Athira Prakash Assignment on Data pre processing.ipynb

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   "cell_type": "markdown",
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   "source": [
    "## Assignment on Preprocessing \n",
    "####                                                                                                                                                               Submitted By: <font color='blue'>ATHIRA PRAKASH</font>\n",
    "Do the following on titanic dataset\n",
    "1. Load the dataset into python environment\n",
    "2. Make ‘PassengerId’ as the index column\n",
    "3. Check the basic details of the dataset\n",
    "4. Fill in all the missing values present in all the columns in the dataset\n",
    "5. Check and handle outliers in at least 3 columns in the dataset\n",
    "6. Do min max scaling on the feature set (Take ‘Survived’ as target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e93fb19e",
   "metadata": {},
   "source": [
    "###  1.Load the dataset into python environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d439516b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b971e74",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Embarked</th>\n",
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       "      <td>1</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data=pd.read_csv(r\"D:\\DSA\\data\\titanic_dataset.csv\")\n",
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61855812",
   "metadata": {},
   "source": [
    "###  2.Make ‘PassengerId’ as the index column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d33f6f6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.set_index('PassengerId',inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d96bda3",
   "metadata": {},
   "outputs": [
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       "      <th>Survived</th>\n",
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       "      <th>Name</th>\n",
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       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>0</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "225572a4",
   "metadata": {},
   "source": [
    "###  3.Check the basic details of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c7b9fce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f3d8532a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Name      891 non-null    object \n",
      " 3   Sex       891 non-null    object \n",
      " 4   Age       714 non-null    float64\n",
      " 5   SibSp     891 non-null    int64  \n",
      " 6   Parch     891 non-null    int64  \n",
      " 7   Ticket    891 non-null    object \n",
      " 8   Fare      891 non-null    float64\n",
      " 9   Cabin     204 non-null    object \n",
      " 10  Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "62ee5524",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
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      ],
      "text/plain": [
       "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "601a800f",
   "metadata": {},
   "source": [
    "Insights:\n",
    "          There are 891 rows and 11 coloumns\n",
    "                                                                                                     \n",
    "                                                                                                     \n",
    "        Out of those 11 coloumn there are float 4 are of int and 5 are of object dtypes\n",
    "        \n",
    "        Columns Age,Embarked and Cabin have some missing values\n",
    "       \n",
    "Each column represents one feature of our data. The PassengerId is a consecutive number identifying each row. Survived is the indicator on whether the passenger survived (0 = No, 1 = Yes). Pclass is the ticket class (1 = 1st, 2 = 2nd, 3 = 3rd). Then we have self-explanatory Name, Sex, and Age.\n",
    "\n",
    "SibSp denotes the number of this passenger's siblings or spouses aboard the Titanic. Parch denotes the number of this passenger's parents or children aboard the Titanic.\n",
    "\n",
    "Then, there are the Fare the passenger paid, the Cabin number and the port of embarkation (embarked) (C = Cherbourg, Q = Queenstown, S = Southampton).\n",
    "\n",
    "Our data has different types. There is numerical data, such as the Age, SibSp, Parch, and the Fare. There is categorial data. Some of the categories are represented by numbers (Survived, Pclass). Some are represented by text (Sex and Embarked). And there is textual data (Name, Ticket, and Cabin).\n",
    "\n",
    "This is quite a mess. For a computer. Furthermore, when having another look at the result oftitanic_data.info(), you can see that the counts vary for different columns. While we have 891 values for most columns, we only have 714 for Age, 204 for Cabin, and 889 for Embarked."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7da15dc0",
   "metadata": {},
   "source": [
    "### 4.Fill in all the missing values present in all the columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9687dbb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Name          0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Ticket        0\n",
       "Fare          0\n",
       "Cabin       687\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "28a0be8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "freqgraph = titanic_data.select_dtypes(include=['float','object','int'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c64bf87d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freqgraph.hist(figsize=(20,15));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f78a9784",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data['Age'].fillna(round(titanic_data['Age'].mean(),1),inplace=True)\n",
    "titanic_data[\"Cabin\"].fillna(value='Not Known',inplace=True)\n",
    "#titanic_data[\"Embarked\"].fillna(titanic_data[\"Embarked\"].mode(),inplace=True)\n",
    "titanic_data['Embarked'].fillna(titanic_data['Embarked'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d78962f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    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       0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a93f52ec",
   "metadata": {},
   "source": [
    "######  Insights:\n",
    "Missing values in Age and Embarked columns are filled with average and mode.Cabin  column's missing values are filled with \"Not known\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030e4fa2",
   "metadata": {},
   "source": [
    "### 5.Check and handle outliers in at least 3 columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1813684a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
       "       'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "79599b7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['Age', 'SibSp','Fare' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c6c0743e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in columns:\n",
    "    plt.figure()\n",
    "    plt.boxplot(titanic_data[col]);\n",
    "    plt.title(\"Box Plot of \"+col)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8525ddce",
   "metadata": {},
   "source": [
    "####  Outlier handling Of Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "61de3ce4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5\n",
      "54.5\n"
     ]
    }
   ],
   "source": [
    "Q1=np.percentile(titanic_data['Age'],25,interpolation='midpoint')\n",
    "Q2=np.percentile(titanic_data['Age'],50,interpolation='midpoint')\n",
    "Q3=np.percentile(titanic_data['Age'],75,interpolation='midpoint')\n",
    "IQR=Q3-Q1\n",
    "low_lim=Q1-1.5*IQR\n",
    "upp_lim=Q3+1.5*IQR\n",
    "print(low_lim)\n",
    "print(upp_lim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cbbf72d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier=[]\n",
    "for x in titanic_data['Age']:\n",
    "    if((x>upp_lim) or (x<low_lim)):\n",
    "        outlier.append(x)\n",
    "titanic_data['Age']=np.where(titanic_data['Age']>upp_lim,upp_lim,np.where(titanic_data['Age']<low_lim,low_lim,titanic_data['Age']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "78a19d22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'box plot of Age')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGxCAYAAADCo9TSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAguUlEQVR4nO3dbXBU5f3/8c9iyCYxm1XudkkJEkswUYSSQCNUSkTJFJFKI7UCsdAZFQasjdRiEaaGjiZCZ5DaeFPvIBYYtHg/IiQtGnQAG0JRJxMUpkkJI2uEQhJISASu/wP+2R9LIrCwuZYN79fMmcy59pyz39QHeffsLuswxhgBAABY0i3cAwAAgEsL8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QGESX5+vhwOh/bv3x/uUYKyYsUKORwO1dTUBH3uunXrlJ+fH/KZJOkvf/mLBg4cqOjoaDkcDh06dOis5zz11FNyOBwaPHhwp8wEoGPEBwBr1q1bp0WLFoX8ujt27NADDzygm266SRs3btSWLVvkcrnOet7LL78sSaqsrNQnn3wS8rkAdIz4ABDxKisrJUn33nuvbrzxRt1www267LLLznjOtm3b9Omnn2rChAmSpJdeeqnT5wRwEvEBhFltba1ycnKUkJAgt9ut3NxcffPNNwHHnDhxQkuWLFFqaqqcTqf69OmjX/7yl9q7d6//mFmzZikmJkYVFRUB5918883yeDzat2/fd85QU1Mjh8OhJUuW6PHHH1f//v0VExOj4cOH65///Oc5/R4vv/yyhg4dqpiYGPXo0UM/+9nPVFVV5X98xowZevrppyVJDofDv53t5ZuzXTcrK0u5ubmSpMzMTDkcDs2YMeOs87bFxhNPPKFRo0ZpzZo1ampqanfc3r17NXnyZLlcLl1xxRWaNm2aysvL5XA4tGLFioBjt23bpp/+9Kfq0aOHYmJiNGzYML322mtnnQW45BgAYfHoo48aSeaqq64yv/vd78yGDRvM0qVLzeWXX26GDRtmWltb/cfed999RpK5//77zfr1681zzz1nevfubZKSksw333xjjDGmubnZ/OAHPzBXX321OXjwoDHGmD/84Q+mW7dupqSk5IyzVFdXG0kmKSnJ3Hjjjeb11183f//7382IESNM9+7dzebNm/3HLl++3Egy1dXV/rWCggIjyUyZMsW899575pVXXjFXX321cbvd5ssvvzTGGLN7924zefJkI8ls2bLFvx09evQ75zqX61ZWVpqFCxcaSWb58uVmy5YtZvfu3Wf8fZuamozb7TYjRowwxhjz4osvGklmxYoVAccdPnzYDBw40PTo0cM8/fTTZsOGDebBBx80ycnJ/udrs3HjRhMdHW1Gjx5tXn31VbN+/XozY8aMdscBMIb4AMKkLT4efPDBgPVVq1YZSWblypXGGGOqqqqMJDN79uyA4z755BMjyTzyyCP+tV27dpmEhAQzadIk849//MN069bNLFy48KyztMVHYmKiaW5u9q83NDSYHj16mFtuucW/dnp8HDx40MTGxppbb7014Jp79uwxTqfTTJ061b82Z84cc67/nyeY67bNVF5efk7XfuWVV4wk89xzzxljjGlsbDTx8fFm9OjRAcc9/fTTRpJ5//33A9ZnzpzZLipSU1PNsGHDzLfffhtw7G233Wb69u1rjh8/fk6zAZcCXnYBwmzatGkB+3feeaeioqL0wQcfSJL/5+kvJfzwhz9UWlpawMsiAwcO1AsvvKC33npLt912m0aPHh3Up0tycnIUExPj33e5XJo4caI2bdqk48ePd3jOli1b1Nzc3G6+pKQkjR079pxftrF1XenkSy6xsbG66667JEnx8fH6+c9/ro8++ki7du3yH1dWViaXy6Wf/OQnAedPmTIlYH/37t3auXOn/7/lsWPH/Nutt96qffv26YsvvjjveYGuhvgAwszr9QbsR0VFqWfPnjpw4IAk+X/27du33bmJiYn+x9tMmDBBHo9HR48e1dy5c8/6xsszzdK21traqsOHD3d4TrDznavOuu7u3bu1adMmTZgwQcYYHTp0SIcOHdLkyZMl/d8nYNpm8Hg87a5x+trXX38tSXrooYfUvXv3gG327NmSFHEfqQY6U1S4BwAudT6fT9/73vf8+8eOHdOBAwfUs2dPSfL/3Ldvn/r16xdw7ldffaVevXoFrM2aNUuNjY267rrr9MADD2j06NG68sorz3mWjtaio6MVHx/f4Tmnzne6juY7V5113ZdfflnGGK1du1Zr165t93hxcbEee+wxXXbZZerZs6f+9a9/tTvm9P+d2maZP3++cnJyOnzea6655rzmBboi7nwAYbZq1aqA/ddee03Hjh1TVlaWJGns2LGSpJUrVwYcV15erqqqKt18883+tRdffFErV65UUVGR3nnnHR06dEi/+tWvznmWN954Q0ePHvXvNzY26t1339Xo0aO/8w7KyJEjFRsb226+vXv3auPGjQHzOZ1OSVJzc/NZZwnmuufq+PHjKi4u1ve//3198MEH7bbf/va32rdvn95//31J0pgxY9TY2Ojfb7NmzZqA/WuuuUYpKSn69NNPNXz48A63c/l3R4BLRrjfdAJcqk7/tEtJSYl58sknTXx8vBk6dKhpaWnxH3vfffcZh8Nh8vLyzIYNG8xf//pX06dPH5OUlGT2799vjDHms88+M7GxsWb69On+89auXWskmSeffPKMs5z+aZc33njDrF271owYMcJERUWZjz/+2H/smT7tcvfdd5t169aZv/3tb2bgwIEBn0o59dxHH33UbN261ZSXlwf8nqcL9rpne8Ppu+++aySZxYsXd/j4N998Y5xOp5k0aZIxJvDTLs8884wpKSkxDz74oBkwYICRZIqLi/3nbty40TidTpOdnW1Wr15tysrKzJtvvmkKCgrM5MmTzzgXcKkhPoAwaYuPiooKM3HiRBMfH29cLpeZMmWK+frrrwOOPX78uFm8eLEZNGiQ6d69u+nVq5fJzc01tbW1xpiTfyRTU1PNtddea44cORJw7pw5c0z37t3NJ5988p2ztMXH4sWLzaJFi0y/fv1MdHS0GTZsmNmwYUPAsR3FhzEnP646ZMgQEx0dbdxut7n99ttNZWVlwDEtLS3mnnvuMb179zYOh6PD65zuXK57rvExadIkEx0dberq6r7zmLvuustERUUZn89njDn56ZqcnBz/f5877rjDrFu3zkgyb7/9dsC5n376qbnzzjtNnz59TPfu3Y3X6zVjx471f6oGwEkOY4yxf78FwMWkpqZGycnJ+tOf/qSHHnoo3ONc9AoKCrRw4ULt2bOn3ftwAJwdbzgFgDMoKiqSJKWmpurbb7/Vxo0b9dRTTyk3N5fwAM4T8QEAZxAXF6cnn3xSNTU1amlpUf/+/fXwww9r4cKF4R4NiFi87AIAAKzio7YAAMAq4gMAAFhFfAAAAKsuujecnjhxQl999ZVcLpccDke4xwEAAOfAGKPGxkYlJiaqW7cz39u46OLjq6++UlJSUrjHAAAA56G2tvasH0O/6OKj7fsPamtrlZCQEOZpAADAuWhoaFBSUtI5fY/RRRcfbS+1JCQkEB8AAESYc3nLBG84BQAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACrLrovlgNw8WlqatLOnTsv+DrNzc2qqanRgAEDFBsbG4LJpNTUVMXFxYXkWgDsID4AnNXOnTuVkZER7jE6VFFRofT09HCPASAIxAeAs0pNTVVFRcUFX6eqqkq5ublauXKl0tLSQjDZydkARBbiA8BZxcXFhfTuQlpaGncrgEsYbzgFAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVUHFR35+vhwOR8Dm9Xr9jxtjlJ+fr8TERMXGxiorK0uVlZUhHxoAAESuoO98XHfdddq3b59/+/zzz/2PLVmyREuXLlVRUZHKy8vl9Xo1btw4NTY2hnRoAAAQuYKOj6ioKHm9Xv/Wu3dvSSfveixbtkwLFixQTk6OBg8erOLiYjU1NWn16tUhHxwAAESmoONj165dSkxMVHJysu666y795z//kSRVV1fL5/MpOzvbf6zT6dSYMWO0efPm77xeS0uLGhoaAjYAANB1BRUfmZmZeuWVV7Rhwwa98MIL8vl8GjVqlA4cOCCfzydJ8ng8Aed4PB7/Yx0pLCyU2+32b0lJSefxawAAgEgRVHyMHz9ed9xxh66//nrdcssteu+99yRJxcXF/mMcDkfAOcaYdmunmj9/vurr6/1bbW1tMCMBAIAIc0Eftb388st1/fXXa9euXf5PvZx+l6Ourq7d3ZBTOZ1OJSQkBGwAAKDruqD4aGlpUVVVlfr27avk5GR5vV6Vlpb6H29tbVVZWZlGjRp1wYMCAICuISqYgx966CFNnDhR/fv3V11dnR577DE1NDRo+vTpcjgcysvLU0FBgVJSUpSSkqKCggLFxcVp6tSpnTU/AACIMEHFx969ezVlyhTt379fvXv31g033KCtW7fqqquukiTNmzdPzc3Nmj17tg4ePKjMzEyVlJTI5XJ1yvAAACDyOIwxJtxDnKqhoUFut1v19fW8/wPoYrZv366MjAxVVFQoPT093OMACKFg/n7z3S4AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArLqg+CgsLJTD4VBeXp5/zRij/Px8JSYmKjY2VllZWaqsrLzQOQEAQBdx3vFRXl6u559/XkOGDAlYX7JkiZYuXaqioiKVl5fL6/Vq3LhxamxsvOBhAQBA5Duv+Dh8+LCmTZumF154QVdeeaV/3RijZcuWacGCBcrJydHgwYNVXFyspqYmrV69OmRDAwCAyHVe8TFnzhxNmDBBt9xyS8B6dXW1fD6fsrOz/WtOp1NjxozR5s2bO7xWS0uLGhoaAjYAANB1RQV7wpo1a1RRUaFt27a1e8zn80mSPB5PwLrH49F///vfDq9XWFioRYsWBTsGAACIUEHd+aitrdVvfvMbrVq1SjExMd95nMPhCNg3xrRbazN//nzV19f7t9ra2mBGAgAAESaoOx8VFRWqq6tTRkaGf+348ePatGmTioqK9MUXX0g6eQekb9++/mPq6ura3Q1p43Q65XQ6z2d2AAAQgYK683HzzTfr888/144dO/zb8OHDNW3aNO3YsUNXX321vF6vSktL/ee0traqrKxMo0aNCvnwAAAg8gR158Plcmnw4MEBa5dffrl69uzpX8/Ly1NBQYFSUlKUkpKigoICxcXFaerUqaGbGgAARKyg33B6NvPmzVNzc7Nmz56tgwcPKjMzUyUlJXK5XKF+KgAAEIEcxhgT7iFO1dDQILfbrfr6eiUkJIR7HAAhtH37dmVkZKiiokLp6enhHgdACAXz95vvdgEAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALAqKtwDAOg8u3btUmNjY7jH8Kuqqgr4eTFxuVxKSUkJ9xjAJYH4ALqoXbt2adCgQeEeo0O5ubnhHqFDX375JQECWEB8AF1U2x2PlStXKi0tLczTnNTc3KyamhoNGDBAsbGx4R7Hr6qqSrm5uRfVXSKgKyM+gC4uLS1N6enp4R7D70c/+lG4RwAQZrzhFAAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4KKj2effVZDhgxRQkKCEhISNHLkSL3//vv+x40xys/PV2JiomJjY5WVlaXKysqQDw0AACJXUPHRr18/PfHEE9q2bZu2bdumsWPH6vbbb/cHxpIlS7R06VIVFRWpvLxcXq9X48aNU2NjY6cMDwAAIk9Q8TFx4kTdeuutGjRokAYNGqTHH39c8fHx2rp1q4wxWrZsmRYsWKCcnBwNHjxYxcXFampq0urVqztrfgAAEGHO+z0fx48f15o1a3TkyBGNHDlS1dXV8vl8ys7O9h/jdDo1ZswYbd68+Tuv09LSooaGhoANAAB0XUHHx+eff674+Hg5nU7NmjVLb775pq699lr5fD5JksfjCTje4/H4H+tIYWGh3G63f0tKSgp2JAAAEEGigj3hmmuu0Y4dO3To0CG9/vrrmj59usrKyvyPOxyOgOONMe3WTjV//nzNnTvXv9/Q0ECAACHgOHZUw7zdFHvoS+krPth2JrGHvtQwbzc5jh0N9yjAJSHo+IiOjtbAgQMlScOHD1d5ebn+/Oc/6+GHH5Yk+Xw+9e3b1398XV1du7shp3I6nXI6ncGOAeAsYg7v0faZ8dKmmdKmcE9zcUuTtH1mvKoO75E0KtzjAF1e0PFxOmOMWlpalJycLK/Xq9LSUg0bNkyS1NraqrKyMi1evPiCBwUQnKPx/ZX+18NatWqV0lJTwz3ORa1q505NmzZNL93aP9yjAJeEoOLjkUce0fjx45WUlKTGxkatWbNGH374odavXy+Hw6G8vDwVFBQoJSVFKSkpKigoUFxcnKZOndpZ8wP4DiYqRv/2nVDzFYOkxB+Ee5yLWrPvhP7tOyETFRPuUYBLQlDx8fXXX+vuu+/Wvn375Ha7NWTIEK1fv17jxo2TJM2bN0/Nzc2aPXu2Dh48qMzMTJWUlMjlcnXK8AAAIPIEFR8vvfTSGR93OBzKz89Xfn7+hcwEAAC6MN4CDwAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwKircAwDoHE1NTZKk7du3h3mS/9Pc3KyamhoNGDBAsbGx4R7Hr6qqKtwjAJcU4gPoonbu3ClJuvfee8M8SeRwuVzhHgG4JBAfQBc1adIkSVJqaqri4uLCO8z/V1VVpdzcXK1cuVJpaWnhHieAy+VSSkpKuMcALgnEB9BF9erVS/fcc0+4x+hQWlqa0tPTwz0GgDDhDacAAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwKqj4KCws1IgRI+RyudSnTx9NmjRJX3zxRcAxxhjl5+crMTFRsbGxysrKUmVlZUiHBgAAkSuo+CgrK9OcOXO0detWlZaW6tixY8rOztaRI0f8xyxZskRLly5VUVGRysvL5fV6NW7cODU2NoZ8eAAAEHmC+hdO169fH7C/fPly9enTRxUVFfrxj38sY4yWLVumBQsWKCcnR5JUXFwsj8ej1atXa+bMme2u2dLSopaWFv9+Q0PD+fweAAAgQlzQez7q6+slST169JAkVVdXy+fzKTs723+M0+nUmDFjtHnz5g6vUVhYKLfb7d+SkpIuZCQAAHCRO+/4MMZo7ty5uvHGGzV48GBJks/nkyR5PJ6AYz0ej/+x082fP1/19fX+rba29nxHAgAAEeC8v1ju/vvv12effaaPP/643WMOhyNg3xjTbq2N0+mU0+k83zEAAECEOa87H7/+9a/1zjvv6IMPPlC/fv38616vV5La3eWoq6trdzcEAABcmoKKD2OM7r//fr3xxhvauHGjkpOTAx5PTk6W1+tVaWmpf621tVVlZWUaNWpUaCYGAAARLaiXXebMmaPVq1fr7bfflsvl8t/hcLvdio2NlcPhUF5engoKCpSSkqKUlBQVFBQoLi5OU6dO7ZRfAAAARJag4uPZZ5+VJGVlZQWsL1++XDNmzJAkzZs3T83NzZo9e7YOHjyozMxMlZSUyOVyhWRgAAAQ2YKKD2PMWY9xOBzKz89Xfn7++c4EAAC6ML7bBQAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVQcfHpk2bNHHiRCUmJsrhcOitt94KeNwYo/z8fCUmJio2NlZZWVmqrKwM1bwAACDCBR0fR44c0dChQ1VUVNTh40uWLNHSpUtVVFSk8vJyeb1ejRs3To2NjRc8LAAAiHxRwZ4wfvx4jR8/vsPHjDFatmyZFixYoJycHElScXGxPB6PVq9erZkzZ17YtAAAIOKF9D0f1dXV8vl8ys7O9q85nU6NGTNGmzdv7vCclpYWNTQ0BGwAAKDrCml8+Hw+SZLH4wlY93g8/sdOV1hYKLfb7d+SkpJCORIAALjIdMqnXRwOR8C+MabdWpv58+ervr7ev9XW1nbGSAAA4CIR9Hs+zsTr9Uo6eQekb9++/vW6urp2d0PaOJ1OOZ3OUI4BAAAuYiG985GcnCyv16vS0lL/Wmtrq8rKyjRq1KhQPhUAAIhQQd/5OHz4sHbv3u3fr66u1o4dO9SjRw/1799feXl5KigoUEpKilJSUlRQUKC4uDhNnTo1pIMDAIDIFHR8bNu2TTfddJN/f+7cuZKk6dOna8WKFZo3b56am5s1e/ZsHTx4UJmZmSopKZHL5Qrd1AAAIGI5jDEm3EOcqqGhQW63W/X19UpISAj3OABCaPv27crIyFBFRYXS09PDPQ6AEArm7zff7QIAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGBVVLgHAHDxa2pq0s6dOy/4OlVVVQE/QyE1NVVxcXEhux6Azkd8ADirnTt3KiMjI2TXy83NDdm1KioqlJ6eHrLrAeh8xAeAs0pNTVVFRcUFX6e5uVk1NTUaMGCAYmNjQzDZydkARBaHMcaEe4hTNTQ0yO12q76+XgkJCeEeBwAAnINg/n7zhlMAAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKzqtPh45plnlJycrJiYGGVkZOijjz7qrKcCAAARpFPi49VXX1VeXp4WLFigf//73xo9erTGjx+vPXv2dMbTAQCACNIpXyyXmZmp9PR0Pfvss/61tLQ0TZo0SYWFhWc8ly+WAwAg8gTz9zsq1E/e2tqqiooK/f73vw9Yz87O1ubNm9sd39LSopaWFv9+fX29pJO/BAAAiAxtf7fP5Z5GyONj//79On78uDweT8C6x+ORz+drd3xhYaEWLVrUbj0pKSnUowEAgE7W2Ngot9t9xmNCHh9tHA5HwL4xpt2aJM2fP19z58717584cUL/+9//1LNnzw6PBxC5GhoalJSUpNraWl5WBboYY4waGxuVmJh41mNDHh+9evXSZZdd1u4uR11dXbu7IZLkdDrldDoD1q644opQjwXgIpKQkEB8AF3Q2e54tAn5p12io6OVkZGh0tLSgPXS0lKNGjUq1E8HAAAiTKe87DJ37lzdfffdGj58uEaOHKnnn39ee/bs0axZszrj6QAAQATplPj4xS9+oQMHDuiPf/yj9u3bp8GDB2vdunW66qqrOuPpAEQIp9OpRx99tN1LrQAuLZ3y73wAAAB8F77bBQAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAKzZt2qSJEycqMTFRDodDb731VrhHAhAmxAcAK44cOaKhQ4eqqKgo3KMACLNO+2I5ADjV+PHjNX78+HCPAeAiwJ0PAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFbxaRcAVhw+fFi7d+/271dXV2vHjh3q0aOH+vfvH8bJANjmMMaYcA8BoOv78MMPddNNN7Vbnz59ulasWGF/IABhQ3wAAACreM8HAACwivgAAABWER8AAMAq4gMAAFhFfAAAAKuIDwAAYBXxAQAArCI+AACAVcQHAACwivgAAABWER8AAMCq/wc3StbUbO2W2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(titanic_data['Age'])\n",
    "plt.title('box plot of Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f21f630",
   "metadata": {},
   "source": [
    "####  Outlier handling Of SibSp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a6363006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.5\n",
      "2.5\n"
     ]
    }
   ],
   "source": [
    "Q1=np.percentile(titanic_data['SibSp'],25,interpolation='midpoint')\n",
    "Q2=np.percentile(titanic_data['SibSp'],50,interpolation='midpoint')\n",
    "Q3=np.percentile(titanic_data['SibSp'],75,interpolation='midpoint')\n",
    "IQR=Q3-Q1\n",
    "low_lim=Q1-1.5*IQR\n",
    "upp_lim=Q3+1.5*IQR\n",
    "print(low_lim)\n",
    "print(upp_lim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c4f2dabe",
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier=[]\n",
    "for x in titanic_data['SibSp']:\n",
    "    if((x>upp_lim) or (x<low_lim)):\n",
    "        outlier.append(x)\n",
    "titanic_data['SibSp']=np.where(titanic_data['SibSp']>upp_lim,upp_lim,np.where(titanic_data['SibSp']<low_lim,low_lim,titanic_data['SibSp']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "25dc3687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'box plot of SibSp')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g5wsLZeN33nknaHnt2rWKi4tTeXm5xo0b1+Y+hYWFSkpKUkFBgSQpLS1Nu3fv1rJlyzRt2rSuTQ0AAHqNi7pnpKGhQZLUv3//drfZtWuXsrOzg9ZNnjxZu3fv1hdffNHmPk1NTfL7/UEPAADQO3U5Rpxzys3N1W233aYRI0a0u119fb3i4+OD1sXHx+vs2bM6duxYm/vk5+crNjY28PD5fF0dEwAAfM11OUbmzp2rjz76SL/97W873Nbj8QQtO+faXN8iLy9PDQ0NgUdtbW1XxwQAAF9zId0z0mLevHl6++23VVZWpsGDB19w20GDBqm+vj5o3dGjRxUWFqYBAwa0uY/X65XX6+3KaAAAoIcJ6cqIc05z587Vxo0b9e677yolJaXDfTIzM1VaWhq0buvWrRozZozCw8NDmxYAAPQ6IcXInDlzVFRUpDfeeEPR0dGqr69XfX29Tp8+HdgmLy9PM2bMCCzPnj1bn3zyiXJzc1VVVaU1a9Zo9erVWrhwYfc9CwAA0GOFFCOrVq1SQ0ODJkyYoISEhMBjw4YNgW3q6upUU1MTWE5JSVFJSYm2b9+uG2+8UT/96U+1YsUKPtYLAAAkhXjPSMuNpxeybt26VuvGjx+vPXv2hHIqAABwmeC7aQAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmAo5RsrKyjR16lQlJibK4/Fo06ZNF9x++/bt8ng8rR779u3r6swAAKAXCQt1h1OnTmnkyJF6/PHHNW3atE7vt3//fsXExASWr7766lBPDQAAeqGQYyQnJ0c5OTkhnyguLk5XXXVVyPsBAIDe7Su7Z2TUqFFKSEjQpEmTtG3btgtu29TUJL/fH/QAAAC90yWPkYSEBL366qsqLi7Wxo0blZqaqkmTJqmsrKzdffLz8xUbGxt4+Hy+Sz0mAAAwEvLbNKFKTU1VampqYDkzM1O1tbVatmyZxo0b1+Y+eXl5ys3NDSz7/X6CBACAXsrko7233HKLDhw40O7PvV6vYmJigh4AAKB3MomRiooKJSQkWJwaAAB8zYT8Ns3Jkyd18ODBwHJ1dbUqKyvVv39/JSUlKS8vT4cPH9b69eslSQUFBUpOTlZ6erqam5tVVFSk4uJiFRcXd9+zAAAAPVbIMbJ7925NnDgxsNxyb8fMmTO1bt061dXVqaamJvDz5uZmLVy4UIcPH1ZUVJTS09O1efNmTZkypRvGBwAAPZ3HOeesh+iI3+9XbGysGhoauH8E6GX27NmjjIwMlZeXa/To0dbjAOhGnf39zXfTAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwFXKMlJWVaerUqUpMTJTH49GmTZs63GfHjh3KyMhQZGSkhg4dqsLCwq7MCgAAeqGQY+TUqVMaOXKkXn755U5tX11drSlTpigrK0sVFRVatGiR5s+fr+Li4pCHBQAAvU9YqDvk5OQoJyen09sXFhYqKSlJBQUFkqS0tDTt3r1by5Yt07Rp00I9PQAA6GVCjpFQ7dq1S9nZ2UHrJk+erNWrV+uLL75QeHh4q32amprU1NQUWPb7/Zd6TOCyceDAATU2NlqPEVBVVRX079dFdHS0hg0bZj0GcFm45DFSX1+v+Pj4oHXx8fE6e/asjh07poSEhFb75Ofna/HixZd6NOCyc+DAAV177bXWY7Rp+vTp1iO08vHHHxMkwFfgkseIJHk8nqBl51yb61vk5eUpNzc3sOz3++Xz+S7dgMBlouWKSFFRkdLS0oyn+dLp06d16NAhJScnKyoqynocSV9epZk+ffrX6goS0Jtd8hgZNGiQ6uvrg9YdPXpUYWFhGjBgQJv7eL1eeb3eSz0acNlKS0vT6NGjrccIuPXWW61HAGDokv+dkczMTJWWlgat27p1q8aMGdPm/SIAAODyEnKMnDx5UpWVlaqsrJT05Ud3KysrVVNTI+nLt1hmzJgR2H727Nn65JNPlJubq6qqKq1Zs0arV6/WwoULu+cZAACAHi3kt2l2796tiRMnBpZb7u2YOXOm1q1bp7q6ukCYSFJKSopKSkq0YMECvfLKK0pMTNSKFSv4WC8AAJDUhRiZMGFC4AbUtqxbt67VuvHjx2vPnj2hngoAAFwG+G4aAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmuhQjK1euVEpKiiIjI5WRkaGdO3e2u+327dvl8XhaPfbt29floQEAQO8Rcoxs2LBBTz31lJ5++mlVVFQoKytLOTk5qqmpueB++/fvV11dXeAxbNiwLg8NAAB6j5BjZPny5XriiSc0a9YspaWlqaCgQD6fT6tWrbrgfnFxcRo0aFDg0bdv3y4PDQAAeo+QYqS5uVnl5eXKzs4OWp+dna333nvvgvuOGjVKCQkJmjRpkrZt23bBbZuamuT3+4MeAACgdwopRo4dO6Zz584pPj4+aH18fLzq6+vb3CchIUGvvvqqiouLtXHjRqWmpmrSpEkqKytr9zz5+fmKjY0NPHw+XyhjAgCAHiSsKzt5PJ6gZedcq3UtUlNTlZqaGljOzMxUbW2tli1bpnHjxrW5T15ennJzcwPLfr+fIAEAoJcK6crIwIED1bdv31ZXQY4ePdrqasmF3HLLLTpw4EC7P/d6vYqJiQl6AACA3imkGImIiFBGRoZKS0uD1peWlmrs2LGdPk5FRYUSEhJCOTUAAOilQn6bJjc3V48++qjGjBmjzMxMvfrqq6qpqdHs2bMlffkWy+HDh7V+/XpJUkFBgZKTk5Wenq7m5mYVFRWpuLhYxcXF3ftMAABAjxRyjDz44IM6fvy4XnjhBdXV1WnEiBEqKSnRkCFDJEl1dXVBf3OkublZCxcu1OHDhxUVFaX09HRt3rxZU6ZM6b5nAQAAeiyPc85ZD9ERv9+v2NhYNTQ0cP8IcBH27NmjjIwMlZeXa/To0dbjfG3xOgHdo7O/v/luGgAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgqksxsnLlSqWkpCgyMlIZGRnauXPnBbffsWOHMjIyFBkZqaFDh6qwsLBLwwIAgN4n5BjZsGGDnnrqKT399NOqqKhQVlaWcnJyVFNT0+b21dXVmjJlirKyslRRUaFFixZp/vz5Ki4uvujhAQBAzxcW6g7Lly/XE088oVmzZkmSCgoKtGXLFq1atUr5+fmtti8sLFRSUpIKCgokSWlpadq9e7eWLVumadOmtXmOpqYmNTU1BZb9fn+oYwJow5nG/9OoQX30yftvK+rzj7t8nKamJh05cqQbJ+s+iYmJ8nq9F3WM+upqjRrUR56zZ7ppKgAXElKMNDc3q7y8XD/+8Y+D1mdnZ+u9995rc59du3YpOzs7aN3kyZO1evVqffHFFwoPD2+1T35+vhYvXhzKaAA64V97/6o9/9NPOvq/0tGLO9aN3TLRJVB78YdIkzTlf/qpxh2/+IMB6FBIMXLs2DGdO3dO8fHxQevj4+NVX1/f5j719fVtbn/27FkdO3ZMCQkJrfbJy8tTbm5uYNnv98vn84UyKoA2ZN37hN56S0pOTlZkZGSXj9Pbr4xI0pVXXqmkUZO6YSIAHQn5bRpJ8ng8QcvOuVbrOtq+rfUtvF5vt/zHBECwgQk+3fu957vlWDd2y1EAIMQbWAcOHKi+ffu2ugpy9OjRVlc/WgwaNKjN7cPCwjRgwIAQxwUAAL1NSDESERGhjIwMlZaWBq0vLS3V2LFj29wnMzOz1fZbt27VmDFj2rxfBAAAXF5C/mhvbm6uXnvtNa1Zs0ZVVVVasGCBampqNHv2bElf3u8xY8aMwPazZ8/WJ598otzcXFVVVWnNmjVavXq1Fi5c2H3PAgAA9Fgh3zPy4IMP6vjx43rhhRdUV1enESNGqKSkREOGDJEk1dXVBf3NkZSUFJWUlGjBggV65ZVXlJiYqBUrVrT7sV4AAHB58biWu0m/xvx+v2JjY9XQ0KCYmBjrcQAAQCd09vc3300DAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMBUl76196vW8nfZ/H6/8SQAAKCzWn5vd/T3VXtEjDQ2NkqSfD6f8SQAACBUjY2Nio2NbffnPeLPwZ8/f15HjhxRdHS0PB6P9TgAupHf75fP51NtbS1f9wD0Ms45NTY2KjExUX36tH9nSI+IEQC9F989BYAbWAEAgCliBAAAmCJGAJjyer167rnn5PV6rUcBYIR7RgAAgCmujAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAE2VlZZo6daoSExPl8Xi0adMm65EAGCFGAJg4deqURo4cqZdfftl6FADGesS39gLofXJycpSTk2M9BoCvAa6MAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFN8mgaAiZMnT+rgwYOB5erqalVWVqp///5KSkoynAzAV83jnHPWQwC4/Gzfvl0TJ05stX7mzJlat27dVz8QADPECAAAMMU9IwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMDU/wOcYnbHwhritgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(titanic_data['SibSp'])\n",
    "plt.title('box plot of SibSp')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495efed8",
   "metadata": {},
   "source": [
    "####  Outlier handling Of Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f566a332",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-26.724\n",
      "65.6344\n"
     ]
    }
   ],
   "source": [
    "Q1=np.percentile(titanic_data['Fare'],25,interpolation='midpoint')\n",
    "Q2=np.percentile(titanic_data['Fare'],50,interpolation='midpoint')\n",
    "Q3=np.percentile(titanic_data['Fare'],75,interpolation='midpoint')\n",
    "IQR=Q3-Q1\n",
    "low_lim=Q1-1.5*IQR\n",
    "upp_lim=Q3+1.5*IQR\n",
    "print(low_lim)\n",
    "print(upp_lim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "258c2269",
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier=[]\n",
    "for x in titanic_data['Fare']:\n",
    "    if((x>upp_lim) or (x<low_lim)):\n",
    "        outlier.append(x)\n",
    "titanic_data['Fare']=np.where(titanic_data['Fare']>upp_lim,upp_lim,np.where(titanic_data['Fare']<low_lim,low_lim,titanic_data['Fare']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "87466156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'box plot of Fare')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(titanic_data['Fare'])\n",
    "plt.title('box plot of Fare')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c7ef1c",
   "metadata": {},
   "source": [
    "### 6.Do min max scaling on the feature set (Take ‘Survived’ as target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b3fa32f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
       "       'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2835f674",
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Not Known</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>65.6344</td>\n",
       "      <td>C85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Not Known</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Not Known</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name  Sex   Age  \\\n",
       "PassengerId                                                                 \n",
       "1                                      Braund, Mr. Owen Harris    1  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0   \n",
       "3                                       Heikkinen, Miss. Laina    0  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0   \n",
       "5                                     Allen, Mr. William Henry    1  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare      Cabin  Embarked  \n",
       "PassengerId                                                                \n",
       "1              1.0      0         A/5 21171   7.2500  Not Known         2  \n",
       "2              1.0      0          PC 17599  65.6344        C85         0  \n",
       "3              0.0      0  STON/O2. 3101282   7.9250  Not Known         2  \n",
       "4              1.0      0            113803  53.1000       C123         2  \n",
       "5              0.0      0            373450   8.0500  Not Known         2  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "for col in ['Sex', 'Embarked']:\n",
    "    le.fit(titanic_data[col])\n",
    "   \n",
    "    titanic_data[col] = le.transform(titanic_data[col])\n",
    "titanic_data.head()\n",
    "\n",
    "   \n",
    "     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1b50fc14",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = titanic_data[['Pclass','Sex', 'Age', 'SibSp','Parch','Fare','Embarked']]\n",
    "                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5332ca01",
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>65.6344</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "PassengerId                                                    \n",
       "1                 3    1  22.0    1.0      0   7.2500         2\n",
       "2                 1    0  38.0    1.0      0  65.6344         0\n",
       "3                 3    0  26.0    0.0      0   7.9250         2\n",
       "4                 1    0  35.0    1.0      0  53.1000         2\n",
       "5                 3    1  35.0    0.0      0   8.0500         2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b2a2f56a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_344\\1644014173.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x[col] = le.transform(x[col])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>65.6344</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "PassengerId                                                    \n",
       "1                 3    1  22.0    1.0      0   7.2500         2\n",
       "2                 1    0  38.0    1.0      0  65.6344         0\n",
       "3                 3    0  26.0    0.0      0   7.9250         2\n",
       "4                 1    0  35.0    1.0      0  53.1000         2\n",
       "5                 3    1  35.0    0.0      0   8.0500         2"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "for col in ['Sex', 'Embarked']:\n",
    "    le.fit(x[col])\n",
    "    x[col] = le.transform(x[col])\n",
    "    \n",
    "    \n",
    "             \n",
    "            \n",
    "x.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8fe2e69f",
   "metadata": {},
   "outputs": [],
   "source": [
    " from sklearn.preprocessing import MinMaxScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "db6b4c4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0275f76a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "279e584d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = scaler.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8976801d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x =pd.DataFrame(x,columns=['Pclass','Sex', 'Age', 'SibSp','Parch','Fare','Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ca66da89",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Sex</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
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       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.654321</td>\n",
       "      <td>0.647587</td>\n",
       "      <td>0.516865</td>\n",
       "      <td>0.170595</td>\n",
       "      <td>0.063599</td>\n",
       "      <td>0.366375</td>\n",
       "      <td>0.768238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.418036</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>0.231962</td>\n",
       "      <td>0.283298</td>\n",
       "      <td>0.134343</td>\n",
       "      <td>0.312056</td>\n",
       "      <td>0.395752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.120522</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.523077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.220223</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.472313</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "           Pclass         Sex         Age       SibSp       Parch        Fare  \\\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean     0.654321    0.647587    0.516865    0.170595    0.063599    0.366375   \n",
       "std      0.418036    0.477990    0.231962    0.283298    0.134343    0.312056   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.500000    0.000000    0.375000    0.000000    0.000000    0.120522   \n",
       "50%      1.000000    1.000000    0.523077    0.000000    0.000000    0.220223   \n",
       "75%      1.000000    1.000000    0.625000    0.400000    0.000000    0.472313   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         Embarked  \n",
       "count  891.000000  \n",
       "mean     0.768238  \n",
       "std      0.395752  \n",
       "min      0.000000  \n",
       "25%      0.500000  \n",
       "50%      1.000000  \n",
       "75%      1.000000  \n",
       "max      1.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "328b5e3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "</table>\n",
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      "text/plain": [
       "   Pclass  Sex       Age  SibSp  Parch      Fare  Embarked\n",
       "0     1.0  1.0  0.375000    0.4    0.0  0.110460       1.0\n",
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       "2     1.0  0.0  0.451923    0.0    0.0  0.120745       1.0\n",
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       "4     1.0  1.0  0.625000    0.0    0.0  0.122649       1.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
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   ],
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  {
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   "id": "8f52a4f3",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "185af71c",
   "metadata": {},
   "outputs": [],
   "source": []
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