exploratory-data-analysis-by-pandas-library.ipynb

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   "source": [
    "# **Exploratory Data Analysis by Pandas Library**"
   ]
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   "source": [
    "Auther: Tassawar Abbas\n",
    "\n",
    "Email: abbas829@gmail.com\n",
    "\n",
    "[github](https://github.com/abbas829)"
   ]
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   "source": [
    "# Pandas Library: A Comprehensive Guide and Cheat Sheet\n",
    "\n",
    "## Introduction to Pandas\n",
    "\n",
    "Pandas is a powerful Python library used for data manipulation and analysis. It provides easy-to-use data structures and functions to work with structured data. Pandas is widely used in data science, machine learning, and data analysis tasks due to its versatility and efficiency.\n",
    "\n",
    "## Key Features of Pandas\n",
    "\n",
    "### 1. Data Structures\n",
    "\n",
    "Pandas offers two main data structures: Series and DataFrame.\n",
    "\n",
    "- **Series**: A one-dimensional labeled array capable of holding any data type.\n",
    "- **DataFrame**: A two-dimensional labeled data structure with columns of potentially different types, similar to a spreadsheet or SQL table.\n",
    "\n",
    "### 2. Data Operations\n",
    "\n",
    "Pandas provides a plethora of functions for data manipulation, cleaning, and analysis:\n",
    "\n",
    "- Loading and saving data from various sources (e.g., CSV, Excel, SQL).\n",
    "- Cleaning and preprocessing data (e.g., handling missing values, removing duplicates).\n",
    "- Indexing, slicing, and filtering data.\n",
    "- Grouping and aggregating data.\n",
    "- Performing operations on columns and rows.\n",
    "- Merging, joining, and concatenating datasets.\n",
    "\n",
    "### 3. Data Visualization\n",
    "\n",
    "Although Pandas itself is not a visualization library, it integrates seamlessly with Matplotlib and Seaborn for data visualization purposes. It allows users to create insightful plots directly from DataFrame objects.\n",
    "\n",
    "## Pandas Cheat Sheet\n",
    "\n",
    "Pandas cheat sheets are handy references that summarize the most commonly used functions and methods in Pandas. These cheat sheets provide quick access to essential information for data manipulation tasks. Let's take a look at some common operations and their corresponding Pandas functions:\n",
    "\n",
    "| Operation                          | Pandas Function(s)                         |\n",
    "|------------------------------------|--------------------------------------------|\n",
    "| Loading Data                       | `pd.read_csv()`, `pd.read_excel()`, etc.   |\n",
    "| Viewing Data                       | `.head()`, `.tail()`, `.info()`, etc.      |\n",
    "| Cleaning Data                      | `.dropna()`, `.fillna()`, `.drop_duplicates()`, etc. |\n",
    "| Indexing and Slicing               | `.loc[]`, `.iloc[]`, `.ix[]`, etc.         |\n",
    "| Grouping and Aggregating           | `.groupby()`, `.agg()`, `.pivot_table()`, etc. |\n",
    "| Data Visualization                 | `.plot()`, `.hist()`, `.scatter()`, etc.   |\n",
    "| Saving Data                        | `df.to_csv()`, `df.to_excel()`, etc.       |\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Pandas is an indispensable tool for data manipulation and analysis in Python. Its intuitive interface, rich functionality, and seamless integration with other libraries make it a preferred choice for data scientists and analysts worldwide. With the help of Pandas cheat sheets, users can quickly access key functions and methods, enhancing their productivity and efficiency in handling structured data.\n",
    "\n",
    "For those new to Pandas, investing time in learning its core concepts and functionalities can significantly boost their data analysis skills and unlock a wide range of possibilities in their data projects.\n",
    "\n"
   ]
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   "source": [
    "# Imoprt libraries \n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
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   "source": [
    "# load the dataset from seaborn library\n",
    "df = sns.load_dataset('titanic')"
   ]
  },
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   "source": [
    "## *Basic Information*"
   ]
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       "      <th></th>\n",
       "      <th>survived</th>\n",
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       "      <td>man</td>\n",
       "      <td>True</td>\n",
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       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
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    "df.head()\n",
    "# this command shows the first 5 rows of the dataset"
   ]
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   survived     891 non-null    int64   \n",
      " 1   pclass       891 non-null    int64   \n",
      " 2   sex          891 non-null    object  \n",
      " 3   age          714 non-null    float64 \n",
      " 4   sibsp        891 non-null    int64   \n",
      " 5   parch        891 non-null    int64   \n",
      " 6   fare         891 non-null    float64 \n",
      " 7   embarked     889 non-null    object  \n",
      " 8   class        891 non-null    category\n",
      " 9   who          891 non-null    object  \n",
      " 10  adult_male   891 non-null    bool    \n",
      " 11  deck         203 non-null    category\n",
      " 12  embark_town  889 non-null    object  \n",
      " 13  alive        891 non-null    object  \n",
      " 14  alone        891 non-null    bool    \n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.7+ KB\n"
     ]
    }
   ],
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    "df.info()\n",
    "# the command shows the information of the dataset that how much values are there in each column and their data type"
   ]
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       "      <th>pclass</th>\n",
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       "      <td>0.42</td>\n",
       "      <td>20.1250</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>38.0</td>\n",
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       "      <th>sibsp</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0000</td>\n",
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       "    <tr>\n",
       "      <th>parch</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0000</td>\n",
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       "    <tr>\n",
       "      <th>fare</th>\n",
       "      <td>891.0</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.9104</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>31.0</td>\n",
       "      <td>512.3292</td>\n",
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       "          count       mean        std   min      25%      50%   75%       max\n",
       "survived  891.0   0.383838   0.486592  0.00   0.0000   0.0000   1.0    1.0000\n",
       "pclass    891.0   2.308642   0.836071  1.00   2.0000   3.0000   3.0    3.0000\n",
       "age       714.0  29.699118  14.526497  0.42  20.1250  28.0000  38.0   80.0000\n",
       "sibsp     891.0   0.523008   1.102743  0.00   0.0000   0.0000   1.0    8.0000\n",
       "parch     891.0   0.381594   0.806057  0.00   0.0000   0.0000   0.0    6.0000\n",
       "fare      891.0  32.204208  49.693429  0.00   7.9104  14.4542  31.0  512.3292"
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   "source": [
    "df.describe().T\n",
    "# this commands shows the descriptive statistics of the dataset, T stands for transpose\n",
    "# count: shows the number of values in each column \n",
    "# mean: shows the mean of each column \n",
    "# std: shows the standard deviation of each column\n",
    "# min: shows the minimum value of each column \n",
    "# 25%: shows the first quartile of each column \n",
    "# 50%: shows the median of each column \n",
    "# 75%: shows the third quartile of each column \n",
    "# max: shows the maximum value of each column "
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     "text": [
      "['male' 'female']\n",
      "['S' 'C' 'Q' nan]\n"
     ]
    }
   ],
   "source": [
    "# Unique values in a column \n",
    "print(df['sex'].unique()) \n",
    "print(df['embarked'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "506732e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:29.997002Z",
     "iopub.status.busy": "2024-08-25T06:42:29.996167Z",
     "iopub.status.idle": "2024-08-25T06:42:30.018566Z",
     "shell.execute_reply": "2024-08-25T06:42:30.017203Z"
    },
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     "duration": 0.037538,
     "end_time": "2024-08-25T06:42:30.021231",
     "exception": false,
     "start_time": "2024-08-25T06:42:29.983693",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sex   age     fare\n",
       "0      male  22.0   7.2500\n",
       "1    female  38.0  71.2833\n",
       "2    female  26.0   7.9250\n",
       "3    female  35.0  53.1000\n",
       "4      male  35.0   8.0500\n",
       "..      ...   ...      ...\n",
       "886    male  27.0  13.0000\n",
       "887  female  19.0  30.0000\n",
       "888  female   NaN  23.4500\n",
       "889    male  26.0  30.0000\n",
       "890    male  32.0   7.7500\n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'] # to select one column by name\n",
    "df[['sex', 'age']] # to select two columns\n",
    "df[['sex', 'age', 'fare']] # to select more than 2 column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3414f7d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:30.047087Z",
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     "shell.execute_reply": "2024-08-25T06:42:30.053642Z"
    },
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     "duration": 0.023973,
     "end_time": "2024-08-25T06:42:30.057080",
     "exception": false,
     "start_time": "2024-08-25T06:42:30.033107",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.nunique() # find number of unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "85a7f0ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:30.082792Z",
     "iopub.status.busy": "2024-08-25T06:42:30.082355Z",
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     "shell.execute_reply": "2024-08-25T06:42:30.095125Z"
    },
    "papermill": {
     "duration": 0.029528,
     "end_time": "2024-08-25T06:42:30.098575",
     "exception": false,
     "start_time": "2024-08-25T06:42:30.069047",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         2\n",
       "pclass           3\n",
       "sex              2\n",
       "age             88\n",
       "sibsp            7\n",
       "parch            7\n",
       "fare           248\n",
       "embarked         3\n",
       "class            3\n",
       "who              3\n",
       "adult_male       2\n",
       "deck             7\n",
       "embark_town      3\n",
       "alive            2\n",
       "alone            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique() # to find number of unique values in all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b892ab0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:30.125072Z",
     "iopub.status.busy": "2024-08-25T06:42:30.124084Z",
     "iopub.status.idle": "2024-08-25T06:42:30.132359Z",
     "shell.execute_reply": "2024-08-25T06:42:30.131073Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n",
       "       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',\n",
       "       'alive', 'alone'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns # to print the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3786566f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:30.163641Z",
     "iopub.status.busy": "2024-08-25T06:42:30.162337Z",
     "iopub.status.idle": "2024-08-25T06:42:30.172728Z",
     "shell.execute_reply": "2024-08-25T06:42:30.171571Z"
    },
    "papermill": {
     "duration": 0.026666,
     "end_time": "2024-08-25T06:42:30.175429",
     "exception": false,
     "start_time": "2024-08-25T06:42:30.148763",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "embark_town\n",
       "Southampton    644\n",
       "Cherbourg      168\n",
       "Queenstown      77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['embark_town'].value_counts()\n",
    "# to count the number of values in a column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "01351b99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:30.202543Z",
     "iopub.status.busy": "2024-08-25T06:42:30.202071Z",
     "iopub.status.idle": "2024-08-25T06:42:30.214523Z",
     "shell.execute_reply": "2024-08-25T06:42:30.213204Z"
    },
    "papermill": {
     "duration": 0.029092,
     "end_time": "2024-08-25T06:42:30.217160",
     "exception": false,
     "start_time": "2024-08-25T06:42:30.188068",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived  who  \n",
       "0         child     34\n",
       "          man      449\n",
       "          woman     66\n",
       "1         child     49\n",
       "          man       88\n",
       "          woman    205\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# groupby\n",
    "df.groupby(['survived', 'who']).size()\n",
    "# to group by two columns "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5fa513f",
   "metadata": {
    "papermill": {
     "duration": 0.012582,
     "end_time": "2024-08-25T06:42:30.242606",
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     "start_time": "2024-08-25T06:42:30.230024",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## *Correlation*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "667132ab",
   "metadata": {
    "papermill": {
     "duration": 0.012645,
     "end_time": "2024-08-25T06:42:30.269557",
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     "start_time": "2024-08-25T06:42:30.256912",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Understanding Correlation Matrix in Data Analysis\n",
    "\n",
    "Correlation matrix is a vital tool in data analysis to explore relationships between variables within a dataset. It helps in understanding the extent to which variables are related and aids in identifying patterns and dependencies in the data.\n",
    "\n",
    "## What is a Correlation Matrix?\n",
    "\n",
    "A correlation matrix is a square matrix displaying correlation coefficients between pairs of variables. Each cell in the matrix represents the correlation coefficient, ranging from -1 to 1, indicating the strength and direction of the relationship between two variables.\n",
    "\n",
    "- A correlation coefficient close to 1 implies a strong positive relationship.\n",
    "- A correlation coefficient close to -1 implies a strong negative relationship.\n",
    "- A correlation coefficient close to 0 implies a weak or no linear relationship.\n",
    "\n",
    "## Why Use a Correlation Matrix?\n",
    "\n",
    "- **Relationship Identification**: Helps identify and quantify relationships between variables.\n",
    "- **Feature Selection**: Useful in selecting relevant features by identifying highly correlated variables.\n",
    "- **Multicollinearity Detection**: Detects multicollinearity among independent variables in regression models.\n",
    "- **Decision Making**: Provides insights for decision-making processes based on the relationships between variables.\n",
    "\n",
    "## Creating a Correlation Matrix in Python with Pandas\n",
    "\n",
    "```python\n",
    "import pandas as pd\n",
    "\n",
    "# Load dataset\n",
    "df = pd.read_csv('data.csv')\n",
    "\n",
    "# Compute correlation matrix\n",
    "correlation_matrix = df.corr()\n",
    "\n",
    "# Display correlation matrix\n",
    "print(correlation_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e99cd53",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:30.297389Z",
     "iopub.status.busy": "2024-08-25T06:42:30.296924Z",
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     "shell.execute_reply": "2024-08-25T06:42:30.304559Z"
    },
    "papermill": {
     "duration": 0.025864,
     "end_time": "2024-08-25T06:42:30.308190",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# correlation matrix    \n",
    "correlation_df = df[['fare', 'age', 'sibsp', 'parch', 'survived']].corr()\n",
    "# correlation between numeric columns \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "17802a75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:30.336176Z",
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    },
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     "duration": 0.470681,
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     "start_time": "2024-08-25T06:42:30.320983",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v9v+w790W6YmpSH7xEtaNaqLPstF4cu4Ogq+82dr8yvaT6PblYET7RyD6SQRaDeoMi7rVcGjKhnI9f2Xz3XkGLusmIc4vDDH3Q9ByXE9o6orx+NeC6+ny/SSkxSTh2rcFUxBqmuowq18dAKCupQF9K1NYNKmF7PQspETE/tunF4YdX4qPpvVF4MmbsG5RBw4juuLvhbtVc5JlxH/HGXT4fhISHobh5b0QNJ7QExo6YgQfKbh2HTZOQkZ0Eu6tKbh2Tad+ihZzB+HK9C1Ie5Yg+cs7N/01cjOyINJQx8fbZ8LUwQ7/jFkHkbqapE52chryc6rOPf4hP51Gq42TkfQgFEn3QlB3Qi+o62oj8nDBtWu1aQpeRyfiyeqCz3H96a5oNG8w7k79ERnP4iF+69rlZRRMNwdvOYmPfpqJhBsBSLj2BJafNId1j1a4OnClak6yLHDkQa5SJw//efLkCSIjI5GdnS1V3rdv3/cOqqJ4FBCEL2YskLxeu2k7AKBfL2esWjxHVWGVG7+TN6BnaohuswfDwMIY0f4R2DtmDdITUgEARtXNILw1LxjpG4RfZ22G85wh6DFvGF6Gx+DAxPWIC3wzBWFgaYxeiz+DvrkRXsUl4f7vV3Fx0+9SX/f6bi9oiDXRe8ko6BrrIdo/Ens+80RiZFz5nHgZCfzrJnRMDeHkPgi6FkaIfxKB46PWIuPf62lQzRxC/ps7p/WtTPCZ12rJa8fJfeA4uQ+e+fjj6LBVAIDYh6H4a+IGdFwwDO1m9UfKs3h4f7MfASeul+/JlbHwP29CbGqIFnMHFWwS9TgCFz5bi9f/Xju9Qteu4ehuUBdr4uMd0lOhD9b9jgfrf4eutQlqurQGALieXy1V5+zgVYj18S/jMyo/L/64AS0zQzSePxhiC2OkPI6Aj9saZP177XSrm0n9oqw9xhnqYk202TVbqp+A744h4LtjAIDoM3dwf8EuNJjRD81WjkFaSBRujduAxFtPy+/EylhVfRqmspRqnwcACA0NxYABA+Dn5ydZ6wC8WehWkjUPslT0fR4qsrLa5+FDUJ77PFRF5bXPQ1VUXvs8VFVlvc9D5uW9SutLp/PnSuuroij1T85Zs2ahdu3aiIuLg66uLh4/fozLly/D0dER3t7eZRAiERFROeM+D3KVetrCx8cH//zzD8zNzaGmpgY1NTV07NgRnp6emDlzJveAICKiyo+3aspV6pGHvLw8GBgYAADMzc0RFVWwoNDW1hZPn1ad+S4iIvqAceRBrlKPPNjb2+PBgweoXbs22rZti7Vr10JLSwvbt29HnTp1yiJGIiIiqkBKNPLw8OFD5P+bPS1evFiySHL58uUICwtDp06dcPr0afzwww9lFykREVF5EfKVd1RBJRp5aNmyJaKjo2FpaYkpU6bg9u3bAIB69eohICAAiYmJMDExkdxxQUREVKlV0ekGZSnRyIOxsTHCwgp2GQsPD5eMQvzH1NSUiQMREdEHokQjD4MGDUKXLl1gY2MDkUgER0dHqKury6xb0i2qiYiIKqwqOt2gLCVKHrZv346BAwciODgYM2fOxIQJEyR3XBAREVU5nLaQq8R3W/Ts2RMAcPfuXcyaNYvJAxER0Qeq1Ldq7tmzpyziICIiqjg48iAXN/YnIiIqTIW3am7evBl2dnbQ1tZG27ZtcevWLbn1N2zYgIYNG0JHRwc1a9bE7Nmz8fr1a0XPvESYPBAREVUQR44cgbu7O5YtWwZfX180b94cLi4uiIuT/VThgwcPYuHChVi2bBn8/f2xa9cuHDlyBF999VWZxsnkgYiIqDAlbk+dlZWF1NRUqSMrK0vml12/fj0mTJiAsWPHokmTJti2bRt0dXWxe/dumfWvX7+ODh06YMSIEbCzs0OPHj3g5ub2ztGK98XkgYiIqDAlTlt4enrCyMhI6vD09CzyJbOzs3H37l04OztLytTU1ODs7AwfHx+ZYbZv3x53796VJAuhoaE4ffo0evfuXTbX5V+lXjBJRERU5SlxwaSHhwfc3d2lysRicZF6CQkJyMvLg5WVlVS5lZUVAgICZPY9YsQIJCQkoGPHjhAEAbm5uZg8eTKnLYiIiCozsVgMQ0NDqUNW8qAIb29vrF69Glu2bIGvry9+//13nDp1CitWrFBK/8XhyAMREVFhKthh0tzcHOrq6oiNjZUqj42NhbW1tcw2S5YswahRozB+/HgAgIODA9LT0zFx4kQsWrQIamplM0bAkQciIqLClLhgsqS0tLTQunVrXLhw4a0w8nHhwgU4OTnJbJORkVEkQfjv8RH/PQG7LHDkgYiIqIJwd3fHmDFj4OjoiDZt2mDDhg1IT0/H2LFjAQCjR49G9erVJQsuXV1dsX79erRs2RJt27ZFcHAwlixZAldX12KfQaUMTB6IiIgKU9EOk8OGDUN8fDyWLl2KmJgYtGjRAl5eXpJFlJGRkVIjDYsXL4ZIJMLixYvx4sULWFhYwNXVFatWrSrTOEVCWY5rlEJOAp/GqaivHRerOoRKyzyfM3fvwyxP1RFUXoZ53P74ffSPOVim/Wce+UZpfekMW6a0vioK/uQkIiKiUuG0BRERUWF8MJZcTB6IiIgKY/IgF6ctiIiIqFQ48kBERFSYCjaJqkyYPBARERXGaQu5mDwQEREVVjF2MaiwuOaBiIiISoUjD0RERIVx2kIuJg9ERESFMXmQq8IkD9xiWXFf31mp6hAqLZ1qnVQdQqVma2il6hAqrdo6FqoOoVLrr+oAPnAVJnkgIiKqMHirplxMHoiIiAoR8nm3hTy824KIiIhKhSMPREREhXHBpFxMHoiIiArjmge5OG1BREREpcKRByIiosK4YFIuJg9ERESFcc2DXEweiIiICmPyIBfXPBAREVGpcOSBiIioMD6SWy4mD0RERIVx2kIuTlsQERFRqXDkgYiIqDDeqikXkwciIqLCuMOkXJy2ICIiolLhyAMREVFhnLaQi8kDERFRIQLvtpCL0xZERERUKhx5ICIiKozTFnIxeSAiIiqMd1vIxeSBiIioMI48yMU1D0RERFQqHHkgIiIqjHdbyMXkgYiIqDBOW8j1XtMWwcHBOHv2LDIzMwEAAh9hSkREVOUplDy8fPkSzs7OaNCgAXr37o3o6GgAwLhx4zBnzhylBkhERFTuhHzlHVWQQsnD7NmzoaGhgcjISOjq6krKhw0bBi8vL6UFR0REpBL5gvKOKkihNQ/nzp3D2bNnUaNGDany+vXrIyIiQimBERERUcWkUPKQnp4uNeLwn8TERIjF4vcOioiISJX4bAv5FJq26NSpE/bt2yd5LRKJkJ+fj7Vr16Jr165KC04Z2o7qjrlXN+Lrp3sx+cRy1GheV259+95t8eWF7/D1072Y4bUGDT5uIfW+nrkhBn03CQtubsYy/z0Y8/MCmNlZF+mnZqv6+OLgIix7shtL/HZi/JEl0BBrKvPUKrQ79/0wbf4ydO07EvYdeuHC5euqDqnC+HrZXDyL8MWrlGCcPXMY9erVllt/0sTR8L17HokJAUhMCMDVy3+ip4v052z8uJG4cP43JCYEIDf7BYyMDMvyFFTmy4WT4fP4LB4/u459x7bCrk5NufU/cmqF7Qc24PqjswhJ8EX3Xh8XqWNmYYq1m77G9Udn8SjyGvYc+fGd/VYGY+aMxpE7B3Eq6E+sPbgG1e2qvbNN3zGu2H/9Z5wO+gub/tyIhi0aSt6zqmGFv5+dlXl07tMJANBjSPdi6xibGZXZuZYJTlvIpVDysHbtWmzfvh29evVCdnY25s+fD3t7e1y+fBnffvutsmNUmMOn7dB78Wf4Z+Pv2NxnEWKeROLzfQuhZyb7B2utVvUx9IfpuHPEG5t7fwX/c3cxcrs7LBu8mZ75bPscmNS0xP4J67C5z1dIfpGAsfs9oKnzZsSlZqv6+HzvAgRfeYit/ZZga78luLHv3Ad1N0pm5ms0rFcHi+ZMVXUoFcq8uVMxfdoXmDp9Idp3dEV6RgZOnzwgd8TuxYtoLFrkiTbteqGtU29c9L6G34/tRpMmDSR1dHV1cPacN9Z8u6k8TkMlJs4YgzET3LBk7moMdBmDjIxM7Pl1M7TEWsW20dXVRsCjQHw9f02xdbbtW4+adjUwadRsuH4yAi+eR2PfsW3Q0dUui9MoF8OmDMWAsf2w8atNmO46C68zX2PN/tXQlPMHzMeuXTB5yUT8suEAJveehtAnoVjzyyrJL/34qHgMaTVc6tj73T5kpGXg1sXbAADvvy4VqXPb+w4e+DxA8suUcjl3Kh8KJQ/29vYIDAxEx44d0a9fP6Snp2PgwIG4d+8e6taV/5d9eeowvjfuHL4I398uIT74Bf5YtAs5mVloPbSLzPpOX/RE0KUHuLr9JOJDovD3+t8Q9TgMTmN6AADMalujVqv6+HPxbrx4GIqE0Gj8uWg3NLW10Lyvk6Sf3ks+g8/es7i89S/EBb1AQmg0Hp26ibzs3HI574qgk9NHmDlxDJy7dFB1KBXKzBnjsdpzI/766xz8/Pzx+dhZqFbNCv36uRTb5uSp8zjj9Q+Cg8MQFBSKJUu/RVpaOtq2aSWp88OmnVj7v824edO3PE5DJcZOHoHN63fi7zOX8PRJEOZOXQorawv06P1xsW0uXbiO9Z5bcO70RZnv29WthVYfNcPSuavhd+8JwoIjsGTuamhri+E6sGcZnUnZGziuPw5sOoTr53wQFhCGb79cCzMrM3RwaV9sm0ETBuL0IS+c/fUcIoMiscHjB2S9zkLPYQXfm/n5+UiKT5I6OvZsj0snL+N1xmsAQPbrbKn38/Py0aJ9c5w5fLZczlupOPIgl8L7PBgZGWHRokX49ddfcfr0aaxcuRI2NjbKjO29qGuqo5p9bQRfeyQpEwQBwdceoVar+jLb1GpZHyFv1QeA4MsPUfPf+hpaBVl7blaOVJ+52bmw/ahgeE/PzBC1WtZH2ssUTDz2NTxub8X4I0tg69gQ9GGrXbsWbGyscOGfq5Ky1NRXuHXrHtq1bV2iPtTU1DB0aF/o6enixs27ZRVqhVPTtjosrSxw7dJNSVnaqzTc932Elo7NFO5XS6tg1CIrK1tSJggCsrOz4di2hcL9qpJNLWuYWZnB98qbRDL9VQb87wegSavGMttoaGqggUN9+F5900YQBPheuYcmrZvIbFPfoR7q2deTmxh0H+yMrMwsXD59RcGzUSHeqimXQsnDw4cPZR5+fn4ICgpCVlaWsuMsNV0TA6hrqCMtQXqoLC0+BfoWxjLb6FsYy6xvYF5QPz4kCknP49Fj/nBoG+pBXVMdnSa7wriaGQwsTQAAprUsAQDdvhyEO4cvYu/naxD1KAxfHPhK5toI+nBYWxV8b8TGxkuVx8YlwNraUm5be/tGSE4MREZaGLb8uAaDh4yHv39QmcVa0VhYmgEAEuITpcoT4l7Cwspc4X5Dg8Lx4lk05i6eDkMjA2hqamDijDGwqW4NCyuL94pZVUwsTAEASQnJUuXJ8ckwtTSV2cbI1BDqGupIipduk5SQBBMLE5lteg3viYjACDy5+6TYWHoNc8E/f1xE9uvsYutUWCocedi8eTPs7Oygra2Ntm3b4tatW3LrJycnY9q0abCxsYFYLEaDBg1w+vRpRc+8RBRKHlq0aIGWLVuiZcuWaNGiheR1ixYt0KhRIxgZGWHMmDF4/fq1zPZZWVlITU2VOnKFvPc6kfKQn5uHg5M3wLyONZY83IFl/ntRx6kJnl68L1mZKxKJAAC3Dv4D398uIfpxBE6v2I+E0Ohip0uoanJzG4DkxEDJoamp+G7wT5+GoPVHPdC+w6f4afs+7N61AY0byx5Bqwr6Du6Fh+FXJYfGe1w7eXJzczH187moXdcW90Iu4dGz63Dq+BG8z1+tNKvtP+nfFX8FnJAcGprqZf41tbS18Em/rjhzpPhRh8atGsO2gS3OHObeP6Vx5MgRuLu7Y9myZfD19UXz5s3h4uKCuLg4mfWzs7PRvXt3hIeH4+jRo3j69Cl27NiB6tWrl2mcCn0ijx8/jgULFmDevHlo06YNAODWrVtYt24dli1bhtzcXCxcuBCLFy/Gd999V6S9p6cnvvnmG6myjkb26GzsoEg4MmUkvUJebh70zaVX+OpbGCGtUHb9n7T4ZJn1X72VwUc9CsOPvb+C2EAH6poayEh8hcknluPFw1AAwKu4grpxQc+l+okLeQGjaor/hUSVz19/ncOtW/ckr8X/LuyzsrJATMybHwRWlua4/+Cx3L5ycnIQEhIOAPC95wfH1i0wY/p4TJ22QPmBVwAXvC7hwd03U4ha/04ZmluYIj42QVJubmkGf7+n7/W1Hj3wh2tXN+gb6ENLSwOJL5Nx7OzP8Lvv/179lhef8zcQcP/NNdD891qZmBsjMe7NSI2xhTFCHofI7CMlMRV5uXkwKTQqa2JugqT4pCL1O/fuBLGOGOeP/l1sXL3deiL4UTCC/IJLczoVhqCitQrr16/HhAkTMHbsWADAtm3bcOrUKezevRsLFy4sUn/37t1ITEzE9evXoalZ8N/ezs6uzONUaORh1apV2LhxI8aNGwcHBwc4ODhg3Lhx+P7777Fu3TqMHDkSmzZtwvHjx2W29/DwQEpKitTR3kj2vJqi8nLyEPUoDHXbN5WUiUQi1G3fFJG+sod7I+8FoW57e6myuh0d8ExG/axXmchIfAUzO2tUd6gD//MF889Jz+ORGpMIizrSt0WZ17ZB8ouEIv1Q1ZWWlo6QkHDJ8eRJIKKjY/FJ146SOgYG+mjTpmWp1y+oqalJkpGqKD0tAxFhzyRH0NNQxMXGo33nNpI6+vp6aNHKHvfuPFTK10x7lYbEl8mwq1MTDi2a4O8z3krpt6xlpmciKjxKckQERuBl7Eu07NhSUkdXXxeNWzTCE1/ZCVFuTi4C/YLQqsObNiKRCC07tpA5LdFruAt8zt9ASqLsOyi0dbXR5dPOlXOh5H+UOG0ha7Rd1vR+dnY27t69C2dnZ0mZmpoanJ2d4ePjIzPMP//8E05OTpg2bRqsrKxgb2+P1atXIy+vbEfzFUoe/Pz8YGtrW6Tc1tYWfn5+AAqmNv575kVhYrEYhoaGUoeGSPlDbdd2noajW1e0HNQJFnWroe+qL6Clq427v10CAAxeNwU95g+T1PfZ7YX6XZqhw/jeMK9bDZ98OQjVHerA5+dzkjr2vduidrvGMKlpicbdW2Psfg88OXcHwVf8JHWubD8Jp89d0LRXG5jaWsHZfQgs6lbD3SOyV3xXRRkZmQgIDEFAYMFfOi+iYhEQGILoGNlDbx+KHzbtxFceM/Hpp91hb98Ie/dsRFRULP74480P2XNeRzB1yueS16tWLkSnjm1ha1sD9vaNsGrlQnTp4oRDh36X1LGyskDz5k1Rt64dAMDBvhGaN28KExPjcjqzsrdn20FMcx+Pbj07o0Hjevhuy3LExsTj3GlvSZ1fft+GUePefKZ19XTQ2L4BGtsX3NZaw7Y6Gts3gE31N+uPevV1RtsOrVHTtjqce3XBz0e34vxpb1z1vlFu56Zsv+86gZEz3ODUvR1qN7LDgg3z8DL2Ja6dfbPfytpDa9BvTF/J62M7fkdvt17oPtgZterVxKzVM6Ctow2vX89J9V3Nrhoc2jrInY742LUL1DXU8ffxC8o/uUrI09MTRkZGUoenp2eRegkJCcjLy4OVlZVUuZWVFWJiYmT2HRoaiqNHjyIvLw+nT5/GkiVLsG7dOqxcubJMzuU/Ck1bNGrUCGvWrMH27dslq5VzcnKwZs0aNGrUCADw4sWLIhegvPmdvAE9U0N0mz0YBhbGiPaPwN4xa5CekAoAMKpuBuGtlbCRvkH4ddZmOM8Zgh7zhuFleAwOTFyPuMA3UxAGlsbotfgz6Jsb4VVcEu7/fhUXN/0u9XWv7/aChlgTvZeMgq6xHqL9I7HnM08kRn44vzgfBQThixlvhtTXbtoOAOjXyxmrFn+4D0/733dboKeni21b1sLY2BDXrt1GH9fPpP4KqVPHFubmbxa2WViYY8/ujbCxsURKyiv4+fmjd58R+PvCmxXskyaOwtIlb66r98WCUb8vxs3Gvl9+LYczK3vbN/0MXT0drFq3GIZGBrhz8z7GDpuO7LfulKhlVwMmZsaS1w4tmuDgHzskrxevLLhGxw79ifkzvgYAWFqZY9EKd5hZmCE+NgHHj5zEj+vetKmMjmz9Fdq62pi9Zhb0DfXx6PZjLBy1CDlv3SlWzdYGRqZv9rzx/usSjEyN8Pmc0TCxMEHIk1B4jFqE5EILL3sOc0FCdALuXCp+tKzX8J64euYa0lPTlX5u5UaJa148PDzg7u4uVaas3Zjz8/NhaWmJ7du3Q11dHa1bt8aLFy/wv//9D8uWLVPK15BFJCiwc9H169fRt29fqKmpoVmzgtuk/Pz8kJeXh5MnT6Jdu3b45ZdfEBMTg3nz5pWoz0V2I0obBv3r6ztlm2FWZTrVOqk6hErN1lC1fyBUZrV1KufdHBXF38/Kdkrk1dReSuvLYMuZEtXLzs6Grq4ujh49iv79+0vKx4wZg+TkZPzxxx9F2nTp0gWampr4++8360/OnDmD3r17IysrS/IHvrIpNPLQvn17hIWF4cCBAwgMDAQADBkyBCNGjICBgQEAYNSoUcqLkoiIqIrT0tJC69atceHCBUnykJ+fjwsXLmD69Oky23To0AEHDx5Efn4+1NQKViIEBgbCxsamzBIHQMHkAQAMDAzQuXNn2NnZITu7YNjw4sWCOf2+ffvKa0pERFSxqehuC3d3d4wZMwaOjo5o06YNNmzYgPT0dMndF6NHj0b16tUlayamTJmCH3/8EbNmzcKMGTMQFBSE1atXY+bMmWUap0LJQ2hoKAYMGAA/Pz+IRCIIgiDZ3wBAma/yJCIiKkuqehbRsGHDEB8fj6VLlyImJgYtWrSAl5eXZA1hZGSkZIQBAGrWrImzZ89i9uzZaNasGapXr45Zs2ZhwYKyvY1boeRh1qxZqF27Ni5cuIDatWvj5s2bSExMxJw5c2Tu60BEREQlM3369GKnKby9vYuUOTk54caN8r07SKHkwcfHB//88w/Mzc2hpqYGdXV1dOzYEZ6enpg5cybu3bv37k6IiIgqqir6QCtlUWifh7y8PMnCSHNzc0RFRQEo2Ofh6dP32+2NiIhI5fhUTbkUGnmwt7fHgwcPULt2bbRt2xZr166FlpYWtm/fjjp16ig7RiIionKlqu2pKwuFkofFixcjPb1g84/ly5fj008/RadOnWBmZoYjR44oNUAiIiKqWBRKHlxcXCT/rlevHgICApCYmAgTExOpuy6IiIgqJY48yKW059yamsp+TjwREVGlUzmeyK4yCi2YJCIiog+X0kYeiIiIqgoumJSPyQMREVFhTB7k4rQFERERlQpHHoiIiArjgkm5mDwQEREVwjUP8nHagoiIiEqFIw9ERESFcdpCLiYPREREhXDaQj4mD0RERIVx5EEurnkgIiKiUuHIAxERUSECRx7kYvJARERUGJMHuThtQURERKXCkQciIqJCOG0hH5MHIiKiwpg8yMVpCyIiIioVjjwQEREVwmkL+Zg8EBERFcLkQT4mD0RERIUweZCPax6IiIioVDjyQEREVJggUnUEFVqFSR7M8zkIoiidap1UHUKllRl1RdUhVGqRH09RdQiV1txULVWHQHJw2kI+/sYmIiKiUqkwIw9EREQVhZDPaQt5mDwQEREVwmkL+ThtQURERKXCkQciIqJCBN5tIReTByIiokI4bSEfpy2IiIioVDjyQEREVAjvtpCPyQMREVEhgqDqCCo2Jg9ERESFcORBPq55ICIiolLhyAMREVEhHHmQj8kDERFRIVzzIB+nLYiIiKhUOPJARERUCKct5GPyQEREVAi3p5aP0xZERERUKkweiIiIChHylXeU1ubNm2FnZwdtbW20bdsWt27dKlG7w4cPQyQSoX///qX/oqXE5IGIiKiQfEGktKM0jhw5And3dyxbtgy+vr5o3rw5XFxcEBcXJ7ddeHg45s6di06dOr3PaZcYkwciIqIylJWVhdTUVKkjKytLZt3169djwoQJGDt2LJo0aYJt27ZBV1cXu3fvLrb/vLw8jBw5Et988w3q1KlTVqch5b2TB0EQIPCGWCIiqkIEQaS0w9PTE0ZGRlKHp6dnka+ZnZ2Nu3fvwtnZWVKmpqYGZ2dn+Pj4FBvr8uXLYWlpiXHjxpXJtZBF4eRh165dsLe3h7a2NrS1tWFvb4+dO3cqMzYiIiKVEPJFSjs8PDyQkpIidXh4eBT5mgkJCcjLy4OVlZVUuZWVFWJiYmTGefXqVezatQs7duwok+tQHIVu1Vy6dCnWr1+PGTNmwMnJCQDg4+OD2bNnIzIyEsuXL1dqkEREROVJmQPqYrEYYrFYeR3+69WrVxg1ahR27NgBc3Nzpfcvj0LJw9atW7Fjxw64ublJyvr27YtmzZphxowZTB6IiIhKydzcHOrq6oiNjZUqj42NhbW1dZH6ISEhCA8Ph6urq6QsP7/g9g4NDQ08ffoUdevWLZNYFZq2yMnJgaOjY5Hy1q1bIzc3972DIiIiUiVlTluUlJaWFlq3bo0LFy5IyvLz83HhwgXJKP/bGjVqBD8/P9y/f19y9O3bF127dsX9+/dRs2ZNpVwLWRQaeRg1ahS2bt2K9evXS5Vv374dI0eOVEpgREREqlLaWyyVxd3dHWPGjIGjoyPatGmDDRs2ID09HWPHjgUAjB49GtWrV4enp6dkveHbjI2NAaBIubIpvD31rl27cO7cObRr1w4AcPPmTURGRmL06NFwd3eX1CucYBAREZFsw4YNQ3x8PJYuXYqYmBi0aNECXl5ekkWUkZGRUFNT/S4LIkGB+yy7du1ass5FIvzzzz8lqvt9rc9KGwb9a17MRVWHUGllRl1RdQiVWuTHU1QdQqU1N1VL1SFUascj/yrT/v1qu767Ugk5hJVtrKqg0MjDxYv8ZUVERFUXty+STyljH6mpqThx4gQCAgKU0R0RERFVYAqNPAwdOhSdO3fG9OnTkZmZCUdHR4SHh0MQBBw+fBiDBg1SdpxK1Xy0M1pP6gM9CyPE+0fi4tJ9iH0QKrOuWYPqcHIfBEuH2jCqaQHvb37BvV1ni9TTszJBJ4/hsOvaDJo6YiSHx+Lc3O2IfRhW1qejEl8vm4txX4yAsbEhrl+/g2kzPBAcXPy5Tpo4GpMmjYKdbcHq3ydPArFy1ffwOvtmFGv8uJFwG94fLVs6wNDQAGYWjZGSklrm51LR3Lnvhz0Hj+JJQDDiXyZio+cSdOvcXtVhqZzhcFcYjx0MdXNTZD8NRcLqLch69PSd7fR7dYHV/75C+oXriJn1jaRcz7kDDIf2gbhJfagbG+LZoCnIfir750Bl5OY+Es4jekDPUA8Bd/zx01dbEB0eLbdNr9G90X/SQBhbmCDcPww7l/6EoAdBkvcne05D847NYWJlitfpr/H0rj/2ef6MFyHPJXVkTSesm7YWV/+qXFOEqlowWVkoNPJw+fJlycM3jh8/DkEQkJycjB9++AErV65UaoDK1sC1LTovGYkbG47jQJ/FSPCPxMD9C6BjZiizvoa2GCmR8bi65gjS45Jl1hEb6WLY70uRn5uH46P/h5+7LcClFQfwOiW9DM9EdebNnYrp077A1OkL0b6jK9IzMnD65AG5m6C8eBGNRYs80aZdL7R16o2L3tfw+7HdaNKkgaSOrq4Ozp7zxppvN5XHaVRYmZmv0bBeHSyaM1XVoVQYej27wHz+RCRtPYDnQ6Yh+2kobH5aBXVTI7ntNKpZwWzOBGTe8SvynkhHG699H+Pl97vKKmyVGTBlEPqM/RQ/eWzBgr5zkZXxGkv3L4emWLPYNh1cO2LskvE4suEQ5vT5EuH+YVi6fzmMzN5c4xC/YGyasxEzPpmK5aOWASIRlu1fXmQB3w/uGzC29SjJcfPcjTI717KizO2pqyKFkoeUlBSYmpoCALy8vDBo0CDo6uqiT58+CAoKekdr1Wo1vhceHbqIJ79dRmJQFP722IPczCzYD+sis37sw1BcWX0IgX/dQG5Wjsw6H01xRVp0YsFIw4NQpD6LR+SVR0iJkP8UtMpq5ozxWO25EX/9dQ5+fv74fOwsVKtmhX79XIptc/LUeZzx+gfBwWEICgrFkqXfIi0tHW3btJLU+WHTTqz932bcvOlbHqdRYXVy+ggzJ46Bc5cOqg6lwjAePRCpR73w6sQ55IRGIn75DxBeZ8FgQPHfc1BTg+W3C5C45RfkPC/6F3faXxeQtO0AMn3ulWHkqvHpuL74bdOvuHX+JiICwrFx9vcwtTRF2x7tim3Td3x/nD90Fv/8dgHPg55hm8cWZGVmoduw7pI65w+exZNbjxH/PA6hj0Jw8H/7YVHdApY1LaX6Sk9NR3J8suTIKeZnJ1VeCiUPNWvWhI+PD9LT0+Hl5YUePXoAAJKSkqCtra3UAJVJTVMdVg61EXn18ZtCQUDk1cewaVVP4X7rdG+F2Ieh6LN1Bib5bsbI0yth7/bx+wdcAdWuXQs2Nla48M9VSVlq6ivcunUP7dq2LlEfampqGDq0L/T0dHHj5t2yCpWqCg0NiJvUR8aNt5JKQUDmjXvQbt6k2GYmU0YiLzEZr34vOs1YlVnVsoKppSkeXL0vKct4lYGg+4Fo2LqRzDYamhqo61APD64+kJQJgoCHV++jYauGMtuIdcT4ZKgzYiJjkBCVIPXexJWT8fP9A1j75zp0G+oss31FJwjKO6oihdY8fPnllxg5ciT09fVha2uLjz/+GEDBdIaDg8M722dlZRV5HGmukAcNkboi4ZSYjqkB1DTUkZGQIlWekZACk7o2CvdrVNMCzT7rBt+dXrj145+wbl4HXb8ZjfycPDw5Wrnm+d7F2qrgL4zY2Hip8ti4BFhbW8pqImFv3whXL/8JbW0x0tLSMXjIePj7V+yRKlI9dRNDiDTUkfcyWao892USdGrL3kFPu2VTGA5wwbPBH97Uj7GFCQAgJSFZqjw5IVnyXmEGpoZQ11BHSkJSkTbV69aQKus5qjdGf/U5dPR08Dz4Ob4ZuQS5OW92Fj743X74XX+IrMwstOjcEhNXToG2ng5O7alctytyzYN8Co08TJ06FT4+Pti9ezeuXr0qme+qU6dOidY8yHo86d+pj9/ZrqISqakh7lE4rq39FfGPI+B38CL8Dl2Ew8hPVB3ae3NzG4DkxEDJoamp8L5iePo0BK0/6oH2HT7FT9v3YfeuDWjcuL4SoyUCRLo6sPScj/ivNyA/ueovuO3cvwsO+v8qOTQ0FP+MlsTlE96Y02sWFg1eiKiwF5i7ZYHUWorffjiCgDv+CHsciuNbj+HEtt/Rf9KAMo2pLHDNg3wKf5c5OjrC0dERgiBAEASIRCL06dOnRG09PDykdqEEgJ+aTlI0lBLLTHyF/Nw86JpLL7LSNTdCRnxKMa3eLT0uGS+DoqTKEoOiUL/XRwr3WVH89dc53Lr1Zk5YLC7Y2MbKygIxMW/WdFhZmuP+A/kJYE5ODkJCwgEAvvf84Ni6BWZMH4+p0xYoP3CqMvKSUiHk5kHdzFiqXMPMBHmF/lIGAM2aNtCsYQ3rH996QJ9awQ/wOvdPI9J1HHKfyb/roDK5df4WAu8FSl7/94vcyNwYSXFvro+xuTHCnsi+m+RVYirycvNgZC49MmFsbozkeOlrnPEqAxmvMhAdHo3Ae0/xi98htHVxwtU/L8vsO/D+Uwz9cjg0tDSQm81nH1UVCu/zsGvXLtjb20NbW1uyv/bOnTtL1FYsFsPQ0FDqKOspCwDIz8lDrF8YanZo+qZQJELNDk0R7RuscL9RdwJhWmjaw6SONVKfJxTTovJIS0tHSEi45HjyJBDR0bH4pGtHSR0DA320adOy1OsX1NTUJMkIUbFyc5H1JAi6bVu+KROJoNO2BV4/eFKkek7YMzzrPxHPB0+RHBkXbyDz1gM8HzwFudHxRdpUZq/TMxETES05ngVGIjEuEc06NJfU0dHXQf0WDfD0ruy9eHJzchHiF4xmHZpJykQiERw6NMdTXzm3w4oK6mlqFX8XR+0mdfAq+VWlSxzyBZHSjqpIoZGHpUuXYv369ZgxY4bkSV8+Pj6YPXs2IiMjK/QjuX13noHLukmI8wtDzP0QtBzXE5q6Yjz+9RIAwOX7SUiLScK1b38FULDI0qx+dQCAupYG9K1MYdGkFrLTs5ASEftvn14YdnwpPprWF4Enb8K6RR04jOiKvxfuVs1JlrEfNu3EVx4zERQcivDwZ/jm63mIiorFH3+8WZh2zusITvxxBlu27gUArFq5EF5eFxH57AUMDPThNrw/unRxQu8+IyRtrKwsYG1tibp17QAADvaN8CotHZGRL5CUlFyOZ6haGRmZiHz+ZiTrRVQsAgJDYGRoAJt3rCupqpL3/Q7LVXOR9TgQrx89hdFnAyDS0carE+cAAJar5yE3LgGJG/ZAyM5BdnCEVPu8V2lQB6TK1QwNoGFjAQ1LMwCA5r/rJ/ISkpD3suiIRmVyctefGDJzGKLDoxAbGYsRcz9DYlyi1C2T3xxaiRtePjjz8ykAwJ87T2DmutkI8QtG0P1AfDquH7R1tXHh178BFCzE7ODaCfcv30Pqy1SY2Zhh4NTByH6dBd+LdwAAjs4fwdjcBIG+AcjOykHzTi0waPoQ/LH9ePlfhPdURdc5Ko1CycPWrVuxY8cOuLm5Scr69u2LZs2aYcaMGRU6eQj86yZ0TA3h5D4IuhZGiH8SgeOj1iIjoWBu1KCaOYT8N982+lYm+MxrteS14+Q+cJzcB898/HF02CoABbdz/jVxAzouGIZ2s/oj5Vk8vL/Zj4AT18v35MrJ/77bAj09XWzbshbGxoa4du02+rh+JrUItk4dW5ibm0peW1iYY8/ujbCxsURKyiv4+fmjd58R+PvCmwWlkyaOwtIlcySvvS8W/MD5Ytxs7Pvl13I4s4rhUUAQvpjxZipn7abtAIB+vZyxavGc4ppVaelel/DSxAgm00dDw9wEWQGhiJ68SLKIUsPGAkJ+fqn61OvaDpar5kpeW3/3FQAgccsvSNqyX2mxq8LxrcegraONKZ7ToWeoB/87T7Bi1DKpWyata1nD0PTN/jbX/roKQ1MjDHcfCRMLE4Q9CcXyUcskCy+zs3LQ5KOmcP2iL/SM9JGSkIzHNx9j4YD5SHlZMO2bl5OHXqN744ul4wCRCDHh0dizYhfOH/yw7nj5ECj0YCxjY2Pcvn0b9etLL3YLDAxEmzZtkJycXOpA+GAsxfHBWIrjg7HeDx+MpTg+GOv9lPWDsa7bKG+n5PbRx5TWV0Wh0JqHUaNGYevWrUXKt2/fjpEjR753UERERKrEuy3kK/G0xdt3R4hEIuzcuRPnzp1Du3YFO5bdvHkTkZGRGD16tPKjJCIiogqjxMnDvXvSW7i2bl2wm2BISAgAwNzcHObm5nj8uPLu10BERAQApVtB8+EpcfJw8SLn1YmI6MMgoGpONyiLwvs8EBER0YepxCMPAwcOxN69e2FoaIiBAwfKrfv777+/d2BERESqks+NHuQqcfJgZGQEkUgk+TcREVFVlc9pC7lKnDzs2bNH8u8tW7YgPz8fenp6AIDw8HCcOHECjRs3houLi/KjJCIiKkdc8yCfQmse+vXrh19++QUAkJycjHbt2mHdunXo37+/zP0fiIiIqOpQKHnw9fVFp06dAABHjx6FlZUVIiIisG/fPvzwww9KDZCIiKi85SvxqIoUerZFRkYGDAwMAADnzp3DwIEDoaamhnbt2iEiIuIdrYmIiCo2TlvIp9DIQ7169XDixAk8e/YMZ8+eRY8ePQAAcXFxMDQ0fEdrIiIiqswUSh6WLl2KuXPnws7ODm3btpU8lvvcuXNo2bKlUgMkIiIqb5y2kE+haYvBgwejY8eOiI6ORvPmzSXl3bp1w4ABA5QWHBERkSpU1V/6yqJQ8gAA1tbWsLa2lipr06bNewdEREREFZvCyQMREVFVxQWT8jF5ICIiKiSfuYNcfDAWERERlQpHHoiIiArhsy3kY/JARERUCB+qKR+TByIiokJ4q6Z8XPNAREREpcKRByIiokLyRVzzIA+TByIiokK45kE+TlsQERFRqXDkgYiIqBAumJSPyQMREVEh3GFSPk5bEBERUalw5IGIiKgQ7jApH5MHIiKiQni3hXyctiAiIqJSqTAjD2Z5qo6g8rI1tFJ1CJVW5MdTVB1CpVbLe6uqQ6i06jsuUnUIJAcXTMrHkQciIqJC8pV4lNbmzZthZ2cHbW1ttG3bFrdu3Sq27o4dO9CpUyeYmJjAxMQEzs7OcusrC5MHIiKiQgQlHqVx5MgRuLu7Y9myZfD19UXz5s3h4uKCuLg4mfW9vb3h5uaGixcvwsfHBzVr1kSPHj3w4sWL0p5yqTB5ICIiqiDWr1+PCRMmYOzYsWjSpAm2bdsGXV1d7N69W2b9AwcOYOrUqWjRogUaNWqEnTt3Ij8/HxcuXCjTOCvMmgciIqKKQplrHrKyspCVlSVVJhaLIRaLpcqys7Nx9+5deHh4SMrU1NTg7OwMHx+fEn2tjIwM5OTkwNTU9P0Dl4MjD0RERIUoc82Dp6cnjIyMpA5PT88iXzMhIQF5eXmwspJeBG9lZYWYmJgSxb1gwQJUq1YNzs7OpT/pUuDIAxERURny8PCAu7u7VFnhUQdlWLNmDQ4fPgxvb29oa2srvf+3MXkgIiIqRJkPxpI1RSGLubk51NXVERsbK1UeGxsLa2truW2/++47rFmzBn///TeaNWv2XvGWBKctiIiIChFEyjtKSktLC61bt5Za7Pjf4kcnJ6di261duxYrVqyAl5cXHB0d3+e0S4wjD0RERBWEu7s7xowZA0dHR7Rp0wYbNmxAeno6xo4dCwAYPXo0qlevLlkz8e2332Lp0qU4ePAg7OzsJGsj9PX1oa+vX2ZxMnkgIiIqRJnTFqUxbNgwxMfHY+nSpYiJiUGLFi3g5eUlWUQZGRkJNbU3kwZbt25FdnY2Bg8eLNXPsmXL8PXXX5dZnEweiIiIClFV8gAA06dPx/Tp02W+5+3tLfU6PDy87AOSgWseiIiIqFQ48kBERFQIH8ktH5MHIiKiQvhUTfmYPBARERWiyjUPlQHXPBAREVGpcOSBiIioEI48yMfkgYiIqBAumJSP0xZERERUKhx5ICIiKoR3W8jH5IGIiKgQrnmQT+HkIT8/H8HBwYiLi0N+vvRl7ty583sHRkRERBWTQsnDjRs3MGLECEREREAQpJeViEQi5OXlKSU4IiIiVeCCSfkUSh4mT54MR0dHnDp1CjY2NhCJODlERERVRz7TB7kUSh6CgoJw9OhR1KtXT9nxEBERUQWn0K2abdu2RXBwsLJjISIiqhDylXhURSUeeXj48KHk3zNmzMCcOXMQExMDBwcHaGpqStVt1qyZ8iIkIiIqZ5y0kK/EyUOLFi0gEomkFkh+8cUXkn//9x4XTBIRUWVXVUcMlKXEyUNYWFhZxkFERESVRImTB1tb27KMg4iIqMLgDpPyKbRg0tPTE7t37y5Svnv3bnz77bfvHRQREZEq5UNQ2lEVKXSr5k8//YSDBw8WKW/atCmGDx+OBQsWvHdgZanhGGc0ndIHOhZGSHwSiVtL9uHl/VCZdeuP+Bh1BneCccMaAIBEvzD4rvlVUl+koY6W8wej+ictoG9rgZzUTERffQTf1UeQGZtcXqdU7r5cOBnDRg2AoaEB7t56gKXzViM89Fmx9T9yaoUJ00fDvnljWFlbYPIod5w/4y1Vx8zCFAuWzkTHrk4wNNTHbZ97+MbjW7n9VjaGw11hPHYw1M1Nkf00FAmrtyDr0dN3ttPv1QVW//sK6ReuI2bWN5JyPecOMBzaB+Im9aFubIhng6Yg+6ns7+UPxZ37fthz8CieBAQj/mUiNnouQbfO7VUdVrlyGtUdXSa5wsDCCNH+kfhj2V48exBSbH2H3m3hMmcITGpYICEsBmfWHEKA933J+1q6YvRa4IamPRyhZ2KAxGdxuLb3LG4c+BsAoGOkhx6zh6BBJwcYVzdH2stUPD53B+fW/4rXrzLL+nRJBRQaeYiJiYGNjU2RcgsLC0RHR793UGXJrm9bOC4biQfrj+Nkz8VIehIJ5wMLoG1mKLO+lVNjhP/hg3NDV+FM36+RHpWI7gcXQMfaBACgoaMFUwc7PNx4Aqd6LoH3hA0wrGODrnvcy/O0ytXEGWMwZoIblsxdjYEuY5CRkYk9v26Gllir2Da6utoIeBSIr+evKbbOtn3rUdOuBiaNmg3XT0bgxfNo7Du2DTq62mVxGuVOr2cXmM+fiKStB/B8yDRkPw2FzU+roG5qJLedRjUrmM2ZgMw7fkXeE+lo47XvY7z8fldZhV3pZGa+RsN6dbBozlRVh6ISzT9tB9fFo/D3xmPY2OcrRD+JwLh9C6FXzM8421b1MeKHGbh9xBsbe3vg8bk7GL19Dqwa1JDUcV08Cg27NMfh2ZvxnfMcXN19Bv2++RxNnFsDAAytTGBoZYyTqw9gXY95+HXuNjTs0hyDv51ULudcFgQlHlWRQslDzZo1ce3atSLl165dQ7Vq1d47qLLUeEIvBB28iJBfLyMlKAo3Fu5BXmYW6g3vIrP+1Rlb8fTnv5H0OBKpIdHwmbsDUFODTcemAICcV5n42+1bRPx1E6kh0UjwDcGtxftg3rwO9KqZleeplZuxk0dg8/qd+PvMJTx9EoS5U5fCytoCPXp/XGybSxeuY73nFpw7fVHm+3Z1a6HVR82wdO5q+N17grDgCCyZuxra2mK4DuxZRmdSvoxHD0TqUS+8OnEOOaGRiF/+A4TXWTAY4FJ8IzU1WH67AIlbfkHO86KJedpfF5C07QAyfe6VYeSVSyenjzBz4hg4d+mg6lBUotP4Prh5+B/c+e0S4oJf4PdFu5CTmY2Phn4ss37HL3oh8NIDXNp+EnEhUTi3/je8eByGDmPefF/atm6Au8cuI/SGP5KeJ+DmoX8Q7R+Bms3rAgBiA5/jlykb4H/BF4mRcQjxeQyv746gSbdWUFNX6NeMynGfB/kU+q86YcIEfPnll9izZw8iIiIQERGB3bt3Y/bs2ZgwYYKyY1QaNU11mDWrjegrj98UCgKirz6GReuS7ZapriOGmoY6spLTiq2jZagDIT8f2akZ7xtyhVPTtjosrSxw7dJNSVnaqzTc932Elo6K7++hpVUwapGVlS0pEwQB2dnZcGzbQuF+KwwNDYib1EfGDd83ZYKAzBv3oN28SbHNTKaMRF5iMl79frYcgqTKTl1THdXtayP42iNJmSAICLr2CLat6stsU6tlfQS9VR8AAi8/RK236kfcDUQT59YwtCoYca3r1AQWtW0QeOUhiqNjoIvXaZnIz6uqvz4/bAqteZg3bx5evnyJqVOnIju74Ie9trY2FixYAA8Pj3e2z8rKQlZWllRZjpAHTZG6IuGUmNjUAGoa6shMSJEqz4xPgWHdotMwsrReNByZsUnSCchb1MSaaPXVcISd8EFOWtWb67OwLBhNSYhPlCpPiHsJCytzhfsNDQrHi2fRmLt4OhbPWYXMjEyMnTwSNtWtYWFl8V4xVwTqJoYQaagj72WyVHnuyyTo1K4ps412y6YwHOCCZ4M/zOF3Kj09E0Ooa6jjVaGfcWnxKbCsK3tU2MDCGGky6huYG0ten/h6LwZ5TsDim1uQl5MLIV/AUY8dCLsVILNPXRMDdJsxADcPXXi/E1KhqrrQUVlKPfKQl5eHK1euYOHChYiPj8eNGzfw4MEDJCYmYunSpSXqw9PTE0ZGRlLHyVeyfxlXJPbTXGHXtx0ujt+A/KycIu+LNNTRZdsMQCTCTY+95R9gGeg7uBcehl+VHBqaCj/FXa7c3FxM/Xwuate1xb2QS3j07DqcOn4E7/NXIeR/eH+5iHR1YOk5H/Ffb0B+cqqqw6EPXIcxLrBtUQ97xv0PG10X4eSq/RiwfCzqdbAvUlesr4Mv9sxHbPALnN9wTAXRKgfXPMhX6t8E6urq6NGjB/z9/VG7dm189NFHpf6iHh4ecHeXXlD4W6OyX1iTlfgK+bl50DGXXqCmY2GE1/EpxbQq0GRSb9hP+xTnh69Bsn/R1f//JQ56NcxwfqhnlRl1uOB1CQ/uvhnS1NIq2Irc3MIU8bEJknJzSzP4+737rgF5Hj3wh2tXN+gb6ENLSwOJL5Nx7OzP8Lvv/179VgR5SakQcvOgbmYsVa5hZoK8hKQi9TVr2kCzhjWsf1z+plCt4MbzOvdPI9J1HHKfVezFyVT+0pNSkZebB4NCP+P0LYzwKj5ZZptX8cnQl1U/oaC+hlgTPecNx75J6xFwsWBtTUxAJKo1sUWXiZ9KTZGI9bQx7ueFyErLxL5J65Gfy92GqyqF1jzY29sjNFTx28HEYjEMDQ2ljrKesgCA/Jw8vHwYJlnsCAAQiWDdsSni7xb/oK+mU/qg2Zf98fdna/HyYdGdNv9LHAxqW+H8sDXISip+PURlk56WgYiwZ5Ij6Gko4mLj0b5zG0kdfX09tGhlj3t3ip//LI20V2lIfJkMuzo14dCiCf4udEtnpZSbi6wnQdBt2/JNmUgEnbYt8PrBkyLVc8Ke4Vn/iXg+eIrkyLh4A5m3HuD54CnIjY4vx+CpssjLycOLR2Go1/7NiIBIJEK99k0R4Rsks03kvSDUa99Uqqx+RwdE/ltfXVMDGloaEATpEcD8/HyIRG92UhLr62D8Lx7Iy8nF3vHfIVfG6GxlwgWT8ik0Br1y5UrMnTsXK1asQOvWraGnpyf1vqGh7FuCKgL/HWfQ4ftJSHgYhpf3QtB4Qk9o6IgRfOQSAKDDxknIiE7CvTW/AgCaTv0ULeYOwpXpW5D2LAHaFgUZem76a+RmZEGkoY6Pt8+EqYMd/hmzDiJ1NUmd7OQ05OdUvcx7z7aDmOY+HuGhkXgWEQV3jymIjYnHudPekjq//L4N505dxC+7jgAAdPV0YPvW3H4N2+pobN8AyUmpiH4RAwDo1dcZiS+TEPU8Bg2b1MOSVfNw/rQ3rnrfKNfzKyvJ+36H5aq5yHociNePnsLoswEQ6Wjj1YlzAADL1fOQG5eAxA17IGTnIDs4Qqp93qs0qANS5WqGBtCwsYDGv2tRNP+9xnkJSch7WXRE40OQkZGJyOdRktcvomIREBgCI0MD2FhbqjCy8nFl5ykMXTcFz/1C8ex+MDqO6wUtXTHu/FbwM27YuilIiU2C19rDAICru89g8pGl6Dy+D/wv3kMLVyfUcKiDYx47AABZaZkIufEEfTxGIud1NpKeJ6BOu8ZoPbAz/lr5C4A3iYOWthiHvlwHsYEOxAY6AID0l6kQ8ivf4D3XPMinUPLQu3dvAEDfvn2lMs/K8GCs8D9vQmxqiBZzBxVsEvU4Ahc+W4vXCQXzynrVzKW+0RuO7gZ1sSY+3jFLqp8H637Hg/W/Q9faBDVdCu51dj2/WqrO2cGrEOtT+YfcC9u+6Wfo6ulg1brFMDQywJ2b9zF22HRkv3WnRC27GjB5a4jeoUUTHPxjh+T14pVzAADHDv2J+TO+BgBYWplj0Qp3mFmYIT42AcePnMSP6960qezSvS7hpYkRTKaPhoa5CbICQhE9eZFkEaWGjUWp13fodW0Hy1VzJa+tv/sKAJC45RckbdmvtNgrk0cBQfhixpuN6tZu2g4A6NfLGasWz1FVWOXmwckb0DM1RI/Zg2FgYYwo/wjsGrNGsijSuLq51AMOI3yDcHDWj+g5Zyh6zhuGhPAY7Ju4DrGBzyV1Dsz4Ab3mD4fbhunQNdZH0ot4eP3vCG7sL9gkqrq9HWxbFtydsfDyRql4PDvOQNLzBFQ2TB3kEwlvfxeV0KVLl+S+36WL7D0T5NlX/bNSt6EC32QVHfamkjlnXfnv5FClWt5bVR1CpbXIcZGqQ6jU1oYfKtP+Z9sNV1pf34cfVlpfFYVCIw+KJAdERESVRVVdq6As73XfXUZGBiIjIyV7PfynWTPFNwsiIiJSNYETF3IplDzEx8dj7NixOHPmjMz3K/KaByIiIno/Ct2q+eWXXyI5ORk3b96Ejo4OvLy88PPPP6N+/fr4888/lR0jERFRueKtmvIpNPLwzz//4I8//oCjoyPU1NRga2uL7t27w9DQEJ6enujTp4+y4yQiIio3vFVTPoVGHtLT02FpWXC/tImJCeLjCzascXBwgK+vr7ymREREVMkplDw0bNgQT58WbEXcvHlz/PTTT3jx4gW2bdsGG5uSPWCKiIioouKzLeRTaNpi1qxZiI4u2Fd/2bJl6NmzJ/bv3w8tLS38/PPPSg2QiIiovHHaQj6FkofPPnuzoVOrVq0QERGBgIAA1KpVC+bmij+WmYiIiCo+haYtAGDXrl2wt7eHtrY2TExMMHr0aJw4cUKJoREREakG77aQT6GRh6VLl2L9+vWYMWMGnJycAAA+Pj6YPXs2IiMjsXz58nf0QEREVHFxkyj5FEoetm7dih07dsDNzU1S1rdvXzRr1gwzZsxg8kBERJVaVR0xUBaFpi1ycnLg6OhYpLx169bIzc1976CIiIio4lIoeRg1ahS2bi36NL3t27dj5MiR7x0UERGRKglK/F9VpPCDsXbt2oVz586hXbt2AICbN28iMjISo0ePhru7u6Te+vXr3z9KIiKicsRpC/kUGnl49OgRWrVqBQsLC4SEhCAkJATm5uZo1aoVHj16hHv37uHevXu4f/++ksMlIiKq2jZv3gw7Oztoa2ujbdu2uHXrltz6v/32Gxo1agRtbW04ODjg9OnTZR6jQiMPFy9eVHYcREREFUa+oJrphiNHjsDd3R3btm1D27ZtsWHDBri4uODp06eSx0K87fr163Bzc4Onpyc+/fRTHDx4EP3794evry/s7e3LLE6F93kgIiKqqpS5PXVWVhZSU1OljqysLJlfd/369ZgwYQLGjh2LJk2aYNu2bdDV1cXu3btl1t+4cSN69uyJefPmoXHjxlixYgVatWqFH3/8UWnXQhYmD0RERGXI09MTRkZGUoenp2eRetnZ2bh79y6cnZ0lZWpqanB2doaPj4/Mvn18fKTqA4CLi0ux9ZVF4QWTREREVZUyn23h4eEhdSMBAIjF4iL1EhISkJeXBysrK6lyKysrBAQEyOw7JiZGZv2YmJj3jFo+Jg9ERESFKPMWS7FYLDNZqMw4bUFERFQBmJubQ11dHbGxsVLlsbGxsLa2ltnG2tq6VPWVhckDERFRIap4MJaWlhZat26NCxcuvIkjPx8XLlyQPEeqMCcnJ6n6AHD+/Pli6ysLpy2IiIgKUeaah9Jwd3fHmDFj4OjoiDZt2mDDhg1IT0/H2LFjAQCjR49G9erVJQsuZ82ahS5dumDdunXo06cPDh8+jDt37mD79u1lGieTByIiokJUta30sGHDEB8fj6VLlyImJgYtWrSAl5eXZFFkZGQk1NTeTBq0b98eBw8exOLFi/HVV1+hfv36OHHiRJnu8QAweSAiIqpQpk+fjunTp8t8z9vbu0jZkCFDMGTIkDKOShqTByIiokL4bAv5mDwQEREVIqhoe+rKgndbEBERUalw5IGIiKgQVd1tUVkweSAiIiqEax7kqzDJg2Ee/1MpqraOhapDqLTmpmqpOoRKrb7jIlWHUGmturNK1SEQKazCJA9EREQVhar2eagsmDwQEREVwjUP8vFuCyIiIioVjjwQEREVwn0e5GPyQEREVAiX8MvH5IGIiKgQLpiUj2seiIiIqFQ48kBERFQI77aQj8kDERFRIVwwKR+nLYiIiKhUSjzykJqaWuJODQ0NFQqGiIioIuC0hXwlTh6MjY0hEolKVDcvL0/hgIiIiFSNd1vIV+Lk4eLFi5J/h4eHY+HChfj888/h5OQEAPDx8cHPP/8MT09P5UdJREREFUaJk4cuXbpI/r18+XKsX78ebm5ukrK+ffvCwcEB27dvx5gxY5QbJRERUTnK54JJuRRaMOnj4wNHR8ci5Y6Ojrh169Z7B0VERKRKghKPqkih5KFmzZrYsWNHkfKdO3eiZs2a7x0UERERVVwK7fPw/fffY9CgQThz5gzatm0LALh16xaCgoJw7NgxpQZIRERU3ni3hXwKjTz07t0bgYGBcHV1RWJiIhITE+Hq6orAwED07t1b2TESERGVq3wISjuqIoV3mKxZsyZWr16tzFiIiIgqBO4wKZ/CO0xeuXIFn332Gdq3b48XL14AAH755RdcvXpVacERERFRxaNQ8nDs2DG4uLhAR0cHvr6+yMrKAgCkpKRwNIKIiCo9TlvIp1DysHLlSmzbtg07duyApqampLxDhw7w9fVVWnBERESqICjxf1WRQsnD06dP0blz5yLlRkZGSE5Oft+YiIiIqAJTKHmwtrZGcHBwkfKrV6+iTp067x0UERGRKgmCoLSjKlIoeZgwYQJmzZqFmzdvQiQSISoqCgcOHMDcuXMxZcoUZcdIRERUrrjmQT6FbtVcuHAh8vPz0a1bN2RkZKBz584Qi8WYO3cuZsyYoewYiYiIqAJRKHkQiURYtGgR5s2bh+DgYKSlpaFJkybQ19dXdnxERETlrqpONyiLQsnD/v37MXDgQOjq6qJJkybKjomIiEilqup0g7IotOZh9uzZsLS0xIgRI3D69Gnk5eUpOy4iIiKqoBRKHqKjo3H48GGIRCIMHToUNjY2mDZtGq5fv67s+IiIiMod93mQT6HkQUNDA59++ikOHDiAuLg4fP/99wgPD0fXrl1Rt25dZcdIRERUrvIFQWlHVaTwg7H+o6urCxcXFyQlJSEiIgL+/v7KiKtM1R7bHfWnfgqxhRFSnkTi4aKfkXwvRGZd25FdUXNIJxg2qgkASH4YhieeR4rU169fDU0Xu8HcqTFEGmp4FfgCt8ZtQOaLl2V+PmVtzJzR6O3WE/pG+nh8+wk2fvUDXoRHyW3Td4wrhk4aDFMLU4T4h+LHpVvw9P5TAIBVDSsc8Nkns93yyStx+dQV9BjSHfPXz5VZZ3CLoUh+mfJ+J1WO3NxHwnlED+gZ6iHgjj9++moLosOj5bbpNbo3+k8aCGMLE4T7h2Hn0p8Q9CBI8v5kz2lo3rE5TKxM8Tr9NZ7e9cc+z5/xIuS5pM7xyL+K9Ltu2lpc/euK8k6uDDmN6o4uk1xhYGGEaP9I/LFsL549kP05BQCH3m3hMmcITGpYICEsBmfWHEKA933J+1q6YvRa4IamPRyhZ2KAxGdxuLb3LG4c+BsAoGOkhx6zh6BBJwcYVzdH2stUPD53B+fW/4rXrzLL+nQrhDv3/bDn4FE8CQhG/MtEbPRcgm6d26s6LJWoqiMGyqJw8pCRkYHjx4/jwIEDuHDhAmrWrAk3NzccPXpUmfEpXfV+7WD/9Wd4sGA3knyDUXdCL7Q/tBB/d5yD7ITUIvXN2zfBixPX4Xc7CHlZOag/3RUdDi/EhS7z8TomCQCga2uJzn8sQ8QhbwT87yhyXmXCsGEN5GXllPfpKd2wKUMxYGw/rHX/DtGRMRg7bwzW7F+NL7pNQE4x5/exaxdMXjIRG7/aBP97ARg0bgDW/LIKYz8eh+SXKYiPiseQVsOl2vQZ0RtDJw/GrYu3AQDef13Cbe87UnXmr58LLbFmpUocBkwZhD5jP8UP7hsQ+ywWI+aOxNL9yzGz29Rir18H144Yu2Q8tn21GYH3A+E6ri+W7l+O6R9PRsq/5x7iF4zLx70RHxUPA2MDDJvthmX7l2Nyh/HIz8+X9PWD+wbcu3RX8jo9Nb1sT1hJmn/aDq6LR+H3xbsQeS8Ynb7ohXH7FuJ/n8xB+suin1PbVvUx4ocZ8Fp7GP4XfNGiXweM3j4HGz/1QGxgQULlungU6rZvisOzNyPpeTwadGqG/iu+QGpsEp78fReGViYwtDLGydUHEBv0HCbVLTBw1TgYWplg/9QN5XwFVCMz8zUa1quDAX164MuvVqo6HKrAFJq2GD58OCwtLTF79mzUqVMH3t7eCA4OxooVK9CoUSNlx6hUdSf1RsSBi4g8fAmvAl/g/vxdyMvMgu3wLjLr3522GWF7/0bK4wikBUfhnvt2QE0Ei072kjpNPIYh9sJ9PF5xCCmPIpAREYeYc74yk5HKZuC4/jiw6RCun/NBWEAYvv1yLcyszNDBpfi/RgZNGIjTh7xw9tdziAyKxAaPH5D1Ogs9h7kAAPLz85EUnyR1dOzZHpdOXsbrjNcAgOzX2VLv5+flo0X75jhz+Gy5nLeyfDquL37b9Ctunb+JiIBwbJz9PUwtTdG2R7ti2/Qd3x/nD53FP79dwPOgZ9jmsQVZmVnoNqy7pM75g2fx5NZjxD+PQ+ijEBz8335YVLeAZU1Lqb7SU9ORHJ8sOYpLWCqaTuP74Obhf3Dnt0uIC36B3xftQk5mNj4a+rHM+h2/6IXASw9waftJxIVE4dz63/DicRg6jHGR1LFt3QB3j11G6A1/JD1PwM1D/yDaPwI1mxdMtcYGPscvUzbA/4IvEiPjEOLzGF7fHUGTbq2gpq7wA4grlU5OH2HmxDFw7tJB1aGoHKct5FPoE6Guro5ff/0V0dHR+PHHH+Hk5KTsuMqESFMdxs1qI/7yozeFgoD4K49g6li/RH1o6IihpqGB7OS0fzsVwcq5BdJCY+B0aCF6PdqKzqeXw6anYxmcQfmyqWUNMysz+F5587Cz9FcZ8L8fgCatGstso6GpgQYO9eF79U0bQRDge+UemrSWfVtvfYd6qGdfT25i0H2wM7Iys3D5dOUYcgcAq1pWMLU0xYOr9yVlGa8yEHQ/EA1by06yNTQ1UNehHh5cfSApEwQBD6/eR8NWDWW2EeuI8clQZ8RExiAhKkHqvYkrJ+Pn+wew9s916DbU+f1Pqhyoa6qjun1tBF978zkVBAFB1x7BtpXsz2mtlvUR9FZ9AAi8/BC13qofcTcQTZxbw9DKBABQ16kJLGrbIPDKw2Jj0THQxeu0TOTn5Rdbh6omLpiUT6FpiwMHDrzXF83KypI8xvs/OUIeNEXq79Xvu4hNDaCmoY7X8dLD3lnxKdCvV61EfTRZ4obXsUmSBERsbghNfR3Un+EK/zW/4cnKQ7Ds2gxtdn+Jq4NW4qVPgNLPo7yYWJgCAJISkqXKk+OTYWppKrONkakh1DXUkRQv3SYpIQk169WU2abX8J6ICIzAk7tPio2l1zAX/PPHRWS/zi75CaiYsUXBL6mUwtcvIVnyXmEG/16/lISkIm2q160hVdZzVG+M/upz6Ojp4Hnwc3wzcglyc3Il7x/8bj/8rj9EVmYWWnRuiYkrp0BbTwen9hRdC1GR6JkUXINXCdKf07T4FFjWlf05NbAwRpqM+gbmxpLXJ77ei0GeE7D45hbk5eRCyBdw1GMHwm7J/ozqmhig24wBuHnowvudEFEVVOLk4YcffsDEiROhra2NH374QW7dmTNnyn3f09MT33zzjVTZMD17DNd3KGk4KlF/uitq9HPC1YErkP/v8K9ITQQAiPa6i5DtZwAAKY8jYPpRA9Qe7VypkodP+nfF7DWzJK8Xfb6kzL+mlrYWPunXFft/OFhsncatGsO2gS3WfLm2zON5H537d8Fkz2mS16s+X16mX+/yCW88uHIPJpam6DdpAOZuWQCPgfMlUxO//XBEUjfscSi0dbTRf9KACp88lJUOY1xg26Ie9oz7H5JeJKBOm0YYsHwsUmOTpEY5AECsr4Mv9sxHbPALnN9wTEURkypV1ekGZSlx8vD9999j5MiR0NbWxvfff19sPZFI9M7kwcPDA+7u7lJlZ+tPKGkoCstKfIX83DxoWxhJlYstjJAVlyy3bb0pfdBgRl9cG7oaqf7PpPvMycWrwBdS9V8FvYBZG9nDzBWVz/kbCPj3jggA0NTSBACYmBsjMS5RUm5sYYyQx7JXvackpiIvNw8mFsZS5SbmJkiKTypSv3PvThDriHH+6N/FxtXbrSeCHwUjyK/ok1wrklvnbyHwXqDktaa44PoZmRsjKe7NuRubGyPsSajMPl79e/2MzKVHJozNjZFc6PplvMpAxqsMRIdHI/DeU/zidwhtXZxw9c/LMvsOvP8UQ78cDg0tDeRm58qsUxGkJxVcAwNz6c+pvoURXhUa0frPq/hk6Muq/++oj4ZYEz3nDce+SesRcPEeACAmIBLVmtiiy8RPpZIHsZ42xv28EFlpmdg3aT3yc7kJ3oeoqk43KEuJ1zyEhYXBzMxM8u/ijtBQ2T8U3yYWi2FoaCh1lPWUBQAIOXlIfhgGi05N3xSKRLDo2BSJd4KKbVdv2qdoOHsArrt9i+QHYUX6TLofCoO6NlLl+nVskPFcev65ostMz0RUeJTkiAiMwMvYl2jZsaWkjq6+Lhq3aIQnvrJvyc3NyUWgXxBadXjTRiQSoWXHFjKnJXoNd4HP+RtISZR9B4W2rja6fNq5UiyUfJ2eiZiIaMnxLDASiXGJaNahuaSOjr4O6rdogKd3ZY9I5ebkIsQvGM06NJOUiUQiOHRojqe+T2W2KahUUO+/hE+W2k3q4FXyqwqdOABAXk4eXjwKQ732bxYli0Qi1GvfFBG+sj+nkfeCUK99U6my+h0dEPlvfXVNDWhoaUAQpNcu5OfnQyQSSV6L9XUw/hcP5OXkYu/475BbSRaYEpU3hRZMXr16VdlxlJuQn04X7N0wtBP061dD82+/gLquNiIPXwIAtNo0BU2+GiapX3+6KxrPH4J7s39CxrN4iC2MILYwgrquWFIneMtJVO/nBNuRXaFnZ4XaX/SAdY9WCNtb/F/TlcXvu05g5Aw3OHVvh9qN7LBgwzy8jH2Ja2ff7Ca69tAa9BvTV/L62I7f0dutF7oPdkatejUxa/UMaOtow+vXc1J9V7OrBoe2Djhz2KvYr/+xaxeoa6jj7+OVc9755K4/MWTmMHzUvQ1qNbTFrO/dkRiXiJvnbkjqfHNoJXqN6SN5/efOE+ju5oKugz9BjXo1MGn1VGjrauPCrwXfT1a1rDBw2mDUcagL82oWaNi6EeZtXYjs11nwvVhwe6uj80dwHt4DtRrUgrWtDVw+64VB04fg9N6T5XsBFHRl5ym0ceuK1oM6w7JuNQxY9QW0dMW481vB53TYuinoOf/N7b5Xd59Bwy7N0Xl8H1jUrYbuXw5CDYc6uPZzQdKZlZaJkBtP0MdjJOq0awyTGhZoPbgzWg/sjEfnCm4P/i9x0NLRxm/zf4LYQAf6FkbQtzCSTE9WdRkZmQgIDEFAYMHI4ouoWAQEhiA6Jk7FkZW/ynC3RWJiIkaOHAlDQ0MYGxtj3LhxSEtLk1t/xowZaNiwIXR0dFCrVi3MnDkTKSmlv/1doQWTn3zyCapXrw43NzeMHDkSTZs2fXejCuLFHzegZWaIxvMHQ2xhjJTHEfBxW4Osf2+r1K1uBrx1n3ztMc5QF2uiza7ZUv0EfHcMAd8VzIVGn7mD+wt2ocGMfmi2cgzSQqJwa9wGJN6S85diJXFk66/Q1tXG7DWzoG+oj0e3H2PhqEVSt/xVs7WBkamh5LX3X5dgZGqEz+eMhomFCUKehMJj1CIkF1o42HOYCxKiE3DnrX0ICus1vCeunrlWafYnKOz41mPQ1tHGFM/p0DPUg/+dJ1gxapnU9bOuZQ3Dt67ftb+uwtDUCMPdR8LEwgRhT0KxfNQyycLL7KwcNPmoKVy/6As9I32kJCTj8c3HWDhgvmQfiLycPPQa3RtfLB0HiESICY/GnhW7cP5gxR/BAYAHJ29Az9QQPWYPhoGFMaL8I7BrzBrJokjj6uZSTz2M8A3CwVk/ouecoeg5bxgSwmOwb+I6yR4PAHBgxg/oNX843DZMh66xPpJexMPrf0dwY39BUlbd3g62LQvuzlh4eaNUPJ4dZyCpko0kKuJRQBC+mLFA8nrtpu0AgH69nLFq8RxVhaUSlWHaYuTIkYiOjsb58+eRk5ODsWPHYuLEiTh4UPYasqioKERFReG7775DkyZNEBERgcmTJyMqKqrUezSJBAWeO5qQkIDDhw/j0KFD8PHxQbNmzTBy5Ei4ubmhRo0a7+5AhhPWIxRqR8CPmpV/F0tVMRBpqTqESq2+mr6qQ6i0Vt1ZpeoQKjVN8zpl2n8d85bvrlRCoQn3lNbXf/z9/dGkSRPcvn0bjo4FWwN4eXmhd+/eeP78OapVK9kdhL/99hs+++wzpKenQ0Oj5OMJCk1bmJubY/r06bh27RpCQkIwZMgQ/Pzzz7Czs8Mnn3yiSJdEREQVhiDkK+3IyspCamqq1FF4u4LS8vHxgbGxsSRxAABnZ2eoqanh5s2bJe4nJSUFhoaGpUocAAWTh7fVrl0bCxcuxJo1a+Dg4IBLly69b5dEREQqlQ9BaYenpyeMjIykDk9Pz/eKLyYmBpaW0jvKamhowNTUFDExMSXqIyEhAStWrMDEiRNL/fXfK3m4du0apk6dChsbG4wYMQL29vY4derU+3RJRESkcoIgKO3w8PBASkqK1OHh4SHz6y5cuBAikUjuERDw/vsHpaamok+fPmjSpAm+/vrrUrdXaMHkwoULceTIEURFRaF79+7YuHEj+vXrB11dXUW6IyIiqrLEYjHEYvG7KwKYM2cOPv/8c7l16tSpA2tra8TFSd8Fk5ubi8TERFhbW8tt/+rVK/Ts2RMGBgY4fvw4NDWLv8W7OAolD1euXMG8efMwdOhQmJubK9IFERFRhZWvorstLCwsYGFh8c56Tk5OSE5Oxt27d9G6dWsAwD///IP8/Hy0bdu22HapqalwcXGBWCzGn3/+CW1tbYXiLPW0RU5ODho2bIhevXoxcSAioipJmdMWZaFx48bo2bMnJkyYgFu3buHatWuYPn06hg8fLrnT4sWLF2jUqBFu3boFoCBx6NGjB9LT07Fr1y6kpqYiJiYGMTExyMsr3U6qpU4eNDU1cewY93onIiJSpQMHDqBRo0bo1q0bevfujY4dO2L79u2S93NycvD06VNkZGQAAHx9fXHz5k34+fmhXr16sLGxkRzPnj0r7svIpNC0Rf/+/XHixAnMnj373ZWJiIgqmcrwYCxTU9NiN4QCADs7O6mRj48//lhpIyEKJQ/169fH8uXLce3aNbRu3Rp6enpS77/rwVhEREQVWWXYYVKVFEoedu3aBWNjY9y9exd370pvLVySp2oSERFR5aVQ8hAWFvbuSkRERJVUWS10rCoUSh6IiIiqMlXdqllZKJQ8fPHFF3Lf3717t0LBEBERUcWnUPKQlJQk9TonJwePHj1CcnIyH4xFRESVHqct5FMoeTh+/HiRsvz8fEyZMgV169Z976CIiIhUqTLcqqlK7/1UTUlHampwd3fH999/r6wuiYiIVKKi7zCpakpLHgAgJCQEubm5yuySiIiIKhiFpi3c3d2lXguCgOjoaJw6dQpjxoxRSmBERESqwrst5FMoebh3757UazU1NVhYWGDdunXvvBODiIiooquq0w3KolDycOrUKQiCINmWOjw8HCdOnICtrS00NLh1BBERUVWm0JqH/v3745dffgEAJCcno127dli3bh369++PrVu3KjVAIiKi8pYvCEo7qiKFkgdfX1906tQJAHD06FFYWVkhIiIC+/btww8//KDUAImIiMqboMT/VUUKJQ8ZGRkwMDAAAJw7dw4DBw6Empoa2rVrh4iICKUGSERERBWLQslDvXr1cOLECTx79gxnz55Fjx49AABxcXEwNDRUaoBERETljdMW8imUPCxduhRz586FnZ0d2rZtCycnJwAFoxAtW7ZUaoBERETljZtEyafQrRGDBw9Gx44dER0djebNm0vKu3XrhgEDBigtOCIiIqp4FL6v0traGtbW1lJlbdq0ee+AiIiIVK2qLnRUFm7KQEREVEhVnW5QFiYPREREhTB5kE+pD8YiIiKiqo8jD0RERIVw3EE+kcCxGbmysrLg6ekJDw8PiMViVYdT6fD6KY7XTnG8du+H14/ehcnDO6SmpsLIyAgpKSncAEsBvH6K47VTHK/d++H1o3fhmgciIiIqFSYPREREVCpMHoiIiKhUmDy8g1gsxrJly7hoSEG8forjtVMcr9374fWjd+GCSSIiIioVjjwQERFRqTB5ICIiolJh8kBERESlwuSBiIiISuWDTB4EQcDEiRNhamoKkUiE+/fvqzok+sB8/vnn6N+/v+T1xx9/jC+//FJl8XzowsPD+bNABjs7O2zYsKFMv4a3tzdEIhGSk5PL9OuQcn2QD8by8vLC3r174e3tjTp16sDc3FzVIdEHZuPGjXzkL1V4t2/fhp6enqrDoArog0weQkJCYGNjg/bt2yvcR05ODjQ1NZUYFX1IjIyMVB3CB0MQBOTl5UFD44P8cSdTdnY2tLS03lnPwsKiHKKhyuiDm7b4/PPPMWPGDERGRkIkEsHOzg5eXl7o2LEjjI2NYWZmhk8//RQhISGSNv8NaR45cgRdunSBtrY2Dhw4AADYuXMnGjduDG1tbTRq1AhbtmxR1amVq3dds+vXr6NFixbQ1taGo6MjTpw4UWRY+NGjR+jVqxf09fVhZWWFUaNGISEhQQVnU3aOHj0KBwcH6OjowMzMDM7OzkhPTy8ybQEAubm5mD59OoyMjGBubo4lS5ZIjU5s2bIF9evXh7a2NqysrDB48GDJex9//DGmT58ut31l8a5z+eWXX+Do6AgDAwNYW1tjxIgRiIuLk7T/bxj8zJkzaN26NcRiMa5evYr8/HysXbsW9erVg1gsRq1atbBq1Sqprx0aGoquXbtCV1cXzZs3h4+PT7meuzzFfS/JmvLq378/Pv/8c8lrOzs7rFixAqNHj4ahoSEmTpyI9u3bY8GCBVLt4uPjoampicuXL0va/TdtMWLECAwbNkyqfk5ODszNzbFv3z4AQH5+Pjw9PVG7dm3o6OigefPmOHr0qFSb06dPo0GDBtDR0UHXrl0RHh7+/heHyp/wgUlOThaWL18u1KhRQ4iOjhbi4uKEo0ePCseOHROCgoKEe/fuCa6uroKDg4OQl5cnCIIghIWFCQAEOzs74dixY0JoaKgQFRUl7N+/X7CxsZGUHTt2TDA1NRX27t2r4rMse/KuWUpKimBqaip89tlnwuPHj4XTp08LDRo0EAAI9+7dEwRBEJKSkgQLCwvBw8ND8Pf3F3x9fYXu3bsLXbt2Ve2JKVFUVJSgoaEhrF+/XggLCxMePnwobN68WXj16pUwZswYoV+/fpK6Xbp0EfT19YVZs2YJAQEBwv79+wVdXV1h+/btgiAIwu3btwV1dXXh4MGDQnh4uODr6yts3LixxO0rk3edy65du4TTp08LISEhgo+Pj+Dk5CT06tVL0v7ixYsCAKFZs2bCuXPnhODgYOHly5fC/PnzBRMTE2Hv3r1CcHCwcOXKFWHHjh2CILz5jDdq1Eg4efKk8PTpU2Hw4MGCra2tkJOTo5Lr8DZ530tdunQRZs2aJVW/X79+wpgxYySvbW1tBUNDQ+G7774TgoODheDgYOHHH38UatWqJeTn50vqbdq0SarM1tZW+P777wVBEISTJ08KOjo6wqtXryT1//rrL0FHR0dITU0VBEEQVq5cKTRq1Ejw8vISQkJChD179ghisVjw9vYWBEEQIiMjBbFYLLi7u0v+21pZWQkAhKSkJOVfOCozH1zyIAiC8P333wu2trbFvh8fHy8AEPz8/ARBePODZcOGDVL16tatKxw8eFCqbMWKFYKTk5PSY67o3r5mW7duFczMzITMzEzJ+zt27JBKHlasWCH06NFDqo9nz54JAISnT5+WZ+hl5u7duwIAITw8vMh7spKHxo0bS/0gX7BggdC4cWNBEATh2LFjgqGhoeSHdGHval+ZlPZcbt++LQCQ/FL7L3k4ceKEpE5qaqogFoslyUJh/33Gd+7cKSl7/PixAEDw9/dXxmm9F3nfSyVNHvr37y9VJy4uTtDQ0BAuX74sKXNychIWLFgg1e6/5CEnJ0cwNzcX9u3bJ3nfzc1NGDZsmCAIgvD69WtBV1dXuH79utTXGTdunODm5iYIgiB4eHgITZo0kXp/wYIFTB4qoQ9u2kKWoKAguLm5oU6dOjA0NISdnR0AIDIyUqqeo6Oj5N/p6ekICQnBuHHjoK+vLzlWrlwpNXxfVcm7Zk+fPkWzZs2gra0tqd+mTRup9g8ePMDFixelrl2jRo0AoMpcv+bNm6Nbt25wcHDAkCFDsGPHDiQlJRVbv127dhCJRJLXTk5OCAoKQl5eHrp37w5bW1vUqVMHo0aNwoEDB5CRkVHi9pWNvHO5e/cuXF1dUatWLRgYGKBLly4A5H9e/f39kZWVhW7dusn9us2aNZP828bGBgCkpkRUpbTfS7K8fT2AgvUMPXr0kEzBhoWFwcfHByNHjpTZXkNDA0OHDpXUT09Pxx9//CGpHxwcjIyMDHTv3l3qc71v3z7JZ9rf3x9t27aV6tfJyalU50EVA1cQAXB1dYWtrS127NiBatWqIT8/H/b29sjOzpaq9/aq47S0NADAjh07inwY1NXVyz5oFSvpNStOWloaXF1d8e233xZ5778f2pWduro6zp8/j+vXr+PcuXPYtGkTFi1ahJs3b5a6LwMDA/j6+sLb2xvnzp3D0qVL8fXXX+P27dswNjZWfvAV1OvXr+Hi4gIXFxccOHAAFhYWiIyMhIuLi9zPq46OTon6f3sR9H/JS35+vhIifz/yvpfU1NSKrG3Jyckp0oesuyZGjhyJmTNnYtOmTTh48CAcHBzg4OBQbBwjR45Ely5dEBcXh/Pnz0NHRwc9e/YE8OZn4qlTp1C9enWpdnzAVtXzwY88vHz5Ek+fPsXixYvRrVs3NG7cuEQZvZWVFapVq4bQ0FDUq1dP6qhdu3Y5RK4677pmDRs2hJ+fH7KysiRlt2/fluqjVatWePz4Mezs7Ipcv6p0a5hIJEKHDh3wzTff4N69e9DS0sLx48dl1i2cVNy4cQP169eXJKMaGhpwdnbG2rVr8fDhQ4SHh+Off/4pcfvKpLhzCQgIwMuXL7FmzRp06tQJjRo1KtHIQP369aGjo4MLFy6UVchlrrjvJQsLC0RHR0vq5eXl4dGjRyXqs1+/fnj9+jW8vLxw8ODBYkcd/tO+fXvUrFkTR44cwYEDBzBkyBBJwtWkSROIxWJERkYW+UzXrFkTANC4cWPcunVLqs8bN26U5jJQBfHBjzyYmJjAzMwM27dvh42NDSIjI7Fw4cIStf3mm28wc+ZMGBkZoWfPnsjKysKdO3eQlJQEd3f3Mo5cdd51zUaMGIFFixZh4sSJWLhwISIjI/Hdd98BePPX3LRp07Bjxw64ublh/vz5MDU1RXBwMA4fPoydO3dWyl94hd28eRMXLlxAjx49YGlpiZs3byI+Ph6NGzfGw4cPi9SPjIyEu7s7Jk2aBF9fX2zatAnr1q0DAJw8eRKhoaHo3LkzTExMcPr0aeTn56Nhw4Ylal/ZFHcutWrVgpaWFjZt2oTJkyfj0aNHWLFixTv709bWxoIFCzB//nxoaWmhQ4cOiI+Px+PHjzFu3LhyOKP3I+97SU9PD+7u7jh16hTq1q2L9evXl3jDJT09PfTv3x9LliyBv78/3Nzc3tlmxIgR2LZtGwIDA3Hx4kVJuYGBAebOnYvZs2cjPz8fHTt2REpKCq5duwZDQ0OMGTMGkydPxrp16zBv3jyMHz8ed+/exd69exW8KqRSql50oQqFF0yeP39eaNy4sSAWi4VmzZoJ3t7eAgDh+PHjgiC8WUz132K/tx04cEBo0aKFoKWlJZiYmAidO3cWfv/99/I5ERV61zW7du2a0KxZM0FLS0to3bq1cPDgQQGAEBAQIOkjMDBQGDBggGBsbCzo6OgIjRo1Er788kuphXKV2ZMnTwQXFxfBwsJCEIvFQoMGDYRNmzYJgiB7weTUqVOFyZMnC4aGhoKJiYnw1VdfSa7FlStXhC5duggmJiaCjo6O0KxZM+HIkSMlbl+ZvOtcDh48KNjZ2QlisVhwcnIS/vzzT6nP538LJgsvwMvLyxNWrlwp2NraCpqamkKtWrWE1atXC4Ig+zOelJQkABAuXrxYDmctn7zvpezsbGHKlCmCqampYGlpKXh6espcMPnfwsfCTp8+LQAQOnfuXOQ9We2ePHkiABBsbW2LfH/l5+cLGzZsEBo2bChoamoKFhYWgouLi3Dp0iVJnb/++kuoV6+eIBaLhU6dOgm7d+/mgslKSCQIlfBGcKp0Dhw4gLFjxyIlJaXE889Uch9//DFatGhR5lsJl4eqdC5EVdUHP21BZWPfvn2oU6cOqlevjgcPHmDBggUYOnQoEwcioiqAyQOViZiYGCxduhQxMTGwsbHBkCFDiuzmR0RElROnLYiIiKhUPvhbNYmIiKh0mDwQERFRqTB5ICIiolJh8kBERESlwuSBiIiISoXJAxEREZUKkwciIiIqFSYPREREVCr/Bzn+UqQQlAUTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(correlation_df, annot=True)\n",
    "# to show the correlation matrix in the heatmap "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1626410",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:30.820744Z",
     "iopub.status.busy": "2024-08-25T06:42:30.820310Z",
     "iopub.status.idle": "2024-08-25T06:42:53.883864Z",
     "shell.execute_reply": "2024-08-25T06:42:53.882668Z"
    },
    "papermill": {
     "duration": 23.083985,
     "end_time": "2024-08-25T06:42:53.889329",
     "exception": false,
     "start_time": "2024-08-25T06:42:30.805344",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_stats/counting.py:137: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  bin_edges = np.histogram_bin_edges(vals, bins, binrange, weight)\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_stats/counting.py:176: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  hist, edges = np.histogram(vals, **bin_kws, weights=weights, density=density)\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_stats/counting.py:137: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  bin_edges = np.histogram_bin_edges(vals, bins, binrange, weight)\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_stats/counting.py:176: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  hist, edges = np.histogram(vals, **bin_kws, weights=weights, density=density)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7feb4fde9870>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x2000 with 72 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8105b7b9",
   "metadata": {
    "papermill": {
     "duration": 0.017532,
     "end_time": "2024-08-25T06:42:53.924984",
     "exception": false,
     "start_time": "2024-08-25T06:42:53.907452",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Missing values and imputing them"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38b0a1f2",
   "metadata": {
    "papermill": {
     "duration": 0.017695,
     "end_time": "2024-08-25T06:42:53.960860",
     "exception": false,
     "start_time": "2024-08-25T06:42:53.943165",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Handling Missing Values in Data: Strategies and Rules for Imputation\n",
    "\n",
    "Missing values are a common occurrence in datasets and can significantly impact the results of data analysis and modeling. In this blog post, we'll explore the importance of handling missing values, along with some rules and strategies for imputing them to ensure clean and reliable data.\n",
    "\n",
    "## Importance of Handling Missing Values\n",
    "\n",
    "Missing values pose several challenges in data analysis:\n",
    "\n",
    "- They can skew statistical estimates and summary statistics, leading to biased results.\n",
    "- They reduce the sample size available for analysis, potentially affecting the validity and generalizability of conclusions.\n",
    "- Many machine learning algorithms cannot handle missing values and may produce errors or biased predictions if not addressed properly.\n",
    "\n",
    "## Rules for Imputing Missing Values\n",
    "\n",
    "When imputing missing values, it's essential to follow certain rules to ensure the integrity and reliability of the data:\n",
    "\n",
    "### 1. Understand the Mechanism of Missingness:\n",
    "   Investigate the reasons for missing values and determine whether they occur randomly or systematically. Understanding the mechanism of missingness can guide the choice of imputation method.\n",
    "\n",
    "### 2. Preserve the Distribution:\n",
    "   Choose imputation methods that preserve the distribution of the original data as much as possible. Imputed values should reflect the underlying patterns and relationships present in the dataset.\n",
    "\n",
    "### 3. Consider Variable Type:\n",
    "   Different imputation methods may be suitable for different types of variables (e.g., numerical, categorical). For numerical variables, mean, median, or regression-based imputation may be appropriate, while mode or probabilistic imputation may be used for categorical variables.\n",
    "\n",
    "### 4. Assess Impact on Analysis:\n",
    "   Evaluate the impact of imputation on subsequent data analysis tasks, such as descriptive statistics, hypothesis testing, or predictive modeling. Imputation methods should not introduce bias or distort the results of analysis.\n",
    "\n",
    "### 5. Sensitivity Analysis:\n",
    "   Perform sensitivity analysis to assess the robustness of results to different imputation methods. Compare the outcomes obtained using various imputation techniques to ensure consistency and reliability.\n",
    "\n",
    "## Imputation Strategies\n",
    "\n",
    "### 1. Mean/Median/Mode Imputation:\n",
    "   Replace missing values with the mean, median, or mode of the corresponding variable. This method is simple and preserves the overall distribution but may not capture underlying patterns accurately.\n",
    "\n",
    "### 2. Regression Imputation:\n",
    "   Predict missing values based on the relationship with other variables using regression models. This method can capture complex relationships but requires careful selection of predictors and assumption validation.\n",
    "\n",
    "### 3. Multiple Imputation:\n",
    "   Generate multiple imputed datasets using statistical techniques such as Markov Chain Monte Carlo (MCMC) and combine the results to obtain robust estimates. This method accounts for uncertainty in imputed values and provides more accurate estimates.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Handling missing values is a critical step in data preprocessing to ensure the integrity and reliability of data analysis results. By following the rules and strategies outlined in this blog post, analysts can effectively impute missing values and produce clean, reliable datasets for further analysis and modeling.\n",
    "\n",
    "Remember that there is no one-size-fits-all approach to imputation, and the choice of method should be guided by the characteristics of the data and the goals of the analysis.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "57dc4fcb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:53.999248Z",
     "iopub.status.busy": "2024-08-25T06:42:53.998766Z",
     "iopub.status.idle": "2024-08-25T06:42:54.010309Z",
     "shell.execute_reply": "2024-08-25T06:42:54.009056Z"
    },
    "papermill": {
     "duration": 0.033427,
     "end_time": "2024-08-25T06:42:54.012862",
     "exception": false,
     "start_time": "2024-08-25T06:42:53.979435",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age            177\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cheaking missing values \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "90737a41",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:54.052115Z",
     "iopub.status.busy": "2024-08-25T06:42:54.051691Z",
     "iopub.status.idle": "2024-08-25T06:42:54.063085Z",
     "shell.execute_reply": "2024-08-25T06:42:54.061801Z"
    },
    "papermill": {
     "duration": 0.03414,
     "end_time": "2024-08-25T06:42:54.065736",
     "exception": false,
     "start_time": "2024-08-25T06:42:54.031596",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deck           688\n",
       "age            177\n",
       "embarked         2\n",
       "embark_town      2\n",
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort missing values in descending order\n",
    "df.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "184f8222",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:54.105108Z",
     "iopub.status.busy": "2024-08-25T06:42:54.104666Z",
     "iopub.status.idle": "2024-08-25T06:42:54.116065Z",
     "shell.execute_reply": "2024-08-25T06:42:54.114939Z"
    },
    "papermill": {
     "duration": 0.034077,
     "end_time": "2024-08-25T06:42:54.118402",
     "exception": false,
     "start_time": "2024-08-25T06:42:54.084325",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived        0.000000\n",
       "pclass          0.000000\n",
       "sex             0.000000\n",
       "age            19.865320\n",
       "sibsp           0.000000\n",
       "parch           0.000000\n",
       "fare            0.000000\n",
       "embarked        0.224467\n",
       "class           0.000000\n",
       "who             0.000000\n",
       "adult_male      0.000000\n",
       "deck           77.216611\n",
       "embark_town     0.224467\n",
       "alive           0.000000\n",
       "alone           0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the percentage of missing values \n",
    "df.isnull().mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cf1e5337",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:54.158879Z",
     "iopub.status.busy": "2024-08-25T06:42:54.158432Z",
     "iopub.status.idle": "2024-08-25T06:42:54.812966Z",
     "shell.execute_reply": "2024-08-25T06:42:54.811738Z"
    },
    "papermill": {
     "duration": 0.678373,
     "end_time": "2024-08-25T06:42:54.815906",
     "exception": false,
     "start_time": "2024-08-25T06:42:54.137533",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show missing values in heatmap \n",
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9eabee63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:54.856952Z",
     "iopub.status.busy": "2024-08-25T06:42:54.856532Z",
     "iopub.status.idle": "2024-08-25T06:42:54.863369Z",
     "shell.execute_reply": "2024-08-25T06:42:54.862267Z"
    },
    "papermill": {
     "duration": 0.03027,
     "end_time": "2024-08-25T06:42:54.865803",
     "exception": false,
     "start_time": "2024-08-25T06:42:54.835533",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#1- drop the column having more than 70% of missing values from dataset\n",
    "df.drop('deck', axis=1, inplace=True)\n",
    "# inplace=True: to make changes in the dataset \n",
    "# axis=1: to drop column \n",
    "# axis=0: to drop row \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "01c7cb26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:54.907107Z",
     "iopub.status.busy": "2024-08-25T06:42:54.906677Z",
     "iopub.status.idle": "2024-08-25T06:42:54.915103Z",
     "shell.execute_reply": "2024-08-25T06:42:54.913759Z"
    },
    "papermill": {
     "duration": 0.032121,
     "end_time": "2024-08-25T06:42:54.917774",
     "exception": false,
     "start_time": "2024-08-25T06:42:54.885653",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].mean()\n",
    "# to find the mean of age column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b99c6bbd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:54.959948Z",
     "iopub.status.busy": "2024-08-25T06:42:54.959013Z",
     "iopub.status.idle": "2024-08-25T06:42:54.968310Z",
     "shell.execute_reply": "2024-08-25T06:42:54.967009Z"
    },
    "papermill": {
     "duration": 0.033181,
     "end_time": "2024-08-25T06:42:54.970879",
     "exception": false,
     "start_time": "2024-08-25T06:42:54.937698",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].median()\n",
    "# to find the median of age column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ed5d5b1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:55.013233Z",
     "iopub.status.busy": "2024-08-25T06:42:55.012748Z",
     "iopub.status.idle": "2024-08-25T06:42:55.022701Z",
     "shell.execute_reply": "2024-08-25T06:42:55.021427Z"
    },
    "papermill": {
     "duration": 0.033913,
     "end_time": "2024-08-25T06:42:55.025106",
     "exception": false,
     "start_time": "2024-08-25T06:42:54.991193",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].mode()[0]\n",
    "# to find the mode of age column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0a6b997d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:55.067872Z",
     "iopub.status.busy": "2024-08-25T06:42:55.067423Z",
     "iopub.status.idle": "2024-08-25T06:42:55.074797Z",
     "shell.execute_reply": "2024-08-25T06:42:55.073700Z"
    },
    "papermill": {
     "duration": 0.031573,
     "end_time": "2024-08-25T06:42:55.077305",
     "exception": false,
     "start_time": "2024-08-25T06:42:55.045732",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17/2478795461.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['age'].fillna(df['age'].mean(), inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# 2- filling missing values of age column by the mean of age\n",
    "df['age'].fillna(df['age'].mean(), inplace=True)\n",
    "# this command fill the missing values of age column by the mean of age "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f493e57e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:55.120235Z",
     "iopub.status.busy": "2024-08-25T06:42:55.119748Z",
     "iopub.status.idle": "2024-08-25T06:42:55.127794Z",
     "shell.execute_reply": "2024-08-25T06:42:55.126577Z"
    },
    "papermill": {
     "duration": 0.033013,
     "end_time": "2024-08-25T06:42:55.130707",
     "exception": false,
     "start_time": "2024-08-25T06:42:55.097694",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17/994958194.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['age'].fillna(df['age'].median(), inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df['age'].fillna(df['age'].median(), inplace=True)\n",
    "# this command fill the missing values of age column by the median of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "51dd6074",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:55.174942Z",
     "iopub.status.busy": "2024-08-25T06:42:55.174510Z",
     "iopub.status.idle": "2024-08-25T06:42:55.185983Z",
     "shell.execute_reply": "2024-08-25T06:42:55.184639Z"
    },
    "papermill": {
     "duration": 0.036476,
     "end_time": "2024-08-25T06:42:55.188602",
     "exception": false,
     "start_time": "2024-08-25T06:42:55.152126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived       0\n",
       "pclass         0\n",
       "sex            0\n",
       "age            0\n",
       "sibsp          0\n",
       "parch          0\n",
       "fare           0\n",
       "embarked       2\n",
       "class          0\n",
       "who            0\n",
       "adult_male     0\n",
       "embark_town    2\n",
       "alive          0\n",
       "alone          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cheak again the missing values after imputation\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "79851157",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:55.233346Z",
     "iopub.status.busy": "2024-08-25T06:42:55.232846Z",
     "iopub.status.idle": "2024-08-25T06:42:55.242728Z",
     "shell.execute_reply": "2024-08-25T06:42:55.241471Z"
    },
    "papermill": {
     "duration": 0.036749,
     "end_time": "2024-08-25T06:42:55.246378",
     "exception": false,
     "start_time": "2024-08-25T06:42:55.209629",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17/936356459.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['embarked'].fillna(df['embarked'].mode()[0], inplace=True)\n",
      "/tmp/ipykernel_17/936356459.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['embark_town'].fillna(df['embark_town'].mode()[0], inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# fill embark and embarked_town with mode\n",
    "df['embarked'].fillna(df['embarked'].mode()[0], inplace=True)\n",
    "df['embark_town'].fillna(df['embark_town'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2f31c181",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:55.291701Z",
     "iopub.status.busy": "2024-08-25T06:42:55.291250Z",
     "iopub.status.idle": "2024-08-25T06:42:55.971736Z",
     "shell.execute_reply": "2024-08-25T06:42:55.970539Z"
    },
    "papermill": {
     "duration": 0.706913,
     "end_time": "2024-08-25T06:42:55.974837",
     "exception": false,
     "start_time": "2024-08-25T06:42:55.267924",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ba916d1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:56.021795Z",
     "iopub.status.busy": "2024-08-25T06:42:56.021321Z",
     "iopub.status.idle": "2024-08-25T06:42:56.037666Z",
     "shell.execute_reply": "2024-08-25T06:42:56.036245Z"
    },
    "papermill": {
     "duration": 0.043204,
     "end_time": "2024-08-25T06:42:56.040464",
     "exception": false,
     "start_time": "2024-08-25T06:42:55.997260",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Youngs\n",
       "1      Middle Aged\n",
       "2           Youngs\n",
       "3      Middle Aged\n",
       "4      Middle Aged\n",
       "          ...     \n",
       "886         Youngs\n",
       "887         Youngs\n",
       "888         Youngs\n",
       "889         Youngs\n",
       "890    Middle Aged\n",
       "Name: age, Length: 891, dtype: category\n",
       "Categories (7, object): ['Infants' < 'Toddlers' < 'Kids' < 'Teens' < 'Youngs' < 'Middle Aged' < 'Old']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# binning of age column into 7 categories\n",
    "bins = [0,1,5,12,18,30,50,80]\n",
    "labels = ['Infants', 'Toddlers', 'Kids', 'Teens', 'Youngs', 'Middle Aged', 'Old']\n",
    "# this command bin the age column into 7 categories \n",
    "\n",
    "pd.cut(df['age'], bins=bins, labels=labels)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a76cc8",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "# renaming a column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "42232c84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-25T06:42:56.132341Z",
     "iopub.status.busy": "2024-08-25T06:42:56.131884Z",
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   "outputs": [],
   "source": [
    "# rename the column Class with category\n",
    "df = df.rename(columns={'class': 'category'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1f6d63c9",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</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",
       "      <th>category</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked category  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S    Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C    First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S    Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S    First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S    Third   \n",
       "\n",
       "     who  adult_male  embark_town alive  alone  \n",
       "0    man        True  Southampton    no  False  \n",
       "1  woman       False    Cherbourg   yes  False  \n",
       "2  woman       False  Southampton   yes   True  \n",
       "3  woman       False  Southampton   yes  False  \n",
       "4    man        True  Southampton    no   True  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  }
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