \n",
       "..                                                 ...  ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    1  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith    0  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"    0  24.0      1   \n",
       "889                              Behr, Mr. Karl Howell    1  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    1  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  Q  S  1  2  3  \n",
       "0        0         A/5 21171   7.2500   NaN        S  0  1  0  0  1  \n",
       "1        0          PC 17599  71.2833   C85        C  0  0  1  0  0  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  0  1  0  0  1  \n",
       "3        0            113803  53.1000  C123        S  0  1  1  0  0  \n",
       "4        0            373450   8.0500   NaN        S  0  1  0  0  1  \n",
       "..     ...               ...      ...   ...      ... .. .. .. .. ..  \n",
       "886      0            211536  13.0000   NaN        S  0  1  0  1  0  \n",
       "887      0            112053  30.0000   B42        S  0  1  1  0  0  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  0  1  0  0  1  \n",
       "889      0            111369  30.0000  C148        C  0  0  1  0  0  \n",
       "890      0            370376   7.7500   NaN        Q  1  0  0  0  1  \n",
       "\n",
       "[891 rows x 17 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.concat([df,embarked,pclass],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dfbe7b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df['Survived']  #Splitting data into Target column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bac0fa9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "104853a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(['PassengerId','Survived','Pclass','Name','Ticket','Cabin','Embarked'],axis=1)  #Splitting data to Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "caf36045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex      0\n",
       "Age      0\n",
       "SibSp    0\n",
       "Parch    0\n",
       "Fare     0\n",
       "Q        0\n",
       "S        0\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "598dc660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex      0\n",
       "Age      0\n",
       "SibSp    0\n",
       "Parch    0\n",
       "Fare     0\n",
       "Q        0\n",
       "S        0\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d992ac4c",
   "metadata": {},
   "source": [
    "# Operations on Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b54f51c4",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex   \n",
       "0          892       3                              Kelly, Mr. James    male  \\\n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df_test=pd.DataFrame(test_data)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b1e82fa3",
   "metadata": {},
   "outputs": [
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     "execution_count": 34,
     "metadata": {},
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   "source": [
    "emb=pd.get_dummies(df_test['Embarked'],dtype=int,drop_first=True)\n",
    "emb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c62bac71",
   "metadata": {},
   "outputs": [
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       "   1  2  3\n",
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     "execution_count": 35,
     "metadata": {},
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   "source": [
    "PClass=pd.get_dummies(df_test['Pclass'],dtype=int)\n",
    "PClass.head()"
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   "cell_type": "code",
   "execution_count": 36,
   "id": "0df6a50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df_test['Pclass']: #test data \n",
    "    for j in df_test['Age']:\n",
    "        if(i==1 and pd.isna(j)):\n",
    "            df_test['Age']=df_test['Age'].replace(np.nan,38)\n",
    "        if(i==2 and pd.isna(j)):\n",
    "            df_test['Age']=df_test['Age'].replace(np.nan\n",
    "                                        ,29)\n",
    "        if(i==3 and pd.isna(j)):\n",
    "            df_test['Age']=df_test['Age'].replace(np.nan,24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1aced945",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df_test['Sex']:\n",
    "    if(i==\"male\"):\n",
    "        df_test['Sex']=df_test['Sex'].replace(\"male\",1)\n",
    "    if(i==\"female\"):\n",
    "        df_test['Sex']=df_test['Sex'].replace(\"female\",0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2eb93c4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_test['Fare']=df_test['Fare'].replace(np.nan,df['Fare'].median())\n",
    "df_test.isnull().sum()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 39,
   "id": "309fab60",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=pd.concat([df_test,emb,PClass],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "965a42a2",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  Sex   \n",
       "0            892       3                              Kelly, Mr. James    1  \\\n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)    0   \n",
       "2            894       2                     Myles, Mr. Thomas Francis    1   \n",
       "3            895       3                              Wirz, Mr. Albert    1   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0   \n",
       "..           ...     ...                                           ...  ...   \n",
       "413         1305       3                            Spector, Mr. Woolf    1   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina    0   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen    1   \n",
       "416         1308       3                           Ware, Mr. Frederick    1   \n",
       "417         1309       3                      Peter, Master. Michael J    1   \n",
       "\n",
       "      Age  SibSp  Parch              Ticket      Fare Cabin Embarked  Q  S  1   \n",
       "0    34.5      0      0              330911    7.8292   NaN        Q  1  0  0  \\\n",
       "1    47.0      1      0              363272    7.0000   NaN        S  0  1  0   \n",
       "2    62.0      0      0              240276    9.6875   NaN        Q  1  0  0   \n",
       "3    27.0      0      0              315154    8.6625   NaN        S  0  1  0   \n",
       "4    22.0      1      1             3101298   12.2875   NaN        S  0  1  0   \n",
       "..    ...    ...    ...                 ...       ...   ...      ... .. .. ..   \n",
       "413  24.0      0      0           A.5. 3236    8.0500   NaN        S  0  1  0   \n",
       "414  39.0      0      0            PC 17758  108.9000  C105        C  0  0  1   \n",
       "415  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  0  1  0   \n",
       "416  24.0      0      0              359309    8.0500   NaN        S  0  1  0   \n",
       "417  24.0      1      1                2668   22.3583   NaN        C  0  0  0   \n",
       "\n",
       "     2  3  \n",
       "0    0  1  \n",
       "1    0  1  \n",
       "2    1  0  \n",
       "3    0  1  \n",
       "4    0  1  \n",
       "..  .. ..  \n",
       "413  0  1  \n",
       "414  0  0  \n",
       "415  0  1  \n",
       "416  0  1  \n",
       "417  0  1  \n",
       "\n",
       "[418 rows x 16 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6fb23e29",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=df_test.drop(['PassengerId','Pclass','Name','Ticket','Cabin','Embarked'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4522e863",
   "metadata": {},
   "outputs": [
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       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</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>3</th>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sex   Age  SibSp  Parch      Fare  Q  S  1  2  3\n",
       "0      1  34.5      0      0    7.8292  1  0  0  0  1\n",
       "1      0  47.0      1      0    7.0000  0  1  0  0  1\n",
       "2      1  62.0      0      0    9.6875  1  0  0  1  0\n",
       "3      1  27.0      0      0    8.6625  0  1  0  0  1\n",
       "4      0  22.0      1      1   12.2875  0  1  0  0  1\n",
       "..   ...   ...    ...    ...       ... .. .. .. .. ..\n",
       "413    1  24.0      0      0    8.0500  0  1  0  0  1\n",
       "414    0  39.0      0      0  108.9000  0  0  1  0  0\n",
       "415    1  38.5      0      0    7.2500  0  1  0  0  1\n",
       "416    1  24.0      0      0    8.0500  0  1  0  0  1\n",
       "417    1  24.0      1      1   22.3583  0  0  0  0  1\n",
       "\n",
       "[418 rows x 10 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301528f6",
   "metadata": {},
   "source": [
    "# Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a9756dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "86723e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=5, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=5, random_state=1)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)\n",
    "model.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "df9bff24",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fae9d013",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Random=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "584534da",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Random['PassengerId']=test_data['PassengerId']\n",
    "df_Random['Survived']=prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2d781acf",
   "metadata": {},
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Random.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b7e8fdf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Random.reset_index(drop=True, inplace=True)\n",
    "df_Random.set_index('PassengerId', inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e51bc78f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Random.to_csv(\"RandomForest.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c477f4ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f7a8760",
   "metadata": {},
   "source": [
    "df[\"Embarked\"] = [0 if i == \"S\" else i for i in df[\"Embarked\"]] \n",
    "df[\"Embarked\"] = [2 if i == \"C\" else i for i in df[\"Embarked\"]] \n",
    "df[\"Embarked\"] = [1 if i == \"Q\" else i for i in df[\"Embarked\"]] \n",
    "\n",
    "df_test=pd.DataFrame(test_data) \n",
    "df_test[\"Embarked\"] = [0 if i == \"S\" else i for i in df_test[\"Embarked\"]] \n",
    "df_test[\"Embarked\"] = [1 if i == \"C\" else i for i in df_test[\"Embarked\"]] \n",
    "df_test[\"Embarked\"] = [2 if i == \"Q\" else i for i in df_test[\"Embarked\"]] \n",
    " \n",
    "    \n",
    "    \n",
    "#It is not advisable to assign values to categorical columns like this because of multicollinearity/ model might think\n",
    "#that there is a closer relation between S and C rather and not a close relation between S and Q, and may give wrong predictions\n",
    "#So we should perform one hot encoding.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d421a9dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,y_train,x_test,y_test=train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4ce4eeaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f012a68c",
   "metadata": {},
   "outputs": [],
   "source": [
    " X.columns = X.columns.astype(str) #We need to convert DataTypes of all columns to string as LogisticRegression takes str inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a5d96d35",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=lr.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bfa8a8ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test=df_test  #Assign the test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a5e7f0ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.columns = X_test.columns.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d1c65d4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aarus\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:432: UserWarning: X has feature names, but LogisticRegression was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "prediction=lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a934dc60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1fed2e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3979eddb",
   "metadata": {},
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ed583253",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df['PassengerId']=test_data['PassengerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "243848b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df['Survived']=prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "754e33ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df.reset_index(drop=True, inplace=True)\n",
    "result_df.set_index('PassengerId', inplace=True)  #Since we have to make the Id as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "25d63520",
   "metadata": {},
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived\n",
       "PassengerId          \n",
       "892                 0\n",
       "893                 0\n",
       "894                 0\n",
       "895                 0\n",
       "896                 0\n",
       "...               ...\n",
       "1305                0\n",
       "1306                0\n",
       "1307                0\n",
       "1308                0\n",
       "1309                0\n",
       "\n",
       "[418 rows x 1 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e53b74e3",
   "metadata": {},
   "source": [
    "# Breaking down the working of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "411e87d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2012649907315183"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w0=model.intercept_[0]\n",
    "w0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "d2b07834",
   "metadata": {},
   "outputs": [],
   "source": [
    "w1,w2,w3,w4,w5,w6,w7,w8,w9,w10=model.coef_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e80ca6da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction based on w0 and coefficients obtained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7043995c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Feature_names=[\"Sex\",\"Age\",\"SibSp\",\"Parch\",\"Fare\",\"Q\",\"S\",\"1\",\"2\",\"3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "9baa8fea",
   "metadata": {},
   "outputs": [],
   "source": [
    "prob=X_test.iloc[45][Feature_names].values.reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2e5e9be2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aarus\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "y=model.predict_proba(prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f501eb2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted value: 0.8946156128464782\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted value:\",y[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "49756e5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "actual value: Survived    0\n",
      "Name: 937, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"actual value:\",result_df.iloc[45])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d50cb74d",
   "metadata": {},
   "source": [
    "# Most important Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ed257196",
   "metadata": {},
   "outputs": [],
   "source": [
    "w=[w1,w2,w3,w4,w5,w6,w7,w8,w9,w10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "6d175df1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-2.5893910054950515,\n",
       " -0.035139967055491196,\n",
       " -0.30074574824103417,\n",
       " -0.0784923774539004,\n",
       " 0.002953579826570136,\n",
       " -0.08882417050164153,\n",
       " -0.3941182817060152,\n",
       " 1.489147641387017,\n",
       " 0.6952703987218793,\n",
       " -0.5014432947745414]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "02f1f5ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "Feature_importance=pd.DataFrame(Feature_names,columns=[\"Features\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "895dc354",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "49d59996",
   "metadata": {},
   "outputs": [],
   "source": [
    "Feature_importance['Importance']=pow(math.e,np.array(w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e0b48266",
   "metadata": {},
   "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>Features</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sex</td>\n",
       "      <td>0.075066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>0.605656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>S</td>\n",
       "      <td>0.674274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>0.740266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.915006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parch</td>\n",
       "      <td>0.924509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.965470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fare</td>\n",
       "      <td>1.002958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2.004251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>4.433315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Features  Importance\n",
       "0      Sex    0.075066\n",
       "9        3    0.605656\n",
       "6        S    0.674274\n",
       "2    SibSp    0.740266\n",
       "5        Q    0.915006\n",
       "3    Parch    0.924509\n",
       "1      Age    0.965470\n",
       "4     Fare    1.002958\n",
       "8        2    2.004251\n",
       "7        1    4.433315"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature_importance.sort_values(by=\"Importance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb3d6084",
   "metadata": {},
   "source": [
    "## Save the DataFrame as a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "73aca840",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df.to_csv('TitanicPrediction.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4935dcd9",
   "metadata": {},
   "source": [
    "# KNN Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "280bad72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting K-NN classifier to the training set  \n",
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "classifier= KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2 )  \n",
    "classifier.fit(X, Y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "ad45aae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting the test set result  \n",
    "y_pred= classifier.predict(X_test)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "c55b76af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "62b0f71e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_KNN=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "85c25937",
   "metadata": {},
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "18e3453a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_KNN['PassengerId']=test_data['PassengerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "1fc1a93a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_KNN['Survived']=y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "b187373a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
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       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         0\n",
       "1            893         0\n",
       "2            894         0\n",
       "3            895         1\n",
       "4            896         0\n",
       "..           ...       ...\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         1\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "ff845589",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_KNN.reset_index(drop=True, inplace=True)\n",
    "df_KNN.set_index('PassengerId', inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "6930233a",
   "metadata": {},
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived\n",
       "PassengerId          \n",
       "892                 0\n",
       "893                 0\n",
       "894                 0\n",
       "895                 1\n",
       "896                 0\n",
       "...               ...\n",
       "1305                0\n",
       "1306                1\n",
       "1307                0\n",
       "1308                0\n",
       "1309                1\n",
       "\n",
       "[418 rows x 1 columns]"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "4c37764c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_KNN.to_csv(\"TitanicPredictionKNN.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8907fbad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8679fce8",
   "metadata": {},
   "source": [
    "# decision tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c3bfe9ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "std=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "48c9ed93",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=std.fit_transform(X)\n",
    "X_test=std.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bebf5c4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "42e29fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "67c23b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "90e8420a",
   "metadata": {},
   "outputs": [],
   "source": [
    "survived=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6c294d7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tree=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "38ff2cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tree['PassengerId']=test_data['PassengerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f18db722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
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       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId\n",
       "0            892\n",
       "1            893\n",
       "2            894\n",
       "3            895\n",
       "4            896\n",
       "..           ...\n",
       "413         1305\n",
       "414         1306\n",
       "415         1307\n",
       "416         1308\n",
       "417         1309\n",
       "\n",
       "[418 rows x 1 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1f12ee40",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tree['Survived']=survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e105e094",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         0\n",
       "1            893         0\n",
       "2            894         1\n",
       "3            895         1\n",
       "4            896         1\n",
       "..           ...       ...\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         1\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d232bd27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tree.reset_index(drop=True, inplace=True)\n",
    "df_tree.set_index('PassengerId', inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c1b307ea",
   "metadata": {},
   "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",
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       "    }\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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived\n",
       "PassengerId          \n",
       "892                 0\n",
       "893                 0\n",
       "894                 1\n",
       "895                 1\n",
       "896                 1\n",
       "...               ...\n",
       "1305                0\n",
       "1306                1\n",
       "1307                0\n",
       "1308                0\n",
       "1309                1\n",
       "\n",
       "[418 rows x 1 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f2cb3051",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tree.to_csv(\"DecisionTreeTitanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "018297cc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "979f6b2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd4d7e9c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "991b7ac4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d341c333",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "debb4717",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c48558b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3abe6088",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32077d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9c634eca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(714, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ea19b978",
   "metadata": {},
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f8345300",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d5b7fce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "kbin_age = KBinsDiscretizer(n_bins=15,encode='ordinal',strategy='quantile')\n",
    "kbin_fare = KBinsDiscretizer(n_bins=15,encode='ordinal',strategy='quantile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c638d96d",
   "metadata": {},
   "outputs": [],
   "source": [
    "trf = ColumnTransformer([\n",
    "    ('first',kbin_age,[0]),\n",
    "    ('second',kbin_fare,[1])\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e72b9419",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[23], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X_train_trf \u001b[38;5;241m=\u001b[39m trf\u001b[38;5;241m.\u001b[39mfit_transform(\u001b[43mX_train\u001b[49m)\n\u001b[0;32m      2\u001b[0m X_test_trf \u001b[38;5;241m=\u001b[39m trf\u001b[38;5;241m.\u001b[39mtransform(X_test)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X_train' is not defined"
     ]
    }
   ],
   "source": [
    "X_train_trf = trf.fit_transform(X_train)\n",
    "X_test_trf = trf.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c536f347",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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