✍️ Data Science - 🎗️ Breast Cancer Pre-processing Code.ipynb

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   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "94e8cd8b",
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       "<p>4024 rows × 16 columns</p>\n",
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      "text/plain": [
       "      Age   Race Marital Status T Stage  N Stage 6th Stage  \\\n",
       "0      68  White        Married       T1      N1       IIA   \n",
       "1      50  White        Married       T2      N2      IIIA   \n",
       "2      58  White       Divorced       T3      N3      IIIC   \n",
       "3      58  White        Married       T1      N1       IIA   \n",
       "4      47  White        Married       T2      N1       IIB   \n",
       "...   ...    ...            ...      ...     ...       ...   \n",
       "4019   62  Other        Married       T1      N1       IIA   \n",
       "4020   56  White       Divorced       T2      N2      IIIA   \n",
       "4021   68  White        Married       T2      N1       IIB   \n",
       "4022   58  Black       Divorced       T2      N1       IIB   \n",
       "4023   46  White        Married       T2      N1       IIB   \n",
       "\n",
       "                  differentiate Grade   A Stage  Tumor Size Estrogen Status  \\\n",
       "0         Poorly differentiated     3  Regional           4        Positive   \n",
       "1     Moderately differentiated     2  Regional          35        Positive   \n",
       "2     Moderately differentiated     2  Regional          63        Positive   \n",
       "3         Poorly differentiated     3  Regional          18        Positive   \n",
       "4         Poorly differentiated     3  Regional          41        Positive   \n",
       "...                         ...   ...       ...         ...             ...   \n",
       "4019  Moderately differentiated     2  Regional           9        Positive   \n",
       "4020  Moderately differentiated     2  Regional          46        Positive   \n",
       "4021  Moderately differentiated     2  Regional          22        Positive   \n",
       "4022  Moderately differentiated     2  Regional          44        Positive   \n",
       "4023  Moderately differentiated     2  Regional          30        Positive   \n",
       "\n",
       "     Progesterone Status  Regional Node Examined  Reginol Node Positive  \\\n",
       "0               Positive                      24                      1   \n",
       "1               Positive                      14                      5   \n",
       "2               Positive                      14                      7   \n",
       "3               Positive                       2                      1   \n",
       "4               Positive                       3                      1   \n",
       "...                  ...                     ...                    ...   \n",
       "4019            Positive                       1                      1   \n",
       "4020            Positive                      14                      8   \n",
       "4021            Negative                      11                      3   \n",
       "4022            Positive                      11                      1   \n",
       "4023            Positive                       7                      2   \n",
       "\n",
       "      Survival Months Status  \n",
       "0                  60  Alive  \n",
       "1                  62  Alive  \n",
       "2                  75  Alive  \n",
       "3                  84  Alive  \n",
       "4                  50  Alive  \n",
       "...               ...    ...  \n",
       "4019               49  Alive  \n",
       "4020               69  Alive  \n",
       "4021               69  Alive  \n",
       "4022               72  Alive  \n",
       "4023              100  Alive  \n",
       "\n",
       "[4024 rows x 16 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "# uploading and showing our data\n",
    "data = pd.read_csv (\"D:/IMT/3- Data Science/3- Data pre-processing (2)/Breast_Cancer.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1d3e2db",
   "metadata": {},
   "source": [
    "The key challenges against it's detection is how to classify tumors into **malignant** (cancerous) or **benign** (non cancerous).\n",
    "<br>Our **target** for this dataset is to find out wether the patient is *Alive* or *Dead*, the last column, **Status**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e13a010c",
   "metadata": {},
   "source": [
    "It should be checked that what was important on the result of surviving (Alive Status) or dying (Dead Status) from cancer, which of the columns were influential and should be kept in the formation of the DataFrame.<br>\n",
    "The columns that are considered should be kept in the DataFrame and the rest should not enter the DataFrame.<br>\n",
    "In this dataset, all the columns seem to be important, so they are kept in DataFrame (df) as mentioned below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec6cf38",
   "metadata": {},
   "source": [
    "Here, two primary DataFrames were created. One with all the columns and the other with only a part of the columns.\n",
    "\n",
    "We will proceed with all columns for better analysis (df).\n",
    "<br>But we will also have a DataFrame and we will check that it looks at the whole case more easily (df0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "558c6745",
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       "      <td>3</td>\n",
       "      <td>69</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4022</th>\n",
       "      <td>58</td>\n",
       "      <td>Black</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>T2</td>\n",
       "      <td>N1</td>\n",
       "      <td>IIB</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>2</td>\n",
       "      <td>Regional</td>\n",
       "      <td>44</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4023</th>\n",
       "      <td>46</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>T2</td>\n",
       "      <td>N1</td>\n",
       "      <td>IIB</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>2</td>\n",
       "      <td>Regional</td>\n",
       "      <td>30</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4024 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age   Race Marital Status T Stage  N Stage 6th Stage  \\\n",
       "0      68  White        Married       T1      N1       IIA   \n",
       "1      50  White        Married       T2      N2      IIIA   \n",
       "2      58  White       Divorced       T3      N3      IIIC   \n",
       "3      58  White        Married       T1      N1       IIA   \n",
       "4      47  White        Married       T2      N1       IIB   \n",
       "...   ...    ...            ...      ...     ...       ...   \n",
       "4019   62  Other        Married       T1      N1       IIA   \n",
       "4020   56  White       Divorced       T2      N2      IIIA   \n",
       "4021   68  White        Married       T2      N1       IIB   \n",
       "4022   58  Black       Divorced       T2      N1       IIB   \n",
       "4023   46  White        Married       T2      N1       IIB   \n",
       "\n",
       "                  differentiate Grade   A Stage  Tumor Size Estrogen Status  \\\n",
       "0         Poorly differentiated     3  Regional           4        Positive   \n",
       "1     Moderately differentiated     2  Regional          35        Positive   \n",
       "2     Moderately differentiated     2  Regional          63        Positive   \n",
       "3         Poorly differentiated     3  Regional          18        Positive   \n",
       "4         Poorly differentiated     3  Regional          41        Positive   \n",
       "...                         ...   ...       ...         ...             ...   \n",
       "4019  Moderately differentiated     2  Regional           9        Positive   \n",
       "4020  Moderately differentiated     2  Regional          46        Positive   \n",
       "4021  Moderately differentiated     2  Regional          22        Positive   \n",
       "4022  Moderately differentiated     2  Regional          44        Positive   \n",
       "4023  Moderately differentiated     2  Regional          30        Positive   \n",
       "\n",
       "     Progesterone Status  Regional Node Examined  Reginol Node Positive  \\\n",
       "0               Positive                      24                      1   \n",
       "1               Positive                      14                      5   \n",
       "2               Positive                      14                      7   \n",
       "3               Positive                       2                      1   \n",
       "4               Positive                       3                      1   \n",
       "...                  ...                     ...                    ...   \n",
       "4019            Positive                       1                      1   \n",
       "4020            Positive                      14                      8   \n",
       "4021            Negative                      11                      3   \n",
       "4022            Positive                      11                      1   \n",
       "4023            Positive                       7                      2   \n",
       "\n",
       "      Survival Months Status  \n",
       "0                  60  Alive  \n",
       "1                  62  Alive  \n",
       "2                  75  Alive  \n",
       "3                  84  Alive  \n",
       "4                  50  Alive  \n",
       "...               ...    ...  \n",
       "4019               49  Alive  \n",
       "4020               69  Alive  \n",
       "4021               69  Alive  \n",
       "4022               72  Alive  \n",
       "4023              100  Alive  \n",
       "\n",
       "[4024 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame (data) # DataFrame with all the columns\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2b8c07d",
   "metadata": {},
   "source": [
    "But if we want to look at the dataset more simply, we can keep only these columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b225c9be",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Race</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>differentiate</th>\n",
       "      <th>Survival Months</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>Poorly differentiated</td>\n",
       "      <td>60</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
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       "      <td>62</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58</td>\n",
       "      <td>White</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>75</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>Poorly differentiated</td>\n",
       "      <td>84</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>Poorly differentiated</td>\n",
       "      <td>50</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4019</th>\n",
       "      <td>62</td>\n",
       "      <td>Other</td>\n",
       "      <td>Married</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>49</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4020</th>\n",
       "      <td>56</td>\n",
       "      <td>White</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>69</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4021</th>\n",
       "      <td>68</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>69</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4022</th>\n",
       "      <td>58</td>\n",
       "      <td>Black</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>72</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4023</th>\n",
       "      <td>46</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>100</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4024 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age   Race Marital Status              differentiate  Survival Months  \\\n",
       "0      68  White        Married      Poorly differentiated               60   \n",
       "1      50  White        Married  Moderately differentiated               62   \n",
       "2      58  White       Divorced  Moderately differentiated               75   \n",
       "3      58  White        Married      Poorly differentiated               84   \n",
       "4      47  White        Married      Poorly differentiated               50   \n",
       "...   ...    ...            ...                        ...              ...   \n",
       "4019   62  Other        Married  Moderately differentiated               49   \n",
       "4020   56  White       Divorced  Moderately differentiated               69   \n",
       "4021   68  White        Married  Moderately differentiated               69   \n",
       "4022   58  Black       Divorced  Moderately differentiated               72   \n",
       "4023   46  White        Married  Moderately differentiated              100   \n",
       "\n",
       "     Status  \n",
       "0     Alive  \n",
       "1     Alive  \n",
       "2     Alive  \n",
       "3     Alive  \n",
       "4     Alive  \n",
       "...     ...  \n",
       "4019  Alive  \n",
       "4020  Alive  \n",
       "4021  Alive  \n",
       "4022  Alive  \n",
       "4023  Alive  \n",
       "\n",
       "[4024 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame with some of the columns (\"Status\" is *y* or *target*)\n",
    "df0 = pd.DataFrame (data, columns=[\"Age\", \"Race\", \"Marital Status\", \"differentiate\", \"Survival Months\", \"Status\"])\n",
    "df0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2c86558",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Tumor Size</th>\n",
       "      <th>Regional Node Examined</th>\n",
       "      <th>Reginol Node Positive</th>\n",
       "      <th>Survival Months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4024.000000</td>\n",
       "      <td>4024.000000</td>\n",
       "      <td>4024.000000</td>\n",
       "      <td>4024.000000</td>\n",
       "      <td>4024.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.972167</td>\n",
       "      <td>30.473658</td>\n",
       "      <td>14.357107</td>\n",
       "      <td>4.158052</td>\n",
       "      <td>71.297962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.963134</td>\n",
       "      <td>21.119696</td>\n",
       "      <td>8.099675</td>\n",
       "      <td>5.109331</td>\n",
       "      <td>22.921430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>107.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age   Tumor Size  Regional Node Examined  \\\n",
       "count  4024.000000  4024.000000             4024.000000   \n",
       "mean     53.972167    30.473658               14.357107   \n",
       "std       8.963134    21.119696                8.099675   \n",
       "min      30.000000     1.000000                1.000000   \n",
       "25%      47.000000    16.000000                9.000000   \n",
       "50%      54.000000    25.000000               14.000000   \n",
       "75%      61.000000    38.000000               19.000000   \n",
       "max      69.000000   140.000000               61.000000   \n",
       "\n",
       "       Reginol Node Positive  Survival Months  \n",
       "count            4024.000000      4024.000000  \n",
       "mean                4.158052        71.297962  \n",
       "std                 5.109331        22.921430  \n",
       "min                 1.000000         1.000000  \n",
       "25%                 1.000000        56.000000  \n",
       "50%                 2.000000        73.000000  \n",
       "75%                 5.000000        90.000000  \n",
       "max                46.000000       107.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # describe in the numeric way of the numeric columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9ccfcd",
   "metadata": {},
   "source": [
    "As you can see, there is no **Missing Value** in the *non-alphabetic* columns of the DataFrame!<br>\n",
    "Mean *Age*, *Tumor Size*, and *Survival Months* are so..<br>\n",
    "and the 'min' and 'max' are so.."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6195e39c",
   "metadata": {},
   "source": [
    "And the rest of the non-numeric columns are described as follows;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c86a662",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Race</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>T Stage</th>\n",
       "      <th>N Stage</th>\n",
       "      <th>6th Stage</th>\n",
       "      <th>differentiate</th>\n",
       "      <th>Grade</th>\n",
       "      <th>A Stage</th>\n",
       "      <th>Estrogen Status</th>\n",
       "      <th>Progesterone Status</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4024</td>\n",
       "      <td>4024</td>\n",
       "      <td>4024</td>\n",
       "      <td>4024</td>\n",
       "      <td>4024</td>\n",
       "      <td>4024</td>\n",
       "      <td>4024</td>\n",
       "      <td>4024</td>\n",
       "      <td>4024</td>\n",
       "      <td>4024</td>\n",
       "      <td>4024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>T2</td>\n",
       "      <td>N1</td>\n",
       "      <td>IIA</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>2</td>\n",
       "      <td>Regional</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3413</td>\n",
       "      <td>2643</td>\n",
       "      <td>1786</td>\n",
       "      <td>2732</td>\n",
       "      <td>1305</td>\n",
       "      <td>2351</td>\n",
       "      <td>2351</td>\n",
       "      <td>3932</td>\n",
       "      <td>3755</td>\n",
       "      <td>3326</td>\n",
       "      <td>3408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Race Marital Status T Stage  N Stage 6th Stage  \\\n",
       "count    4024           4024     4024    4024      4024   \n",
       "unique      3              5        4       3         5   \n",
       "top     White        Married       T2      N1       IIA   \n",
       "freq     3413           2643     1786    2732      1305   \n",
       "\n",
       "                    differentiate Grade   A Stage Estrogen Status  \\\n",
       "count                        4024  4024      4024            4024   \n",
       "unique                          4     4         2               2   \n",
       "top     Moderately differentiated     2  Regional        Positive   \n",
       "freq                         2351  2351      3932            3755   \n",
       "\n",
       "       Progesterone Status Status  \n",
       "count                 4024   4024  \n",
       "unique                   2      2  \n",
       "top               Positive  Alive  \n",
       "freq                  3326   3408  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=object) # describe in the alphabetic way of the verbal columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "801611d5",
   "metadata": {},
   "source": [
    "As you can see, there is also no **Missing Value**!<br>\n",
    "Description of this table is.."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f948e2d",
   "metadata": {},
   "source": [
    "**Also, another thing that could be done was that if we saw that some of the columns of the dataset were not describe(), we would create a separate and NEW DataFrame with those columns completely SEPARATELY, then we would describe this newly created one.<br>\n",
    "In this way, the columns that were not included in normal describe were described.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cdf28a4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                       53.972167\n",
       "Tumor Size                30.473658\n",
       "Regional Node Examined    14.357107\n",
       "Reginol Node Positive      4.158052\n",
       "Survival Months           71.297962\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(numeric_only=True) # np.mean(df) : this command has warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d1fc3ae",
   "metadata": {},
   "source": [
    "We can also calculate this but it's not that usefull. So, not important to be considered!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5468e360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                        80.337778\n",
       "Tumor Size                446.041563\n",
       "Regional Node Examined     65.604732\n",
       "Reginol Node Positive      26.105265\n",
       "Survival Months           525.391932\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.var(numeric_only=True) # np.var(df) : this command has warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7aff5f8",
   "metadata": {},
   "source": [
    "We can also calculate this, too. But it's not that usefull again. So, not important to be considered!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb885f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4024, 16)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # (rows, columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c9317fe",
   "metadata": {},
   "source": [
    "If we want to take two columns as an example and compare them, we can:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "229ea3b7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Married</td>\n",
       "      <td>Alive</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Married</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Divorced</td>\n",
       "      <td>Alive</td>\n",
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       "      <th>3</th>\n",
       "      <td>Married</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Married</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4019</th>\n",
       "      <td>Married</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4020</th>\n",
       "      <td>Divorced</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4021</th>\n",
       "      <td>Married</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4022</th>\n",
       "      <td>Divorced</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4023</th>\n",
       "      <td>Married</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4024 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Marital Status Status\n",
       "0           Married  Alive\n",
       "1           Married  Alive\n",
       "2          Divorced  Alive\n",
       "3           Married  Alive\n",
       "4           Married  Alive\n",
       "...             ...    ...\n",
       "4019        Married  Alive\n",
       "4020       Divorced  Alive\n",
       "4021        Married  Alive\n",
       "4022       Divorced  Alive\n",
       "4023        Married  Alive\n",
       "\n",
       "[4024 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame (data, columns=[\"Marital Status\", \"Status\"])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bff7cfd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4019</th>\n",
       "      <td>62</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4020</th>\n",
       "      <td>56</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4021</th>\n",
       "      <td>68</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4022</th>\n",
       "      <td>58</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4023</th>\n",
       "      <td>46</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4024 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Status\n",
       "0      68  Alive\n",
       "1      50  Alive\n",
       "2      58  Alive\n",
       "3      58  Alive\n",
       "4      47  Alive\n",
       "...   ...    ...\n",
       "4019   62  Alive\n",
       "4020   56  Alive\n",
       "4021   68  Alive\n",
       "4022   58  Alive\n",
       "4023   46  Alive\n",
       "\n",
       "[4024 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame (data, columns=[\"Age\", \"Status\"])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c77d8d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Race</th>\n",
       "      <th>Tumor Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>White</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>White</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>White</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>White</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4019</th>\n",
       "      <td>Other</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4020</th>\n",
       "      <td>White</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4021</th>\n",
       "      <td>White</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4022</th>\n",
       "      <td>Black</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4023</th>\n",
       "      <td>White</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4024 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Race  Tumor Size\n",
       "0     White           4\n",
       "1     White          35\n",
       "2     White          63\n",
       "3     White          18\n",
       "4     White          41\n",
       "...     ...         ...\n",
       "4019  Other           9\n",
       "4020  White          46\n",
       "4021  White          22\n",
       "4022  Black          44\n",
       "4023  White          30\n",
       "\n",
       "[4024 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame (data, columns=[\"Race\", \"Tumor Size\"])\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44b75d9d",
   "metadata": {},
   "source": [
    "***\n",
    "***\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e3068d",
   "metadata": {},
   "source": [
    "If we had any Missing Value problem and the x and y axis values were not equal to draw the plot, we should use the following command.<br>\n",
    "But here, since we didn't have any Missing Value, there was no need to use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ff8eec04",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Race</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>T Stage</th>\n",
       "      <th>N Stage</th>\n",
       "      <th>6th Stage</th>\n",
       "      <th>differentiate</th>\n",
       "      <th>Grade</th>\n",
       "      <th>A Stage</th>\n",
       "      <th>Tumor Size</th>\n",
       "      <th>Estrogen Status</th>\n",
       "      <th>Progesterone Status</th>\n",
       "      <th>Regional Node Examined</th>\n",
       "      <th>Reginol Node Positive</th>\n",
       "      <th>Survival Months</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>T1</td>\n",
       "      <td>N1</td>\n",
       "      <td>IIA</td>\n",
       "      <td>Poorly differentiated</td>\n",
       "      <td>3</td>\n",
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       "      <td>4</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>T2</td>\n",
       "      <td>N2</td>\n",
       "      <td>IIIA</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>2</td>\n",
       "      <td>Regional</td>\n",
       "      <td>35</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>62</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58</td>\n",
       "      <td>White</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>T3</td>\n",
       "      <td>N3</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>2</td>\n",
       "      <td>Regional</td>\n",
       "      <td>63</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>75</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>T1</td>\n",
       "      <td>N1</td>\n",
       "      <td>IIA</td>\n",
       "      <td>Poorly differentiated</td>\n",
       "      <td>3</td>\n",
       "      <td>Regional</td>\n",
       "      <td>18</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>T2</td>\n",
       "      <td>N1</td>\n",
       "      <td>IIB</td>\n",
       "      <td>Poorly differentiated</td>\n",
       "      <td>3</td>\n",
       "      <td>Regional</td>\n",
       "      <td>41</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4019</th>\n",
       "      <td>62</td>\n",
       "      <td>Other</td>\n",
       "      <td>Married</td>\n",
       "      <td>T1</td>\n",
       "      <td>N1</td>\n",
       "      <td>IIA</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>2</td>\n",
       "      <td>Regional</td>\n",
       "      <td>9</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4020</th>\n",
       "      <td>56</td>\n",
       "      <td>White</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>T2</td>\n",
       "      <td>N2</td>\n",
       "      <td>IIIA</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>2</td>\n",
       "      <td>Regional</td>\n",
       "      <td>46</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>69</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4021</th>\n",
       "      <td>68</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>T2</td>\n",
       "      <td>N1</td>\n",
       "      <td>IIB</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>2</td>\n",
       "      <td>Regional</td>\n",
       "      <td>22</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Negative</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>69</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4022</th>\n",
       "      <td>58</td>\n",
       "      <td>Black</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>T2</td>\n",
       "      <td>N1</td>\n",
       "      <td>IIB</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>2</td>\n",
       "      <td>Regional</td>\n",
       "      <td>44</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4023</th>\n",
       "      <td>46</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>T2</td>\n",
       "      <td>N1</td>\n",
       "      <td>IIB</td>\n",
       "      <td>Moderately differentiated</td>\n",
       "      <td>2</td>\n",
       "      <td>Regional</td>\n",
       "      <td>30</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4024 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age   Race Marital Status T Stage  N Stage 6th Stage  \\\n",
       "0      68  White        Married       T1      N1       IIA   \n",
       "1      50  White        Married       T2      N2      IIIA   \n",
       "2      58  White       Divorced       T3      N3      IIIC   \n",
       "3      58  White        Married       T1      N1       IIA   \n",
       "4      47  White        Married       T2      N1       IIB   \n",
       "...   ...    ...            ...      ...     ...       ...   \n",
       "4019   62  Other        Married       T1      N1       IIA   \n",
       "4020   56  White       Divorced       T2      N2      IIIA   \n",
       "4021   68  White        Married       T2      N1       IIB   \n",
       "4022   58  Black       Divorced       T2      N1       IIB   \n",
       "4023   46  White        Married       T2      N1       IIB   \n",
       "\n",
       "                  differentiate Grade   A Stage  Tumor Size Estrogen Status  \\\n",
       "0         Poorly differentiated     3  Regional           4        Positive   \n",
       "1     Moderately differentiated     2  Regional          35        Positive   \n",
       "2     Moderately differentiated     2  Regional          63        Positive   \n",
       "3         Poorly differentiated     3  Regional          18        Positive   \n",
       "4         Poorly differentiated     3  Regional          41        Positive   \n",
       "...                         ...   ...       ...         ...             ...   \n",
       "4019  Moderately differentiated     2  Regional           9        Positive   \n",
       "4020  Moderately differentiated     2  Regional          46        Positive   \n",
       "4021  Moderately differentiated     2  Regional          22        Positive   \n",
       "4022  Moderately differentiated     2  Regional          44        Positive   \n",
       "4023  Moderately differentiated     2  Regional          30        Positive   \n",
       "\n",
       "     Progesterone Status  Regional Node Examined  Reginol Node Positive  \\\n",
       "0               Positive                      24                      1   \n",
       "1               Positive                      14                      5   \n",
       "2               Positive                      14                      7   \n",
       "3               Positive                       2                      1   \n",
       "4               Positive                       3                      1   \n",
       "...                  ...                     ...                    ...   \n",
       "4019            Positive                       1                      1   \n",
       "4020            Positive                      14                      8   \n",
       "4021            Negative                      11                      3   \n",
       "4022            Positive                      11                      1   \n",
       "4023            Positive                       7                      2   \n",
       "\n",
       "      Survival Months Status  \n",
       "0                  60  Alive  \n",
       "1                  62  Alive  \n",
       "2                  75  Alive  \n",
       "3                  84  Alive  \n",
       "4                  50  Alive  \n",
       "...               ...    ...  \n",
       "4019               49  Alive  \n",
       "4020               69  Alive  \n",
       "4021               69  Alive  \n",
       "4022               72  Alive  \n",
       "4023              100  Alive  \n",
       "\n",
       "[4024 rows x 16 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df.dropna()\n",
    "df_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c6bff2",
   "metadata": {},
   "source": [
    "Now we want to use plots to plot this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "6da5589c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.scatter(df_new['Age'], df_new['Tumor Size'], s=50, c='rebeccapurple', alpha=0.5, marker=r'$\\clubsuit$', label='luck')\n",
    "\n",
    "plt.legend (['Age'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Age', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels \n",
    "plt.ylabel('Tumor Size', fontsize=15)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/1-Age&Tumor_Size.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "0393db10",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.scatter(df_new['Tumor Size'], df_new['Status'], s=30, c='rebeccapurple', alpha=0.5, marker=r'$\\clubsuit$', label='luck')\n",
    "\n",
    "plt.legend (['Tumor Size'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Tumor Size', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "plt.ylabel('Status', fontsize=15)\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/2-Tumor_Size&Status.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "14e336e0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.scatter(df_new['6th Stage'], df_new['N Stage'], s=200, c='rebeccapurple', alpha=0.5, marker=r'$\\clubsuit$', label='luck')\n",
    "\n",
    "plt.legend (['6th Stage'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('6th Stage', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "plt.ylabel('N Stage', fontsize=15)\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/3-6th_Stage&N_Stage.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "48a02763",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XVxEbG5upmNRITU0VFStWNBi/evXqZiXvs2fPNuizWLFiJl12Y833XsoZeBcOomxQoUIFDBo0CH/99RdOnTqFypUrW3yMWbNmwcEhc58Tfv78Of73v/8ZlI8dOxbVqlXLdEwtWrRA27ZtDco3bNiQ6T4zQ6PRYPjw4Qbl58+fN6n92x/kAoDChQubHde7QulhQNZ+mEd2K1WqFLp162ZQbupxlZHZs2dnqt0HH3yAvHnzGpRfunTJpPZKT4xcv349nJycMhWTGn/88QeCg4MNyhcsWGDWw2s+//xzg4cJRUZG4rfffst0n4B5772UczCBJsoGr169gkajMfuWcEpKly6N9957L9Ptd+/ejaSkpHRlGo0GX3zxhZmRAd7e3gZlp06dMrtftWSJvJ+fn0ltZUnDjRs3zI7pXSG78woA5M+fP4sjyXrmHFfGtGzZEnXq1MlU23z58qF27doG5devX8+w7ZMnT3DixAmD8gYNGqBdu3aZiketbdu2GZRVqVIF77//vln9Ojk5oUePHgbl5rwfmfveSzkHE2iibPDkyRNs3rwZnTt3hru7O06ePGnR/jt16gR7e/tMt5fdgqxx48ZwcXExJywAQKtWrQzKbty4ofh4amspXbq0QdmDBw/w8uXLDNtWqFDBoGzx4sV4/vy5RWLL6WS3dgNg9Ud32wLZcWVKopoRWWKuRo0aNQzKZH9Jedvhw4el92keNWqUWfGYSqfT4ciRIwblvXv3tkj/svcjc24tZ+57L+Uc/BsDUSa0bdvWpLMviYmJePHiBUJDQxEQEICHDx8a1AkMDETbtm3x7bffYubMmRaJr379+ma1P3funEFZs2bNzOrztYoVK8LBwSHd2XchBC5fvmxWkpCSkoKbN2/i3r17iI2NRWxsLOLj46X3wjUmOjo6wzOlbdu2xa+//pqu7OHDh2jatCmWLl36rz8DpZRAKCXWtiw+Ph5Xr15FWFgYYmNjERcXh5cvXyo+/EN2qYHSPcjVaNSokVntZZcYvXjxIsN2Sg986tixo1nxmCooKEi6fpZ6P3JzczMoe32v6ly5cqnuz9z3Xso5mEATZUKrVq3w7bffqm537do1rFq1CqtXr06X2Ol0OsyaNQuJiYmYP3++2fHJzjaZ6vHjx9JrWM3p821FihQxGOPZs2eq+7l16xa2bduG/fv348aNGxZ5mlhMTEyGZ9o//vhjTJkyBfHx8enK79y5gy5duqBq1aro27cvunXrhvr16//rzkjJrrcFkOV/ZcgMIQROnz6Nbdu24ejRowgODjb7SZOWmLe51487OzsblJnyIJ9r164ZlJUqVUr6VxhrkD1wCrDc+1HRokUNyrRaLaKjo1GiRAnV/VnyfZJsGy/hIMpCtWvXxsqVK3HmzBnpEwcXLFhg9Ol2pipUqFCm2yo9nveTTz6BRqOxyD9Zgm7Kn5Nfu337Njp37ozq1atj1qxZuHLlisUexWtKslO0aFGjH+i6ffs2Zs+ejUaNGqFo0aLo2rUr5s6dixMnThhcW/4ukiVrgGlnPLPT0aNHUaNGDbRq1Qo//fQT7t+/b5HHtFsigTbnexoA7OwMf9yb8tcZ2V/NatWqZVYsaii9H7m5uVnkvahmzZrS/tW8H73J3H2inIMJNFE2aNy4Mfbt2weNRmPw2qefforExESz+n/7k+VqPH782KyxM8vUP3P/+OOPqFOnjvS6SEsw9YOdX3zxBT7//PMM67148QJ//PEHvv32W7Rp0waFCxdGx44dsXr1aov8ad8WlS1bVlpuq2egU1NTMWTIEHTq1Am3bt2yeP9qLyOSya67OkRGRhqUZWWSaOvvR28z572XchYm0ETZpGXLlujTp49B+dOnT/Hzzz+b1Xdmrt17TekOCtZmypnZZcuW4bPPPkNycnIWRJSxJUuWYOvWrShTpozJbRITE/HXX39h1KhRKFWqFEaOHInQ0FArRpn1ypcvLy0PDg62uTPwWq0WH3/8MTZt2pTdodgk2S/zWZlA2/L7kYw5772UszCBJspGAwcOlJZv3749iyP5h7VurWeuixcv4ssvv1R8vUGDBvjyyy+xdetWnDp1CsHBwYiMjMTLly+h1Woh0h4cle6fJXz88ce4e/cuVq5ciQYNGqhqm5ycjDVr1qBatWpYt26dReKxBVWrVoWjo6NBeUpKCq5cuZINESlbunQpdu3aJX0tV65c6NChA6ZPn469e/fiwoULCAsLQ1RUFBITE6XHlI+PTxbPIOvJ/nJmLbb6fkTEDxESZSPZLZSAtE++JycnZ8ttv5QeTDB27NhMfajGVEpr8dpnn30GnU5nUN6hQwcsXLgQ7u7uqsaz5FnsvHnzYvTo0Rg9ejRCQkJw7Ngx+Pr64uTJk9JrSN+WmJiITz75BI8fP87Uh1NtjYODA+rWrQt/f3+D1/z9/c2+o4SlPH36VPHON2PHjsXkyZOlt6Uzxlb+OmIpefPmNfiwbFZeiiN7P8qXLx8mT55s1XErVqxo1f4p52MCTZSNnJ2dUaBAAYMfUImJiQgLC0OlSpWyPCbZhxsBoFu3bll266q3BQYGSh9G0bdvX2zbtk36AamMZPZDQhlxdXXFsGHDMGzYMABAaGgoTp48iRMnTuCPP/5AeHi4Yttp06ahadOmaN++vVViy0pt2rSRJtAnTpzAZ599lg0RGdqxYwdiY2MNylesWJHpGK11XGWXYsWKGbw/ZeW1+7L3o8TERHz11VfSv3IQZRVewkGUzZTuWJBdD+VQun71/v37WRzJP37//XeDMmdnZ/z000+ZSp4B+YejrKFcuXLo378/1qxZg7CwMJw/fx7e3t7SW9sJIfDNN99kSVzWpvSUuP3792fZ2mdEdly1bt3arATfVuZmKbL3g6x86qZsfCEEQkJCsiwGIhkm0ETZTOnWXll5neGbKlasKL2Pr9IDFbKC7Eyml5eXWR9munjxohkRZV7jxo2xadMmnD17Vnp2zc/P7534UGHz5s1Rrlw5g/Lk5GSb+cCe7LgaNGiQWX1m13FlLbLHhz969MikS5MsQek2c9n5fkQEMIEmylYvXrxQfHS0uQ9OyCwHBwd4enoalB88eNBiH7xT68mTJwZltWvXNqvPs2fPmtXeXI0aNcLixYulr50+fdpoW6Wz7tm1PzL29vb6y1jetmrVqmx/KmFSUpL0l9ecflxZWuPGjaXlf/75Z5aMX79+felnQWR/PSDKSkygibLRiRMnpOV2dnZW/cBeRrp06WJQ9uTJExw9ejQbopFfziJ7NLGpEhMTsXv3bnNCsog+ffpIb3sl+4XhTQUKFJCWm3v/cEsbPXo0nJycDMrv37+f7ZeqKF0iZc5xdeHCBdy+fTvT7W1Rp06dpH8N++mnn7Jk/Lx586Jt27YG5YcOHcq2y9yIACbQRNlq8+bN0vKmTZtm6wdk+vXrJz3LOXXq1GyIBsifP79BmTk/PLds2WITH/bKmzcvihUrZlCe0T1oZUkpkH3XzSspXrw4xo8fL31tyZIlGZ5pN9fChQsVLzWQHVOAeWu4ZMmSTLe1VSVKlEC7du0Myv/++2/4+vpmSQz9+/c3KIuPj8d///vfLBmfSIYJNFE28fHxwa+//ip9zcvLK4ujSc/V1VUag7+/P5YtW5bl8RQvXtygLLPJ1+PHjzFlyhRzQ7KI1NRUacKW0YNZcuXKhaJFixqUW+MpeuaaNGkSqlevblCu0+nQp08fq5yxTUxMxPDhw/HVV19Jb30IpD0xTnZpQGaPq6NHj2LHjh2ZamvrlD5UOWzYMIM7dFjDhx9+iFKlShmUL1u2jNdCU7ZhAk2UDc6ePYuePXtKr1l1dnY2+4NMljBnzhzpnSLGjx9v0esPQ0NDM3xcb/369Q3KDh8+jJs3b6oa69WrV+jfv79ZZxljYmJw/PjxTLd/0759+6T3Da5WrVqGbT08PAzKDhw4YImwLCpPnjz4+eefpX9Refz4MVq2bIlTp05ZbLxz587B09MT69evz7BuvXr1DMp+/PFH1U+hCwkJwZAhQ2zqGnRL6t69u/R78P79++jfv7/Vny7p6OiIGTNmGJQnJSXBy8vLoncIkn2wlEiGCTRRFrp69SpGjx6Nli1bKj6MYObMmdn2AcI31axZE19//bVBuVarRffu3TF79mzFs3umuHz5MgYOHIhKlSohKCjIaN1OnTpJ4+jbt6/J96SNjo7Ge++9Z3byGxMTg/bt26N+/frYtm1bpj8Md/v2bYwZM8agvEKFCmjYsGGG7WUf7rpy5QqmTZtmc09va9iwoWJC+/TpU7Rt2xYTJ05UvCONKW7duoVBgwahefPmJt9mTXZcBQcH45NPPjH52L527RpatmyJiIgIVfHmJBqNBqtWrZL+Qv3bb7+hc+fOGf4SLBMTE4M5c+ZgxYoVGdb95JNP0LJlS4Pyx48fw9PTE7/99pvq8V/TarU4cOAA2rRpYzMP+SHbxwepEGXCyZMnMWfOnAzrvXr1CrGxsXj48CEuX76c4a2fvLy8pElVdpk5cybOnTtn8HhinU6HadOmYcuWLZgwYQI+/PDDDD989erVKwQEBOD333/Hnj17VF1u0Lp1a9SqVQvXr19PV3716lXUq1cPy5cvx/vvvy/9sFNcXBx++eUXTJs2Ld09ej09PfH333+bHMPbLl++jP79+8PJyQkffPABevfujebNm0svN3lTVFQU1q5di++++076EI9x48aZdAvDQYMG4bvvvjMonz17NlavXo1WrVqhUqVKyJ8/v/R69rFjx6JgwYIZjmMp/fv3R3R0NMaNG2dwplar1eKHH37A2rVrMWTIEPTr1w+NGjXK8B7fz549w5EjR7B161YcPXpU9S90w4YNw/fff2/wV4AtW7YgNDQUS5YsUXzCZXh4OBYvXoxly5YhJSVFX27ucWWrGjVqhFmzZkk//Onr6ws3NzdMmDABQ4cOVbyXPJB21vj48ePYsWMHfv31V8TFxWH69OkZjm9nZ4etW7eicePGePToUbrXoqOj4eXlhTZt2mDixIno0KFDhp8hiYmJwblz5/Drr79i3759ePbsWYYxEL1JI97VvzkRWYCvr6/0E+DW4OXlhV27dknvyqBEKb7g4GC4urpaJK4XL16gY8eORv+0qdFoUKdOHdStWxdFixZFoUKFkJiYiBcvXiAqKgo3b95EUFCQ4plRHx8ftGnTxmgc+/fvR/fu3RVfd3FxQcuWLVGuXDk4Ojri6dOnuH//Pk6ePGnwJ2YnJyf4+/tLr83NKJaQkBCjj/ktX748PDw8UKJECRQuXBj58+dHYmIiHj9+jBs3buDSpUvQarXStk2aNMHp06elZ/pkevbsqXgdfUYseYyo8csvv+CTTz7J8My9k5MT6tWrh8qVK6N48eLIly8fUlNTERcXhwcPHuDmzZsm/RKW0Ty//PJLox/+q1OnDho3boySJUtCp9Ph8ePHuHr1Ki5evGjwi0CdOnUwf/58vPfeewb9mPqj1lrf0zNmzDB4bHnr1q1VfxBwxIgRWLt2rdE6tWrVgru7O0qUKIGCBQsiNjYWz58/x/Xr1xEYGJjuFw4AmD59uvQSDZnAwEB06NDBaMLr6OiIRo0aoUqVKihSpIj+aa8xMTF49uwZAgMDjT6IxZS9yor3XrJtPANNlM2cnZ0xb948fPrpp9n28BRjChYsCB8fH/Tv3x/79++X1hFCIDAwEIGBgVaLw8vLCxMmTMAPP/wgfT0sLAzbt2/PsB9HR0fs3bvXpOuM1cqTJw+eP3+OY8eOGa0j4+7ujj179gCAYoL9ttWrV+POnTsIDg5WHatOpzN5HEv66KOPUKdOHQwfPtzoNew6nQ4XL140+mASpTtpAGm3o5swYQJKly5tdJ7fffed4qPigbTrfGXX2ObLlw9A2l69evUK5cuXxx9//IG7d+8qjvUuWL16NZydnRW/DwHg+vXrBn8tspS6devizJkz+OCDDxQv/UpKSsKpU6csem090duYQBNlkwoVKsDb2xujRo1C6dKlszsco/Lnz499+/Zh9erV+Oabbyx6uzQPDw+ULVvWpLrz58+HEAKLFi3K1FglS5bEnj170Lx580y1B+RPiCxXrhzWrFmT4eUbGQkPD0d4eLiqNpl9ql90dLTJ149bQ1bdR/jatWsZ1pk/f75ZY7x48QJubm5wcXF55xNojUaDhQsXolmzZhgzZozB5RSZkdGlOm9zc3PDxYsX8c0332DlypUGZ7Qzy97eHh06dLBIX/TuYwJNZEUODg5wdnaGs7MzihQpglq1aqF+/fpo3LgxmjRpYpNnnI0ZOXIk+vbtix9//BGrV6/O9ON8a9eujS5dumDAgAHSRwUrsbOzww8//IDWrVtj8uTJJn9YLG/evBg6dChmz55t1oMygLRffIKCgnDw4EEcPHgQZ8+exZo1a1C9enW4uLjkuD0l8wgh8OjRI8TExEgfXf6u6tmzJzp27IgVK1ZgxYoVqj9EaW9vjzZt2uCTTz5Bjx49VI+fL18+LF68GKNHj8bixYvxyy+/IC4uTnU/uXLlQpMmTeDl5YWPP/7Y5k9mkO3gNdBElClCCPj7++P48ePw9/fH3bt3ER4ejvj4eKSmpiJ//vxwcnJCkSJF4ObmhurVq6Nu3bpo06aNRe4yIoTA4cOHceTIEZw5cwYRERF4/vw5hBBwcnJC+fLlUatWLbRv3x5eXl7SxFn2J+Dy5cvr/zxvilevXuH69etwd3eHgwPPSfwbpaam4sqVKwgJCUHbtm3x9OlTgzqy6+3fFTqdDqdOncJff/0Ff39/3Lt3D0+ePEFCQgIcHBzg5OSEwoULw83NDTVq1ECzZs3Qvn17i36I9fWHE0+dOoVLly4hODgYT548wcuXLwGkXVPv5OSEkiVLolq1aqhevToaNGiAli1bGr0UiEgJE2giIjNotVoEBATAw8PD5A//0bvl9TFw+vRplCtXDj179szukIjIyngfaCIiIgsoUqQIQkNDbe4+3ERkeUygiYiILMDOzg46nc6sBwwRUc7ABJqI6B2zd+9eNGjQAB4eHqhRowbat29v8aTup59+wuLFi83qQ6PRID4+3qDc19cXGo0GX3zxRbryQYMGQaPRmHRnDWOWLFmS7jrlGTNmYOLEiWb1SUT/LvzECxHRO+Tx48cYNWoU/P39UaFCBQDApUuXVN8dJDU11eiHIkeNGmVWnBmpVq0a9u3bh/nz5yN37tyIjY3F2bNnTb7loTFLlixBhw4dUKJECQtESkT/RjwDTUSUTZISU3Dxr3sIvR2ZcWUTPXr0CA4ODihatKi+rH79+voE2tXVNd0ZXE9PT/3T6Nq0aYNvvvkG7du3R6dOnTB8+PB0D8wIDg5GqVKlkJKSku6sbdWqVdM98GTjxo36D9ItWrQIDRs2RL169dCoUSPFB5a8rUCBAmjXrp3+4T07duxAr1690iX1d+/eRYcOHVC3bl14eHhg3759+tc0Gg3++9//onHjxqhYsSI2btwIAJg1axYiIiLQu3dveHh4ICAgAAAQERGBbt26oWbNmmjXrh2ioqIAAOfPn9efza9duzZWrVplUvxE9G5jAk1ElE2CLoTj9L5bOLHrBrSplrnEwt3dHU2bNkX58uXRo0cPLFiwQNXDWQICAnD48GEcO3YMQ4cOTfeglk2bNqF///4Gj5sfPHiwPkF9XW/IkCEAgIEDB8Lf3x+XL1/GsmXLMGzYMJNjGTp0KDZs2AAA2LBhA4YOHZru9f79++PDDz9EYGAgdu3ahWHDhiE0NFT/ep48eeDn54c//vgD48aNQ2pqKqZNm4YyZcpg9+7d+runAICfnx82b96MGzduoESJEli9ejUAYN68eZgwYQICAgJw7do19OvXz+T4iejdxQSaiCgbxEUnIvDUA+i0OsRGJcL/6D2L9GtnZ4c9e/bg7Nmz6Ny5M86cOYNatWqZ/IS8gQMH6hPkZs2aISUlBX///TeEENi8ebM+MX6Tt7c3du7cieTkZNy7dw+3b9/Ge++9BwC4fPkyWrdujdq1a2PUqFG4ceMGkpOTTYqlRYsWePDgAY4cOQIHB4d0j1+Pi4tDQECAPiF3c3NDixYtcPr0aX2d/v37AwBq1KgBBwcHPH78WHGs9957D0WKFAEANG3aFPfupe1H27ZtMWfOHMyaNQunT582+0E8RPRuYAJNRJQNhEi7zMCpSF7kym0PlU8zzlD16tUxcuRI7Nu3D02aNMFvv/0GIO3pmFqtVl/v1atX6doVKFAg3deDBw/Gpk2bcPz4cZQoUQK1a9c2GKts2bKoX78+fvvtN2zatAkDBw6Eg4MDkpOT0atXLyxatAjXrl3DyZMnIYQwOYEG0j44OGDAAIPE/fUjDN6+tvvNr/PkyaP/v729vdHbyynV/eKLL3DgwAGULl0aU6ZMwejRo02OnYjeXfwQIRFRNnAukhc9xjTCpePBKO7iDLd6lnmEcHh4OEJCQtC8eXMAQHR0NIKDg1G5cmUAQOXKleHn5wd3d3dcuHABt27dMtqft7c3PDw88OTJE+nZ59deX25x/fp1HDp0CEBacp6SkqJ/xPXy5ctVz+f1ZRt9+/ZNV+7s7AwPDw/9WfF79+7hzJkzWLFiRYZ9Ojs748WLFyaNf+vWLVSrVg2VKlVCuXLlMGXKFNVzIKJ3DxPobODo6IjixYtndxhEZAF58+bFjh07MtU2f8E8aNmjhkXjSU1NxaxZsxAcHIx8+fIhNTUV3t7e8PLyAgDMnTsX3t7eWL9+PerXr49atWoZ7a906dLw9PTEgQMHsHbtWsV6Xl5e+PTTT+Hm5oaaNWsCSEtUZ82ahUaNGqF8+fL44IMPVM+nRIkSmDRpkvS1rVu3YuTIkViyZAk0Gg3WrVunT9aNGTduHIYMGYJ8+fKlu8ZbZvny5fDx8UHu3Llhb2+f7kOVb/vpp5+g0+mwaNEi8CG/RDnfs2fPkJSUJH2Nj/LOBi4uLggLC8vuMIjIAvgob3p9DAQFBeHp06f47LPPkDt37uwOi4jMZCxf4zXQREREREQqMIEmIiIiIlKBCTQRkQXwarh/r9d7z2OA6N+DHyIkIjKDvb09cufOjUePHqF06dKqH5lNOZsQAhEREdBqtUygif5FmEATEZnJzc1N/wEy+vfRarV48OABtFot7O3t+WFSon8BJtBERGbKkycPKleujJ07dyIxMRH58+fP7pAoC+l0Ouh0OsTExMDd3Z0JNNG/ABNoIiILcHJyQvfu3eHr64vo6OjsDoeymIODAxo2bIgWLVpkdyhElAWYQBMRWUjRokXRq1ev7A6DiIisjHfhICIiIiJSgQk0EREREZEKTKCJiIiIiFRgAk1EREREpEK2JNB79+5FgwYN4OHhgRo1aqB9+/bQ6XTZEUo6S5YsyfR9XAcPHowVK1ZYOCIiIiIisjVZnkA/fvwYo0aNwt69exEQEICbN29iwYIFWfL0rtTUVKOvm5NAv6uSElNw8a97CL0dmd2hWExsVAJ2LjoL/z/vZqp9TlkTNfM0d07WWJOcss5A9q+fbK+zMqas2qvI8Fgs/+IQDm++nKn25s7JGu2ttU+yctn6qWlvLmvM39RxLDG+qe+p1ho/q77PzP0Z+W+R5Qn0o0eP4ODggKJFi+rL6tevD41Ggzt37qBr165o2LAh3N3d8eOPP+rraDQazJgxA82bN0fVqlWxfft2/WsDBgyAp6cn6tati/fff1+fBPv6+sLDwwPjxo1D06ZN8euvv2Lbtm1o3Lgx6tWrBw8PD/zxxx8AgFmzZiEiIgK9e/eGh4cHAgICkJKSgkmTJqFRo0bw8PBAv379EBMTAwAIDw9H+/btUbduXXh5eSEy0vZ/yGdG0IVwnN53Cyd23YA2Nfv/SmAJx7dfw/mDd3Bg9UUkvzL+S5VMTlkTNfM0d07WWJOcss5A9q+fbK+zMqas2qs9y/xw41wYDm8MQGJ8sur25s7JGu2ttU+yctn6qWlvLmvM39RxLDG+qe+p1ho/q77PzP0Z+W+R5Qm0u7s7mjZtivLly6NHjx5YsGABwsPDodVq8fHHH+OHH36Av78/zp07h59++gmXLl3St9VoNDhz5gwOHz6MsWPHIjQ0FEDameO///4bgYGBaNGiBWbNmqVvExgYiA8//BDnzp1Dnz590KlTJ5w/fx6XL1/Gvn37MHz4cKSkpGDatGkoU6YMdu/ejYCAAHh4eGDBggUoUKAALly4gICAANSqVQvTp08HAIwbNw6tWrVCYGAgFi9ejBMnTijOedGiRXBxcdH/i4+Pt9LqWlZcdCICTz2ATqtDbFQi/I/ey+6QzPYoOBqXjt+H0AkkxCVj56KzqtrnlDVRM09z52SNNckp6wxk//rJ9jorY8qqvbob8Bg3zocBAkhO0uLHiUdUtTd3TtZob619kpXL1k9Ne3NZY/6mjmOJ8U19T7XW+Fn1fWbuz8h/kyx/kIqdnR327NmDoKAgnDhxAocOHcLcuXNx8uRJXL9+Hf369dPXjYuLw40bN1C/fn0AwPDhwwEAlSpVQosWLXDq1Cl8/PHH2Lp1K7Zs2YKkpCQkJiaiVKlS+j6qVq2a7slQwcHB6N+/P8LCwuDg4IDIyEg8ePAAVapUMYh13759iI2Nxe7duwEAycnJqFy5MgDAx8cHy5Yt08fTvn17xTmPHz8e48eP13/t4uKiet2ygxBpv7Q4FckLoROwewc+cip0AhqNBnny54IQgJ29uknllDVRM09z52SNNckp6wxk//rJ9jorY8qqvRI6HTQawD63HSDSfpaoam/mnKzR3lr7JB1Lsn5q2pvLGvM3dRxLjG/qe6rVxs+y7zPzfkb+qwgb0KlTJzFnzhxRrlw5xToARFhYmP5rLy8vsW3bNnHq1ClRpUoV8fTpUyGEEPv37xcNGjQQQgjh4+Oj//9rlStXFr/++qv+68KFC4urV68KIYSoUKGC/v9CCOHp6SmOHTsmjadw4cIG8Sxfvtyk+ZYtW9akerYgPiZRnNx7Q9y8ECZSU7TZHY5FPHkYI9ZPPS6O/BwgkhJTVLfPKWuiZp7mzskaa5JT1lmI7F8/2V5nZUxZtVfB15+Ied57xZa5J0RCXJLq9ubOyRrtrbVPsnLZ+qlpby5rzN/UcSwxvqnvqdYaP6u+z8z9GfkuMZavZXkCHRYWJk6fPq3/OioqSlStWlXs27dPVKtWTWzevFn/2p07d8Tz58/TAgXErFmzhBBCBAcHi6JFi4qHDx+K3377TdSvX1+kpqaKpKQk0aVLF6MJdJEiRcTly5eFEEJs2bJFANAnzXXq1EkX26xZs0TXrl3Fy5cvhRBCvHz5Uly7dk0IIUSvXr3SxVOgQIF3MoEmIiIi+jcylq9l+SUcqampmDVrFoKDg5EvXz6kpqbC29sbXl5eqFmzJr788kssXLgQWq0WxYsXx9atW/VtHR0d0bx5czx79gzLly9HuXLlULp0afzyyy+oXr06XFxc0KxZMxw5onxt3NKlS9GjRw+ULVtWfy32a+PGjcOQIUOQL18+bNq0CZMmTcLMmTPRuHFj/V1Cvv76a9SqVQtLly7FoEGDsGvXLlStWhUdOnSw3qIRERERkc3QCCFEdgdhCo1Gg7i4OBQoUCC7QzGbi4sLwsLCsjsMIiIiIlJgLF/j1eFERERERCpk+SUcmZVDTpQTERER0TuOZ6CJiIiIiFRgAk1EREREpAITaCIiIiIiFZhAExERERGpwASaiIiIiEgFJtBERERERCowgSYiIiIiUoEJNBERERGRCkygiYiIiIhUYAJNRERERKQCE2giIiIiIhWYQBMRERERqcAEmoiIiIhIBSbQREREREQqMIEmIiIiIlKBCTQRERERkQpMoImIiIiIVGACTURERESkAhNoIiIiIiIVmEATEREREanABJqIiIiISAUm0EREREREKjCBJiIiIiJSgQk0EREREZEKTKCJiIiIiFRgAk1EREREpAITaCIiIiIiFZhAExERERGpwASaiIiIiEgFJtBERERERCowgSYiIiIiUoEJNBERERGRCkygiYiIiIhUYAJNRERERKQCE2giIiIiIhWYQBMRERERqcAEmoiIiIhIBSbQREREREQqMIEmIiIiIlKBCTQRERERkQpMoImIiIiIVGACTURERESkAhNoIiIiIiIVmEATEREREanABJqIiIiISAUm0EREREREKjCBJiIiIiJSgQk0EREREZEKTKCJiIiIiFRgAk1EREREpAITaCIiIiIiFZhAExERERGpwASaiIiIiEgFJtBERERERCowgSYiIiIiUoEJNBERERGRCkygiYiIiIhUYAJNRERERKQCE2giIiIiIhWYQBMRERERqcAEmoiIiIhIBSbQREREREQqMIEmIiIiIlKBCTQRERERkQpMoImIiIiIVGACTURERESkAhNoIiIiIiIVmEATEREREanABJqIiIiISAUm0EREREREKjCBJiIiIiJSgQk0EREREZEKTKCJiIiIiFSwqQR6xowZ+OSTT/Rf+/r6QqPR4MyZM/qyYcOGYdasWejfv7+0j5CQEBQrVszqsb5t06ZN6N27d5aPS0RERERZy6YS6LZt28LHx0f/ta+vLxo3bmxQ1rZtW2zdujU7QvzXiY1KwM5FZ+H/512j9ZISU3Dxr3sIvR1p0XGs1W9WUBO7LE6l9qbWtdaamto+MjwWy784hMObL1usTzV1rbH+atbU1DI18as5JmTrf/tSBCZ03IwlYw9k2F5GaU9Drj/FlA+2Ye03fxqte/b3IIxsuAZfdfk5w/aymGT1lOrK1iriXhTmDNiDHQtPZ9inqXWV1k42f1l7pfFl8cv6VNoTWbksVtk8leIydU5KTB1fKSZZe9kxrbQmph7nSuObu6amHpNq2svmrzRPU9dKzfpdP/cQ41pvwLwhe422VzpOTF1Tc39uWYNNJdBNmjRBREQEwsLCAKQly9OmTYOvry8AIDQ0FI8ePUJiYiI8PT317VauXIkqVaqgZcuWWLduXbo+t2zZgjp16qBu3bro2rUrwsPDAQBNmzbFuXPnAADjx4+Hi4uLvk358uURGhqqb9+4cWPUr18frVu3xrVr1wAAycnJGDlyJKpWrYq2bdvCz8/POouSzY5vv4bzB+/gwOqLSH6Vqlgv6EI4Tu+7hRO7bkCbqrPYONbqNyuoiV0Wp1J7U+taa01Nbb9nmR9unAvD4Y0BSIxPtkifaupaY/3VrKmpZWriV3NMyNZ/3TfHER+dhJvnIhD1ON5oexmlPd0wzQfPI+Lx99FgvIhMUKy7Ze4pQACxT1/hYVCk0faymGT11OzflrknERr0HCd330R8zCujfZpaV2ntZPOXtVcaXxa/rE+lPZGVy2KVzVPNnirFL2Pq+EoxydrLjmmlNTH1OFca39w1NfWYVNNeNn+leZq6VmrWb903x5H0MhUhVyPxLCxWsb3ScWLqmpr7c8sabCqBdnR0RNOmTeHj44OkpCSEhYWhS5cuePDgAZKTk+Hj44PmzZsjd+7c+jaBgYGYO3cuzpw5g1OnTiEqKkr/2rVr1/DVV1/h8OHDCAwMRLNmzTBixAgAQIcOHfDnn2m/Cfn6+qJs2bIICgrCrVu3kDdvXpQrVw5nzpzBjh07cPLkSVy6dAlz5szRXzqyevVqBAcH4/r16zh48CD8/f0V57Vo0SK4uLjo/8XHxyvWtSWPgqNx6fh9CJ1AQlwydi46K60XF52IwFMPoNPqEBuVCP+j9ywyjrX6zQpqYpfFqdTe1LrWWlNT298NeIwb58MAASQnafHjxCMWWStT61pj/dWsqallauap5piQrb/v7ut48eyfH1wz++1SbC+jtKd+h+7gyYMXaZUEMGfAHmndX+adhC5V6PubO2CvYntZTLJ6avYv8NQDhFx/BgDQagUWDN+v2KepdZXWTjZ/WXul8WXxy/pU2hNZuSxW2TzV7KlS/DKmjq8Uk6y97JhWWhNTj3Ol8c1dU1OPSTXtZfNXmqepa6Vm/Q7/HICE2H8S7Bl9d6o6TkxdU3N/blmLQ3YH8La2bdvC19cX5cqVQ+PGjQEADRs2hJ+fn/7yjTf5+vqia9euKFmyJABgxIgR2LlzJwDAx8cH77//PsqWLQsAGD16NObMmQMhBDp06ICpU6di1KhRyJUrFz788EP89ddf0Gg06NChAwBg//79uHLlij4OAHj27Jk+mff29kauXLmQK1cuDBgwAKdPp/9zy2vjx4/H+PHj9V+/ebbblgmdgEajQZ78uSAEYGcv/31LCECj0cCpSF4InYCdyl/LlMaxVr9ZQU3ssjiV2pta12pramJ7odNBowHsc9sBArAzMpCqtTJ1fCusv5o1NbVMzTxVHROS9X/7rI1Go1FsL18n+Z5qU9PKhfinX+n4KfKzRvL2hjHJ6qnZP502rb3GDoAANHYaxT5Nrat4TCis/9vtFeckPf4M+1TaE3ldw1hl81S1pwrxy48f08ZXiknpmHiTUpxK7WWUxzdvTU09JtW0l89fPk9T10rN+r39Pa32ODF1Tc39uWU1wsacPn1aVKxYUUyfPl2sXr1aCCHETz/9JGbOnClcXV3F2bNnhY+Pj2jQoIEQQoglS5aI4cOH69tfvnxZFC1aVAghxNKlS9O9Fh0dLfLkySN0Op1ISkoShQsXFmvXrhWTJ08WAQEBonv37qJ79+5i7969QgghJk6cKKZOnSqNs3v37uKXX37Rf7148WLRq1cvk+ZYtmxZFSuSvZ48jBHrpx4XR34OEEmJKYr14mMSxcm9N8TNC2EiNUVrsXGs1W9WUBO7LE6l9qbWtdaamto++PoTMc97r9gy94RIiEuySJ9q6lpj/dWsqallauJXc0zI1v/sgSAxpvk6Ma3XDvH8SZzR9jJKe3rp+D3xZbuNYp73XhET+VKx7u6lZ8WIBqvFZ83XiYf3nhltL4tJVk/N/t3wCxWTu20Vyz//Q8RFJxrt09S6Smsnm7+svdL4svhlfSrtiaxcFqtsnmr2VCl+GVPHV4pJ1l52TCutianHudL45q6pqcekmvay+SvN09S1UrN+Pjuvik8brxFfd/lFPI14odhe6TgxdU3N/bmVWcbyNZtLoJOTk0X+/PmFm5ubuHXrlhBCiKCgIFG1alWRP39+kZycnC6BvnLliihTpox48uSJEEKIMWPG6BPoa9euiTJlyohHjx4JIYSYN2+e6Nq1q36szp07iwoVKohjx44JnU4nqlatKooUKSKio6OFEEKcOHFCVKhQQTx8+FAIIYRWqxX+/v5CCCGWL18uOnXqJFJSUkRCQoJo2LDhO5lAExEREf0bGcvXbO4Sjly5cqF58+YIDAxE1apVAQDVqlVDbGwsWrRogVy5cqWrX7duXUyZMgXNmjVDqVKl0LVrV/1rtWrVwrx58/Cf//wHAFCuXDmsWbNG/3rHjh1x4sQJNG/eHBqNBg0aNMC9e/dQqFAhAECrVq3w3XffwcvLC1qtFikpKejatSs8PT0xYsQIBAYGombNmnBxcUHLli3x4MEDK68OEREREWU3jRCvr0qhrOLi4qK/0wgRERER2R5j+ZqtXIpNRERERJQjMIEmIiIiIlKBCTQRERERkQpMoImIiIiIVGACTURERESkAhNoIiIiIiIVmEATEREREanABJqIiIiISAUm0EREREREKjCBJiIiIiJSgQk0EREREZEKTKCJiIiIiFRgAk1EREREpAITaCIiIiIiFZhAExERERGpwASaiIiIiEgFJtBERERERCowgSYiIiIiUoEJNBERERGRCkygiYiIiIhUYAJNRERERKQCE2giIiIiIhVMTqAjIiJw69Yt/ddarRbz589Hv379sGHDBqsER0RERERkaxxMrThy5EiUL18eK1euBADMnj0bs2bNQqFChbBr1y7kzp0bAwYMsFqgRERERES2wOQz0JcuXULbtm31X69duxZffvkloqKiMGLECH1iTURERET0LjM5gX7+/DlKlSoFALh58yYePXqEwYMHAwB69eqV7vIOIiIiIqJ3lckJdMGCBfH06VMAwMmTJ1GkSBHUqVMHAKDRaJCcnGydCImIiIiIbIjJ10A3atQI//3vf5ErVy4sXboU//nPf/Sv3b9/H2XKlLFKgEREREREtsTkM9CzZ8/G/fv34eXlhSdPnuCbb77Rv7Zv3z40atTIKgESEREREdkSk89Ae3h44MGDBwgKCkKVKlXg7Oysf2306NFwc3OzSoBERERERLZEI4QQ2R3Ev42LiwvCwsKyOwwiIiIiUmAsXzP5DPTJkyczrNOqVSvToyIiIiIiyoFMTqDbtGkDjUZjtI5WqzU7ICIiIiIiW2ZyAu3j42NQFhkZif379+PMmTN8kAoRERER/StY5BroUaNGIU+ePFiyZIkFQnr38RpoIiIiIttmLF8z+TZ2xvTo0QM7duywRFdERERERDbNIgl0dHQ0kpKSLNEVEREREZFNM/ka6IcPHxqUJSUlITAwEJMnT0aTJk0sGhgRERERkS0yOYF2dXWV3oVDCIFq1aphxYoVFg2MiIiIiMgWmZxAb9iwwSCBzpMnD1xdXdGwYUPY2VnkahAiIiIiIptmcgI9ePBgK4ZBRERERJQzmHzauFKlSrhy5Yr0tWvXrqFSpUoWC4qIiIiIyFaZnECHhIQo3mnj1atXePDggcWCIiIiIiKyVaouXFZ6lPf9+/fh5ORkkYCIiIiIiGyZ0WugN2/ejM2bN+u//vTTT+Hs7JyuTmJiIq5cuYLWrVtbJ0IiIiIiIhtiNIFOSEjAs2fPAKSdfY6JiTG4jMPR0RF9+/bFzJkzrRclEREREZGN0AghhCkVK1asiH379sHd3d3aMb3zjD1bnYiIiIiyn7F8zeTb2AUHB1ssICIiIiKinMrkBPpNz549Q2JiokF5+fLlzQ6IiIiIiMiWqUqg58yZg2XLluH58+fS17VarUWCIiIiIiKyVSbfxm7Dhg34/vvvMW7cOAghMGXKFEyePBkuLi5wc3PDunXrrBknEREREZFNMDmBXrlypT5pBoAePXpgzpw5CAoKgpOTEyIjI60WJBERERGRrTA5gb579y6aNGkCO7u0JsnJyQCAvHnzYsKECVizZo11IiQiIiIisiEmJ9AODmmXS2s0Gjg7O6e7rUexYsUQHh5u+eiIiIiIiGyMyQm0m5sbQkNDAQANGzbE2rVrkZKSAq1WizVr1sDV1dVaMRIRERER2QyT78LRpUsXnDx5Et7e3pg8eTI6deqEQoUKwcHBAfHx8diwYYM14yQiIiIisgkmP4nwbf7+/tixYwc0Gg26du2Ktm3bWjq2dxafREhERERk2yzyJMK3NWzYEA0bNsx0UEREREREOZHJ10Db29vjwoUL0tcuXrwIe3t7iwVFRERERGSrTE6gjV3podPpoNFoLBIQEREREZEtMzmBBqCYJF+8eBEFCxa0SEBERERERLbM6DXQS5cuxdKlSwGkJc/du3eHo6NjujqJiYl4+vQpevfubb0oiYiIiIhshNEEukSJEqhVqxYAICQkBJUqVUKhQoXS1XF0dESdOnXw+eefWy1IIiIiIiJbYfJt7Nq2bYtVq1ahevXq1o7pncfb2BERERHZNovcxs7Hx8diARERERER5VRGP0QYHx+Phw8fGpTfuXMH/fr1Q+3atdGpUyccP37cagESEREREdkSo5dwjB07FkePHsWtW7f0ZZGRkahRowaeP3+OQoUKITY2Fvb29jh16hQaNWqUJUHndLyEg4iIiMi2GcvXjJ6BPnv2LPr165eubOnSpXj+/DkWL16MqKgoPHz4EK6urli4cKHlIiYiIiIislFGE+iHDx/C3d09Xdkff/yBcuXK6e+6UaZMGXzxxRc4e/as9aIkIiIiIrIRGV4DXaxYMf3Xr169QmBgIFq3bp2uXo0aNfDs2TPrREhEREREZEOMJtBlypRBSEiI/usLFy5Aq9XC09MzXT2tVov8+fNbJUAiIiIiIltiNIFu1qwZli9fjoSEBADA6tWrodFo0KlTp3T1rl27hrJly1ovSiIiIiIiG2H0PtDffPMNGjRogFKlSsHZ2RkRERHo0aMHqlWrlq7enj170KRJE6sGSkRERERkC4yega5evTpOnz6NHj16oF69epgzZw62b9+ers7jx49RqFAh9O3b16qBEhERERHZApMf5U2Ww/tAExEREdm2TN8HOifYu3cvGjRoAA8PD9SoUQPt27eHTqcDAHTp0gX37t0zq//BgwdjxYoVlgiViIiIiN4BOTqBfvz4MUaNGoW9e/ciICAAN2/exIIFC6DRaACk3bO6cuXK2RxlzpCUmIKLf91D6O3IDMsjw2Ox/ItDOLz5sr4sNioBOxedhf+fd9O1l9WV9ak0vqmxKo0vK1czVsS9KMwZsAc7Fp422qesnpr41cxJtqYySu3N3VNT11SpvYy5x4SafTZ1rrJ6Svts6vyV9i7k+lNM+WAb1n7zp9E+ZfUA4MSe6xjpuQZfdtxotOz2pQhM6LgZS8YeSNc+wPc+Pmu2DjP6/U9fdv3cQ4xrvQHzhuzVl/kfuYNPG6/BlO5b07WX1T22PRAjPddgXNv1GcZ/9vcgjGy4Bl91+dnoWLI5AcD+n/ww0nMNRjZZY3R8U9dJzfoprYmpc9qz/Gxa7I3WpGsv2xPZ+ErHpGxPzI1fFpOsntJYsjVVc5zI2svGl81dqdzUY19NXdmclNZE1qfSe5esX9n8lb7PTV0rpfay9d82/2Ta8eu5xmifSseZrNzUYy+75egE+tGjR3BwcEDRokX1ZfXr19cn0K6urrh27RoAoE2bNvj666/RsmVLVK5cGaNGjdK3CQ8PR/v27VGrVi28//77eP/996VnnVNSUjBp0iQ0atQIHh4e6NevH2JiYqw7ySwSdCEcp/fdwoldN6BN1Rkt37PMDzfOheHwxgAkxicDAI5vv4bzB+/gwOqLSH6Vqm8vqyvrU2l8U2NVGl9WrmasLXNPIjToOU7uvon4mFeKfcrqqYlfzZxkayqj1N7cPTV1TZXam7Mmauaq1N7UucrqKe2zqfNX2rsN03zwPCIefx8NxovIBMU+ZfUAYPt/zwAAEqJTEHztqWLZum+OIz46CTfPRSDqcby+/fqpvkhN1uHR3Rd4HBKjr5v0MhUhVyPxLCwWALBp5gnotMDzsJcIvxulby+ru3PReQBAUpwWdwMeG41/y9xTgABin77Cw6BIxbFkcwKAP9ZdSftPKnD17EPF8U1dJzXrp7Qmps7p6Oa0n1HQAQGnQjLck7fHVzomZXtibvyymGT1lMaSrama40TWXja+bO5K5aYe+2rqyuaktCayPpXeu2T9Ks1f9n1u6loptZet/4mdQfrX/Y/dVexT6TiTlZt67GW3HJ1Au7u7o2nTpihfvjx69OiBBQsWIDw8XLH+vXv34Ovri2vXruHIkSM4d+4cAGDcuHFo27Ytrl+/jh9//BEnT56Utl+wYAEKFCiACxcuICAgALVq1cL06dMzjHPRokVwcXHR/4uPj8+wTVaKi05E4KkH0Gl1iI1KhP/Re4rldwMe48b5MEAAyUla/DjxCB4FR+PS8fsQOoGEuGTsXJT2VEpZXVmfSuObGqvS+LJyNWMFnnqAkOtpDwjSagUWDN8v7VNWT038auYkW1MZpfbm7qmpa6rU3pw1UaJmn02dq6ye0j6bOn+lvfM7dAdPHrxIm4wA5gzYI+1TVg8ANsw4DvHG7xffD94nLfPdfR0vnv2TjMzstwsAsP8nfyQnpr5RvhOHfw5AQuw/Cf6Mvjuxc/FZpCb/0+msj3cDgLTu6sl/Am98umbB8N8U4/9l3knoUv+pPHfAXulYsjkBwMJRv+FNK8Ydlo5v6joB8jWVrZ/Smpg6p++H7UsX+6ovjwKQ74lsfKVjUrYn5sYvi0lWD4B0LNmaqjlOZO1l48vmrrQmph77gOnfJ7I5Ka2JrE+l9y5Zv7L5K32fm7pWSu1l6z+r/268ad3Xx1W9d8jKTT32bIHR29jZOjs7O+zZswdBQUE4ceIEDh06hLlz5+Lvv/9GlSpVDOr369cP9vb2yJs3Lzw8PHDv3j00bdoUPj4+WLZsGQCgfPnyaN++vXS8ffv2ITY2Frt3p21+cnKySZeIjB8/HuPHj9d/7eLikpnpWo0QgEajgVORvBA6ATs75XKh00GjAexz2wEibQ+ETkCj0SBP/lwQArCzT+tAWlfWp8L4psaqPL5huZqxdNq0+DV2AASgsdNI+5TVUxW/qjkZrql0HKX2Zu+paWuq1N6cNVFsr2KfTZ2rrJ7SPps6f6W906amlb/+OLdGo5H2mZpiWA8AtCmGZ+elZW+dxVdurzEo02gMy5TGUqorm6eq+E0c32p9StbP3Jh0JrfXSMdXOiale2Lx+I20N3GsrDhOlNrLY5If+/KxVH6fmLQm8u99+fjmfp+bfpwotZcdv+a8d5hepvwzNkuJd0ynTp3EDz/8IIQQokKFCuLq1atCCCFat24tfv/9d329Xr16iY0bNwohhChcuLAIDw/Xv9a9e3exfPlyIYQQ3t7e+v97enqKY8eOmR1j2bJlze7D0uJjEsXJvTfEzQthIjVFa7Q8+PoTMc97r9gy94RIiEsSQgjx5GGMWD/1uDjyc4BISkzRt5fVlfWpNL6psSqNLytXM9YNv1AxudtWsfzzP0RcdKJin7J6auJXMyfZmsootTd3T01dU6X25qyJmrkqtTd1rrJ6Svts6vyV9u7S8Xviy3YbxTzvvSIm8qVin7J6Qgixff5JMaLBavFpk7Ui5PYTxbKzB4LEmObrxLReO8TzJ3H69oc3XxIjPVeL8R02i0eh0UIIIXx2XhWfNl4jvu7yi3ga8UIIIcRvqy+IEQ1Wi3GtN4iwkOf69rK6P8/2ESMarBajGq0R928+Mhr/7qVnxYgGq8VnzdeJh/eeKY4lm5MQQqwc/4cY0WC1GNFgtQi69EBxfFPXSc36Ka2JqXNaOvZ3few3/ION7olsfKVjUrYn5sYvi0lWT2ks2ZqqOU5k7WXjy+auVG7qsa+mrmxOSmsi61PpvUvWr2z+St/npq6VUnvZ+s8f/qv++A08c0exT6XjTFZu6rGXFYzla0ZvY6d0KYOSVq1amZ3QqxEeHo6QkBA0b94cABAdHY0mTZpg/vz58PLygqurKw4cOIDatWujTZs2mDhxIt5//30AQO/evfH+++9j8ODB6NmzJzw9PTFlyhSEhoaidu3amDt3LsaMGYPBgwfD09MTY8aMwezZs+Hn54edO3ciX758SEhIQHBwMGrVqqUqbt7GjoiIiMi2GcvXjF7C0aZNG/3pe2OESPtzpVarzVyEmZSamopZs2YhODgY+fLlQ2pqKry9veHl5aWqn6VLl2LQoEH43//+h6pVq6J58+YoWLCgQb1JkyZh5syZaNy4sX5dvv76a9UJNBERERHlXEbPQJ84cUJVZ61btzY7oOyQmJiIXLlywcHBAY8ePULDhg1x7Ngxg0eWWwrPQBMRERHZtkyfgc6pCbFad+7cwaBBgyCEQEpKCqZPn2615JmIiIiIcrYcfRcOS6lbty4CAgKyOwwiIiIiygFUJdBRUVHYtm0bbt68icTExHSvaTQarF+/XqElEREREdG7weQE+uHDh2jYsCESEhKQkJCAYsWKISoqClqtFoULF5Z+6I6IiIiI6F1j8pMIJ02ahFq1auHJkycQQuDQoUN4+fIlli9fjjx58uDgwYPWjJOIiIiIyCaYnECfO3cOn376KfLkyQMg7dZ1uXPnxmeffYZhw4bhq6++slqQRERERES2wuQE+smTJyhdujTs7Oxgb2+P2NhY/WutW7fG6dOnrRIgEREREZEtMTmBLlmyJKKiogAArq6u+Pvvv/WvhYSEwMGBN/QgIiIionefyVlvkyZNcPnyZXzwwQfo2bMnZs2ahaSkJOTOnRsLFixAu3btrBknEREREZFNMDmBnjhxIkJCQgAA06ZNw82bNzF9+nQIIdCqVSssWbLESiESEREREdkOo4/yzkhsbCw0Gg2cnJwsGdM7j4/yJiIiIrJtxvI1k6+BnjVrFiIiItKVOTs7w8nJCY8ePcKsWbPMi5KIiIiIKAcwOYGeOXOmYhYeERGBmTNnWiwoIiIiIiJbZXICbexKj/j4eOTKlcsiARERERER2TKjHyIMDAxEQECA/us//vgDQUFB6eokJiZi69atqFy5slUCJCIiIiKyJUYT6F9//VV/aYZGo1G8zjlv3rzYuHGj5aMjIiIiIrIxRu/C8ejRI0REREAIgUaNGmHjxo2oXbt2ujqOjo6oXLky8ubNa/Vg3xW8CwcRERGRbTOWrxk9A126dGmULl0aAODj44P69evzlnVERERE9K9m8oNUWrdubc04iIiIiIhyBKMJ9NChQzF16lRUrFgRQ4cONdqRRqPB+vXrLRocEREREZGtMZpA+/j44PPPPwcAHD9+HBqNRrGusdeIiIiIiN4VRhPo4OBg/f9DQkKsHQsRERERkc0z6UEqr169wpQpU3Dx4kVrx0NEREREZNNMSqDz5MmDxYsX4+XLl9aOh4iIiIjIppn8KO8aNWqku6SDiIiIiOjfyOQEeurUqZgzZw7u3btnzXiIiIiIiGyayfeB3rhxIxISElCjRg3UrVsXpUuXTnfnDY1Gg/3791slSCIiIiIiW2FyAh0YGIjcuXOjbNmyeP78OZ4/f57udd7GjoiIiIj+DUxOoHkbOyIiIiIiFddAExERERGRijPQb3r27BkSExMNysuXL292QEREREREtkxVAj1nzhwsW7bM4Prn17RarUWCIiIiIiKyVSZfwrFhwwZ8//33GDduHIQQmDJlCiZPngwXFxe4ublh3bp11oyTiIiIiMgmmJxAr1y5Up80A0CPHj0wZ84cBAUFwcnJCZGRkVYLkoiIiIjIVpicQN+9exdNmjSBnV1ak+TkZABA3rx5MWHCBKxZs8Y6ERIRERER2RCTE2gHh7TLpTUaDZydnREWFqZ/rVixYggPD7d8dERERERENsbkBNrNzQ2hoaEAgIYNG2Lt2rVISUmBVqvFmjVr4Orqaq0YiYiIiIhshsl34ejSpQtOnjwJb29vTJ48GZ06dUKhQoXg4OCA+Ph4bNiwwZpxEhERERHZBI0QQmSmob+/P3bs2AGNRoOuXbuibdu2lo7tneXi4pLuEhgiIiIisi3G8rVMPUgFSLuMo2HDhpkOioiIiIgoJ+KjvImIiIiIVDB6Brpdu3Ymd6TRaHDs2DGzAyIiIiIismVGE2hfX184OzujXLlyWRUPEREREZFNM5pAV6pUCffv30fBggUxdOhQ9O3bF/nz58+q2IiIiIiIbI7Ra6Dv3r0LHx8fVKpUCWPHjkXp0qUxfPhwnD17NqviIyIiIiKyKRl+iLB169b4+eef8ejRI8yfPx9Xr15FixYtUKNGDSxYsABPnjzJijiJiIiIiGyCyXfhcHZ2xqhRo+Dn54fAwEC0b98eU6ZMwejRo60ZHxERERGRTVF9G7ubN29i8+bN2L17N4QQqFatmjXiIiIiIiKySSY9SCU+Ph7bt2/Hhg0b4Ofnh8qVK2PcuHEYPHgwypQpY+0YiYiIiIhshtEE+uTJk1i/fj327NkDIQR69+6N77//Hq1bt86q+IiIiIiIbIpGCCGUXrSzs4OzszP69u2Ljz76CM7OzkY7q1+/vsUDfBcZe7Y6EREREWU/Y/lahgk0kPaUQWOEENBoNNBqtWaE+e/BBJqIiIjIthnL14xewrFx40arBERERERElFMZTaC9vb2zKg4iIiIiohxB9W3siIiIiIj+zZhAExERERGpwASaiIiIiEgFJtBERERERCowgSYiIiIiUoEJNBERERGRCkygiYiIiIhUYAJNRERERKQCE2giIiIiIhWYQBMRERERqcAEmoiIiIhIBSbQREREREQqMIEmIiIiIlKBCTQRERERkQpMoImIiIiIVGACTURERESkAhNoIiIiIiIVmEATEREREanABJqIiIiISAUm0EREREREKjCBJiIiIiJSwSYTaFdXV1SvXh3u7u5wc3ODl5cXzp49CwD46aefsHjx4myO0NDgwYOxYsWK7A6DiIiIiKzMJhNoANi9ezeuXLmCO3fuYOjQoejSpQv8/PwwatQofPnllxYbJzU11WJ95RRJiSm4+Nc9hN6OzLBubFQCdi46C/8/7xptr6ZPU8dRM76a9kp1ZcyZq1K9yPBYLP/iEA5vvqy6T6X2sjKleZo6JzUxZeUxYSrZmqjpV9Y+5PpTTPlgG9Z+82eGfao5Tk09JgJ87+OzZuswo9//0rWPuBeFOQP2YMfC00bLlOKXlcti8j9yB582XoMp3bemay+LS9b+9qUITOi4GUvGHshwfNlYSvM/tj0QIz3XYFzb9Ubby8b/36LTGOm5BiM912Q4p+vnHmJc6w2YN2Svvuzg+r8x0nMNPm2Wvv3Z34MwsuEafNXlZ6PtleYk23/Znp7Ycx0jPdfgy44b07WXzVU2lppjQramsjmpmb9sHKU5ycpl7ZXmZOqaKM1Jtv5q3ntlccnGVzomZOWyOSl9n8rqyuKX7R0g/z4z971HFpOs/ZGfL2Ok5xp81nItbIXNJtBv8vLywujRo7Fw4ULMmDEDEydOBABUrVoVFy9e1NfbuHEjevbsCQC4e/cuOnTogLp168LDwwP79u3T19NoNPjhhx/Qpk0bTJ48GS9evMDw4cNRp04duLu7Y+jQoQCAlJQUTJo0CY0aNYKHhwf69euHmJgYAEB4eDjat2+PunXrwsvLC5GRmUsSskPQhXCc3ncLJ3bdgDZVZ7Tu8e3XcP7gHRxYfRHJr1IV26vp09Rx1Iyvpr1SXRlz5qpUb88yP9w4F4bDGwOQGJ+sqk+l9rIypXmaOic1MWXlMWEq2Zqo6VfWfsM0HzyPiMffR4PxIjLBaJ9qjlNTj4n1U32RmqzDo7sv8DgkRt9+y9yTCA16jpO7byI+5pVimVL8snJZTJtmnoBOCzwPe4nwu1H69rK4ZO3XfXMc8dFJuHkuAlGP442OLxtLaf47F50HACTFaXE34LFie9n4x7fd0Pdz0fe+0Tmt++Y4kl6mIuRqJJ6FxQIAflt1CQCgSwZu+IW9sSenAAHEPn2Fh0GRiu2V5iTbf9mebv/vGQBAQnQKgq891beXzVU2lppjQmlN356TmvnLxlGak6xc1l5pTqauifKcDNdfzXuvLC7Z+ErHhFKsb89J6ftUVlcWv2zvAPn3mbnvPbKYZO33LvMHAKQmCtz6OwK2IEck0ADQsGFDXL9+PV3Z4MGDsXHjP7+Jbtq0CUOGDAEA9O/fHx9++CECAwOxa9cuDBs2DKGhofq6SUlJ8PX1xYIFC/DFF18gb968uHLlCq5cuYL//ve/AIAFCxagQIECuHDhAgICAlCrVi1Mnz4dADBu3Di0atUKgYGBWLx4MU6cOKEY+6JFi+Di4qL/Fx8fr1jX2uKiExF46gF0Wh1ioxLhf/SeYt1HwdG4dPw+hE4gIS4ZOxedlbZX06ep46gZX017pbqmrpWpc1WqdzfgMW6cDwMEkJykxY8Tj6haP1l7WZnSPE2dk5qYsvKYMJVsTdT0K2vvd+gOnjx4kVZBAHMG7FHsU81xauoxsf8nfyQn/vPDeGa/nQCAwFMPEHL9GQBAqxVYMHy/tEwpflm5LKadi88iNfmfX4RmfbwbAKRxydr77r6OF88S3qi3S3F82VhK8189+U9A/LN3C4b/Jm0vG3+ud/ozjGsm/qU4p8M/ByAh9p9fxGb03YklYw+ma7/0sz8AAL/MOwld6j9BzR2wV9peaU6y/Zft6YYZxyHe+N30+8H7AEA6V9lYao4J2ZrK5qRm/rJxlOYkK5e1V5qTqWuiNCfZ+qt575XFJRtf6ZiQlcvmpPR9Kqsri1+2d4D8+8zc9x5ZTLL2KyYcxpsWjUr/F6zs4pDdAZhKCGFQ5u3tjXr16mHRokUIDQ3F7du38d577yEuLg4BAQEYNmwYAMDNzQ0tWrTA6dOn8dFHHwGA/iwzABw4cAAXL16EnV3a7xPFixcHAOzbtw+xsbHYvTvtAExOTkblypUBAD4+Pli2bBkAoFKlSmjfvr1i7OPHj8f48eP1X7u4uGR6HcwlRNoZeKcieSF0AnZGfoUSOgGNRoM8+XNBCMDO3k7aXk2fpo6janw17RXqmrpWps5VqZ7Q6aDRAPa57QAB2NnJ56S8VpL20jKFNTFxTqpiysJjwvT2hmuipl9Ze21qWtnrtyKNRqPYp7rj1LRjQpvy9ln8tPF12rT2GjsAAtDYaaRlSvHLyqXzNxg/jSwupfVLV8vI+LKxlOZvWl1Ix9epmNPbZUpxmhqTvP3/P6Yk+y/dU6XxJXOVzknFMWHqnFTN38RxFPuUtTcyJ4PxTdxnQL7+at57TZur8jGhtH8ZzwmK8zfv+9wy7z1vx6TmOM92wgZVqFBBXL16NV3ZpEmTRJ8+fcT06dPFhAkT9OWdOnUSu3btEt9++6346quvhBBCvHjxQuTOnVtotVp9vQ8++EBs375dCCEEABEXF6d/rVixYuLBgwcGcXh6eopjx45JYyxcuLAICwvTf+3l5SWWL19u0vzKli1rUj1riY9JFCf33hA3L4SJ1BSt0bpPHsaI9VOPiyM/B4ikxBTF9mr6NHUcNeOraa9UV8acuSrVC77+RMzz3iu2zD0hEuKSVPWp1F5WpjRPU+ekJqasPCZMJVsTNf3K2l86fk982W6jmOe9V8REvjTap5rj1NRj4vDmS2Kk52oxvsNm8Sg0Wt/+hl+omNxtq1j++R8iLjpRsUwpflm5LKbfVl8QIxqsFuNabxBhIc/17WVxydqfPRAkxjRfJ6b12iGeP4kzOr5sLKX5/zzbR4xosFqMarRG3L/5SLG9bPwfRu0XIxqsFiMarBbXzt8zOiefnVfFp43XiK+7/CKeRrwQQgix+usjae09V4vbV0L17XcvPStGNFgtPmu+Tjy890yxvdKcZPsv29Pt80+KEQ1Wi0+brBUht5/o28vmKhtLzTEhW1PZnNTMXzaO0pxk5bL2SnMydU2U5iRbfzXvvbK4ZOMrHROyctmclL5PZXVl8cv2Tgj595m57z2ymGTt10/9S4xosFqMbLha3L0WLrKKsXxNI4Tk1G42c3V1xYEDB1C7dm0AwP79++Ht7Y3Dhw/j8OHDiI+Px8KFCwEAO3fuxKZNm3D9+nUcOnQINWvWBAA0btwYo0aNwpAhQ3Dv3j00btwYly9fRrly5aDRaBAXF4cCBQoAAIYNG4Z8+fJh6dKlsLOzw7Nnz1C8eHHMnj0bfn5+2LlzJ/Lly4eEhAQEBwejVq1a6N27N9zd3TF16lSEhISgTp06mDdvHsaMGZPh/FxcXBAWFpZhPSIiIiLKHsbyNZu9Bvp1glqlShWsX78ef/zxB5o0aWJQz8vLC35+fihdurQ+eQaArVu34pdffoG7uzt69eqFdevWoVy5ctKxFi9ejISEBNSuXRseHh6YMmUKAGDSpEnw8PBA48aNUbduXTRp0gQBAQEAgKVLl8LX1xd169bFxIkT0aFDB8svAhERERHZHJs8A/2u4xloIiIiItuWI89AExERERHZIibQREREREQqMIEmIiIiIlKBCTQRERERkQpMoImIiIiIVGACTURERESkAhNoIiIiIiIVmEATEREREanABJqIiIiISAUm0EREREREKjCBJiIiIiJSgQk0EREREZEKTKCJiIiIiFRgAk1EREREpAITaCIiIiIiFZhAExERERGpwASaiIiIiEgFJtBERERERCowgSYiIiIiUoEJNBERERGRCkygiYiIiIhUYAJNRERERKQCE2giIiIiIhWYQBMRERERqcAEmoiIiIhIBSbQREREREQqMIEmIiIiIlKBCTQRERERkQpMoImIiIiIVGACTURERESkAhNoIiIiIiIVmEATEREREanABJqIiIiISAUm0EREREREKjCBJiIiIiJSgQk0EREREZEKTKCJiIiIiFRgAk1EREREpAITaCIiIiIiFZhAExERERGpwASaiIiIiEgFJtBERERERCowgSYiIiIiUoEJNBERERGRCkygiYiIiIhUYAJNRERERKQCE2giIiIiIhWYQBMRERERqcAEmoiIiIhIBSbQREREREQqMIEmIiIiIlKBCTQRERERkQpMoImIiIiIVGACTURERESkAhNoIiIiIiIVmEATEREREanABJqIiIiISAUm0EREREREKjCBJiIiIiJSgQk0EREREZEKTKCJiIiIiFRgAk1EREREpAITaCIiIiIiFZhAExERERGpwASaiIiIiEgFJtBERERERCowgSYiIiIiUoEJNBERERGRCkygiYiIiIhUYAJNRERERKQCE2giIiIiIhWYQBMRERERqcAEmoiIiIhIBSbQREREREQqMIEmIiIiIlIhxyXQrq6uKFGiBFJSUvRlx48fh0ajwcSJE83uf9q0afjf//6nut3gwYOxYsUKs8cnIiIiItvmkN0BZEb58uXx22+/oVevXgCADRs2wNPTU1UfqampcHBwMCibNWuWxeK0BUmJKbh25iFKlC+IclWLAQBioxJweFMAKtYpgYYdqwAAIsNjsX3BGbjVK4XO3vX07SPuRWHDdB9U8SiJfhNbKLaX1VOqe/tSBFZ//SfKVS+KL5a/b7R9yPWnWDP5L1SsUxyfzO2o2KesTGksWZ+yekrrIhvr+rmHWD3pL5SuVAiTN/Y0uqaysWR1A3zvY+2U4yhe3gkzdvTVt5eVy8rU7ImsvWydlPqVzd//yB1smOaDwqXz47t9/Y3uqay9rAwAzv4ehM2zTsK5eB4s+GOQ4lhKe2rqXE/suY5t884gX+FcWPznEADAkZ8vY+8yfzjk1WDlqU/0fZo6vix2pbnKxj+2PRA7fzgPRyd7LPMZpm8/re8OPLkXCwBY/fcIAMDXXTYj5mlSurLP263Bq7Rq+jIAmNxtC6IeJaYrH9d2DZLi0teV1QOAb3psRWToy3TlEzttQNzz1HRlY9usQXK84fiz+u9E+K2YdOWTuv6M6Cev0pXN+Oh/eHTnhUF7Wbkspu+H7EHw1eeZntPUPtvxNDguXdmE/6xHfJTWoP23vbbh2YP4DOckW2cAmOL1C56HJ6QrXzjyV9y5+CzDeSqVy2JSil/W/qvOGxEbmZKuTGlPZPGP77gOL6N1GY4vWydAfkzL5iTbJ6V+ZXVlxy4AfNF+DRLTpqov/857Nx5cj0pXNmfgLoTejDZoLxtLFpNsnZXiko2ldEzLvv9la6o0//nD9+JeQGSGscrmKWsLAItG78OtC08zXFPZ8ZTdctwZaAAYOnQoNmzYAAB48eIFzp8/j86dOwMArl69ipYtW6J+/fqoWbMm5s2bp283ePBgjBs3Dp07d4a7uzt8fX3h4eGBcePGoWnTpvj111/TnUlOSUnBpEmT0KhRI3h4eKBfv36IiYkBAISHh6N9+/aoW7cuvLy8EBkZmbWLYKKgC+E4ve8WTuy6AW1q2hvX8e3XcP7gHRxYfRHJr9K+SfYs88ONc2E4vDEAifHJ+vZb5p5EaNBznNx9E/ExrxTby+op1V33zXHERyfh5rkIRD2ON9p+wzQfPI+Ix99Hg/EiMkGxT1mZ0liyPmX1lNZFaU5JL1MRcjUSz8Jija6pbCxZ3fVTfZGarMOjuy/wOCRG315WLitTsyey9rJ1UupXNv9NM09ApwWeh71E+N0oo3sqay8rSxv/FCCA2Kev8DAoUnEspT01da7b/3sGAJAQnYLga2lv8HuX+QMAUhMFbv0doe/T1PFlsSvNVTb+zkXnAQBJcVrcDXisb/86eQaA/y0+BQD6H4oAsHbmXwCg/+EJAMu+PKD//+sftgCwce7x/z/GP3WXfv67Yj0A+kQLALb81xcA9D+AAWDVN0cAQJ88A8CSsb/p//86eQaAPcvT5v06qQCA9bOPAYA+UQOAXUtP6/8vK5fF9Dp5BoDfV59XPafXSQEAbP/hJADokz8A+HHyYf3/Xyd1ALB1wQnFOcnWGYA+WQCAzfN8AECfPAPAoY0XFONUKpfFpBS/rP3rRAkAfpp6FIDynsjif508A8DKrw8pji9bJ0B+TMvmJNsnpX5ldWXHLgB98gwAyyccBAB9ogcA+1edAwB9QgsAv648q/+/bCxZTLJ1VopLNpbSMS37/petqdL8XyfAAHBwnZ9irLJ5ytoC0CfPAHDk57T3V9mayo6n7KYRQojsDkINV1dXHDhwAH369MFff/2F33//HQ8ePICjoyPi4+Mxffp05M6dG46OjkhMTESzZs2wdu1aeHp6YvDgwQgMDMTJkydRoEAB+Pr6ol27djh58iRatEg7kzZ48GB4enpizJgx+O6776DT6fDtt98CAGbPno3IyEgsXboUvXr1Qt26dTF9+nTcv38f7u7umDdvHsaMGWMQ86JFi7Bo0SL91/Hx8fpE3JriohOxb+UFRIbHwTFfLtRvXwkVahTDqolHEBf1Cg657eHeugKadKmK5Z8fQnJiKjT2GlR2L4kJP3VD4KkHWDXxKHRaAWiAUhUKYtSC/xi0r9uygkG9mbv74lFwtEFdl6pFsf37M/oY8xTIhWGz20nb+x26gw1T//lGcS6WF+NXvW/QZ/uP6hiUDZjSCr67rxuM9fHXLQz67Dq8vkG9pb5DcDfgscG6fPx1C4Oxirk449dlF/TtHRzt8OXK96VrKotp7JL3DOpW8SiFP9Zd1tezcwBWnR+B/T/5G5R3HlzPoOzTBZ1M3hOnInkN2g+e3tZgnRYcHig9Jpp+UM1g/q1718Sxrdf+ORjtgNUXRkj3tP3HdQzadxvpaVC28sxw/DLvJE7tCcKb2vevbTDWR//XXLqnsvWTzbVGk7LwO3A33Th1WpfH1RMP05Wt/nsEdi4+a9L4DTtVNoh99d8jcPjnAIO5NuhYyWD8+h0r4tKfwQbtP226BroUZMrqv0dgpOeazDV+3b7RGkCXcV2l9p81X4PUpIzr2ucGtMnpy1b/PQKjm60xKIcdDGJyzGePpAQtMqI0J40DIFLlbQzaN14DZDyUcvuGa4C3fjLndcqFxLi3NloyT8U9sYdJManZU6U9kcVvDsXjVDIn2T4ptVe1p5L2ufPaITkx/ULlyqNByqv0k1/99wiMarLGpLHUjC8bS017NeN/3noDXr3MeAKyNc2T38Gg7eq/R+DL9puQ8CL9ASRbU2hgcDxl1VloFxcXhIWFSV/LkZdwAMDAgQOxefNm7Nu3D1u3bsXWrVsBAImJiRg9ejQCAgJgZ2eH0NBQBAQE6C/x+PDDD1GgQAF9P1WrVtUnz2/bt28fYmNjsXv3bgBAcnIyKleuDADw8fHBsmXLAACVKlVC+/btFWMdP348xo8fr//axcXFjJmbTghAo9HAqUheCJ2AnR0gdAIajQZ58ueCEICdvR2ETgeNBrDPbQcIwM4u7Q8TOm1aucYOgAA0dhppe1k9QD7W67Pgr2k0GsX22tS08te/4mk08vFlZa/bvz2WrE9ZvbT4DddFOqcUw/ZKayobS1b37T7T3kEgLZeVqdoTWZ+SdQLkx4Rs/oZ9Qj9/g/U3of3r8WX9SssU9tTUuZo6jprxTW2vdnxdJpNXizFzfFPjV6onLZeU6dQslKSqUDNPc/dEkhPptLJJqRjfCvErLmlWnZYzc59U7alseMkC6HTyyZs7lnz8rDv/aer3j2yeSm1lx7S0ro2e5s2Rl3AAaWeKly1bhjx58sDNzU1fPmXKFJQsWRKXL1/GlStX0KZNG7x69c+fSN5MnmVfv0kIgR9//BEBAQEICAjAjRs38PvvvyvWtzXORfKix5hGqOZZBi171kCDDpVRpnIRfLa4M9zql0G7frXR+/MmcKtfBl/82BUuVYqgSVc3jJqfdg2oR5uKGLvsPRQpVQC1m5fDxDUfSNvL6gGQ1m3frw4Gz2yN3HnsUcq1IKb9r7di+2bdqmHk/A7IXzA3KtYujm9+6SntU1YGQDqWrE9ZPQDSdZGN1XVYfXz0dTPYO2hQuGR+TN/VR3FNZWPJ6vYc2xg9xzWERgMUKJwH03f3AQBpuaxMzZ7I2svWSemYkM3/o/9rgfdHpF3LnadALkzb1VtxT2XtZWUA4D2tDf4zqA4AIFcee3y7s6d0LKU9NXWun8ztgLZ9awAA7HPZYcr27vh8WRc07pL2C7TGDvi/zWnXNZs6vix2ANK5ysYfvbATWnSvCgCws9dg0i9pe7rabwRy59Pov++7fOqednbG/p/3gpb9qxucsWnY1TWt/d8joMn1T3kb75oGdT3fqyCt97q9veM/5R2G1TZo36yPm0FZg07lAQCrzo1AngL/BPvB2Hppdd84vdNqYA38dH4EHPP/8yPr/c88AEBaLotpxelPkL9Qbn1Z7wme6uZ0YQRy5f2nrPPIugZzator7TMFq/1HwCHPP+X/+aSOdE6ydX49vt0/oaLdkFpYdnIYnIr902m/rxtJ41SMXykmWfxKe/rPYYbmfasq7oks/rfHatKjsnR82Tq97tPgmJbNSbJP+vZvr78Je9qsj9s/7d/QqFtFrDwzAvmc/zmAen7ZAD+eHYG8Tv8M1P3z+mntlcaSHRNvrbNs/GZ93KRjGTum39Swq6tJ7xOv57/81HAUKPLPQfHhVw2lscrmKWsLAEt9h6JgiX8qfzy5sXRNZceTLcixl3DUrl0bGzduRPXq1dG0aVPMmDED8fHxCA4ORsuWLfHFF1/g1q1b8PT01F9a8eblGQDg6+uLiRMn4u+//9b3/2ad2bNnw8/PDzt37kS+fPmQkJCA4OBg1KpVC71794a7uzumTp2KkJAQ1KlTR/ESjrcZ+5MAEREREWW/d/ISDgAYMmSIQdm3336LgQMHYuvWrXB1dUW7du0y3f+kSZMwc+ZMNG7cWP8n4K+//hq1atXC0qVLMWjQIOzatQtVq1ZFhw4dMj0OEREREeUcOe4M9LuAZ6CJiIiIbJuxfC3HXgNNRERERJQdmEATEREREanABJqIiIiISAUm0EREREREKjCBJiIiIiJSgQk0EREREZEKTKCJiIiIiFRgAk1EREREpAITaCIiIiIiFZhAExERERGpwASaiIiIiEgFjRBCZHcQ/zaOjo4oXry4xfuNj49HgQIFLN4vWR73KmfgPuUc3KucgfuUM3Cf0jx79gxJSUnS15hAv0NcXFwQFhaW3WGQCbhXOQP3KefgXuUM3KecgfuUMV7CQURERESkAhNoIiIiIiIVmEC/Q8aPH5/dIZCJuFc5A/cp5+Be5Qzcp5yB+5QxXgNNRERERKQCz0ATEREREanABJqIiIiISAUm0O+IO3fuoFmzZqhatSoaNWqEGzduZHdIBODVq1fo3r07qlatCg8PD3Tu3BkhISEAgKdPn6Jz585wc3ND7dq1cfr06ewNlgAAM2fOhEajwbVr1wBwn2xNUlISxowZAzc3N9SqVQsDBgwAwH2yNUeOHEGDBg1Qr1491K5dG5s3bwbAfbIF48aNg6ura7r3OcD43iQkJOCjjz5ClSpVULVqVezduzc7Qrctgt4Jbdu2FRs3bhRCCLFr1y7RpEmT7A2IhBBCJCYmioMHDwqdTieEEGL58uWiY8eOQgghhgwZIqZPny6EEOLChQuifPnyIiUlJbtCJSHExYsXRefOnUX58uXF1atXhRDcJ1vzxRdfiLFjx+q/pyIiIoQQ3CdbotPpRJEiRcSVK1eEEEIEBwcLR0dHERsby32yASdOnBChoaGiQoUK+vc5IYx/D82cOVN4e3sLIYS4f/++KFmypIiKisrq0G0KE+h3wJMnT0TBggX1B7pOpxMlS5YUwcHB2RsYGfD39xeVK1cWQgiRP39+8fTpU/1rDRs2FD4+PtkUGb169Uo0adJE3L9/P90PFu6T7YiPjxcFCxYUcXFxBq9xn2zH6wT6xIkTQgghrly5IsqUKSOSkpK4Tzbk7QTa2N7UrFlTXLhwQf9anz599Cft/q14Ccc7IDQ0FGXKlIGDgwMAQKPRoHz58nj48GE2R0ZvW7ZsGbp164bnz59Dp9Ole6S7q6sr9ywbTZs2DQMGDEDFihX1Zdwn23Lv3j0ULVoUc+bMgaenJ1q2bIljx45xn2yMRqPBzp070bNnT1SoUAEtWrTA5s2bERcXx32yURl9Dz18+BAVKlSQvvZvxQT6HaHRaNJ9LXh3Qpvz3Xff4c6dO5g7dy4A7pktOXfuHPz9/TF69GiD17hPtiMlJQX3799HzZo18ffff2PFihXo168fUlNTuU82JDU1FfPmzcP+/fvx4MEDHDt2DN7e3gD4/WTLMtqbN1/nvjGBfieUK1cOYWFhSE1NBZB2YIeGhqJ8+fLZHBm9tnDhQuzduxeHDh1Cvnz5ULRoUQDAs2fP9HUePHjAPcsmJ06cQFBQECpWrAhXV1eEhYWhU6dOuHDhAgDuk62oUKEC7Ozs0L9/fwCAu7s7KlasiJs3bwLgPtmKgIAAREREoHnz5gCAhg0bokyZMggMDATAfbJFGf1MKl++vP4D8G+/9m/FBPodUKJECdSrVw+//PILAGDPnj1wdXWFq6tr9gZGAIBFixZh+/bt+PPPP1GoUCF9eZ8+fbBy5UoAgL+/Px4/fowWLVpkU5T/bpMmTUJERARCQkIQEhICFxcXHDlyBO+99x73yYYUK1YM7du3x5EjRwCk/RAPDg5GtWrVuE825PVJnVu3bgEA7t69i3v37qFq1arcJxtmbG/efC04OBgnTpzABx98kG2x2oTsu/yaLCkoKEg0adJEuLm5iQYNGohr165ld0gkhAgNDRUARKVKlYS7u7twd3cXjRo1EkII8fjxY9GxY0dRpUoVUbNmTeHr65vN0dJrb364hvtkW+7duydat24tateuLdzd3cXevXuFENwnW7Nt2zZRu3ZtUbduXVGnTh2xfft2IQT3yRaMHj1alC1bVtjb24uSJUvqP9hubG/i4+PFhx9+KCpXrizc3NzErl27sit8m8FHeRMRERERqcBLOIiIiIiIVGACTURERESkAhNoIiIiIiIVmEATEREREanABJqIiIiISAUm0EREREREKjCBJiIyk5+fH3r06IHy5cvD0dERJUuWRNOmTTFhwoRsi2nGjBkGj+a1tMGDB5v0wKbBgwdDo9HAyckJ8fHxBq8/ePAAdnZ20Gg0mDFjhuUDfcO2bduwZMkSg/KQkBBoNBosXLjQquMT0buBCTQRkRkOHjyIZs2aITY2FvPnz8fRo0exdOlSNG/eHP/73/+yLa7hw4fj3Llz2Tb+23LlyoXU1FTpmmzcuBFOTk5ZEodSAk1EpAYTaCIiM8yfPx8VK1bEkSNH0K9fP7Ru3Rr9+vXDwoUL8fDhQ4uNk5iYCDXPvXJxcUGTJk0sNr65cufOje7du2PDhg3pyoUQ2LRpE/r27ZtNkRERqccEmojIDM+fP0exYsXg4OBg8JqdXfq3WKVLFFxdXTF48GD915s2bYJGo8HRo0cxdOhQFC9eHPny5cP//vc/aDQaHDt2zKCPVatWQaPRIDAwEIDhJRzdu3dHhQoVoNPpDNo2btwY9evX13+9cuVKtGrVCiVKlED+/PlRp04dzJ8/HykpKRmuhzFDhw7F2bNncevWLX3ZX3/9hQcPHmDIkCHSNteuXYOXlxcKFy6MPHnywMPDA5s3b05Xx9fXFxqNBtu3b8c333yDMmXKwNnZGR06dEg3Vps2bXDw4EE8ePAAGo1G/+9tixYtQsWKFVGgQAE0bdoU58+fT/f6/fv30a9fP5QpU0Z/yU779u0REBBgxuoQUU7CBJqIyAxNmzaFn58fxo0bBz8/P7OTzDcNHToUuXLlwpYtW7B792706NEDJUqUwMaNGw3qbtq0CfXr10fdunUV+3r48CGOHz+erjwoKAgXLlxIl8Deu3cPH3/8MbZs2YIDBw5g2LBhWLBgAUaOHGnWfDp06IAKFSqkOwu9fv16tGrVCm5ubgb1b926hWbNmuH69etYtmwZ9u7di5o1a2Lw4MGYP3++Qf0pU6bgwYMHWLduHdasWYM7d+6gW7du0Gq1AIAff/wRzZs3R6lSpXDu3Dn9vzetXLkSf/75J5YsWYKtW7fi5cuX6NKlC168eKGv06VLF1y8eBHz58/Hn3/+iVWrVqFevXqIiYkxa32IKAcRRESUaZGRkaJFixYCgAAgcuXKJZo1aybmzZsn4uLi0tUFIKZPn27QR4UKFYS3t7f+640bNwoAYtCgQQZ1x48fL/LmzStiYmL0ZTdu3BAAxPLly/Vl06dPF2++xaekpIiSJUuKjz/+OF1///d//ydy584tIiMjpfPTarUiJSVF/Pzzz8Le3l5ERUXpX/P29hYVKlSQtnuTt7e3yJ8/vz6uUqVKiZSUFPH8+XPh6OgoNm3aJJ49e2awPv369ROOjo7i4cOH6fp77733RL58+fRr4OPjIwCILl26pKu3c+dOAUCcO3dOX9a1a1dpzMHBwQKAqFOnjkhNTdWXX7hwQQAQ27dvF0Kk7TcAsWTJkgznTUTvLp6BJiIyQ9GiRXHq1Cn4+/vj+++/h5eXF27fvo3JkyejTp06iIyMzHTfvXr1MigbOnQoEhMT030Yb+PGjXB0dMTHH3+s2JeDgwMGDBiAvXv36s+marVabNmyBV5eXihatKi+7uXLl/HBBx+gaNGisLe3R65cuTBo0CBotVrcvn070/MBgCFDhuDJkyc4dOgQtm7dity5c6NPnz7SusePH0f79u1Rrly5dOWDBw9GQkKCwdnjDz74IN3Xr8/GP3jwwOT4unbtCnt7e8U+ihQpgsqVK2PBggVYtGgRLl++LL0shojebUygiYgswNPTE19//TV27dqFiIgIfPnllwgJCZFeamCq0qVLG5TVqlULDRs21F/GodVq8csvv8DLywtFihQx2t/QoUPx6tUr7NixAwBw5MgRPHr0KN3lGw8fPkTLli0RHh6OpUuX6n85WLlyJYC0DzOao0KFCmjfvj02bNiADRs2oF+/fsiXL5+07vPnz6VrUKZMGf3rb3rzlwAAcHR0VB1zRn28vga9U6dOmD9/PurXr4/ixYtj3LhxiIuLM3kcIsrZmEATEVlYrly5MH36dABpH4J7zdHREUlJSQb1304EX1O6j/OQIUNw/vx53Lx5E4cPHzZIgpXUrFkTjRo10iffGzduRJkyZfCf//xHX2ffvn14+fIl9u7diwEDBqBFixbw9PRE7ty5M+zfVEOHDsVvv/2GgIAADB06VLFe0aJF8ejRI4PyiIgIAECxYsUsFpMaFSpUwPr16/H48WPcunULX375JX788Ud89dVX2RIPEWU9JtBERGaQJXgAcPPmTQD/nC0F0u628fouGa8dP35c+nARYz766CPkyZMHmzZtwqZNm1C2bNl0SbAxQ4YMgZ+fH06fPo3ff/8d3t7e6S5ZeJ20vz7zCqTdam7t2rWqYjSmR48e6NGjB4YOHWr0Vnvt27fH8ePH9Qnzaz///DPy5cuXqdv0OTo6mn0W/U1Vq1bFt99+izp16uDSpUsW65eIbJvhfZeIiMhknTp1gouLC7p164bq1atDp9MhICAAP/zwAwoUKIDPP/9cX3fgwIGYOnUqpk2bhtatW+PGjRtYsWIFChYsqGrMQoUKoUePHti0aRNiYmIwceJEg1vmKfnoo48wfvx4fPTRR0hKSkp3+zwA6NixI3Lnzo2PPvoI//d//4dXr15h1apViI6OVhWjMXny5MHu3bszrDd9+nQcOHAAbdu2xbRp01CkSBFs3boVBw8exPz581WvGwDUqVMHe/fuxapVq9CgQQPY2dnB09PT5PaBgYEYM2YM+vTpAzc3N+TOnRvHjx9HYGAgJk2apDoeIsqZmEATEZnh22+/xf79+7F48WI8evQISUlJKF26NDp06IDJkyejRo0a+rpfffUVYmNjsWnTJixcuBCNGjXCzp074eXlpXrcIUOGYPv27QBgkAQbU7BgQfTo0QPbtm1D8+bNUbVq1XSvV69eHXv27MG3336Lnj17omjRovj4448xfvx4vPfee6rjNEe1atVw9uxZTJkyBZ999hkSExNRo0YNbNy4UdWc3/T555/j+vXrmDJlCl68eAEhhKoH1JQqVQqVK1fGjz/+iNDQUGg0GlSqVAk//PADxo4dm6mYiCjn0Qg17xxERERERP9yvAaaiIiIiEgFJtBERERERCowgSYiIiIiUoEJNBERERGRCkygiYiIiIhUYAJNRERERKQCE2giIiIiIhWYQBMRERERqcAEmoiIiIhIhf8HuGVqzWy8JX0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.scatter(df_new['Survival Months'], df_new['Marital Status'], s=10, c='rebeccapurple', alpha=0.5, marker=r'$\\clubsuit$', label='luck')\n",
    "\n",
    "plt.legend (['Survival Months'], loc='upper center', bbox_to_anchor=(0.5, 1.05), ncol=3, fancybox=True, shadow=True)\n",
    "\n",
    "plt.title (\"Breast Cancer\", fontsize=53)\n",
    "\n",
    "plt.xlabel('Survival Months', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "plt.ylabel('Marital Status', fontsize=15)\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/4-Survival_Months&Marital_Status.png')\n",
    "\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "32babb3f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.scatter(df_new['Regional Node Examined'], df_new['Reginol Node Positive'], s=30, c='rebeccapurple', alpha=0.5, marker=r'$\\clubsuit$', label='luck')\n",
    "\n",
    "plt.legend (['Regional Node Examined'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Regional Node Examined', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "plt.ylabel('Reginol Node Positive', fontsize=15)\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/5-Regional_Node_Examined&Reginol_Node_Positive.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75df0e6e",
   "metadata": {},
   "source": [
    "As we saw, none of the values needed to be sorted and all the numbers were in order on both axes, but if they needed to be sorted, we could use this command:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cad8099",
   "metadata": {},
   "source": [
    "df_newer = df_new.sort_values(by=['Regional Node Examined'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e1011e7",
   "metadata": {},
   "source": [
    "Well, we can also use the plot bar:\n",
    "<br>We will try on these five graphs that were drawn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "1dd0d2cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tn/xEnHPOOeKAAw4Qo0ePdlKunnfeeeLhhx8Wtm2PKGPbtrjlllvEYYcdJlpbW8U+++wjzjzzTPH888+7KqZ+FFYhhOjv7xff/OY3xTHHHCPGjx8vxo4dK+bMmSM+97nPjUgAUMp///d/i7PPPlvst99+YtSoUWLq1Kli/vz54otf/KJ46qmnRiSMoMJKSGNiCCFEPSy3hBBCCCGEBAH3sBJCCCGEkFhDhZUQQgghhMQaKqyEEEIIISTWUGElhBBCCCGxhgorIYQQQgiJNS1RNyAM2trasO+++0bdDEIIIYQQ4sL27dtHJHAppSkU1n333RebN2+OuhmEEEIIIcSFmTNnun7HLQGEEEIIISTWUGElhBBCCCGxhgorIYQQQgiJNU2xh5UQQgghJM7Ytg0hRNTNqCuGYSCV0rOVUmElhBBCCImIfD6Pzs5OFAqFqJsSCqNGjcKsWbPQ2tpaUzkqrIQQQgghEdHZ2YkJEyZgypQpMAwj6ubUFSEEuru70dnZiYMPPrimslRYCSGEEEIiwLZtFAoFTJkyBS0tzaGSTZkyBT09PbBtu6btAc3RO4QQQgghMWN4z2qpZdW0TFi2FXhd6VQaLeno1b7hc611v270LSeEEEIIITAtE9c+eC26ersC/+0ZE2dgxZkrYqG06pDMVhNCCCGENBiWbaGrtwvTp09HOpUO9ne3dMGyLU8Ka19fH6ZPn47PfOYz+OEPfxhYO/zAOKyEEEIIITFiePk+qE+tyu99992HY445Br/85S/R399fp7OsDSqshBBCCCHE4Y477sBVV12F448/HqtXrwZQDL/1hS98AYcccgg+8pGP4NJLL8XSpUudMjfffDM++MEP4phjjsFpp52Gd955J9A2RaKwfulLX0J7ezsMw8Arr7xS8f2Pf/xjGIaBhx56yDk2ODiIc889FwcffDAOOeQQPPDAA2E2mRBCCCGk4Xn11VfxzjvvYMmSJbjkkktwxx13AABuv/12dHZ24rXXXsPjjz+OF1980Snz05/+FH/5y1/w3HPP4cUXX8S5556LL37xi4G2K5I9rEuXLsWVV16J4447ruK7zZs34/bbb8fChQtHHL/55pvR1taGt956Cxs2bMCHP/xhnHjiiZg8eXJYzSaEEEIIaWjuuOMOXHjhhUin0zj99NPxj//4j3j99dfx5JNP4oILLkBLSwtaWlpw7rnn4tlnnwUA/PrXv8af/vQnzJ8/HwBgWRbS6eD24AIRWVgXLVqEmTNnSr/7whe+gO985ztoa2sbcfz+++/HZZddBgA48MADsWjRIqxZs6bubSWEEEIIaQYKhQLuvfde3H333Whvb8fBBx+MwcFB/OhHP4IQwjWxgRACX/va19DR0YGOjg78z//8Dzo6OgJtW6yiBPzgBz/AEUccgQ996EMV33V2dmL27NnOv9vb29HZ2Rlm8wiJLaq4fXGJvZc0ouhTXkcSNkkac0lqq1+CjsPq9ffWrFmDgw46CM8//7xz7JVXXsFJJ52Er371q7j33nvxqU99CqZp4v7778eMGTMAAGeddRa++93v4uyzz8bee++NQqGAV155BfPmzQvsHGJzdTds2ID/+q//wu9//3tXmVLNXhVwduXKlVi5cqXz77h4uBFSD6rF7Ut67L0oiKJPeR1J2CRpzCWprX5Ip9KYMXEGurbUJw5rtWgBd9xxB84777wRx+bOnYsZM2Zg+vTpmD59Oo444gjMnDkTxxxzDDKZDADgggsuQHd3NxYvXgzDMGCaJi655JLGVFife+45dHV14fDDDwcAbN26FZdccgmuv/56fP7zn8esWbOwceNG7LvvvgCATZs24bTTTpP+1vLly7F8+XLn327bDwhpBFRx+2qNvUeKRNGnvI4kbJI05pLUVj+0pFuw4swVkWW6evjhh6XH169fDwD42Mc+hgkTJiCXy+Gss87CsmXLHJnLL78cl19+eWDtLSc2V/azn/0sPvvZzzr/Xrx4Mb7yla/gjDPOAAAsW7YMq1atwl133YUNGzbg6aefxm233RZVcwmJHY22JBYHouhTXkcSNkkac0lqqy7DsVPjyMknn4xcLodsNouTTz4ZF110UWh1R9Ijl112GdasWYOtW7fi5JNPxvjx4/HWW28py1xxxRW4+OKLcfDBByOVSmHVqlXYe++9Q2oxIYQQQkhz88c//jGyuiNRWFetWoVVq1YpZZ566qkR/x43bhzuv//+OraKEEIIISQ83Lzum4Faz52ZrgghhBBCIsAwDBiGgUKhEHVTQqNQKDjnXQvx3CRBCCGEENLgGIaBSZMm4b333sP+++/f8BZXIQTee+89TJo0iQorIYQQQkhSmDp1KjZt2oQ333wz6qaEwujRozF16tSay1FhJYQQQgiJiFQqhQMPPBC2bStjzDcChmEgldLbjUqFlRBCCCEkYnQVuWaBvUMIIYQQQmINFVZCCCGEEBJrqLASQgghhJBYQ4WVEEIIIYTEGiqshBBCCCEk1lBhJYQQQgghsYZhrQghRIJt2zAts+K4aZmwbTuCFhFCSPNChZUQQsowLRPrN69Hx86OitiItm1DDAiYlom2UW0RtZAQQpoLKqyEEFKGZVvI5DOYOm0qWtIjp0nTMrFt5zZYthVR6wghpPmgwkoIIS6kjFSFhTVlc+s/IYSEDWdeQgghhBASa6iwEkIIIYSQWEOFlRBCCCGExBoqrIQQQgghJNbQ6YoQkghMy3T1zE+n0hXe/H7LRQFjvzYGSRpzpLlJ0liNT0sIIcQF0zJx7YPXoqu3S/r9jIkzsOLMFdIQVDrlooCxXxuDJI050twkbazGoxWEEKLAsi109XZh+vTpSKfSld9t6YJlWxUTq265KGDs18YgSWOONDdJG6vxaAUhhHhAd4kqbktbKhj7tTFI0pgjzU1SxipnQUIIIYQQEmuosBJCCCGEkFhDhZUQQgghhMQaKqyEEEIIISTWUGElhBBCCCGxhgorIYQQQgiJNVRYCSGEEEJIrKHCSgghhBBCYk38I8USQggAW9gwLbPiuGmZsIUdQYsIIYSEBRVWQkjsMS0THZ0d6NjSUZEFyrZtoFCUaRvVFlELCSGE1BMqrISQ2GPZFgYLg5g6bSpaWkZOW6ZpYtvmbbBsK6LWEUIIqTdUWAkhiSGVSlVYWMv/TQghpPHgTE8IIYQQQmINFVZCCCGEEBJruCWAEEJIzZiW6bpvOJ1KoyXNxwshJDg4oxBCCKkJ0zJx7YPXoqu3S/r9jIkzsOLMFVRaCSGBwdmEEEJITVi2ha7eLkyfPh3pVLryuy1dsGyLCishJDA4mxBCCNGCS/+EkLCg0xUhhBBCCIk1kSisX/rSl9De3g7DMPDKK684xy+++GIceuihOProo7Fo0SJ0dHQ43w0ODuLcc8/FwQcfjEMOOQQPPPBABC0nhBBCCCFhE4nCunTpUqxduxazZ88ecfzss8/Gq6++io6ODlx55ZX41Kc+5Xx38803o62tDW+99RYeffRRXHrppdi5c2fYTSeEEEIIISETyeajRYsWSY+fddZZzt8LFy7Epk2bYNs2UqkU7r//ftx1110AgAMPPBCLFi3CmjVrcNFFF4XQYhI1qhA6QH320umG7WmGcD/ZfBZ5My/9rrWlFaNbR4fcouARELCFDdu2Rxy3hQ0BoSzbDGMgbKIYc2FfRz/16Za1bRumZUp/r3zsk/oT9pizheL6i3hd/9jOmt/97ndx2mmnOWkXOzs7R1hk29vb0dnZKS27cuVKrFy50vl3f39/fRtL6kq1EDpA8GF0dMP2NEO4n2w+i4/e8lFs698m/X7q+Kl44vInEq20mpaJnoEe9G3tq0j9ats2CgMF6SQ/XLbRx0DYRDHmwr6OfurzM1+t37weHTs7pONcDAiYlom2UW2aZ0VqIYox19HZgY4t8uuPAmJ1/WM5Y957771YvXo1nn322RHHDcNw/hbC3cKxfPlyLF++3Pn3zJkzg28kCQ1VCB3n+4DD6OiG7WmGcD95M49t/duwz/77SB+A297dhryZT7TCagkLpm1i3OhxSLeUXUfTQsbOwBJyK0gzjIGwiWLMhX0d/dTnZ77K5DOYOm2qvF93blOubJFgiWLMDRYGi9e/pez6mya2bY7X9Y/djHn//ffjG9/4Bh5//HFMnTrVOT5r1ixs3LgR++67LwBg06ZNOO2006JqJomAKJZSdetshmXflnRLxSTXaBiGMeJFefiYF5phDIRNFGMu7Ovopz7dsikjVWFhS9kMIhQVYY+5VEpy/VPxu/6xatHq1avxta99Db/73e8wa9asEd8tW7YMq1atAgBs2LABTz/99Ig9r4QQQgghpDGJRGG97LLLMHPmTGzevBknn3wyDj74YADAeeedh2w2i49//OM4+uijcfTRR6O7uxsAcMUVVyCTyeDggw/GqaeeilWrVmHvvfeOovmEEEIIISREIlmvWrVqlWMtLaVQKLiWGTduHO6///56NosQQgghhMSQWG0JIIQQQgghpBx6BBBCSEzwE/uVEEIaGSqshBASA/zEfiWEkEaHCishhMQAP7FfCSGk0aHCSgghMcJP7FdCCGlU6HRFCCGEEEJiDS2shDQxpmW6pt5jpiZCCCFxgU8jQpoU0zJx7YPXoqu3S/r9jIkzsOLMFVRaCSGERA6fRIQ0KZZtoau3C9OnT0c6la78bksXLNuiwkoIISRy+CQipMnh0j8hhJC4Q6crQgghhBASa6iwEkIIIYSQWEOFlRBCCCGExBoqrIQQQgghJNZQYSWEEEIIIbGGCishhBBCCIk1VFgJIYQQQkisocJKCCGEEEJiDaOFE0KIC0IICCEqjhGiwhY2TMusOG5aJmxhu5ezFeVs93KkPpiWCcu2pN8x4Ur4sLcJIaQM0zKRK+TQ09uDVGrkQpRt2zALplSxIMS0THR0dqBjS4d07KBQlGkb1VZRbv3m9ejYKS8nBoS0HKkPpmXi2gevRVdvl/T7GRNnYMWZK6i0hgh7mhBCyrCEBRs20m1ppNIjlQfDMpBHHpaQW15Ic2PZFgYLg5g6bSpaWkY+Yk3TxLbN26RWO8u2kMlniuXKlCDTMrFtp7wcqQ+WbaGrtwvTp09HOpWu/G5LFyzbosIaIuxpQghxwwAMw6g4Rkg1UqlUhaW0/N/ScoaknE13k6jg0n984F1ACCGEEEJiDV8bCCGhQkcGQgghtcInAyEkNOjIQAghRAc+FQghoUFHBkIIITrwqUAICR0u/RNCwoLbkBoDXiVCCCGENCTchtQ48AoRQgghpCHhNqTGgVeIEEIIIQ0Nl/6TD+OwEkIIIYSQWMPXjTrADd6EEEJIJarnIxCvZ6QtbJiWWXHctEzYwo6gRc1NPEZFA8EN3oQQQkgl1Z6PQHyekaZloqOzAx1bOipS5dq2DRSKMm2j2iJqYfNBrSlguMGbEEIIqUT1fHS+Vzwjw7R4WraFwcIgpk6bipaWkW0xTRPbNm9TWopJ8FBrqhNxWtYghBBC4oLO8zEqi2cqlaqor/zfJByoURFCCCEk1tDiSaiwEhITkuSMQAghUUCLZ/PCpx8hMSBJzgiEEEJI2PDJR0gM8OuMQAghhDQyfPIREiO47E8IIYRUwo0fhBBCCCEk1kSisH7pS19Ce3s7DMPAK6+84hzftm0blixZgjlz5mDu3LlYu3at893g4CDOPfdcHHzwwTjkkEPwwAMPRNF0QkjCGI7dKPswWw0h4aK6H3lPEhWRrD0uXboUV155JY477rgRx6+++mosXLgQjzzyCNatW4elS5fi7bffRktLC26++Wa0tbXhrbfewoYNG/DhD38YJ554IiZPnhzFKRBCEgCz1RASH1T3I8B7kqiJRGFdtGiR9Pjq1auxYcMGAMCCBQswbdo0rF27FosXL8b999+Pu+66CwBw4IEHYtGiRVizZg0uuuiikFpNSH0xbRPZfFa6h9W0TJh2ZYaXamWrlUsSAgK2sIsPtRJsYUNASMtYtoWBwgD2nbqvtG+2v7vdNZSYEALCErAxsj5hCQghr88vQggIu/gZcdyuT51+QqnZtiLrkO1uJdvVvwv9+X7pd+Nbx2PS+EnqRmugOs96nKMujZ67XhVLFYhfPFUhBGxbMufYdt3mAMuykCvkKvrAtExYVvD9Ygu7+Iwo+2nTjt+Yi413R3d3N2zbxr777usca29vR2dnJwCgs7MTs2fPln5XzsqVK7Fy5Urn3/398smRkLiQzWfx2CuPofBqAQaMiu8FBEaJUcjmsxWWB1VZVbkkYVomegZ60Le1T2opLQwUXB/0PQM96NtWW7n+TD9s28ZA3wAqLocAYBdlgsS0TOTNPHp29EjbapqmtK1+6tMNpWZaJtZvXo+OnXLLtRgQUivZrv5dOOCaA5CxMtL6xqTH4J1/fydQpbXaeQZ9jrpk81l07epCV6YLhlF2HwsB5IoyE8dODKS+KJHFUh0+HhecuaPTZe4YlM8dfsjms3jktUdgbjClY6Al0xLoXJ4tZPHu7nfxrvGufAVqd1FmIuIx5mKjsAKQ36Qu36vebpYvX47ly5c7/545c2ZALSSkPuStPDJWBhOnT0Q6LQlrZVno3dKLvJWvqayqXJKwhAXTNjFu9DikW8rO0bSQsTOwRKX1Qbdc1soW/xiDyp3+NoD+EpmAsIQFGzbSE9NIpUdWalgG8oN5aVu16/MRSs2yLWTymaKlTKLobdspt5L15/uRsTIYPXU0jHTZfG8JZLZl0J/vxyRM8n+Cpefhcp71OEdd8nYepjAxZq8x8r55L4O8nez7OElYwoIpTIzba5x0Xs0MyOcOP+StPLJmFhPHT5TOV719wc7leSsPy7YwunW09ByzdjZWz47YKKxTpkwBAGzfvt2xsm7atAmzZs0CAMyaNQsbN24c8d1pp50WTWMJqROpVKpCWQHUL2iqsvVatooKwzAqXmzL/x1kORiSF2mjzn0qqVNidA8MP6HUUoYk65Bd3UpmpI3KB2T5mmTA6J6n7jlqk6q0NAatGBHvGIYBI1U2d9h1vCEht0CLVP3mHe35MWTiY38HsGzZMqxatQoAsG7dOmzdutVxzCr9bsOGDXj66adx1llnRdZWQgghhBASDpFYWC+77DKsWbMGW7duxcknn4zx48fjrbfewk033YQLLrgAc+bMQWtrK+655x5nY/YVV1yBiy++GAcffDBSqRRWrVqFvffeO4rmE0JIrNB1KiKEkKQQySy2atUqx1payrRp0/DYY49Jy4wbNw73339/vZtGCCGJQtepiBBCkgRnMEIISTC6TkWEEJIkOIMR4kLYMRgJ8QOX/glpDISojLvcaA60OnB2I0RC2DEYCSGENDemZSJXyKGn1yUWcyHYWMxJgworIRLCjsFICCEkXoSdec6JxdzmEosZwcZiThpUWAlREHoMRkIIIZETdua5EYQcizkpUGElhBBCCCkh7MxzpDpUWAkJGFsonLUEnbWagWYYA0II2LZd4YBo2zYdREhdEBCwhWTMCRsCHHONDhVWQgLEtEx0dHagY4vcWQsF0FmrwWmGMWBaJnoGetDX2Sc9x8JgoamdQ0jwOGNuq8uYGwh2zA1Hg8kMZqT1uUWRIfWDCishAWLZFgYLg0VnrZYyZy3TxLbNdNZqdJphDFjCgilMjNtrHNLpstivloXMQEa9XCokYXpoICMKLGHBtE2MGz0O6ZayMWdayNhVxpxGfQICRpsBIzVyA6lhGxAQ3BIQMlRYCakDqZTEWStFZ61mohnGgGHIH+Zu0GpF/GIYRoVDUoWDUh3qJNFDhZUQQkgo0GpFCNGFCishhJDQodWKEFILjbU+RQghhBBCGg5aWAkhNcOQRnWEDkmEEFIBFVZCSE0wpFF9oEMSIYS4Q4WVEFITvkMaESl0SCKEEHeosBJCtKg1pBHxDh2SCCFkJDUrrLt378bzzz+PHTt24LTTTsPkyZPr0S5CCCGEEEIA1Kiw/tu//RtuvPFGZDIZGIaBdevWYfLkyTjppJNwyimn4Oqrr65XO0mTo9q/N7z3L+kIISDs4qfiO1tUdWYSolKGDlDRYGNovJYZSk3LhA3FWBWK66i4lLaQ3x+mZcIW7vWZtolsPouWdOWjwLRMmLb7nlm38eplrOrgJ4+8ZVnIFXIVGcZMy4RluW+z0O1X3XJA+PexrgNlf6YfmUJG+t2YUWMwfsz4QNs5TBLmOdMyldns0qm09J6rV51V60uIo6fnHrv11lvxjW98A5deeik+9rGP4fTTT3e+O+OMM/DAAw9QYSV1wbRMrN+8Hh07K3OzA0MT64BIdH520zKRN/Po2dHjeo6mabo+BHOFHHp6K8vatg2zIC9H6kO2kEXXzi68a76LlFF2PYQNo89AtpDFREwc8Z1pmYAArF4LwhAV5SDgev07OjvQsaXy/rBtGyhAem9k81k89spjKLxagFGuWaOoII4So5DNZyvKqsaraqzqYlomuge60bdd7uiXH8i71pfNZ/HIa4/A3GBWbLUQQqAl0yI9x2w+i65dXejKdEnLIVeUmTh2orzcYFdFvwoIIO9ezrItDGQHKrbaCFsAdlEmSHQdKPsz/Tj0G4eiN9cr/d2JbRPxxtffCFRpNS0TOTOHnkw4Y04X0zJx7YPXoqu3y1VmxsQZWHHmisCU1mp1utWXNEdPz731/e9/H8uXL8e3vvWtijfSOXPm4M033wy8cYQAxdzsmXymmJvdxRK0bWey87NbwoING+mJaaTSlQqrYRnID+alTjdO2bbKsoZlIA95OVIf8lYepjAxeuxopFNlTmm2hWxvFnkrX1HOElbRIjsBlRGybQAZyK+/bWGwMFi8P1rKHkimiW2b5fdG3sojY2UwcfrECuc5oGiV7N3S69pWt/GqGqu6WMKCZVtoHdVacx75vJVH1sxi4viJ0rK9ffJzzNvF6zhmrzEw0mUKpCWQeS+DvK0oN6n2chAA0qhUWI2ihV1Wzg+6DpSZQga9uV5M2G+CtFzv1l5kCplAFVZLWLCFjXRKMuZgIC/iMc9ZtoWu3i5Mnz694v53vt/SBcu2AlNYVXWq6kuao6fn3vrrX/+KU089VfrdhAkTsGvXrqDaRIiUlFGZmx0AUnYD5b8wXBxuvPjgyMrSdycypE5pwsMF0byOqVTl/SG7X6TlJC9JnpZZQx5zfvLIy/pHpDycY6qyHz09xHXLRXAf6zpQptPpipeAupOQea4ey/71rDMJjp6en/R77bUX3nvvPel3GzduxNSpUwNrFCGEEEIIIcN4VlhPOukkfOtb38LAwIBzzDAMmKaJH/zgB67WV0IIIYQQQvzg2Xa8YsUKLFiwAO9///vxiU98AoZh4Pvf/z7Wr1+Pzs5OrF69up7tJISQ5iAhHruEEBImni2sBx98MH7/+9/j8MMPx6233gohBO6++27ss88+ePbZZzFr1qx6tpMQQhoax5k1B9g5e8QHuTIZQghpMmranfv+978fjzzyCHK5HLq7uzF58mSMGTOmXm0jhJCmwXHIaYM8SgA8Ou2QxBO3uKKExAHPFtZnnnkGGzduBAC0tbVhxowZjrLa19eHZ555pi4NJISQZsMo+480Po71XBQTT5T+N7wlhBZ20sx4VlgXL16Mo446Co899ljFd6+99hpOPPHEQBtGCCGENAterOe0sJNmpqYAlu3t7TjzzDNx11131ak5hBBCSHMTtoV9ON1p+YeQOFHTHtbbb78dP/nJT3DJJZegs7MT1157bb3aRQghhJA6wrTOJEnUpLCmUin8x3/8B/bff39cc801eOedd3D77bfXq22kATEt0zWFahSZQQiJFQLuYa3iaPBiCC53EtA3iUzrrNGvtrClirdpmbCFXb3KsK3NOudoK87RjuE5aqClHVx99dWYOXMmLrnkEnR1deHLX/5y0O0iDYhpmbj2wWvR1dsl/X7GxBlYceYKKq2kKTHtoYdNHypTTYoymYgZfghm8hmpZc7t4dkMOH0zmKC+SUC602HlMmNmKtJx27atVEo7OjvQsaVDej1QKMq0jWqrKAcBmAVTXk4g8OuoO3ZMy8T6zevRsVN+jmJAxOYc/aCtGZx//vmYNm0ali5dihdffDHINpEGxbItdPV2Yfr06Uin0pXfbemCZVtUWElTYtt2UUmYAHlYqyw8WUrCwBIWBASMtCQHvWFAQMTPMhcSTt+0SfrGbu6+8YMlLAghYBhGhXJtGAaEkPerZVsYLAxi6rSpaGkZ+WwxTRPbNm+Trvo5v9WCSuU9VSYTELpjx7ItZPKZ4jmWPT9Ny8S2nfE5Rz94drqaPXs22tpGauennHIKnn766co3M0IUDC/9l37KFVhC4oIo+6+GgpVOLF6KGy6fmDKsQMgUiWaHfVMfdPo1lUpJP/Wqzy9a52hIztGI7znWimdT1oYNG6THjz76aLzxxhvo6ekJrFGEEBIlpTExK5TMKjExE7kkTAghMSeQtdcJEyZgwoQJQfwUIYREjrMMJrNuGmUykrJcEq4PsnBLSXAWqZWaLPmkKkII52WxFNu2G3L8NCpKhXXFihX4+7//e8yYMQMrVqxQ/pBhGPjXf/3XQBtHCCFJJq5La0mjGcIvORZ72TZlu0yGeMa0TPQM9KCvs086dgqDhcSPnWZBqbBed911WLJkCWbMmIHrrrtO+UNUWAkhhNSDRIZfqhGn/Sm4epck/RyjwBIWTGFi3F7jkE6XOftaFjIDGfZrQlAqrKXm87h4pxLS0Mji7w0dJxGg2MNKIiAB4ZdIPDEM+RadRkFAwBaSbQ/CbpgtJowfREgMUMW1BOisEzZOvNM8Yh8TlRDS3JiWie6BbvRtl297yA/kG+LZ4TmsVTmPPPIIbr75Zvz0pz9FNpsNrEGPPvoo5s+fj3nz5mHu3Ln48Y9/DADYtm0blixZgjlz5mDu3LlYu3ZtYHUSIqP0jXXEpw5vrCPiWrp86KwTHo6VohVAW9mntUyGEEIixBIWLNtC66hWtLW1jfi0jmqFZVsN8exQWljvuOMO/OY3v8GaNWtGHP/4xz+Ohx56yAni+81vfhPPPvssJk2a5KsxQgh89rOfxZNPPokjjzwSGzduxGGHHYZzzjkHV199NRYuXIhHHnkE69atw9KlS/H2229XBAImJAiifGOlo06MUEQJICRI/IRSIwSANIZqIz1PlBbWn//855g8efKIY/feey8efPBBnHDCCfj1r3+Nf//3f8ebb76Jb33rW4E1ateuXQCA3t5eTJkyBW1tbVi9ejUuu+wyAMCCBQswbdo0WllJ3WiWN1ZCSDyoCKUmSRzBOYc0M0rz5KuvvorPfvazI46tXr0aY8eOxS9/+UtMnjwZZ511Fnp6evCb3/wGN9xwg6/GGIaB1atX45xzzsG4ceOwc+dOPPDAA+jr64Nt29h3330d2fb2dnR2dvqqL26YlilNnwbsyQ6VdFT5nm3hvsTqtqF8+DfrsalcQLjGfVTVp3uOvpE5bFXpFtM2kc1npen8GmWPpkAxBmO5g4Vtexg3fpyuNK4HbLiHNVIMHUtYyJv5ivFlWqZSyRFCQNjFT8V3duXYd/sNHWotNyJjmOR4PRjuH9sY2a+e+kbn+g/LhOzopxPfVve+cuZHieGv6hwp9sRUrWhrjPyK3J4BQAjPAU2SEI9WqQHt2LEDs2fPHnHs6aefxvHHHz/C8nriiSdi1apVvhtjmia++c1vYs2aNfjIRz6CdevW4eyzz8bLL79cYdZWde7KlSuxcuVK59/9/f2+21ZvTMvEtQ9ei67eLun3MybOwIozVyRaaTUtEx2dHejY0iFdZkehKNM2qq2inNvy/HDZoJfos/ksMvkMMjszFZ6lwhZAvigjK9e1qwtdg10wymZkgT3lJo6dGFhbVQ5bKmetbD6Lx155DIVXC9K2jhKjkM1nK65HkhhxHWVziMt13Nm/s/iHwunKkZHUCQGYBRMo1xXtYnlZnf25/mJd8p8FjCGZ8nLZfnT2dGJTbpP0HI0BA/3Z/ooxZ1om8mYePTsqY5sCQ/FNTXl80+Fjpm0iBcm9XCITVDlhC2R3ZqVjXNgi8C062XwWtm1jYOeA/F625ddxIDtQvP6WWfmSMaSMDmQHKsrlCrniHxYqlS+7TCYgsvksLNvCQHZAfn+4nGO2ULyvBnsHpdfDyBvIFuTl3t39Lt6135WmDLWFDfQV5SZi5Hgdvq+snRYso+zGGupXWVvDRvWcA9TPurBxtpiYgJ0qG6wxjP2r1H4mTpyIvr4+599vvPEG+vr6cOyxx46QGz9+fCDaeUdHB7q6uvCRj3wEQHHpf8aMGXj55ZcBANu3b3esrJs2bcKsWbOkv7N8+XIsX77c+ffMmTN9t63eWLaFrt4uTJ8+HelUuvK7LV2wbCvRCqtlWxgsDGLqtKkVe49N08S2zdukFubS5fl0S7rye9NCxg42ll7eLlqrWtpaKuI+2pYNU5jI23lpOVOYGDNpDIx02QPAEsi8l5GW88MIh63ysC2Gu7NW3sojY2UwcfpEaXzC3i29yFvBtjVsdK/jQGFIoRiFyo1TdplMGQW7UPyjxb2sI1Pa1kK+qLBOdim3Y0imjIyZKZ7j2BZ5UP1+ExkzU1HOiW06sTK2KTAU33RQHt/UElZRSZBtjzNQVCwCLicMAWOCUXFfGZYBkQneIdEZFxMk7RUAMpCOnaw5pDTJ4qnaZTIlFKyhMZGWlDPKZAIib+eL55KG/MVcyM8xbw3dV6Mqx5xhG8X7SjJ35K08LNvC6DGjK55zQPEZkd2dlZYt2IViP+wFeb9ul99XYaN6zgHqZ13YOPeMYszFaRuKcg/roYceiv/+7/92/v3b3/4WhmFg0aJFI+TeeecdTJs2zXdjDjjgAGzevBlvvPEGAOCtt97C22+/jUMOOQTLli1zrLjr1q3D1q1bcdxxx/muM24ML/2XfmQ3dpJJpVLSTzWGN5TLPnXDqKzXk9NNqvI89WNyeEenX1KpFFLpso+H65EoaryOzsOkgKKVtfRTKJNRVjvyP0+kXD7VK4ORMkZ8PFUp6RvP4xx6Y067nO796AdDci/Xs07FHtZ61hnm9R+OiVrx8VI+ijGggdtzLq5zq9ZcFTJKc92ll16Kz372s8jlcthvv/2watUqzJo1Cx/96EdHyP3ud7/DEUcc4bsx06ZNw+23346lS5cilUpBCIFbb70V+++/P2666SZccMEFmDNnDlpbW3HPPfcwQgAhpC44e8xa4Woli+M+NEIIaVSUGt9nPvMZdHR04Lvf/S5yuRwOPvhg/PSnPx2xfNjT04Of//znWLFiRSANOvfcc3HuuedWHJ82bRoee+yxQOoghERHZE5pOjCsFUkIbnvVh/e4E5J0qpoob7zxRlx33XUYGBjAlClTKr6fOHEiNm7ciIkTg3MiIYQ0JrqOd4Q0Oo6yqYgSoPI8X795PTp2yu8rMSB4X5HE42lNffTo0Rg9erT8B1papIosIYSUo+t411REENaIRI+tiltWRcayLWTymeJ9JQlRt21nY91X9QhjSOIPN4ESQkJH5nwQV2eEsPAS97ZRYuMSBT62oaQMyX1lN8Z95SW8UpxCMJHgaYyRTAghCcfL/t3Y7fElJCS8hFeKUwgmEjy0sBJCSJzwYWFLQraaqNDJ5hQJ3BJSFxJz/YkrVFgJISTB6GaPahZMy0TOzKEnU5nRS5XNK2ycpACKTFdBJw5oBpzrPxDv60+q40lhzWazWLFiBT75yU9i/vz59W4TCZFsPou8Kc9m1NrSitGtcme7ZqLR38J1LQ+65dxyiVcLazWcQ1yau7xeOe+jsHbVWKclLEehkZ6jXael0qF2VoRMkrU/QixhwRY20qnKjF4GDOSFPJsXAOdcKvq1DufoN9OVgIAt7IrrYQu7upOS2znWgeF5QzZW3Y77wbn+LZLrb1W5/iRWeI4S8J3vfAdLliypd3tIiGTzWXz0lo9iW/826fdTx0/FE5c/0ZRKazNYrXQtT6ZlIlfIoafXpVzB3WKhyiVeLY94Jp9BpjsjTyGZl+cRNy3TyeueEpLrKOTXsVCobsnyIlMLXry4ZTJOfvleQBhypSPoHPTO7+0Or07fDGVIKj/mhjMu+ty9893uj+IfcE06oZw7NLaEmJaJ7oFu9G3vk96T+YG8tM6B7FB64SxgG5IXj1KZADAtE8IWyO7MSp0sbduGsEV95tYarz+JH563BBx++OHYsGFDRVpWklzyZh7b+rdhn/33kYdCeXcb8ma+KRVW3ZznSULX8uTkoG9zsVjA3WKhyiWuyiOet/OwYaNlTEtFnbZlw+wzpTnPnXakUHktU2UyJRSEB4XVg0wt6DpdFayhHOsTIFeQssEvJTt1TnSpM5f85Wtb2MVzHA/5OWbk18M5prCUBu08ZwkLlm2hdVQr0i1l95VpIWNnpOM8Zw69VLRJ2mqXyQTUTmEIGBMMGOnKydWwDIiMSPzcSuqD5ygB//qv/4rrr78eb7/9dj3bQyKgJd2ClpayT5rbm4fRzZWeGHRzc/vNP66ZR9zTMbc6NXLXR5HXXatO3XJi5DKtsyzrZWXWR/9U1BdXfNwfYY4d55pJPlX7N+xxLunTWuYPEjyi7L844lkrufPOOzE4OIjDDz8cRx55JKZPnz5i0jcMA2vWrKlLIwkhzY3u0j5xZ3jPcKaQQcqSL8+6pdHVxYmTKdyXoBlLs3ZMy0TezKNnBx2LiHece01m8LfLZGKAZ4X15ZdfRmtrK/bff390d3eju7t7xPcNaXkiRAM3q1HsLUkxRndpn7hjCQtCCMfCXY4BA0IEuzzLWJr1wdmmM9Flm84gHYtIJSPmVZf19jiNG88K68aNG+vYDEIaA5VTQV0dCpqIipdjvgP4JgqDg1H25hHXZchEQcci0sBwoyIhAaJyKqBDASGEEKJHTQproVDA3Xffjccffxzd3d3YZ599cPLJJ+P888/HqFGj6tVGQiJFaxm/wS0d3PZQR5jpqD6EGGuUxBBe/8TjWWHdvXs3TjrpJLz44osYN24c9ttvP/zhD3/Az372M9x66614/PHHMXHixOo/REgCoHOIO9z2UB+cGKt5VL7ciDIZ4hnHuczMIGVXjtegHctIvDAtE7ZtI5PPSOcr2+b1Twqew1pdc801eOONN3D//fejr68Pb775Jvr6+rB69Wq88cYbuOaaa+rZTkJChc4h7ozY9jCx7DPBgDC47UEHJzZnK4pxMUs/rWUyxDOOc5kkjJJhBO9YRuKFJSwICBhpQ/oR4PVPCp4V1l//+tdYsWIFli1bNuL40qVLcd111+FXv/pV4I0jJA4YZf+RIXzEYSUKooj92iQ0fExlooTXP9l4Vli3b9+OI488UvrdUUcdhR07dgTWKEIIIYQQQobxvId1//33x9q1a3HSSSdVfPf73/8eM2bMCLRhzYjbXprhPThED9MyXff+pVPpqlm9ag63M7S5v/yaec0eFLbTkpvzVF3rs0XF3mBhe3DY0nGcGHJiqriHZM5NLmVrqm+EqEY/6tbpo61ROMrp3lfSsVrn80zCdRyec8oth7Zte7qvdNta8/1oD+3/l8hZtiUPYh8QSXIIrbWtw/OqsMvuDy/zakLwrLB++tOfxg033IAJEybgc5/7HKZMmYLu7m7ce++9uOGGG7B8+fJ6trPhMS0T6zevR8fODrkjy0DRkaVtVFtELUwmpmXi2gevRVdvl/T7GRNnYMWZKyqU1r7+vuIfigwgjkwJ2XwWEIC1y4JllCnJQw+FbD4rbScAmLaJlGThY1jZCtI5IFvIwrItDOQGKgLHC1sAdlEmSLL5LGzLxsCOgYoHqxACsOT9k8sP5TMvAHaq7KLYZTIlDGQHin/0AsKQP1gdmRIKhULxD4UDlCNTxmB2cI+cixLgyJSws3+n9PeqyeQKQ+edg3s++EJl3zjZwwpmxZwDqDOIOX2tOEfZ9dDNrDPcVqvXqriOtlBnOlPdW6r7SretTl9bqOwbxfVwXqgUfepmuOjP9sO2bAz2VI6r4bb0Z/srDvf094xol6ytjkwJw/OcWTCL51lezmWee2/XewAAsVPANCTXS+yRe9/U9408BQ9OrqqxIxvnccuSp9vWZsl05llhve6667B+/Xp85StfwRVXXIGWlhaYpgkhBE499VRcd911dWxm42PZFjL5DKZOm1qhPJmWiW07t9FDWAPLttDV24Xp06cjnUpXfrelC5ZtVfT5gDmkxMj2DxplMiUU7ELx+4mQKw+5IZnydoohi4PbtioDxQd2gM4BeStfrDNdGYJLGEWrVd7KB1YfAOTtfPFcJkGuCG4bkinD6bMWuCplsn7NmbliPRNcymWHZMrrE0O/1aqoT8gV1qw59KCWXUujTKaE/nylQuFFpmANtaNN0VbLZcwBxT6VtVWRQczpa8X94TrOh3+7hsw6lrCc6yh98ci43xvKe0txX+m21enrtKScUSYj+y1Fn7qdY9bMVh3nsjE3WBhScBXn6MiUoHs/9mZ799z/svpsADuG5MrQdYRVjvOYZcnTbWuzZDrzrLC2tbXhkUcewaOPPoonn3wS3d3dmDJlCk466SSccsop9WxjU5EyUhVvSOWhWEjteFn6l6J4eFQrJ1UCvRSVOQPUeUUn9OxRBirGuacXshqvh2NxUSkBKsuN7vV3k/XSrz7GnG5boxhztVAaZs7NUu7FAhfqOI/gOmrPO37Gjk7GMoWCXE+SlCVPq60NHv+75if4qaeeilNPPbUebSGEkEBheLLGwI/1kRDSGGilZh0cHEQ2W7m8sPfee/tuEKkdv05FJCYM7V2TbpD34iBE3InAkSlJJMkpQ8uiR0gzZLpq8HP0rMkMDg7iq1/9Kn7yk5+gp6dyEzbQvJl/okTXqYjEC2dDfB9gK9xkG2HjfJiY9lB/KZynHJkSvETlSHrkDme+tiSObIDSsYiQpOCM35y7w2bSx3izZHPzrMV88YtfxD333IMzzzwThx9+OFpbW+vZLuIRXaciEi9sYRcVqvFwd0bIMNNRrThKpcJ5SqZ4eunnpF8LL05FI+QISSDO+FU4JQY9xodDjEnnFi9hxmqkPJtbKY2Uzc2zFvPggw/im9/8Jr7yla/Usz1EEy79NwiyTfPw7rBFXIjCASZByDK4camdNBphbCcxLRM9Az3o6+xzDRdXGCzUzeLZyBm8atJw5s2bV692EEIIIYQkGktYMIWJcXuNQzqdrvzespAZyDSExTNsPAeWOOecc/DYY4/Vsy2EEEKIElH2H2kyhMsnZhiGASMl+TSwBbTeeLawfvvb38YnP/lJLF++HKeddpo0IsAxxxwTaOMIIYQQZ/k0B3fnuQZwKiHu6GYQI42DZ4U1k8nANE3ccsst+O53vzviu+HNvkn3tIsat43a9dik7dQJAVtI6hQ2rRcR4afftXOlJyisUc3YcE89Wc1vKuywVm4WozpbkeJ+rzsObirnuWpOcEkJ+eMzBJv0HKuVT0D4Nt0MYgCce71inHuZA1D7/CggiqEmJb9t2Vbs77e44llhveSSS7Bu3TpcfvnljBJQB1Qbteu1Sdu0THQPdKNvu7zO/ECeVouQUOYtLzmuzJVtmUgJ7/mnS7MH2UZZxTVkD4oz/Zn+4sNsp4uAMSRTRj5fPSWtm0xpv7opAbJ+dWIp98HVihhkemav1zZWY0DDCS4pYY1clS0PMk5oNsXYkYVv8zKe4pISPG96uCclMgO5gapzwECuMs22MybM2sZNf7YfA7kBDOwecI/4kivK7TtxX9dzIZV4VliffPJJrFy5Ep///Ofr2Z6mRbVRu16btC1hwbIttI5qRbqlrE7TQsbmxvCw8BNiaETZGvNPe25XQslYmWKfTIbcMrdjSKaMvOXh4egio5uVSQhRNR98kJZwr9c26WMgirBGOvhRWG3brjp2kh6+rWB76B+JTK6QqzoHyLYSOGNCYdGVjZuMmSm+JLRUpp8G9hgRMmblvEPUeFZYJ0yYgPb29jo2hQB7NmqPOGbXd5O2W+w2EgFuy3ce9RTdXNkNnz1IN3d5FGGtmiSUVhQkYpz7uf7NEL5Nt626cwA0x41LiMLY9msC8Hz5LrzwQtx33331bAshTcuIjEw5ySdfJkcIIYQ0EZ4trEcddRSuueYafOITn8Dpp58ujRJwzjnnBNo4QiJHxxlBw/lBmZEJUGZlSiqhOnolwKmE1J9YWlRJ7OG4iQeeFdbzzjsPALBx40asWbOm4ntGCSCNhGPJVIRQkVk7dZ0fHGTLXXA5lkBKncvCcIBJklNJ2CidwwClg1iS0HWCCxvb8rCf1EVG6bQZI8eyJI25pPRpM1GT0xUhzYLjaKDYcC9zRtB1fmgWdB2SdEmSU0nYKK8FUJfrEQVhjzldLHh4uXKR8eK0GatzrDbmPPRFvUlKnzYTnhXWE044oZ7tICSeNIMTQ0RoOTLoLu3zetSHpGy1iCi+LakDPuPU1lwuIhhTuxLPCishhETFCKc0na0WJFCSdD18b9MhsUB3e4/u9q4o0I2NPRxn27RNpCCJxY3GyATnWWH96Ec/qvzeMAw8/vjjvhuUy+Xw5S9/GY8++ihaW1sxb9483Hvvvdi2bRsuvPBCvP3222hra8Ntt92G4447znd9hBB9wnqbVzqlNaBDWtxJ0vXgNp3GQHd7j3NtVdu7YnL9dWNjW8IqKrRu2yxEY2xf8Kyw2rZdEVNsx44deOONNzB16lQccsghgTTo6quvRiqVwl/+8hcYhoEtW7Y4xxcuXIhHHnkE69atw9KlS/H222+jpYVGYkLCRDezViDUuLTvOKkolgO9OLvUgnPuijobwdoBILStFl6cW6rKcFtIBX4cvXzVV8XpSllnjdfRGReqPcwxdJ7SjRmsG4s7CXjW9p566inp8b/85S/4+Mc/jq9//eu+GzMwMIA777wTmzdvdjp9+vTpAIDVq1djw4YNAIAFCxZg2rRpWLt2LRYvXuy7XkKId3Qza0WB0smjTg4etofk5F5kyB6aIStbFPhx9PJVX4hOV2GfI6kfmnkf9nDIIYfgiiuuwJVXXum7MW+//TamTJmC66+/HsceeyyOP/54PP744+ju7oZt29h33z15d9vb29HZ2em7zrghhJB+4oZpmcgVctJP3KxHtrBhWqb0UxcPcVHl00AMZ0mTZUtTIirHel37Juxr4WcM6JaxXT61OKTU2je6/Rp2Od2ybn06/KlHW6OYN3yMuYr72OuYi6Ct2veHJn6e5aLsP4+F9ObVhDyrAllPb29vxyuvvOL7dwqFAv7617/i/e9/P2688Ua89NJLOPnkk/HKK69UPAxVF37lypVYuXKl8+/+/n7fbas3pmUiZ+bQM9BTkX/Ytm2YphkbRdC0TFz74LXo6u2Sfj9j4gysOHMFWtLRb9cwLRMdnR3o2NIh7VcUijJto9oCqc/JSa1w8JDlre4d6B1qlMsP22VyCcVpfw6u/RPkOWZzWS0ZYYk9bXJZ1ndkynhv13vFP3rheo6OTAmZbPXc4jKZHf07in8oxpwjU4IzDm0ok1XIxutgfnDP77v0jyNTQk9fT/GPrKROu0ymhGwhO0JGVs6RKUPXQWxX366i/E7pzwLGkEwZ3QPdVdvqyJTg1n4vMgPZgeIfiuvhyJRQMAtV65TJdPcPtV8xxh2ZgBjIDLVf0a+OTAm7B3cX25iFvK3GkEwZgxkPYzxTOcZz+T33VUWs6ZK2OnIlOM94xfwo0wOc38q6O2vJ6nPGhKI+2biJikA0il/+8peYMWOG79+ZPXs2UqmUk6TgqKOOwoEHHojXX38dALB9+3bHyrpp0ybMmjVL+jvLly/H8uXLnX/PnDnTd9vqjSUs2MJGuiWNVHrkTG5YBvIiH5slL8u20NXbhenTpyOdSld+t6ULlm3FQmG1bAuDhUFMnTa1Yr+zaZrYtnlboIHjC1ahqoNHwap8AGSsIUWkylKZIxcHhOTFscpb+WBhaIJvg6vC4sgEQN7Oa8n42UqwK7urKDMR8nPcMSRTRs6qfKB4kenP9avHXG5IpgxnHFYZc7Lxmrfy7mWNMpkSBgpDD7/RLm3tK5EpwXlIK2Jiur3Q6zqI9RX6iucy2aXcjiGZMpy+VrRVej00lUdAf+wUbA91SmQGcgNVx/hATqHouFnwFPOHM/cp+lU2PzrzyRiXtvbL55ys5eEFQiLj9Fe1+0rSr86Kn2qsSlYFnd9SzKuy+nJmrmo5RyYGKDWKZ555BscccwzGjx+Piy++uOL7XC6Hl19+Ga+99hq+9a1v+W7MPvvsg5NOOgmPPvooTjvtNGzatAkbNmzAoYceimXLlmHVqlW47rrrsG7dOmzdurUxowQYkk3TMXUMSKfSsVBKvZBKpSosrOX/DpQg47d6LRsSjoNCzke2qpAcYBwrqKI+N0upbxQPViW6fRNkfV7rDDtOsZ9xE4d+jes5htQ3hcKQ0iQLMQXsUa4KCkU6SWNOl2Y4Rw2U2saJJ56I5557Dh/84AfxxBNPVChSo0ePRnt7O/7lX/4Fn/3sZwNp0G233YaLL74YV111FdLpNP7zP/8T06dPx0033YQLLrgAc+bMQWtrK+655x5GCCAkAhxLv+KtPDarAXS4ICQ2FMSQIioLMQXssT6K6pZf0nwoNb7S5b6NGzfWuy0AgIMOOkgakWDatGl47LHHQmkDIQDcN5/Xc0O6xlJZlOiGXiGE1AHFfstYkYCVpMiI4rmTEGiiJESCs69V4cgS5N5XZw9dlaWyuAS4DptA4nAS0qDoZoEiweN1HpJmrGJWNiVVFdaaQtWQRCILtxHHUFphYgsP2XECDInlLKFXWSqLy1L7MGFZVBmH0wNJsa6RwNHNAkWCx+s8JJNjVjY1VRXWE0880ZNzimEY2L27MjQEiS/DsVR7el1CaRXiE0orMkLajO41m0zQWZl0cCwDJtz3sAZs7SzNse2mlDWrhdWxnClypdO6Fh5hZ48a8XuK+SoOcwfxSEKcoMKmqsK6ePHiEQH7SeNgCQs2bKTbXEJpIT6htBqdKDLA6OKMiRaE5nTl/J4qzFSTjlXHcqbKlU7rWmhE4ehH50LSDFRVWK+99lp88IMfDKMtJCoSFEqLkIZHd2mfVhnSDHDrS9NCpytCCIkB3IdYH7idpDHwsnczyP2dSRo3ztY9xTatRtjeR4U1RrjlG641B3GcERCwhV0xsdjCrpsDjxACti2p07ar92vYb/O6Ya2GylVM2NVCoQzl067o+1pypdfSziDK6qBbX4jn6FthDfIcvZZNAL4c9oZz0JdTLQe9n9BEfu+NWq9blG2tgbAdL5O0zaJZtgVRYY0JpmVC2ALZnVmpA5SwReLfkEzLRPdAN/q290nPMT+QD/wcTctEz0AP+jrldRYGC9I6dcPE6L6VKx1nAKXzjJPrvRcQhjxVqiwfvJNeUZErXZZe0cktrcjN7pZ/OpPPVC3ryJSQL+T3yLj0qyNTwmB2sGp9jkwJsnSkXmWc9JkKJyhZik0v4WpkMl5yfctk8uZQf1UZc45cAERhterPDqVBVeRLd2RKyOaH0m72u5dzZEpwsjQpQhPJMjmZpofr7yLjjCehqFMy5nTD942oz+U6ekk1q0WNW1+cvla1VXI9lKEGFWEGneeJm36osHjq3h+DmerprGUyA5mBqvU5MjFAqbA2c/iEsLGEBWEIGBMMGOmRd59hGRAZkXinEktYsGwLraNakW5Jj/zOtJCxM3Vx1jGFiXF7jUM6XVanZSEzIK9T19ql6xykfEMuKSvNI20VqoZCkSlXuUKuaq50maLr5JZW5Lt2yz/t5DNXlZXlPBeK/NyK7DhOPntFfbKc936sOc4DXmHtkCkBjjKqOEeZwuol17dMxsktXi3rkId8816xq5rtvcnUQsYcegFSZGZzZErIWUP3x3iXcln5WDWFWfV+NEXlddR9YQG8XSPX3PUa4fuc31LdjwGOGz/ozh3KUINe5vIqDrRBPj+yduWLkxeZQcuDoutBJixoYY0bTeAAZRhGxTnWO96vYRgwUmV12h7qDNuRxU8GmKTkStct67ZNwcv2hSTlAw+7viiyDiXpeiSlXBR1NoOjXzPcVwm5jlRYCSGxJ2/mq25fCHTpmokKYkUUWwkcK7hiOwnj28YfYVXfVOtFhkQPFVZCSOwxbbPq9oUgUxZ6cXJsFEfIJKCMU2yUyQSEs2Sr2E7SCI4sjU6SnKeIGiqshMSJBvfY9o3u9gXGbnSHY05NQpZLmwI/EQ1I4qHCSkgM8Lq0yCXI2tB1nmuGmKiOU20VhZXOtyQO6EY0II0DFVbS8AghYFt2hWOXbXmIwxoSXpWfpCtJvgkptmmkCmtIMWOVHsnYcyzwGKXNQgJi//omxPqEEFWjNsRlPif1gQoraWiy+Swy+Qwy72VglD2VBQSQl8dSDBs/FtYkZWSxLQ+KoERGN5aisD3sRVXJhLgc7Jy3Ku6jpG98ZwDS8GZ2HNwU1q4gneCShO71UMZiVsRhBrzNYUHOc2FnnRrxe24vSXWok8QLKqykocnbediw0TKxpSKslbAFzKyJvB39g9WPhVU3dl8U6DpAaMdSjMBZRxcnNqci7qMsfmcU1mDHCc4tfmcuWCe4JKF7PRzroOL6u1kQZfFgdWS8EkUUjWbJ5kTcocJKGh9RtKZKLaxxW0GKInYfcSdJzlpROAfpOsHpEsX14BiIF81wjkQKFVbS0GTzWUAA1m4LllH2xj/0IIrDlgCiRjdVrnZ9HrZQBL3NgqG03JGl69WRqQXdbSjZ3NB8oojf6siU4MdS7mwVUbTVy3Ycr/jZvhA2ToxVRd/I4rDq9qnXeSHI+SPs6x8VVFjrgC1saZ5g0zLrsmQRdn1+EUJUPHjr9SAu2CVpS2UPj2x8UghGho4Fya/DTUjOU7r1BbLkGeY5+rkefsJaheRYlLfyVUMayVLs+kF3G4qzFUIRv1W2XcIWdtVzdFVY4aFsgClvnba6WdgVbS1tk/R4wOhuQ9IuJxQ+BdhzTDl/1DpfwaqaCVB5PglZRaDCGjCmZaKjswMdWzqQSo28k23bBgpFmbZRbYmszw+mZSJXyKGnt0faVrNgShVvv3UCUFo7ZHWG/cbq9beCrDNXGNrTpugbR6aE/mx/8Y/d7uUcmTIGsgN75Fz61ZEpQddZS9Z+LzLZggcnFheZfL664iST0c0ln80OtaMf7ha9bGVbdw/urlqfm5xzfRUWNtkYcKygpntbZZZSZxVEdY4uKyW6llLtsWN5GDsSmVxu6LcU95UjU0bBHGq//LYbKVOCWTD3/L5L3zgyJewe3F1sY9alrYZ83DjXNicpV1JnkNZyx/lPVqfCQdAZE4oxLhs3g/nBPb+tUFgduRL680MXsADXlx1HpoQdfTuK59YrqQ8AjCGZMhxLv+IcZasBUUGFNWAs28JgYRBTp01FS8vI7jVNE9s2bwt26TLk+vxgCQs2bKTb0kilR96NhmUgj3z9NurXmK0mbGcdr78VZJ2OZVnRNzLrc8bMFPtgL5dyO4ZkJDjHFf0qK6t7PbxY3WQyXl6c3GS8OPHJZHQ9r7N2tmq4n6xd+dAZLFQ+MGXI5HLmkOKkcIBxZEpw+rrFpa2QX4+clat6jm5ORTIrqBcZmYLnRUZ37GRFtup9lRVy5cGyrar9I3sOyJz4vMg4L2tjXOrrl7/QOde2TVJuuCyCtZY794ysTkV0AWdMKMa4bNw4ba/ijyA7R2c8jXJvq2zM9WZ7q46d3mylNpuzPdzHdnDOen6hwlonUqlUhRWx/N9Jrs8XBipiotZ90zw36ruj2zd+HG7Cvh669flpZ9h16l4PP45+STlHP3XqEsU5BjkGwu6bKOoMe4zXu86kXH9NYqrREEIIIdERRazRpCDbJqAjQ0gt0MLaAAghYNt2xeRp2/HJ5ETqTBWHCxIBYTsyMORToESW7SwBfaq7lYAQP1BhTTimZaJnoAd9nX1SR6bCYCFwRyYSLxxHHEXWoWYN4h4FYQdVjyIclq5jWZII28IaRSg1XZrB+twsoaKSBBXWhGMJC6YwMW6vcUin0yO/syxkBjKxyXJE6oNt2+qsQ9nkPzySRNgKZBRZh3Sd2ZJE2BbWKK6jLpFZn0MkSVnymgUqrA2CYRgVqUcNO4a7ppNGApbnHBKycb5p0L0ecdhK4LXOZhhzSTrHMMdOs2xDCjLWsNeyuiTpeaUBFVZCJDTDkheJF87yoiImomwJUtfa5YTHkdVXUqeX8E7EP36slrohuHRx4pYq4uLKYpsmiXxhqP2KONWOTAlew0gGGW6yWZ5XVFgJkZCk5bmmIqTMSlHgOKkoYiLKHFl0FR3ngSmrr6TOuMRx9o3OGBDwl9GtBvxsJQk7dXHezleN++olHnGcce41VcYyzfuxFrmgfivpWzQAKqyEqEnScmADo2tBSOREnqR4kQnAUdQUljKZMudYzxTOjDILmy6B7H0Oe77yEzM25jjziWrFQzLnRGFhdWjw5xUVVkJI7NFVPKPwoCfxQjfbXV7k1c6MuSGZoNvpU4YEg9PXihUP2fWIwsLaLFBhjROi+PAsf4AKIeq+UbvioR3DZ7isb4aP169SJGcJOuy2RnGOYVsQErCVwCEK55A4OIjV0k4dB6EkOc/pbl+Iw9wRRZ31vB5RrFwkab7SgAprTDAtExCA1WtBGCNHmC1sQHjLUV1rnbZtIzOYkcZwtW07FjFcTctEzsyhJ9MjTTdr2zZM0wy0rbpODH3ZvuIfiuVHRyYg+jJDv2eh8oElymRKyOQzxT8UbXVkSnCcKRSTY9AOF8LyYCmVyHhZspU6TiQoJmauMJTrW3EdHZkSTNPb/SKTc35PMQZkdQ5kBqqWc2RKcMaTygHGZcxt37W9+IfCQciRKcFL7GKZjG45P/tQ+zJ9xXPLQn6OhssckKm8v73I6J5jLuctL71MzsmcpRgDsuxauv2qvQ1JFb8Ve45JHSg1Y78qHSgbyHmSCmtMsISlXn7KBO/kYwkLAgJGmzwkloCIhWORJSzYwkY6lUYqXamwGjCQF/lA21qwPSisEpmsmS3+MQquy4+OTEBkzKEHimzpyi6TKcGJk6lYKpXF0nQeRArLU9CJCrzEO5TJ6MYLTZLTXcEaGoeK6+jIlOD1GsnknLGvGAOy+yNnVVdYZDKOQqE4RzfFpD/fX9VBqD/f716nApmMr3JVLMFuv52xhu7vMZCfY3+JTAk528P1kMjonqNXRyyZnK4TlO62IG1nRlX8Vuw5JpuvdGO/Kh0oG8h5kgpr3DCKMVXLj9W92vI644isb4aO17POWpYDnUmhIJFTOHgEQhycdep0LRzrqcLyoLTCJuAcfROF01VI/RpI2J4aHYSE7WHM2cGNOUf5UViC3ZQoZTsUMk6fKayWyn5NwJjzvY+9GeaAhECFlZAA0XXwIGqYdaa5icIhyR6+YRVjzpZuGtVDCKFeZcu6K1Ze2iGTcY6p5qsAzzEKkrRaQtRQYSWkHjT4m27iaHBnBF9ORUHUW36sXvi5r3TbmqR7uRlWEnRJ0jk2+nylCRVWQkho+F7arxHH6qZyYki4xdtpvyJeaODWxwT1qxdnzDg4l3r2dA+ySr/bHkighJ2xStfJKyqosMYQxoUkNROFhU3DChD20r5zLynqU95vfiwdIYXf8bOU7FpflTodZVS1XB4XhVXiiFOTTEjXMYqla11nRocg+8ZP2XqWC5Gwx4Dv6x8yVFhjghMiRwC2UTbRizIZQkpwnLgUFrYgHb3CzlvuB12HCz+OGtpeyR4sGUqZGpc8ZZEDvMolMsh9rc46mo5MSXLyCfv+8GodlMllC0PRVRSKpyNT+lu6SUdCXg0CgGx+qP2K8FSOTGlzNNsqCwOmIxMWVFhjAjeGE11sYVe1sAWpPOiG/IoC3YeVH4VM9172bQmsET8pJBOpsNaItiNTgvpGd6zW875yk9MNp1eX8FR1cvR0QropwlNJw75ptjXsOccvsc0C/I1vfAOGYeCVV14BAGzbtg1LlizBnDlzMHfuXKxduzbiFtYPo+w/QjxhuHziWJ9w+dQL3bb6Occw63TrTy/96lZfvftHh7DHDZCcvvFD2OcYxZjTLZekMZeU+jSJpYX1xRdfxPPPP49Zs2Y5x66++mosXLgQjzzyCNatW4elS5fi7bffRktLLE+BECIhbKcCbbw0IQbNBKJxugobx8qriBfaCIHRiTthO4glZq5qImKn7eVyOVx22WX46U9/ihNPPNE5vnr1amzYsAEAsGDBAkybNg1r167F4sWLI2pp/RBh7gQXxb1G5fuNhBDKt0jTNpHNZ9GSHjmETMusnkFHSPY3edw0L907FccN90lxRrDhvl+q2lysUd9wmmGVg5hSuQrLAQZ79pS7lau6HBhSWx2nK0Ump6CdrpRl63WOgDKDXNV9lSG1NbJyUdQZ4jxnwUMmsAAdxOoyV1WpU1k2ZnN5FMROYb322mtx/vnn48ADD3SOdXd3w7Zt7Lvvvs6x9vZ2dHZ2Sn9j5cqVWLlypfPv/v7KtHtxI4rc5aZlFm/yXgvCGDk6h29WWbiXbD6Lx155DIVXCxVbFgQERolRyOazaBvVVlGfbdvIDGaQSo186ti2Ddu2pfU55fKV5aqVdTbh5+BqmZFt1Nclmxv6LYUlyJEpwY8zgq4loHewt9jGXS6FjCGZMnbs2lH8Q3GOjkwZ/YND96LilnRkSnAcDVT9KnFGkB3zIuPHsUw3z/pAZqD4h4XKB4wokyn9SjMSguzaypDJZXKZPe1yedA5MiV093VXLefIyNqgyCDndj6ZfGU7vMhopx/Ne0gHLJGxTQ/3sYtMJuPhemQqz9GZ+1T3lWR+HMwODjVI1sgymRL6s/3u7Syp05ErYdfgrmIbsy5tNYZkysiZHtLPSmTyhaFrpMg85siUMOI+lp2jXSZXgu58NZAdqDqXD2Qr6+vL9BX/UFx/RyYGxEphfe6557Bu3TrceOONFd+Vp+RUvU0vX74cy5cvd/49c+bM4BpZJyIJaSKs4iB1c9bJyOvMW3lkrAwmTp+IdDo98jctC71bel3zswsIGG0GjNTI62nYBgSEtD6nXLqyHFAcG25lHSW2zeUcEWwMRifvtiJzjCw3tx9nBN2xM1gYLF7/yZC3dceQTBn9haGHieIcHZkysla2qjUwa1VOyI6Cp8ojLlECdZVHXWXFT1knf7rM4cIukyn9StOpZNCsvLYyZHKZwpDyo1CSHZkS+vP9Vcs5MrI2KK6/2/nkTQ8KpETG93VU1SeRMeFhrLrIZCwP18OqvB65gof5qlA5X2XNoXtUke7WkZEdc9sjabiXdV4qxri0tV/+4qE7B+TtfNW5SnYdlY5TgHOOMucpL5E7ZDJZO1t1Ls/alX3qKOqq6+9B4Q+LWCmsTz/9NP73f//Xsa5u3rwZp556Kn74wx8CALZv3+5YWTdt2jRijyvxgVH5QuBlw3UqlUIqPXKUe40hW1GfR3TLqSbyuqBbX5WJvC511phjPRB06wyyX+tZzg9xOMco6ozr9QgbP+eYlH71M+aCrLOe82OS5vKE3FexihJw9dVXo6urCxs3bsTGjRsxc+ZMPProo/jYxz6GZcuWYdWqVQCAdevWYevWrTjuuOMibjEhzYeuxSJRiJL/yz6lMuVF/cbhJIHiOwSTYgwEHRItKUSxha0Z+pWoiZWFVcVNN92ECy64AHPmzEFrayvuueceRgggpBSdjfNVnApk+FkujwSNc9SNwQkE8GCNwllHlwS01Xdw/Br3Bkf6whLS9fC9hU3DIcl3vyZgrBI1sdb4Nm7c6Pw9bdo0PPbYY9E1hpCYoqtA+s6QlYBlJO2QT14fZBJkjhheZHSd5xxrtuLBGrTFWzvTkW7WIR8hjSospZKy0nb5DW0W4v2ha/EMOyuX1wx4MjntBAAlWSTdrn9d+kYDJ5Odaqx6yIjXqMRaYSWEVEdbCfCbgz4pVDlHGX4srDKHQy8y2pmVhpVYhfIQdLxIX2OuClJF18f10LWURpHpSJewM1bp4jUDnkxOd+w45626ji7OvtUI2hHazzhvBqiwElIPglx+8lo2ARZPUkd0r38UYy5Jzmw6CLiHfIrjO2CQY6fe55eUeY5zeeBQYSUkQEzTg0OSRMZrhpZmzeTihB5TLXvLwpPpLu372BKgG8M1bGuXn1Bquugu7SfJkc1ZXlbE7wzaISns/nG2mCjid8q2oSivf0nZusxzqmV2WXxrzfvRLHjbfuNVjuyBCitpDkKyBGjH/fS4tBTkElRkzkEamMJDv0pktJfZfSzNRRZFocbr4VWBCVLR0b4eUXqI6zgkVYnfGfhSctgvO8MKniJ+p1QJHP6ySsinIJe9nXtNYbWU3Y/aCquHuaqqHB29pFBhJQ2NriXAN1HExKwRx6HKLZ0fqjhrefntgHAefoq2Kq3BIS5da+/T1LR4+/aC1yjrmxr7NQqF1fc4jyK+cVhLyT5WIADozXOaTnBhO5Ypy1Upm6RVryigwkoaGl1LQDPgTNKyjCwqZ5QoFNbhi6Voa1ycEbTjfoZsfYxiS4AuUWwJSFz4thCJ5AXCg4ObTCZJ8VuT1NYooMLaIAghKibseu7pEhCwbRuGPfJ10bZtiDquXcRln1pVwna6SpqTR9hLXkFejzj2Z1QkqV8bfcz5mQPi0Dde64xDW+vtQKtLg89XVFhjgm6sONMykTNz6BnoQSo10vRk2zZM05Q7o/ggm88ik88gszNTkSpVCAHkizJBMdx+0zaRkqyxDVtIZefp5AhX9KuXXONe0XW4GcwO5UGXbV0AnLY6ciX0Z4dyr/dKyooymRKc/lJtl5D0qe45AsBAZqBqWZmMk89csSVAlvN8sCDPL19NZnd2d9VybjKZbGU+cy8ymcLQMcU5OjIlZHND95pMYRFlMjW2001ON+6nl3lBJtM/WDl+vcoM5jyMAYlMX7av+Ifi/nBkStDtG6vgoZyLTCY/dI163cs6MiX09Ve234uMF6comYyXGMVucrlc9Zz2Mpn+jIexI5HpHRjqTMX1d2RK2yCZh2TI5HRjODv3t6KtsjkgaVBhjQl+YsXZwka6JY1UeqQyZ1gG8iIf+Ab/vJ2HLWy0tLVU1GlbNkxhIm8HpwRawiredG57fwwAQt4/Tjw/Rb8qYwPW6nChuYzoxOWUbV0AHEVEFr8zb+aL5zJRUtYGsEOulDt7dxXbJWT7e/04FWUsD8qcRKZgDV0jxZYAR6YE3ZioGdNDO11kcraHB6tExhm/inOUjXHnXjMk5ewyGVm5KsjkdGNU5kwPfSOR8XU9PCgQUuXBVNyTdplMCbp9Y8LDfeUiU7ALVecA2Tynez/qLs97uR/d5AqisGc+ls3JYkimjGwhW7VctlCpzDkvsorrL3vZ9RNr1ss9KZPJWUPjV9FWR0ZGQiyzVFgbBQMV1s4w4hqGXWdFfYC3G6vGjfO5fG7Pb7vcyI5MAPUF4gCj6+RRY1sDySNeh/4J0nHC+S3F9Xerz/c+tFqdPCJwgPEdCaHWc/SbdUqnziSVG76+qrK1joE6tNVP+D7H10ARLkzmj+A7zFiITldhJ4BI2p5ZKqyESHDe8BWTlVdrgReiCGulSyQZYEKeWJ192Irr77ZXO/QQQxFkxwnbmzlJWaeiICkZkry2wTXUnAFluDBZOQsewozFZOzUM+JHkKmSo4IKKyFxwk9Yq7CXdfyE0NFtq26dYdfnt2yY+HEOCft6JGTpEkByzrHKcnns0B1zuitQUYy5KKzztX4XAVRYCZGQpHh4ztYElUOSavtCiOg6a2jXN2xVUPRNYiJPuKBtXbEUsW2BPWHfrODGue5WgiRZgrTDYfnY2qE7Xzl91gf3ZfYA+1UZ+xlQxn8O3arfBGHNnP5SXf+YPOcAKqzEJ2E/7MOqL0lZoHQdknyjcY6+ly5rrNNZtlfFmlX/gP51DMnC5iWMnExG6eQFKB29dAk7OcJIAYRyPbSVRx/3hq/7qspyeZA4Cl61MSdTWEPe9pBIhbXWsTq8zWICatpmERVUWEnNmJYJiOL/U6IylNbwd0HhbIi3ATsluXnsMrkgqXGJJVLLbFhL0D4sQboWVt+OXjX2jZnzYAl0kdFVrvyECwNQl7Fai5wXdMP2+FFYHYVbEfJHGoFFM9Sg9v3h577ya2VXjJ0gLezOi3MVC6vsBdtRDlXXQ2W5VilzkrbozjnO/FVlq02QK0naBpbhQ6q5Iz76KhVWUjvO5J5C5SBPlckEWV+V/Z1xcEhK0tKlLn6crnStJGE7eumG7QH0LZ5xdNaqRc4LulsC/CisznFFyB9pRjdNRy/tjGU+LIi6Y85PKC0dHAWvmoVVFqdWM/Sj02cqpTzAOUc5bkrbGqCjl+794WeeiwIqrMQXFWGm6rxib0hmgHpm1tImSGcUuBzzUjaGXQMg/FBBje44Aeifox9HPxI8UTj6NcOYS9L8qEtSHD01ocJKiARhKZZ1RJlMAPgJa+UEWlcseXoJ2O4Vx8qncGSKixU57DAxUaBtmVUtlZZ/HxC6bfUT91X7WkawtJ8UItnfqdmvke6bJoFChTVOCLgrSNXuCyG5eUK4l3Rv2JrLDfWB64Ml4HP1tVQiUHNObz+JA5yMKYolzyCzeTltUISJiYsDjICoel/JLPS+H1YaY6C8XRXHXNAOGh7ElgCdtla5HoErrLpbNCJY2h9qjL4lUKesxlj1vUVHYyXJ1/WoNuYk5QKZA3ReaEKcO3yXCxkqrDFhIDdQtI5lILeSGUMyZZiWCdu2kclnkEpVOkDZth2oAxQw0hnBNsruLFEmU9ZWoPhgSUHirAW5s5Zz3rLQGyV1yvrHcRhQ3JAypwIvXvUyGceSqUhfLbN29mWq5/R2k5Olh/Qis3twd/EPt8mxVKYEJ++6wqLrlr9d19qhez36eof6S5HlxpEpIZfJVY3ZmMvIrdaD2cFiXVmXOo0hmfJy/YMj5WS/3S8pl/dwPfKV5Zx0pKakXElZWdrSvqyHHPQSmWw+W7VvsvlK13RnXlCco9s8N5AfqFrWkSlvK6BcSZC11UnzqSonSQXqONTJrocokylDdw7YmdlZ/ENxfzgyJcja70XGq9VVJqdbp+PEpwjdJHP0c8a9auVKcm9kctXT3brJ9eWG7ple93KOTAm66Ye9PHe8PpvCgAprTHCUmDGQW8n65YqOJSwICBhpA0Zq5F1lGAYERODOSNqb0YXlPJQqMAAIlyXvQs499AbghN+Q3ZC6jhO6S15OTm9FmBhpTu+Cx0lOIqdrfXJyrysspbL87M5vtUnK2eo26bbVy0uXTCZv5ateD1nGMtM2qyqsbueSsYf6THEvOzLlvzc8Rt2UMkmdTm5xVc57Wf5xe+h+aZGUKynryJWQNT0oDxKZrD10TNE3jkwJphg6b8U5OjJl6OZZz4uhPlOERHNkSsvZHspJrkdBDM0Lsuthl8m41alAOgbMXNX7Q/bccevrajJ+tj7p1pkXeXXoppz8Ojp9rVq5klwP2ViSIR1zhaG2TnSpc4dcudZ9odd9CYgKKqxxw8em6QoHqBAod4Ly6gCl5azlZ6O+7pKH7vXQzarih7AdLpLkHBKFM0JS2pokp6skOZUk6b6KYr4KG91zDPI6+ikbx3IhQ4WVxB4nS1OVPUGybE7O8rJqS0AMMnn42cNK3GmGcC+RZPPyui+u/FAEjiza2dU0zzFR6J5jPcu5yIUe41qU/N/N2BHw9Xf2b6ueVzFxaI0CKqx1QEDAFnbFzWML27MFMjSGN52X3+huymFQ1dbwUHJbBvMipxu7L2y8TkJxya4VSX0houuoUyYQigNEXbJ5ValT1wHGd79q9Knuy0ekzlO66I4dRbmwHdJqkauJGvtGN36rsr4qdTrPRUWddTFaJGQup8IaMKZlonugG33b+6ROUPmBfOBOULo4ez57AWGUjU7FpnJdHEcsS5KxSpGtymumFaVczJc8/DgjhB1iJpJwL16GgETGsqyiIqfKciTLVuNDsdK1BJmWWXVGls0dXu4PmYzXeUgqp2th1bR2+kqqEHJbI7Ei64Zv0zxH3b3ofl7ME3M9fFiRHUJ6Xuk6a0UFFdaAsYQFy7bQOqoV6Zb0yO9MCxk7EwuLHjC0CVu1GT0bbA76ERmyXPYSxSVzSNgkSmENwvpYI7pWMt0sR37OMewHpHaWIw9OLG5y2hnLdK2dPhTWsK3BUdwf2gqrZlu1Q6n5UVgTcj38OJaFja6zVlRQYa0ThmFUOBZF4RTlCR9vc7o3uq6zVugkZKkEQDycdbzWF9JyedMg4N437J9o0L0/OMbrQ5KuR9h1xnwFchgqrKRmZEuoOjJxJuy87r4SBwzHZlRMcm7xG8PG6TPFEr2sX3UtAVFYZSxzaOyrtiGYwe2ZdLYXKGJpyrYg+No3rbnMnsspHChFmUxpG3w43IS9lKzr6OnLijy85UOVfU6yLUS3b0pjcbudo/QZ4GO5XNt5TpMotj14maelMppbppxjKgU5Rj5eVFhJzfjOcpIAfCustW7w9zHJ+ckCBCDUt3ndJXrb9pCtJkAPYV9bAuDhHIPu4CqxNGX4GXPaWwI0nSD9OED5dp6qEe0XDx9zjq7jne8XOpVzkGwrgQ+nq7CX9nWvhx/jg7aSvGdicX9JqkdWtpChwkqIihqXSnwruhqx+5zfU1lXJHVqW8pFyf/dFF2XOdu3043Kilhrv6r61M9Erml91N5POGw5U1hQanZIhMux4Z/VfZhHGWKq1jGg6cymvZ80iL3PtZ6jpuVau61+LKyWhzo9yHhFd4z78UfQtbBqrwaEvJLoFyqshARIFBOAMxGprCuyWKOalvLIrF1VHASDJBALa41lfZfTDWsVItpKeQSWIG2lPIJtKElBGSoKe44l2RroR2HVtbCG7TwXFVRY64AQAsIWFZYCYYt4Bn7XXRL2sZSsNfHK6vNaZ9jnGOQGf691NjpVtgR4Klt+jDTPmAtzDPgZq0HUW34sTtiQrwYNf1cv/SgJfUOUUGENGNMykTfz6NnRI43Dappm/OKwKpxDZDHYnGMWKicXRTndJWhnCcSUtLOkTtlSSd4cyruck5QVZTKl7dC0Ppqmuee3XSZHR6a0nZL80DJkcrqx9AYyA1XLyWRk/eVVJpPLFP9Q9I8jU4JzbRVbAmTX38RQXyu2SzgypfVh6LcU94YjU4aV8zB2JDJ55KvW6ciUlsvm98i49KkjIytXBZmc7tJlIe+hnERmMDdY/ENxjo5MGZnByvHkRcYseLB2SWR6+3qH/nAv58iUMJAdutcU5+jIlKF7jtlMtmiVV9wf2Uzl8oXumNu2e1txbO9UNNQYkguqrZrzVbZQfdlGJtOf6a9azk1Od8zlTA/PAImM46iquI4Ma9XAWMKCDRvpiWmk0iMVVsMykB/Mx8YhyRmICucQ2WB1jsmWIBXldJXAgj30Wy2S+krrtCUKy/DySZt7W6VLLJp7gmRt8CLjZSnITU7Xgz5T8PCQk8jkbQ8PABcZ59wVFmhZ/xREoapjkSzTmbNEqFguVy4jKu4NN2QKsBcZJ/6ook5ZjFLTNovyij6VjRs/Y85LDFdp/FbNJUjnJU91jpIXQUA/TrFuOSe+9UTIr+MO+f3o9HON1xEYejlIS78aKVN+zC4U26i4P5TzVY1tzRfyxe/3ltQHOP0jezG3bKtqW2XXwxYeHDYDdGbNmt72J8nk8sLD3CqR0X3uJCUT5DBUWOuFIYm7GsO4ZgBqXr72YiEO0orsNSSTUk53ib7WcprhRfx4ljqE5SA23ARVfS7N9O3IoJpYZUqHZqB6P9i27ZoYY4RM+TFdz/vhB7kqjJakb7yGAAoy5JNlWlUVK1nILzdl1IuMtmORJarOETInH6e/VB70MsfCYQcuVcg3F0ewujh7Ks7dt2NZlT2sUgch3QQQw7+lWJ2R1qfpdOdnD6uD7vVQjB1tJ7gYbZugwkpqJuyN2l7eHmuRqyfKrFxGmUwJXvf0xiFsiy+nK12FtQlCqemiG0YrSWMuinSnkWXI0giH1lQOWzoyGg6bun0aRaYr3bGTFEe2YaiwEn3ikFmp3nWGvVHfzzmG6SAm4B5GKez+ievKBSFhQqfExoHXQwoVVtLQaC/rJMii52y/UDkWBbhFw3GMUyyxuTrYhbwE5eYYVatMLTh7SqvJBIT2VgKP2ehkctoxSjWtVn4yXemOOWGLqtsXXLNApaBUOmR96seK7PSZatlbZWFTbUOQjR3TqjrGpdncPIwbNzlti7du7GfNMe51rpXJ+Y5TrUAZi7vGsRoVVFhJQ6O7h9H3EqTuG7KbNURR1pmIVI5FAW7RsGBVdYBy63ffS1A19msUS6VJWWbzs2867Bilfgg9Lq5qn6Zij6YfBETVe1J5PjU6F/qK++lhRSQO8W11y3lxSHST031e6TqW6W5hiwoqrCT2OG+xVZQ5r2/vXnDeYhUKUpAZeXwpD8OWJVWmqyDTlvp4IOs67Gln5XK+hGvfBE3YSlkOueE/XJUOR6YEy6ruAOXIlf+spvXJlzW4mmOZy/W3TKvqk05qDRx+wKusjwHut9btU0Bf8dBOI6vrWGjZnrQO2XnKQrp5kdEeq5rl/IQo1I344syZCgu7bF51HJVV4fs8Oj2HARVWEnuUkzFQlzdBZUYWRTgk3YeVHwcY36GbasSPQqYbDsm3t7+ib4ImbIXV+S1F6DbdLDducmE77DkvQCqnEpeXJN/OU3VwZgs661BSrPpe2xCkM1vYqwFeQlO5yWmHRRRmVQu7bF51QgGqQltKwgVGRZV3pHDJZrM4++yzccghh+Doo4/GkiVLsHHjRgDAtm3bsGTJEsyZMwdz587F2rVro20s0Ue4fEg06F4Pw+WjW1+1eu0qH0KaHd17kjQGDX79Y6WwAsAXvvAFvPHGG+jo6MAZZ5yBL3zhCwCAq6++GgsXLsSbb76JO++8E+edd56n2HwkPgzkhrK05FFcviz95MtkIkZ3Cdr35ndZ35T0T5DOGo7jj+J6BOkcBJTEHuwH0Ff26S+TKcG2hva+7QLQU/bZBcCosoQX4kuSbuIAXeq5R8+1rCT+qBcZX3v0quAmo12n7vYF3fr8OJZpottW3eV5x8GxygurzBHS1/aFauVkcXg1HaBk20tkyOS0M8gVPJSTyDhzreIZ4DWubBjEakvA6NGjcdpppzn/XrhwIW655RYAwOrVq7FhwwYAwIIFCzBt2jSsXbsWixcvjqClMUMUFZJypUQIESvLpZMeVLH84CXNaBhoL+3rBlRXZTkCnP7RjeGqXCpTXA+loqDhWGYJDw5bspS3GFrymuxSbkcVJTDEcFhhbwmIYjk47Dr99GkkWzQsuO+btqo4a6l+u5pMjfdk2A5Jyq1WQH22MOkmOajjFgQ3OS/L7zIZ388rja02URArhbWc733vezjzzDPR3d0N27ax7777Ot+1t7ejs7NTWm7lypVYuXKl8+/+fm+5fZOIaZmAAKxeC8IYObCGPQfdHF2c44pJLshwSA4hKg9Khy2Fs5bu5vdszkP+aZmM1zlBIuesNOTgvuFeshqhm3qwkPPQNy4yjiVDtTdYYu0YkV/epa+85KmvGQ1nrbBDaek+WB1HLNk5AtUdtqogk5H9lhcZPwryAKqv2shkMvCQulgiY2IorNlOl0IpF8dCTecwQN9SqjtWdevz6mcQpD9CPp+vupacz1dag82Ch/B0BUn6YR+hu5w5TPG8ks1zVsHD9ZDI+MkgFwWxVVhvuOEGvPnmm7jtttuQyWQq0pyqtP7ly5dj+fLlzr9nzpxZt3ZGjWOxcsvikXF/+wo7Y1UU6HrP6nqz5ywPD2SJjB9nBMeyqHC6kVkftfO6ayq6xeZoLrOWpmZ1Oce6LF2F6KwVNs64l50joM7MJkT1tKUBWmYSZ0U2AEyB62pA0PVFlpWrxnJ+5jldtFe9hlMeK5BuX/JhYdVOQa1pYfUdfSVkYqmw3nzzzXjggQfwu9/9DmPHjsXYsWMBANu3b3esrJs2bcKsWbOibGa88JMBKszsQW77B+PqeNUM2bx08dM3zFhFGh2O8XgRh+ccFMejIkHP5Ng5Xa1cuRI/+9nP8P/+3//DpEmTnOPLli3DqlWrAADr1q3D1q1bcdxxx0XUyuCpWLqWbESXZmQafvuxikuppZ9hw0hc3pCcuHN9AHrLPn1lMiX4WWIJm0hynms6FSTKwu6ly2I2ucYdX05Xmg4puoTtyAaEb330s9fWsUhaLh8EbLVMkIXVaYbK8VJyOr6d52SOpSXOpbI52dnyoXKCkmwL8eLoJS1XmrXQzQk2JvoDEDML6+bNm/HlL38ZBx10EE488UQAQFtbG/74xz/ipptuwgUXXIA5c+agtbUV99xzD1paYtV8X+guXTtm/hRcXz/ikkK0IArq7QtZlw3lEex70iUKJVDbezZBCmsUGZIaHV8KaxM4liWSkLawJOl+dNqhSnQS5IvH8HYZmWMp4DzrlI5eIcX+BVDVCTZOxErjmzlzpqv1adq0aXjsscdCbhGpC1EslWl4tPsqp4tqGSlu+OmbsPuVECInQUvCiaJZtneFSKwUVlI7jrnehLszSkxM+roe+35wvDhVkRAknp7OxnaFl7hs87vulgCv8RWlS/vDx1QpJCXldB3LfMWL1Fza113yIu74WZ7VDb/jUOMLS5IsrM7WBNWcI9m+EMW2BwdFSk8Z9XTyqioXUppl7W0PfqIE5ApV0yXLIrA4capV6bllWwoLVlXF1EsEgrCgwppwnOV+xVJQXLYE6G578IOTjk5RpzQVqO2hX4NUWH0oD84xVTxVWXQBzTSpfmiG7QvNgG8FMiEWHV/UeI6+0w/rorEkHOmWgBq3PTjtULxA6O63DVopt2FXtXgr61T0jaw+J8qQgrjoDwAV1sTjWE8VN2NcLKxR4DvOnO6DtdZytSyllx/SVJJ1s6r4cSzTziA2bEFQEAenu2YhMotnAyu6kVpYG7hfHSVfsQKlGzNWWs60qlpJHbkynJduhcVbmnDAKlTV5mRxw23brup6H3R2NT9QYU04SXK6igI/MUPDxI+FtZ4ZWQJ3EPMT0aCawkoLa2gkyekmKbBP60PYjkx+4rDasKtavOOy9SUKqLCSeDgW6TpAeS2rS1KctZqFJFmCktTWpMD7ijQDYa3sJQwqrE2MswdTtVQSYPYgL78lzRziwyHJ+T3VlokA96LqlstblfFnZcjkdC2l2tZOD0vvbjLOuav2k8UodzWRE/aWgCQ5Xeli7zH3uTulJvwck4SuhdXrtg2ZnG8re40vdJFuQ9GACmsjoBmWxNkqoFgqcd1OoFGntmLlY4nFUX5UMfhkTlCabdWe5DxuS5DJabdV10EsiK0EupaAJFnYktRWEiyaczKAhreShU2S0s8mKVlFFFBhTTi5wlBe+j64WkkdGTdq9WYdtkgq6pRZLbO5IZdTRegNR6b0Jz1mzJHJ6WbkcfpM0VZZvzoKpcJqLVM6nVAlVbY9SEOa2B4sMzJLqelB8ZTIWKZVdeaoGmKqxjFXsAtVnQMKdnUnMhIMBXhw2PMgUzNJegnoRU2hoiIlpFBRUaA7VnWdrnKo8rxVyOm2dQADxT8Uzx1HpgRaWEmoFCwP2aMk3oF+cEJhKOqUWWZz9tANqgi94ciU1+e1XeXHNENpOfFHFW2VxigdtiyqQkxJrI/K8FuldUrCTGlbkXVDTEWwPJs0SwCpE0mxPhoAJkI+B+wIvzlVCSlDVhTEcfuKm5zvUFo1OpYlDSqsjUIUE3nYdUaROSQpD0ggWW1NkqWMEB2SdD+GTZKyQJHYQIW1ifHjPKODrtOVsBSOOthzzJGLkHruJ61Fzgu6Tld+rJ26WzRoYSWEAKj5ZTfshAx+EgfotrWe+23j5OhHhbWJ8X0j1zhx6CpzymV97DkWZFt1y0WqsNba1igU1iaZWAkhCqoYH5Q0sOU67BfzpBkCqLCSmtENFeUQ4oSj7dHuVZkNs5yLnLbVUjNKgPNioHDUcH150OyfyNJWknjRwM5BhISN41CluK/odEUSjaPgKRTPuGQd0g6lFbJDkp9lpMjeymvMW+21HXF6oycxo4GdgwiJjITcV1RYiT5JWpoJO3NIkOW8liWEkEbGT3xbkniosDYxhYIi9qcokynBccRSxf0M0FkrSU5XUaCdscq2qyrCXrOMeUZzSwAtswSAMs5kw5CkCBpRbNHoRzLGgKxvAG/9k6QxECJUWJuYghhSRhUWPUemBN3Yprp4/a2G2MOo4YzgK41sFYVVWi4CpysqrASAMs5kw5Cklauwl5INAOMldQ7F/44Vsr4B6rtC1+BQYU04luXBAcryoMjF+I3Vq5UvcGtgmPhxutJUWB1qzXTmwwFK1xpMpytCCAB9Zc5C5cuN1xUfnXKy1QCvZcMmIQoyFdaEE7a1Mwq8OnDFxdFLBz9OV0lC2xpMCyshxA8yi6eXR4ahWU62GuC1LJFChTVuNMPeFd1z9OOQ1Az9qgv7hhDSyNBZS01CngFUWGOCbhaoJKEbL9QPlvDQrx5k6k4EWwKELeQWgHKZMvwE8dfdEkAIIb5IirNWFFBhbXKERElQDADtAPcJIgrl0ff+zpCIYktAFMvsSbkehJA64WbthOK4X5LkrBUF3MPanJiWCdu2kclnkEqNvDts24Zt2zAtReaIhAwcX0RxjrXWKUr+7/bmGeDE6ic1q64S6DvdqcZ11I1M4KfOyEhSW0ljkJQxJ7N2AvW16iWlb4grVFgDxhIWBASMtAEjNfJuMAwDAiIeS9BEiaOoqbJ5JXz3vG+FVQPfCishJNm4WTsBbxbPsJO5kNhAhbWOGEaId0NC9qA0DVE4ljUDSRrnQY4BXv8ifvqmGfo1KfdHFPNcUvoGoFLuAhXWhOM4YqnisCbcWStJRLFHM0mhonQdvQghMSRJSiBJPFRYE47jiKVauk64s1Yg6E6sNZbzrQSqnBHihkafMp6qB6gEuMO+iRdRWPSaYQw0wzlqQIW1UWjwpQBdnBBJipzX0jBKXpXZ8kOaobscC2iV/NNSa6pmWx1qHDuBKJ0cr8ET5DKi17JhI8seVO8HOceqO+wbEiJUWElD4zgNKXJeyxyLdB2SfKcQ1cg/TaslaRpk2YO4gERIU0CFlTQHSdnE7sfaFYe2Npp1JQonn2boV1389E1I24J8k4Swf8NEsXTN+8OdBnf0pMJKSIA4S/aKiYNhmwghhJDaoMJKSIA4S++KN9a6Lc8naaN+Utrqp51hn2OQFkSvZcMmKeOG1A+OgaaFCish9SDEJZamSnfKZXaiSzOMgWY4R13YN4mnSkREQggJDjqIEUII0YEWVkIahWawIITtOJOkPo0irBUt3vEiKQ5iSQqllqSxmqS2akALKyEkNGhhJYQQogMtrITEiSQ5wDQDUTh4NINTCfuVhA2vf+KhwkpIDPDqFEXnKc1yUZCktiaFJMUoTVJbk0RSzjFJMbUTArcEEEIIIYSQWEMLKyEq4uDEAJdjxDtJsliE3VY/21CStMyalDGQpD5NEmFn82qW+ypEqLASIqGpYpuS5iYP+YObw9sfVDoICZREbQl488038Td/8zc45JBD8MEPfhCvvfZa1E0iDQq92T0gXD6NVmej0wqgTfJpjbJRdSDssWO4fBoJ3o8kRBKlsP7DP/wDvvCFL+Avf/kLrrzySlxyySVRN4k0Os3w0KmRplLmm+H6u51jLc4hYZUjJClEcV81OIlRWLdt24YXX3wR559/PgDgk5/8JDZs2ICNGzdG2zBCCCGEEFJXErOH9Z133sGMGTPQ0lJssmEYmDVrFjo7O9He3h5t42TYgAWr4piXcp6OBVUuijqDLBdFnTzHIn726IVdJ++r2spFUWe9r0dSxk4U91Uc7kevdfIc41MuZAyREM+RP//5z7jwwgvx6quvOscWLFiAb3/721i0aNEI2ZUrV2LlypXOv7du3Yr99tsvtLaW0t/fj/Hjx0dSdxJg/7jDvnGHfaOG/eMO+8Yd9o0a9o87QfXN9u3bkcvlpN8lRmHdtm0b5syZg+7ubrS0tEAIgenTp+P555+Pp4V1iJkzZ2Lz5s1RNyO2sH/cYd+4w75Rw/5xh33jDvtGDfvHnTD6JjF7WKdOnYp58+bh3nvvBQD88pe/RHt7e6yVVUIIIYQQ4p/E7GEFgNtvvx0XXXQRbrjhBkycOBE//vGPo24SIYQQQgipM4lSWA899FA899xzUTejJpYvXx51E2IN+8cd9o077Bs17B932DfusG/UsH/cCaNvErOHlRBCCCGENCeJ2cNKCCGEEEKaEyqshBBCCCEk1lBhDZC//du/xZFHHomjjz4axx9/PDo6OgAUQ3ItWbIEc+bMwdy5c7F27dpoGxoBbn2zePFiHHTQQTj66KNx9NFH4zvf+U60DY2Qb3zjGzAMA6+88goAjptyyvuHYwdob2/HYYcd5vTB/fffD4BjZxi3/uHYAXK5HL74xS9izpw5OOKII5wskhw77n3DcQPs2rXLOf+jjz4ahxxyCFpaWtDT01P/sSNIYOzcudP5+1e/+pWYN2+eEEKIv/u7vxNf//rXhRBCvPDCC2LWrFmiUChE0MLocOubE044QTz44IMRtSo+/PnPfxZLliwRs2bNEv/zP/8jhOC4KUXWPxw7QsyePdvpj1I4doq49Q/HjhCXX365+D//5/8I27aFEEJ0dXUJITh2hHDvG46bSv7v//2/4owzzhBC1H/sJCpKQNyZNGmS8/fu3buRShUN2KtXr8aGDRsAFLNzTZs2DWvXrsXixYsjaGU0uPUNKb7NX3bZZfjpT3+KE0880TnOcVPErX+IOxw7RMXAwADuvPNObN68GYZhAACmT58OgGNH1TekkjvvvBP//u//DqD+Y4daQ8BceOGFOOCAA/C1r30NP/7xj9Hd3Q3btrHvvvs6Mu3t7ejs7IywldFQ3jfDXHHFFfjABz6AT3/60/jrX/8aYQuj4dprr8X555+PAw880DnGcbMHWf8M0+xjBwDOO+88fOADH8Df//3fY/v27Rw7ZZT3zzDNPHbefvttTJkyBddffz2OPfZYHH/88Xj88cc5duDeN8M087gp57nnnkN3dzfOOOOMUMYOFdaAufvuu/HOO+/g+uuvxxVXXAEAzlvaMKJJI4nJ+uaee+7B66+/jpdffhnHH388zjjjjIhbGS7PPfcc1q1bh0svvbTiO44bdf80+9gBgGeeeQYvvfQSXnzxRUyZMgWf+9znAHDsDOPWP80+dgqFAv7617/i/e9/P/70pz/h+9//Pj7zmc/ANM2mHztufbN9+/amHzfl/OhHP8KFF16IlpbiYn3dx05gmwtIBaNHjxY7duwQY8eOFdu2bXOOL1iwQDz55JPRNSwGDPdNOW1tbdLjjco3v/lNMX36dDF79mwxe/ZskU6nxYwZM8Rvf/tbjhuh7p9ymm3slNPV1SXGjx8vhBAcOxJK+6ecZhs727dvF6lUSpim6RwbHiPNPnZUfVNOs42bUvr7+8WECRPE66+/7hyr99ihhTUgent70dXV5fz7V7/6FaZMmYK9994by5Ytw6pVqwAA69atw9atW3HcccdF1dTQceubiRMn4r333nOO//KXv8S0adMwZcqUKJoZCVdffTW6urqwceNGbNy4ETNnzsSjjz6Kj33sY00/bgD3/jnllFOafuwMDAxg165dzr9/9rOfYd68eQDAsQP3/jFNs+nHzj777IOTTjoJjz76KABg06ZN2LBhAw499NCmHztuffO+972v6cdNKT//+c9x5JFH4rDDDnOO1Xvs0OkqIHbv3o1PfvKTyGQySKVS2HffffHQQw/BMAzcdNNNuOCCCzBnzhy0trbinnvucUzozYBb3+TzeZx++unI5XJIpVLYZ5998Jvf/Cbq5saGZh83KnK5XNOPnffeew+f/OQnYVkWhBA46KCDcPfddwPg2AHc+4djp8htt92Giy++GFdddRXS6TT+8z//E9OnT+fYgbxv9t57b5xwwglNP26GueOOO3DJJZeMOFbvscPUrIQQQgghJNZwSwAhhBBCCIk1VFgJIYQQQkisocJKCCGEEEJiDRVWQgghhBASa6iwEkIIIYSQWEOFlRBCYsD3vvc9GIaBuXPnRt0UQgiJHVRYCSEkBvzoRz8CALz66qv44x//GHFrCCEkXlBhJYSQiPnTn/6El156CaeffjqAYlBuQgghe6DCSgghETOsoN544434m7/5G9x3330YHBwcIbN582YsXboUEyZMwKRJk3Deeedh3bp1MAwDd9111wjZP/3pTzjrrLOw9957Y/To0Zg3bx5Wr14d1ukQQkjgUGElhJAIyWQy+NnPfoYFCxZg7ty5uPjii9HX14ef//znjszAwABOPPFEPPnkk7jpppuwevVqTJs2DZ/+9Kcrfu/JJ5/ERz7yEezatQu33XYb1qxZg6OPPhqf/vSnKxRbQghJCkzNSgghEXLPPffgwgsvxG233YZ/+Id/QH9/P6ZPn4558+bhmWeeAQDceuutuOyyy/Dwww9jyZIlTtl//Md/xO23344777wTF110EQDg8MMPx5gxY/DCCy+MyON95pln4s9//jM2b96MVIq2CkJIsuCsRQghEXLHHXdgzJgx+MxnPgMAGD9+PJYtW4Znn30Wb775JgDg6aefxoQJE0YoqwBw7rnnjvj3W2+9hf/93//FeeedBwAwTdP5nHbaadiyZQveeOONEM6KEEKChQorIYRExFtvvYVnnnkGp59+OoQQ2LVrF3bt2oWlS5cC2BM5oLu7G9OmTasoX37svffeAwB85StfwahRo0Z8Lr30UgDAjh076nlKhBBSF1qqixBCCKkHP/rRjyCEwC9+8Qv84he/qPj+xz/+Ma6//npMmTIFL7zwQsX3W7duHfHvffbZBwDwL//yLzjnnHOkdR566KEBtJwQQsKFCishhESAZVn48Y9/jPe973344Q9/WPH9Qw89hG9/+9t4+OGHccIJJ2D16tV4+OGH8bGPfcyRue+++0aUOfTQQzFnzhy89NJLuOGGG+p+DoQQEhZUWAkhJAIefvhhdHV14aabbsLixYsrvp87dy6+//3v44477sC9996L73znOzj//PNx/fXX4+CDD8bDDz+MRx99FABGOFHdfvvt+NjHPoZTTz0VF110Efbff3/09PTg9ddfx4svvjgi+gAhhCQF7mElhJAIuOOOO9Da2oq/+7u/k36/zz774BOf+AQeeugh9Pf344knnsDixYtx5ZVX4pOf/CQ6Oztx6623AgAmTZrklDvxxBPxwgsvYNKkSbj88stx8skn45/+6Z/wu9/9DieffHIYp0YIIYHDsFaEEJJQbrjhBnzta19DZ2cnZs6cGXVzCCGkbnBLACGEJIDvf//7AIDDDjsMhUIBTzzxBL73ve/h/PPPp7JKCGl4qLASQkgCGDt2LL7zne9g48aNyOVymDVrFq666ip87Wtfi7pphBBSd7glgBBCCCGExBo6XRFCCCGEkFhDhZUQQgghhMQaKqyEEEIIISTWUGElhBBCCCGxhgorIYQQQgiJNVRYCSGEEEJIrKHCSgghhBBCYs3/Bxxv4kDb7PuLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.bar(df_new['Age'], df_new['Tumor Size'], width=0.5, color=\"green\", edgecolor=\"darkgreen\", alpha=0.5)\n",
    "\n",
    "plt.legend (['Age'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Age', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "plt.ylabel('Tumor Size', fontsize=15)\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/1-Age&Tumor_Size(bar).png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "1452d8c3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.bar(df_new['Tumor Size'], df_new['Status'], width=0.5, color=\"green\", edgecolor=\"darkgreen\", alpha=0.5)\n",
    "\n",
    "plt.legend (['Tumor Size'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Tumor Size', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "plt.ylabel('Status', fontsize=15)\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/2-Tumor_Size&Status(bar).png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "aad0f04e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.bar(df_new['6th Stage'], df_new['N Stage'], width=0.5, color=\"green\", edgecolor=\"darkgreen\", alpha=0.5)\n",
    "\n",
    "plt.legend (['6th Stage'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('6th Stage', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "plt.ylabel('N Stage', fontsize=15)\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/3-6th_Stage&N_Stage(bar).png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "eb045596",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.bar(df_new['Survival Months'], df_new['Marital Status'], width=0.5, color=\"green\", edgecolor=\"darkgreen\", alpha=0.5)\n",
    "\n",
    "plt.legend (['Survival Months'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Survival Months', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "plt.ylabel('Marital Status', fontsize=15)\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/4-Survival_Months&Marital_Status(bar).png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "a95b98b0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.bar(df_new['Regional Node Examined'], df_new['Reginol Node Positive'], width=0.5, color=\"green\", edgecolor=\"darkgreen\", alpha=0.5)\n",
    "\n",
    "plt.legend (['Regional Node Examined'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Regional Node Examined', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "plt.ylabel('Reginol Node Positive', fontsize=15)\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/5-Regional_Node_Examined&Reginol_Node_Positive(bar).png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e17297d",
   "metadata": {},
   "source": [
    "***\n",
    "***\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d33c6db",
   "metadata": {},
   "source": [
    "We can also overlap two plots!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "d64415d8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2BCqbo6MjPDw8KnsaRERERFSGGzduGHy4TWn/+EDr4eGBtLS0yp4GEREREZXB19e33Ot5ygERERERKRoDLREREREpGgMtERERESkaAy0RERERKRoDLREREREpGgMtERERESkaAy0RERERKRoDLREREREpGgMtERERESkaAy0RERERKRoD7aOUdxlIqAok2Oq/5102rrl1DEhQFX/dOmZck/mnYU3mn6bHu7nPsO7mPuOaO2cK56PSf79zxrgmNxlIcAYSbPTfc5NNj5d12HC8rMMPNicASPzasC7xa+OalG8Na1K+Na5J22hYk7bR9HjnvzKsO/+VcU3q94Y1qd+b7uvab4Z1134zrrm0zrDm0roHu38AcOUnw7orPxnXXN5gWHN5g+m+0ncY1qXvMK6R85y5vtuwn+u7TY8n5/kupwaQ9+/r3hXgXE0gwV7//d4V45qCLOBiM+Ccu/57QZbp8fIzgPPeQIKT/nt+hnGN3H87cuYlp0Z7D0ifCFzuqf+uvWd6PDl1QgvkxgF3Nui/C+2D96UrADKXAhmv67/rCoxrNHeB1P5AUoj+u+au6fHkzsta5Iz3qOdUGeQ+t55klfE4/xOeW1aiEkKIRz2ov78/nJyc4OjoiLy8PAQHB2PatGlo3769VcfYvn07mjZtWm6dr68v0tLSrDZumRLsAWhMXGEHBKkLa1Rl3z5IyK+xZl8JtgB0JgpsgKAS/7Aey7k/4vGs2RfnbmFfcv59OQIwEaTgAATd1/94wRvQXjMusfUGAtOLL5+rAog84zqVC9Aot3A8uf92ZMxLTk1aP+DuVuMS136Ab4k/cuTU5WwGMl4DdLcA2ANQAzZugHc0ULW/ZX1dnwpkfQrDtbABak4BPOfrL6a0BvKPGPfj1BrwP1R8We68rEXOeI96TpVB7nPrSVYZj/M/4bllAXN5rdICbcmwuXXrVowaNQq//vor2rRpUyFjlOWRBNrCX7Z9Gz8PIAyACoAAcBzbz24FYIeiX8avPvc8Ui8U19QNPI6v/mv4H8m7Ud1x6tDTUk2zNvsxN6bwKGCQkELAh5M64shvXaS6Vt13Y9bnfxj0tWRGG/y+6RmppuvAn/HWJ0W/QGwA6LApJgir5g+TakZP+w4DoxIg/WIuHK/sumInD9XBe1EvSzVzYv6DkDZXjea+dX1DfP3xv6S6cTPX4/l/nTfo6/f/BmDJtBelmrfmr0HX55IMao7v98XMl16Saj5esQJhT6cZjXdodz18/NoYqW7ml9+gTRfDI3wX/q6FyYNfgxA2UKl0WPzjlwhsctOor3Lr5Myr0NE9dTF7/FipZvbylQiPTDUaL+GEJ6YMmyDVLfouGkHNrxv0dfe2AxZMGYKU817wb3gNUxf9ANfqBQ/UV3amE2aMjUJGak14183CJytjUMM9X36N1eeu//d185oL3nnhZWTdqIaaHnewcMN/UMsrD/p/XzYACpCfZ4fVS7rj/CkfNGx2BaPe+g1OLhoADoCtG6C9VnaNrRcQmCGF2TLvo8oFEPkAdOXMvejfjj6olr1eDoWrYGburs8Ad7dCo7bBgd+CkZTgjYCgDLTvfgZ29jrA9XnAd0vZwaSI6/NA9ReBK8OgUWtN9GUL+HwHVB0g9VXumA4NgayFZdfUfAfIizMdZos4tQL8D+t/uV8ZBkBtosi+eF7WImc84NHOqQJo1Foc2JyApPgMBIR6o/2AIP3jXETOc8Z3S0VPs3I96udeZY35mFNEoAWA9957DxcuXMD69esxc+ZM7N69GwUFBQgKCsLy5ctRo0YNrF+/Hp999hkKCgoghMCcOXPQu3dvAMDevXsxYcIEODs7o3Xr1ti2bRt27NhR+YE27zJw2a8wzLYwUfBXYagtCrPGNXUD/5JCrT7MdjCqadZmnz7UqmIAEVUYZrsa1bXq/rsUavVhtrdRTdeBO6VQqw+pLxjVjJ62QR9Wq2wDcp81X4eiMPuKUc2cmH8Xhtr3AMwpDLPDjerGzVwnhVp9mB1lVPPW/NVSqNWHxnFGNR+v+LowPE4E8EVhmB1rVDfzy5VSqL3wdy28Neh1o5olG5cVhtV5AKbLqJMzr6Iw+5JRzezlKwpD7fsA/q8wEL5mVLfouy+lYHj3tgNeaP8ehK74aKfKRmDDgTmF4epjADNl9ZWd6YQRHd41qlm7b64UWM3WqFYDYpR15l41FsjpjJvXXBDVaZpRXzFx8wtDLZCfZ4cREdOQn+cgXe/kUoC1e+cXBkMZNXUuAFcDZa2D2bnX+gu42UJWX3LmrlHbYPqLY5B0tjY0alvY2WsR0Dgd89Z8ow+PAdlAUg2jcYx5QKPOLLsvJ0/gqUQg0dX8mFBBo1aVUwOYPopdSv27QEoDQGvitI4itt5AgyuAygpn0gktkOhrfjwhAJ2JI/oVMacKoFFrMT1yDZKOZ0Cj1sLO3hYBYd6Yt+dFfajV3gMuuJjvKPA+YOtgvk6J5D4XrPk4V8aYCmAurz02K9GqVSv8/fffWLhwIVxdXXH48GHEx8ejSZMmmDVrFgCgZ8+eOHjwII4fP44tW7bgpZdeglqtxv379zFs2DAsW7YMhw8fRseOHXH5sonz5yrD5SaFP4QVfleV+CrZjsIjs8Y1xe0oPDJrXCO1iygAKDwya1xX3I7CI7PGNcXtKDzialwjtec+K68OKDwya1xT3D4HAAqPzBrXFbej8MiscU1xOwqPgBrXFLd/AQCFR2aN64rbgcmDXzNZU9w+XWadnHmh8MiscU1x+/8BQOHRTeO64nZgwZQhhaGq+EvoVFgwZUjRjGT3NWNslMma4nYZNWKU9eae0xkA8M4Lpp9bxe3A6iXdCwNhcU1+ngNWL+kuv+ZqkOx1MDv3my1k9yVn7gd+C0bS2doouG8Pnc4GBfftkXS2Ng78FqwvSDP+Q9i0zPL70t0C0kfIGxPCTI2MMAsAV3oVvuxaDt0t4N5emffRjLy95sfTZgK6Ms6vrog5VYADmxOQdDwDBfka6LQCBfkaJB3PwIHNha+sXX9HXkc33q64SVY2Oc8Faz/OlTHmE+CxCbRFB4q3bNmCtWvXIjQ0FKGhodiwYQOSkvRH3JKTk/HMM8+gadOm6NevH27evIlLly7h3LlzcHFxQadOnQAAQ4YMQfXq1U2Os3jxYvj6+kpfd++W8cYDqyk6z05VxvWqMn5+mBolj2fNvqw3nhCm/6mUbpdX92jnnnLey2SFcbv5vjJSa5qsKNkup0bueHLnnnWjmsm6ku3nT/mYrCnZbr5Gf+6rnPsod+5y+pIz96QEb2jUtgbXa9S2SErw1l9Qp0Ou8vuyB9QX5Y0ps8YszWX9uOWyBzTy72O5tOnmx1PZwPyvUCvOqQIkxeuPzJakUWuRFF94ZFB9QV5HcuuUSM5zwdqPc2WM+QR4bALtkSNH0LRpUwghEB0djfj4eMTHx+PMmTPYtm0bAGDYsGEYP348Tp8+jfj4eLi6uiI/Px+WnDUxefJkpKWlSV+urq4VdZcKFb1cU9YcRRk/P0yNksezZl/WG0+lMn0kqXS7vLpHO3f/hqZfEjVuN9+Xd13TR6RKtsupkTue3LnX9Lhjsq5ke8NmJnYFKNVuvkYfzOTcR7lzl9OXnLkHBGXAzt4wnNjZaxEQVBhO7Gub7MOU8vtSA/b15Y0ps8Ysu3owfS5hSWrATv59LJdtbfPjCR3MH2G24pwqQECot+H5soD+tIPQwj827APldSS3TonkPBes/ThXxphPgMci0G7duhVfffUVJk+ejOeeew6LFy9GXp7+yGZeXh7+/vtvAMCtW7fg7+8PAFi7di1u3dIfkg8KCsK9e/fwxx/6c0M3btyI27dvP/o7Ykq9vwt/OF74XZT4KtkO1A00XVPcDjRrs99kjdSuigEAtOq+22RdcTvQdeDPJmuK24HR074zWSO1V9kmrw7AnJj/mKwpbn8PADBu5nqTdcXtwFvz15isKW4HPl6xwmRNcftEAMDML78xWVfcDiz+8UuTNcXt82TWyZkXMHv5SpM1xe3vAwAWfRdtsq64HZi66AeobIRBjcpGYOqiH4pmJLuvT1bGmKwpbpdRo1ptvblXjQUALNxg+rlV3I7CN1EVGNQ4uRRg1Fu/ya+pkyB7HczOvdZfsvuSM/f23c8goHE6HBzVsLHRwcFRjYDG6WjfvXBbNd8ytsgz4l5+XzZuQO218saEykyNzF9BPr/oxy2PjRvgHCHzPprhEmF+PFt3wMb00fUKmVMFaD8gCAFh3nBwsoONrQoOTnYICNO/MQwA4LlQXkcen1bcJCubnOeCtR/nyhjzCVDp23bl5uYiODgY06dPR/v27aFWq/Hhhx9i69atUKn0Lz1OmzYNw4cPx9q1azFz5kz4+PigXbt2+OGHH6Q3fpV8U1inTp3w/fffPx5vCgO4y0EJ3OVAxrwKcZcDuXO31i4HNQFtRjm7HBRu3fVY7nLQG7i7pZwdB/rpt1dK6w/c3YIyufYr3OVgaDm7HHyv3zKosK9yx3RoCGQtKGeXg6mFuxyY2LO6SNHWXTk/AVeGoux3fX9v3a2M5IwHPNo5VQDzuxzIeM486Vt3PernXmWN+Zh7LHc5eJxwH1pzNdyHtlL64twt7Iv70HIf2gr45c59aPW4Dy33oX0MMNCa8cgCLQDk3QRSAwCRC6iqAHWTAJdahjW3koBr9Ysve10E3AIMazLPAzcaFV/2OAe4NzQe72YCcLNx8eVaZ4FaQYY1d9KAq/7Qv9nFFqiTAlTzNazJvQ6k+gPIB+AE1E0Bqngaj5eVCFwvcS6V5wWgZgPL5wQAib8AmuLdFmD3M9Cgl2FNShyQ37n4slMs4N/JsCbtIHC3XfFl1z8B37bG453fDuieLb5ssw1o2NewJvUAkPt08eUq+4G6Jj4M5NpJ4Fbz4stuJwCvEMOaS38A9yKLLzvvAfw6GtbIuX8AcOUwkFNi/+aqhwCf1oY1l/cBeSVennLZC9Qz8a739L+A2y2LL1c/BtQutZWcnOfM9dNAVrPiyzVPAZ4mXi2R83yXUwPI+/d1LwtIDQR0dwCbakDdC4BzqZeNC+4Cae0BzVXArg7gewBwMHGufX42kNoY0N7S72Fb9yzgVMOwRu6/HTnzklOjLdC/41x9QX9eo8enprdTklMndPp3UWvS9efqOUfA5BZBcvrSaYDsL/VvJrOvD9R4DbCxM6zR5AMZ/yqu8V4P2DkZjyd3XtYiZ7xHPafKIPe59SSrjMf5n/DckomB1oxHGmiJiIiIyGKK2YeWiIiIiOhBMNASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRojHQEhEREZGiMdASERERkaIx0BIRERGRolkUaPPz83Hnzh2Dth9++AHTp0/H77//btWJERERERHJYVGgHTlyJCZNmiRd/vzzzzFs2DAsWLAAPXr0wM6dO60+QSIiIiKi8lgUaA8fPoxevXpJlz///HOMGDEC2dnZGDBgABYtWmT1CRIRERERlceiQHvjxg34+PgAAJKTk5GUlITXX38d1apVw9ixY3H69OkKmSQRERERUVksCrQuLi64ffs2AGDv3r1wdXVFeHg4AMDJyQl37961/gyJiIiIiMphZ0lxs2bN8OWXX8LPzw/R0dHo3LkzVCoVAODy5cvw9vaukEkSERGRPDqdDkKIyp4GkcVUKhVsbB5sAy6LAu3MmTPRt29fhIaGwsHBAbt27ZKu27FjB1q0aPFAkyAiIqKHU1BQgMuXL0OtVlf2VIgemL29PerVqwcHBweLbmdRoO3SpQvOnj2LY8eOITQ0FAEBAQbXhYaGWjQ4ERERWcfly5dRtWpVuLu7S6+eEimJEAKZmZm4fPkyGjRoYNFtLQq0AODn5wc/Pz+j9ldeecXSroiIiMgKdDod1Go13N3dYWdn8a92oseGu7s7srKyoNPpLDr9wKJn/eXLl83W1KtXz5IuiYiI6CEVnTPLI7OkdEXPYUvPA7co0Pr7+5v9x6LVai2aABEREVUSoQXy9gLadMC2NuASAahsK3tWRBaz6K1k33zzjdHXggUL0KFDB/j7+2PFihUVNU8iIiKyppzNQKIvkNYLSH9Z/z3RF8j5ySrd+/v7IygoCKGhoWjUqBHmzZv30H327t0bFy9etMLsjKWkpKBWrVomr4uJiYFKpcLatWultu3bt6NTp04WjxMTE4NBgwZZfLuS61n0debMGYv7eRAVse6dOnXC9u3brdafRUdoo6KiTLa//fbbGDx4MFJTU60xJyIiIqpIOZuBK8MAFO2IcF//TZsBXBkK+HwHVB3w0MNs3LgRTZs2xdWrVxEcHIwuXbqgdevWD9zfzp07H3pOD8rf3x8zZ87E4MGD4ejoWClzKFrPR60y112uB9vsy4SoqCgeoSUiInrcCS2Q8RqKw2xpav31Qme1IevUqYNGjRrh0qVLAICMjAwMGTIErVu3RkhICD744AOpdu/evWjWrBlCQkLw+uuvw8/PT/okUn9/f+nnxMREdOvWDSEhIQgNDcWWLVukPlQqFebPn482bdrgqaeewqpVq6Tr3nnnHbRq1QqhoaGIjIzEhQsXZN2Hli1bonnz5oiOjjZ5/bfffivNu0+fPrhy5QoA/XZqr7zyCho2bIjOnTvj0KFDBrdbtGgRWrdujRYtWqB3794WHxxMSEiAr68vkpKSAAALFy5E7969IYTAqVOnEBERgRYtWiA4OBhz586VbhcVFYXx48eja9eu8PPzwxtvvIHY2Fh07NgR/v7+WLx4sVRbct07deqEadOmISIiAvXr18f48eOlupycHIwbN056XMePHy9tI3fmzBm0adMGLVq0wPDhw5Gfn2/R/TRLWMmWLVtE1apVrdXdI+Pj41PZUyAiInooGo1GnDlzRmg0GvPFd2OFSHAU4izK/kpwFCI37qHm5OfnJ06dOiWEEOLs2bMiICBAXL9+XQghRI8ePcSePXuEEEKo1WrRs2dPsXnzZpGfny98fHzEH3/8IYQQYvPmzQKA1E/JPlu3bi3+/e9/CyGEOH/+vKhZs6a4fPmyEEIIAGLp0qVCCCHOnDkjXF1dhVqtFkIIcePGDWmOGzZsEH369BFCCJGcnCzc3d1N3pdVq1aJgQMHijNnzggvLy9x+/ZtsW3bNhEZGSmEEOLUqVPCy8tLpKWlCSGE+OSTT0Tv3r2FEEJ8/vnnonv37qKgoEDk5uaKli1bioEDBwohhFi3bp0YN26c9LitWbNGPPfcc2WuZ6NGjUTz5s2lr/v37wshhFi/fr1o2bKliI2NFf7+/tJ9vHPnjsjPzxdCCJGXlydCQ0PFkSNHhBBCjBo1Sjz99NMiPz9f5ObmCg8PDzF69Gih1WpFWlqaqFKlisjJyTFa98jISDFw4ECh0WhEXl6e8Pf3FwcOHBBCCDFu3DixZs0aIYQQOp1OjB07VixevFgIIUSLFi1ETEyMEEKIP//8U9jY2Iht27YZ3c+ynsvm8tpD7+2hVqtx8uRJzJo1C82bN3/4hE1EREQVR5sOwB7SaQYm2QOa9IceatCgQVCpVDh37hyWLFkCDw8P5ObmYvfu3bh27ZpUd/fuXSQkJKB+/fpwdnZGREQEAKB///6oUaOGUb85OTmIj4/H2LFjAQCBgYHo0KED9u3bhxdeeAEAMHz4cABA48aNYWdnh4yMDPj6+uJ///sfli1bhpycHOh0Oty5c0f2/WncuDH69u2L+fPno127dlJ7bGws+vbtCx8fHwDAhAkT8Mknn0AIgdjYWIwaNQr29vawt7fHiBEjsG/fPgDAli1bcPToUbRs2RKA/o31trZlvymvrFMOXnjhBcTGxqJnz574/fffpXOB7927hwkTJiA+Ph42NjZITU1FfHw8wsPDAQD9+vWTTp9o1KgRevfuDRsbG/j4+MDNzQ1paWkICgoyGm/YsGGwtbWFs7MzQkNDcfHiRbRr1w5btmzBwYMH8emnn0rjOzg44M6dOzh9+jRGjhwJAGjbti2aNWsme93lsCjQ2tjYlLnLgZubG3799VerTIqIiIgqiG1tlH26QRE1YFf7oYcqCmC7du3Cs88+iy5dukg7Jh05cgT29vYG9SdOnJC19ZgoY5uykpednJykn21tbaHRaHD58mVMmjQJhw8fRkBAAE6ePIkuXbpYdJ8+/PBDNG/e3GBPfiGEwdglfxblbD8lhMCMGTMwZswYi+ZQmkajwenTp1GzZk3pVAcAeO+99+Dl5YXjx4/Dzs4OAwYMMHipv/QamVozU8qqE0Jgy5YtBh+8BQB37typ8C3lLDqH9oMPPjD6mjNnDtavX4/k5GTpLwwiIiJ6TLlEADZu5dfYuAHOEVYbslu3bnj11VcxY8YMVK1aFREREQa7Hly9elU6Gpibm4v9+/cDALZu3Yrs7Gyj/qpVq4bQ0FCsXr0aAHDx4kXs378fTz/9dLnzuH37NhwcHODt7Q0hBL744guL74uPjw9eeuklg/NRu3btip07dyIjIwMAsHz5cnTt2hUqlQpdu3bFt99+C41Gg3v37mH9+vXS7Z577jlER0cjKysLgP5V7+PHj1s8p+nTp6NRo0b4448/8PbbbyMxMREAcOvWLfj6+sLOzg7nzp3Db7/9ZnHflnjuuecwb948KeDeunULiYmJqFatGpo2bYp169YBAA4fPoxTp05ZdWyLjtDOnj3bqoMTERHRI6ayBbyj9bsZmDxSa6+/XmW1940DAGbOnIkGDRrg2LFjWLduHSZPniy97Ozq6orly5fD19cX69evx/jx4+Hs7IzOnTvDy8sL1atXN+pv3bp1eOWVV7B06VKoVCqsWLECdevWLXcOzZo1w+DBg9GkSRPUq1cP3bt3f6D7Mn36dHz99dfS5SZNmmDu3Lno0aMHAKBu3br4z3/+AwB4+eWXcfLkSQQHB8PX1xcRERHSm+NGjhyJzMxMdOrUCSqVChqNBmPHjkVYWJjJcQcNGmRwdHTZsmW4ffs2fvnlFxw+fBguLi5YtGgRBg8ejD///BMzZszAyJEjsW7dOvj7+1t8NNpSS5cuxbRp0xAaGgobGxvY29tj/vz5aNCgAdasWYPRo0djyZIlaNGiBdq0aWPVsVWivGPhpXTp0gXR0dEmz6c4f/48xo8fj927d1t1ghXN19cXaWlplT0NIiKiB6bVanH+/Hk0bNiw3HMwDeRs1u9moLsF/Tm1av2RWe9ooGr/ipxu+dPKyUHVqlUBQDr/NCUlxaKPQSXlKuu5bC6vWXSENi4ursyTp3NycrBnzx5LuiMiIqLKUnUA4NoPuLdX/wYwu9r60wysfGTWUps2bcKSJUug0+ng6OiIDRs2MMySWQ+9y0GR9PR0uLi4WKs7IiIiqmgqG8AlsrJnYSAqKqrMD3IiKovZQLt161Zs3bpVuvzxxx/Dw8PDoObevXuIi4sr85wPIiIiIqKKYjbQnjlzBj/++CMA/TYUu3fvNjr07+joiGbNmuGzzz6rmFkSEREREZXBbKB999138e677wLQ70MbGxv7UJ/DTERERERkTRadQ6vTWe9znYmIiIiIrIFvGyQiIiKr8/f3R1BQEEJDQ9GoUSODD1J4UL1798bFixcfel6nT582ak9JSYFKpcJLL70ktd29e/eBPuEqJSVF+vhZS0RFRcHX1xehoaFo3LgxXnnlFajV5j7VzbSSaxUTE4Pz589L1/33v//FO++880D9Pq4sDrRr165FeHg4qlSpAltbW6MvIiIiIkD/0bfx8fGIjY3FvHnzcPjw4Yfqb+fOnahfv76VZmfM1dUV27dvx5kzZypsDHOmT5+O+Ph4HD9+HCdPnsTy5csfqJ+Sa1U60D733HNYuHChVeb7uLAo0P73v//F6NGjERYWhnv37mH06NF44YUXUKVKFQQGBuKDDz6oqHkSERGRlWnUWvzx/d+Iefd3/PH939CotRUyTp06ddCoUSPpE7IyMjIwZMgQtG7dGiEhIQb5Ye/evWjWrBlCQkLw+uuvw8/PTzqiWvLoaqdOnTBt2jRERESgfv36GD9+vNTHtWvX0L9/fzRr1gxNmzaVPrXLHEdHR0yfPl1671BpR48eRbt27RASEoLWrVtLH9ELAF9++SUaNGiAiIgIrFixwuB2v/76Kzp06ICWLVuiTZs2+OOPP8zOxcnJCRERETh37hy0Wi2mTJmCpk2bomnTpnj99ddRUFAAAFixYgWCg4MRGhqKZs2a4dChQwZrtWLFChw9ehSTJk1CaGgodu7ciZiYGAwaNAiA/mOJN23aJI0bGxuLFi1aANB/xsC4ceOkx2n8+PEPfMS4wgkLtGvXTkydOlVoNBqhUqnEsWPHhBBCpKeniwYNGoiVK1da0t1jwcfHp7KnQERE9FA0Go04c+aM0Gg0sm+jLtCIt9t9I/o7zRHP2n4s+jvNEW+3+0aoC+T3UR4/Pz9x6tQpIYQQZ8+eFQEBAeL69etCCCF69Ogh9uzZo5+HWi169uwpNm/eLPLz84WPj4/4448/hBBCbN68WQCQ+inZZ2RkpBg4cKDQaDQiLy9P+Pv7iwMHDgghhBgyZIiYPn26EEKIa9euCV9fX3Ho0CGjPkpKTk4W7u7u4v79++Kpp54S+/btEzk5OaIoKt2/f1/UrVtX/PLLL0IIIfbu3Su8vb3F3bt3xYkTJ0Tt2rVFRkaGEEKIV199Vbi7uwshhLh48aJo166duH37thBCiAsXLog6deqIgoICozmMGjVKLFu2TAghRFZWlggJCRHffPONiI6OFp06dRL5+flCrVaLZ555RixYsEAIIUS1atXElStXhBBCFBQUiJycHJNrtW3bNmmcVatWiYEDBwohhFi3bp3o06ePdN2LL74oPv/8cyGEEOPGjRNr1qwRQgih0+nE2LFjxeLFi0093FZT1nPZXF6z6AjtuXPn0K1bN+l8Eo1GAwDw9vbGjBkzsHjxYmvnbSIiIqoABzYnIOl4BgryNdBpBQryNUg6noEDmxOsNsagQYPQuHFjBAcHY9KkSfDw8EBubi52794tHTEMDw9HYmIiEhIScO7cOTg7OyMiIgIA0L9/f9SoUaPM/ocNGwZbW1s4OzsjNDRUOmd0165deO211wAAnp6eGDBgAH7//XdZc3ZwcMBHH32EqVOnGrSfO3cODg4O6NmzJwCgQ4cO8PT0xMmTJxEXF4c+ffrAy8sLAPDyyy9Lt/vll1+QmJiIjh07IjQ0VDoympqaanL8efPmISwsDF27dsXAgQMRFRWFXbt2YezYsXB0dISdnR3GjRuHXbt2AQC6dOmCF198EZ999hmSk5Ph6uoq634WGTBgAA4ePIiMjAzk5ORg27Zt+Ne//gUA2LJlCxYuXIjQ0FCEhYVh7969uHDhgkX9PyoW7XKg1Wrh4OAAGxsbVKlSBRkZGdJ19erVQ1JSktUnSERERNaXFJ9hdIqBRq1FUnwGOg5tYpUxNm7ciKZNm2LXrl149tln0aVLF/j7+0OlUuHIkSOwt7c3qD9x4oRFb8JycnKSfra1tZUOtAEw6seSfocPH45PP/3U4IOlhBAm+1CpVBBClNmXEAK9evXCmjVrZI09ffp0TJw40aiPsu7P5s2bcezYMcTFxaF379745JNPMGzYMFljAfo1HDRoENauXQs3Nzd069YN7u7u0rhbtmxBQECA7P4qi0VHaJ966ilcvXoVANC8eXNs2LBBum7jxo2oXbu2dWdHREREFSIg1Bt29oZv5razt0VAqLfVx+rWrRteffVVzJgxA1WrVkVERITBrgdXr15FWloagoKCkJubK52bunXrVmRnZz/QeEXnzd64cQM//fQTunTpIvv2KpUKc+fOxYwZM6S2oKAg3L9/H7t37wYAHDhwANevX0ezZs3QuXNn7Ny5E9evXwcArFy5Urpdjx498MsvvxjsrGDpm+O6d++OmJgYFBQUQKPRYOXKlejWrRs0Gg0uXryI8PBwTJkyBYMGDTLZd7Vq1XD79u0y+x8zZgxiYmKwatUqjB49Wmp/7rnnMG/ePOkPhVu3biExMdGiuT8qFgXarl27Soe433jjDXz//fdo0KABgoODsXz5coMTsomIiOjx1X5AEALCvOHgZAcbWxUcnOwQEOaN9gOCKmS8mTNnYt++fTh27BjWrVuHs2fPolmzZmjWrBkGDhyIzMxMODo6Yv369Rg/fjxat26NAwcOwMvLC9WrV7dorM8//xwnT55ESEgIOnfujPfff9/iD4Xq1auXwZFJBwcHbNq0Ce+//z5CQkLw5ptv4scff0SVKlUQEhKC9957D+3bt0eHDh1Qp04d6XaBgYFYu3YtXnrpJTRv3hyNGze2+JNVX375ZTRv3hwtWrRAaGgo/P39MWnSJGi1WowePRpNmzZFaGgojh07hsmTJ5u8/UcffSS9Kay0orVJTk5Gjx49pPalS5fCzs4OoaGhCAkJQbdu3ZCSkmLR3B8VlSjvOHkp9+/fx/3791GtWjUA+sPc69atg0qlQt++fREVFVVR86wwvr6+SEtLq+xpEBERPTCtVovz58+jYcOGFm2hqVFr9efSxmcgIFQfZksftX3UcnJyULVqVQD6d9yPGjUKKSkpsLHh1vn/BGU9l83lNYvOoXV0dISjo6N0ecCAARgwYMADTJeIiIgqm529LToObWK1c2atYdOmTViyZAl0Oh0cHR2xYcMGhlkyy6JnSEBAAE6cOGHyutOnTyvipGEiIiJ6fEVFReHEiRM4deoUjh49iqeffrqyp0QKYFGgTUlJwf37901el5+fL22YTERERET0qFh8DL+sbS+SkpKkc16IiIjo0Sn63WzB22KIHktFz2FLtlkDZJxDu3r1aqxevVq6/Oqrr0pvCity7949nDhxApGRkRYNTkRERA/PxsYG9vb2yMzMhLu7u8VhgOhxIIRAZmYm7O3tLT5v2mygzcvLw40bNwDo03J2drbRaQeOjo4YOnQoPvzwQ4sGJyIiIuuoV68eLl++jKysrMqeCtEDs7e3R7169Sy+nUXbdj311FPYsmULmjdvbvFAjytu20VERE8SnU7HUw9IkVQqVZlHZq26bVdycrL0c1ZWFhYsWIDTp0/Dx8cHb7zxBoKDgy3pjoiIiKyMW1zRP5HZQDtlyhT88MMPuHz5stSWm5uL8PBwXLp0Sfor8LvvvsPhw4fRqFGjipstEREREVEpZv+MO3DgAIYNG2bQ9sUXXyAlJQVvvvkmsrOzceDAAbi6uhp8LjMRERER0aNgNtAmJSUhPDzcoG3btm3w8PDAggULUK1aNbRt2xaTJ09GXFxcRc2TiIiIiMgks4E2OzsbtWvXli5rNBocOXIEnTp1MviM3bCwMKSnp1fMLImIiIiIymA20Hp5eRkE1b/++gtqtdroqK2NjQ0cHR2tP0MiIiIionKYDbQtW7bE119/Lb35a926dVCpVOjatatBXUJCgsGRXCIiIiKiR8HsLgfTpk3D008/jUaNGqFWrVo4ePAgIiIi0KJFC4O6bdu2oVWrVhU2USIiIiIiU8weoW3Tpg22bt2KOnXqICcnBy+99BJ++ukng5qMjAykpaXh+eefr7CJEhERERGZYtEnhT2J+ElhRERERI83c3mNHydCRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREimZX2RP4R0noDmBXiYZuQNBvpWoaALhYoqE+EJRYquZpAAdKNLQHgvabGK8jgL0lGiKAoD9K1XQAUPK2TwNB+0rVhAM4VqKhJRB01MR4PQCUvD/dgaD/larxBXClRIMPEJRmoi+VcVuQKFVjD0BTosEOCFJb3o/s8R5xX7LHcwJwv0SDIxCUX6rGBcC9Eg3OQFCeib7qAij5ePgCQamlapoC+LtEQxMg6HSpGhnPdQBIGA0gpkRDFBC0qlRNHwA7SzT0BoJ2mOhrAICfSjT0B4I2l6r5EMDsEg2zgaBZpWreAPB5iYZJQNBnJsb7DMCbJRqWAkFvGNYkrQIKxhRfdvgGCBht3Ff6DuB23+LL1bcDtfsY1mQdBq63Kb7seQio2dqw5tYx4Fp48WWvo4BbS+PxMv8EbrQvvuxxAHBvZ1hzNxFICwagBmAP+J4BXBsY96W+DVzpA6gvA/b1AJ8dgH11w5r8DOByKKDLBmxqAPXiASdvy/sBgPs3gMutAe0NwNYDqHcYcPQwrNHcBdJHAuqLgH19oPa3gJ2rYU1BFpAaCWiuAnZ1gLp7AIeaxuPJ6Uvu3HUFwK3o4r7cJgA2DoY12nvA9XcA9QXAPhDwXAjYOhvWCC2QtxfQpgO2tQGXCEBlazyenDq5fcmZlxxy1kAuuXOSex/Jus+ZR0wlhDDxG/LRyMnJQe3atTFs2DCsWLECABATE4Pt27dj48aNOHr0KJYsWYJ169ZV2Bx8fX2RlmYiUFmbqXBSpCikWKtGyeNZsy/OvXL64twrpy+rjmcDwNSvBhUQpCu+eLGBPpiUZt8AqH9B//O5KoAw8ceTygVolCu/HwA4XwPQ3Taus6kBNLyl/zmlNZB/xLjGqTXgf0j/8wVvQHvNuMbWGwhML74spy+5c78+Fcj6FECJ9YMNUHMK4DlffzGtH3B3q3Ffrv0A38I/1nI2AxmvAbpbAOwBqAEbN8A7Gqjav/g2curk9iVnXnLIWQO55M5J7n0k6z5nKoC5vFapgfbrr7/Gt99+i1OnTiE1NRWurq4GgfZReCSBtvAXSN/GzwMIA6CC/pfFcWw/a/gP8qFrgkSljfdi1+eRdbW4rmad41jzu2Ffk4f2xfmT4VJNw5CjWPz9dqO+Jg18HklnivsKCD6OzzcZ9rVkRhv8vukZqabrwJ/x1ieHDGo+fa8tYn/qJdV07v8L3p5z0Gi8lQtC8dOqflJd/9FbMHZqvEFf278PxPLZw6Wa8bPXoe/QC0Z9fftZM3y/fKBUN3T8Jox845RBX38f88a0EeOlmvlrl6NJywyDmuP7fTHzpZekmo9XrEDY02lG4x3dUxezx4+V6mYvX4nwSMOjquWOV6IvOfPKuOKKtwZNwN3bLnCtnoclG6Ph7XPXoCbpXE28NXAitFpb2NpqsWTTFwholGU03s1rLnjnhZeRdaMaanrcwcIN/0EtL8PwU25Nib7u3nbAgilDkHLeC/4Nr2Hqoh/gWr3AoC+N2gYHfgtGUoI3AoIy0L77GdjZ6wxqLl2sgTcHTIS6wB72Dmos3fwF/OpnG42Xn2eH1Uu64/wpHzRsdgWj3voNTi4ag76yM50wY2wUMlJrwrtuFj5ZGYMa7vkP1NdDkz2e/rEve60KQ21hkCuzzr4+oEk3HWaLqFwAu9qmA2ER+/pA/UQpzJY5d5vqgEND0wG0iFMr/VFUU2G2iK0XEJhRdpgt2Zc2y/wa1E8sDHILy+6r5jtAwXnTAa2I6/NA9ReBK8OgP2pemj3g8x1QdYA+dJirA+T1VVZwLDkv3y1lX19Ezhp4LjDfDyB/TnLWoeoAeWM+6az5nKkgj3Wgbdu2LWbOnIl///vf6NevH8aMGWMQaOPi4jBlyhQcPXoUL730Eho3boy3334bAJCcnIx27dohNVX/i3vmzJnYvXs3CgoKEBQUhOXLl6NGjRpm51DhgbbwpVd9KGxhouAvKRxaq8aafckdTx9mjetq1vlLCrX6MNvKqKZhyJHiUIuiMGvcV0DwX1Ko1YfZ3kY1XQfulEKtPsw+Y1TTuf/PxaEWRWHW+C/L/qN/kkKtPsyOMKoZP3ttcahFUZgdZFQ3dPxGKdTqQ+OrRjXz134lhUd9mB1nVPPxiq+LQy2KwuxLRnWzl6+QQq2c8eTWZVxxxUvd3jGqWbFroRRqk87VxKR+bxjVfL7ls8JQ6wbgFm5ec0FUp2lGdTFx86XAar6mCoBc3L3tgBfavwehKz76qLIR2HBgjhRqNWobTH9xDJLO1oZGbQs7ey0CGqdj3ppvpFB76WINvNb3LaPxvty+pDDUPgPgZ+Tn2WFExDTk5xW/VOrkUoC1e+dLwTA70wkjOrxr1NfafXMLQ+0MAJ/I6ssq7NYCmhGyxjO7Vl4JwLUgWWtqFb4pQJr/o1ureunA5dqySs2uwVO3gGR3GB6VLK3oYIE5HgBulH21rTdQ/zJwsR6gzSi/TghAV16w9waeSgQSXcuuKRJ4H7At57QBXQFw3hnlr4EN0PA+YGPmTEjtPeCCi/k5NcgDkgPMr0ODK4DqH/52IqEFEn2t85ypwPU0l9cq7VH8+++/kZqail69emHs2LFYuXJlufVFYbdITEwMhg8fDnt7eyxcuBCurq44fPgw4uPj0aRJE8yaNavszh6povMIwwq/q0p8lWy3Zs2jH09/ZNa4rrgdhUdmjWuK2/X0R2aN64rbUXhk1rimuB2FR2aNa4rb9fRHZo3rittReGTWuKa4XU9/ZNa4rrgdhUdAjWuK21F4ZNa4prhdT39k1riuuF3eeHLr3ho0wWRNcTvw1sCJpmukdv1Lw++88LLJuuJ2OTX6l6wXTBlSGGaLv4ROhQVThkh9HfgtGElna6Pgvj10OhsU3LdH0tnaOPBbsFTz5gDTcy9u/xkAsHpJ98JQVVyXn+eA1Uu6S33NGBtlsq/i9k9k92UVmhGyxzO7VteC5NVZS1pD2XO3isuBskvNrkFqC5Qf5AB5YRYAMsu/WncLyP6y8OXgcmgzAV2W+b7Sjf+IN+nG2+Vffysa5tdAp5+7OdeN/6A2KeNf5tdBdwu4t7f8mn+CvL3We85U4npWWqBduXIlXnzxRdja2qJPnz5ISkrC2bNny6xv37491Go1jh49CiEEVq9ejdGjRwMAtmzZgrVr1yI0NBShoaHYsGEDkpKSTPazePFi+Pr6Sl937941WWd9Khnt1qpR8njW7Itzt3Zfd2+bPjJSsl2rNf3mgNLtWTeqmawr2S6nBgBSznuZrCvZnpTgDY3acA4atS2SEorfnKQusDfZT+n286d8TNaVbM9INfEGIxPtcvqyJjnjyVkrS+oeXoHRHEuy/lqVc5pEKWbXQFvOEVWrswcKLuq/l0dlA/O//u3LPxWkJPUFM9fL7UdGnbmxishZB9jrT4v5p9Omw2rPmUpcz0oJtGq1GmvXrsWaNWvg7++PBg0aIC8vD9988025t4uKikJMTAx2794NT09PNG3aFAAghEB0dDTi4+MRHx+PM2fOYNu2bSb7mDx5MtLS0qQvV1cZL6dYRVl/gYsKqFHyeNbsi3O3dl+u1U3/oi/ZbmurNVlTur2mxx2TdSXb5dQAgH9D0y+DlWwPCMqAnb3hHOzstQgIKn6Zzd7B1Llhxu0Nm10xWVey3buu6aMZpdvl9GVNcsaTs1aW1D08B6M5lmT9tZLxknYhs2tg62HiVhVFDTjUh+lzHEsQOpg/YqrWnwMsh72ZI9qy+5FRZ26sInLWAWr9Odz/dLa1YbXnTCWuZ6UE2q1btyIgIABXrlxBSkoKUlJSsH//fqxZswZqddmLOmrUKPz4449Yvny5dHQWAJ577jksXrwYeXn6X6p5eXn4+++/y+rmEetW+P144XdR4qtkuzVrHv14NeuYrituBxqGHDVZU9yuFxBsuq/idqDrwJ9N1hS3A537/2Kyprhdr//oLSbrituB8bPXmawpbtcbOn6TybridmD+2uUma4rbgY8Ld/0oXVPcrjd7+UqTdcXt8saTW7dkY7TJmuJ2YMmmL0zXSO1uAICFG/5jsq64XU5NFQDA1EU/QGUjDOpUNgJTF/0g9dW++xkENE6Hg6MaNjY6ODiqEdA4He27n5Fqlm42Pffidv0pLfo3IhUY1Dm5FGDUW8Vbk32yMsZkX8XtM2T3ZRV2a2WPZ3atvBLk1VmL73nZc7eKejKPAkLGGtT9C+Z/1Zb16khp7uVfbeMG1HhN/708tu6AjelXEAz6qr1W3rQ8Pi3/ercJML8GNvq5m+NZzhvLSvJeb34dbNwA5wh5/T3JXCKs95ypxPWslDeFPfPMM+jduzdef/11g/awsDCMHDkSBw4cMHpTWJE+ffpg9+7dSE9Pl970pVar8eGHH2Lr1q1QqQrP+5s2DcOHG57faAp3ObDeeNzlQN8XdzngLgdFnuxdDgIBdWI57/BvoN/j1ewuB3UAdWLZNUXbX513A3TZ5exyUKNwl4PDZffl1LpwlwMzb34JTAdS2pjvS5tlfg3qXwCuTwOyynkHf82phbscbCm7xrVf4S4HQ1H2u8y/12+dlPOT+TpAXl9p/c3PS87WXXLWQO7WXXLnJGcduHWXnjWfMxXksd7l4HHAfWgfs/Gs2RfnXjl9ce6V0xf3odX/zH1o5ddxH1rLxnzScR9aZXtkgRYAEl4FUPJl3vFA0FelanoB+LVEQ08g6JdSNaMArCnR8CIQtNrEeGMArCrRMBoIKnWeckIUgJK3HQUExZSqGQbg+xINQ4Gg70yMNwFAyfvzKhAUXaqmE4A9JRoigaA4E33VA1DyKGNdIOhyqZrGABJKNAQBQaXeWCjnk68AIKE2gJJHbbyBoFInt1v1k8JkfGJagi2M/uMPMnF+qqxP7goBUPIocTMg6KSJvjoDiCvR0AkIii1V0w9AyV8kzwNBW0rVyHiuA0DCLAAflWj4AAj6sFTNG5D3yV1TAZR8OfIdIKjUEaGEbwCMLdGwEggaU6pmkf62koVA0BQT420GMLBEwyYgqNQejEm7gIIS78B3+A0I6AYj6X8Bt0t8olf1Y0DtUlvXZSUC10ucP+h5AahZ6pO7biUB10qch+h1EXALMB4v8zxwo1HxZY9zgHtDw5q7GUCaH/RvyHIAfC8Bribe7KXOA672AtSXAHs/oM4vgH2pc1Dzs4HUxoD2FmDrBtQ9CzjVsLwfALh/B0gNLf6ksLrxgGOpNw9q8vXvci/6NCrv9YCdk2FNwV0grX3xJ4X5HgAcTLynQk5fcueu0+jfyV/UV43XjLep0hbodw0o+vQrj0+Nt8QSOv27yTXp+nMWnSNgcqskOXVy+5IzLznkrIFccuck9z6SdZ8zVsZAa8YjDbREREREZLHHdh9aIiIiIiJrYKAlIiIiIkVjoCUiIiIiRWOgJSIiIiJFY6AlIiIiIkVjoCUiIiIiRWOgJSIiIiJFY6AlIiIiIkVjoCUiIiIiRWOgJSIiIiJF+8d/9K2joyM8PDys0tfdu3fh6mris8CpQnHdKwfXvXJw3SsH171ycN0rx+O47jdu3MD9+/fLvP4fH2itydznDFPF4LpXDq575eC6Vw6ue+XgulcOJa47TzkgIiIiIkVjoCUiIiIiRWOgtaLJkydX9hT+kbjulYPrXjm47pWD6145uO6VQ4nrznNoiYiIiEjReISWiIiIiBSNgZaIiIiIFI2B1gouXLiA9u3bo2HDhmjdujXOnDlT2VN6Ik2aNAn+/v5QqVQ4ffq01H79+nX06tULgYGBaNq0Kfbt21eJs3zy5Ofno1+/fmjYsCFCQ0PRq1cvpKSkAODaV7QePXogJCQEoaGhiIiIQHx8PACu+6Pw4YcfGvxfwzWveP7+/ggKCkJoaChCQ0Px/fffA+DaV6T79+9j4sSJCAwMRJMmTTBixAgACl1zQQ+tc+fOYtWqVUIIIX788UfRtm3byp3QE2rPnj0iNTVV+Pn5iVOnTknto0ePFrNmzRJCCHH48GFRr149oVarK2mWT5579+6JHTt2CJ1OJ4QQYtmyZaJ79+5CCK59Rbt165b0808//STCwsKEEFz3inbs2DHRq1cvUa9ePen/Gq55xSv9f3sRrn3FefPNN8Xrr78u/f9+9epVIYQy15yB9iFdu3ZNVK9eXXqgdTqd8PLyEsnJyZU7sSdY6f/0qlSpIq5fvy5dbtWqlYiNja2Emf0zHDlyRNSvX18IwbV/lGJiYkTLli2FEFz3ipSfny/atm0rkpKSDP6v4ZpXvLICLde+Yty9e1dUr15d5OTkGF2nxDW3q+wjxEqXmpqKOnXqwM5Ov5QqlQr16tXD5cuX4e/vX7mT+wfIzMyETqcz+Phif39/XL58uRJn9WT7/PPP8eyzz3LtH5EXX3wRsbGxAIBffvmF617BPvjgA4wYMQJPPfWU1MY1f3SGDx8OnU6HNm3aYO7cubCxseHaV5CLFy/C3d0dn3zyCXbt2gVnZ2fMnj0boaGhilxznkNrBSqVyuCy4E5ojxTX/9GZM2cOLly4gP/7v/8DwLV/FNasWYPU1FR88skneOeddwBw3SvKn3/+iSNHjmDChAlG13HNK94ff/yBEydO4K+//oK7uztGjRoFgGtfUdRqNZKSkhAcHIyjR4/iiy++wLBhw6DRaBS55gy0D6lu3bpIS0uDRqMBoH/QU1NTUa9evUqe2T+Du7s7AODGjRtS26VLl7j+FWDRokXYvHkzfv75Z7i4uHDtH7FRo0ZJR2oBrntF2LNnDxISEvDUU0/B398faWlp6NmzJw4fPgyAa17RitbT3t4eb775Jvbu3cv/ZyqQn58fbGxsMHz4cABA8+bN8dRTT+Hs2bMAlLfmDLQPydPTE2FhYVi7di0AYNOmTfD39+fpBo/Q4MGD8eWXXwIAjhw5goyMDHTo0KGSZ/VkWbx4MTZs2IDffvsNNWrUkNq59hXnzp07uHr1qnT5p59+gru7O2rWrMl1ryDTp0/H1atXkZKSgpSUFPj6+uLXX3/FM888wzWvYLm5ucjOzpYub9iwAWFhYQD4/0xFqVWrFrp27Ypff/0VgD60Jicno1GjRopcc35SmBWcO3cOUVFRyMzMRLVq1bB69Wo0adKksqf1xHnttdewdetWZGRkoFatWnB1dUViYiKuXbuGkSNHIjk5GQ4ODoiOjkZkZGRlT/eJkZaWhrp16yIgIABVq1YFADg6OuLQoUNc+wqUmpqKgQMH4t69e7CxsYGHhwcWLVqE0NBQrvsj4u/vj+3bt6Np06Zc8wqWlJSEgQMHQqvVQgiBgIAAfPbZZ/D39+faV6CkpCSMGTMGmZmZsLW1xaxZs9C/f39FrjkDLREREREpGk85ICIiIiJFY6AlIiIiIkVjoCUiIiIiRWOgJSIiIiJFY6AlIiIiIkVjoCWiBxYTEwOVSiV92dnZoXbt2hg2bBguXLhQ4eN36tQJnTp1qvBx5EhJSYFKpUJMTIzs2wwYMADPP/98mdfHxcUZrK+trS08PDzw7LPP4ujRo1aYdfmioqIqfE/tovsYFxf30H3Nnj0bKpUKnp6eyMnJMbre398fffv2fehxSlKpVJg9e7ZV+yQiyzHQEtFDW7VqFf7880/s2rULEydOxH//+1906NABt27dqtBxo6OjER0dXaFjVJTc3Fz88ssvGDhwoNnaOXPm4M8//0RcXBxmzpyJAwcOIDIyssL/aJg5cyZ++umnCh2jIty4cQMLFiyo7GkQ0SNkV9kTICLla9q0KcLDwwHoj5pqtVrMmjULW7ZswejRoyts3ODg4Arru6Lt3LkTGo0Gzz77rNnawMBAtG3bFgAQERGBGjVqYNSoUVi7di0+/PDDCptj/fr1K6zvitSrVy8sWbIEr732Gry9vSt7OkT0CPAILRFZXVG4vXbtmkH70aNH8dxzz6FmzZpwcnJCWFgYfvjhB6Pb79u3D+3atYOTkxN8fHwwc+ZMrFixAiqVCikpKVKdqVMOsrKyMGHCBPj4+MDBwQEBAQF4//33cf/+fYM6lUqFiRMn4ttvv0Xjxo3h4uKC5s2bY/v27QZ1iYmJGD16NAIDA+Hi4gIfHx88++yzOHXq1EOskP5jsrt06QI3NzeLb1vW+l64cAH/+te/4OnpCUdHRzRu3Fj6+MqS/v77b/To0QMuLi7w8PDAa6+9hh07dhi99G/qlAO56wboH8euXbuiatWqcHFxQfv27bFjxw6z9y8pKQnDhg1DnTp14OjoCC8vL3Tt2hXx8fHmFwfAJ598Ao1GI+tUALnPlzt37mDcuHFwd3eHq6srevXqhfPnz5vsU+7jQETWwyO0RGR1ycnJAICGDRtKbbGxsejVqxfatGmD5cuXo3r16vjuu+8wdOhQ5OXlISoqCgBw8uRJdO/eHQ0bNsTq1avh4uKC5cuXY+3atWbHzc/PR+fOnXHx4kV8+OGHCAkJwd69ezF37lzEx8cbhakdO3bgyJEj+Oijj+Dq6ooFCxagf//+OHfuHAICAgAAV69ehbu7O+bNmwcPDw9kZWVh9erVaNOmDY4fP45GjRpZvD75+fnYsWMHFi9ebPFtAdPre+bMGbRv3x716tXDp59+Cm9vb/z666+YNGkSbt68iVmzZgEA0tPTERkZiSpVquCrr76Cp6cnNmzYgIkTJ8oeX8667dmzB927d0dISAhWrlwJR0dHREdH49lnn8WGDRswdOjQMvvv3bs3tFotFixYgHr16uHmzZs4cOAAsrOzZc3Pz88PEyZMwLJlyzB58mSDdSpJ7vNFCIF+/frhwIED+OCDD9CqVSvs378fzzzzjFGfch8HIrIyQUT0gFatWiUAiIMHDwq1Wi1ycnLEL7/8Iry9vUXHjh2FWq2WaoOCgkRYWJhBmxBC9O3bV9SuXVtotVohhBCDBw8WVapUETdu3JBqtFqtCA4OFgBEcnKy1B4ZGSkiIyOly8uXLxcAxA8//GAwxvz58wUA8b///U9qAyC8vLzEnTt3pLaMjAxhY2Mj5s6dW+Z91mg0oqCgQAQGBoq33npLak9OThYAxKpVq8pfNCHEli1bhK2trbh+/Xq5dbGxsQKA+P7774VarRZ5eXli//79olGjRiI4OFjcunVLqu3Zs6fw9fUVt2/fNuhj4sSJwsnJSWRlZQkhhHjnnXeESqUSf//9t0Fdz549BQARGxsrtY0aNUr4+fkZ1Mldt7Zt2wpPT0+Rk5MjtWk0GtG0aVPh6+srdDqdwX0sGvfmzZsCgFi6dGm5a2PKrFmzBABx48YNcfPmTVG9enUxcOBA6Xo/Pz/Rp08f6bLc58vPP/8sAIjPPvvMoO7//u//BAAxa9YsqU3u40BE1sVTDojoobVt2xb29vaoWrUqevXqBTc3N2zduhV2dvoXgRITE5GQkIDhw4cDADQajfTVu3dvpKen49y5cwD0R/a6dOmCWrVqSf3b2NhgyJAhZuexe/duVKlSBYMGDTJoLzr6+/vvvxu0d+7cGVWrVpUue3l5wdPTE5cuXZLaNBoN5syZg+DgYDg4OMDOzg4ODg64cOECzp49a8EqFdu0aRMiIiLg4eEhq37o0KGwt7eHi4sLnn76ady5cwc7duxAjRo1AOiPNP7+++/o378/XFxcjNY3Pz8fBw8eBKBf36ZNmxqdf/zCCy/Inr+5dcvNzcWhQ4cwaNAguLq6SnW2trYYOXIk0tLSpMe7tJo1a6J+/fpYuHAhFi9ejOPHj0On08meWxF3d3dMmzYNmzZtwqFDh0zWyH2+xMbGAoD0/C3yr3/9y+CyJY8DEVkXAy0RPbQ1a9bgyJEj2L17N1555RWcPXvWICAVnes5ZcoU2NvbG3xNmDABAHDz5k0AQGZmJry8vIzGMNVWWmZmJry9vaFSqQzaPT09YWdnh8zMTIN2d3d3oz4cHR1x79496fLkyZMxc+ZM9OvXD9u2bcOhQ4dw5MgRNG/e3KBOLrVajW3btsna3aDI/PnzceTIEezZswfvv/8+rl27hn79+knneWZmZkKj0WDZsmVG69u7d28A1lnfIubW7datWxBCoHbt2kZ1derUkeZhikqlwu+//46ePXtiwYIFaNGiBTw8PDBp0iSTW3GV580330SdOnUwdepUk9fLfb5kZmbCzs7O6H6XfsOZJY8DEVkXz6EloofWuHFj6Y1KnTt3hlarxYoVK7Bx40YMGjRIOtr67rvvYsCAASb7KDoX1d3d3ejNTgCQkZFhdh7u7u44dOgQhBAGIeX69evQaDQGR33lWrt2LV588UXMmTPHoP3mzZvSEVJL7Nq1C7dv30b//v1l3yYgIEBa344dO8LZ2RkzZszAsmXLMGXKFLi5uUlHP1977TWTfTz11FMAHm595XJzc4ONjQ3S09ONrrt69SoAlPtY+Pn5YeXKlQCA8+fP44cffsDs2bNRUFCA5cuXy56Hs7MzZs+ejZdfftnkm9HkPl/c3d2h0WiQmZlpEGpLr5kljwMRWReP0BKR1S1YsABubm744IMPoNPp0KhRIwQGBuLEiRMIDw83+VX0EnZkZCR2795tcCRLp9Phxx9/NDtu165dcffuXWzZssWgfc2aNdL1llKpVHB0dDRo27FjB65cuWJxX4D+dIO2bdvCx8fngW4PAFOnTkWDBg0wb9485OTkwMXFBZ07d8bx48cREhJicn2LglhkZCROnz6NM2fOGPT53XffPfB8SqtSpQratGmDzZs3GxzF1ul0WLt2LXx9fct8o1ZpDRs2xIwZM9CsWTP89ddfFs9lzJgxaNy4MaZPn2506oLc50vnzp0BAOvWrTOoW79+vcFlSx4HIrIuHqElIqtzc3PDu+++i6lTp2L9+vUYMWIE/v3vf+OZZ55Bz549ERUVBR8fH2RlZeHs2bP466+/pMD6/vvvY9u2bejatSvef/99ODs7Y/ny5cjNzQWgP5+2LC+++CK+/PJLjBo1CikpKWjWrBn27duHOXPmoHfv3ujWrZvF96Vv376IiYlBUFAQQkJCcOzYMSxcuBC+vr4W96XVarF161ZMnz7d4tuWZG9vjzlz5mDIkCH47LPPMGPGDHz22Wfo0KEDIiIi8Oqrr8Lf3x85OTlITEzEtm3bsHv3bgD6l+G/+eYbPPPMM/joo4/g5eWF9evXIyEhAUD562uJuXPnonv37ujcuTOmTJkCBwcHREdH4/Tp09iwYYPRy/xFTp48iYkTJ2Lw4MEIDAyEg4MDdu/ejZMnTz7Qutna2mLOnDnSEfGQkBDpOrnPlx49eqBjx46YOnUqcnNzER4ejv379+Pbb781Gk/u40BEVlbJb0ojIgUr2uXgyJEjRtfdu3dP1KtXTwQGBgqNRiOEEOLEiRNiyJAhwtPTU9jb2wtvb2/RpUsXsXz5coPb7t27V7Rp00Y4OjoKb29v8c4770jvPM/OzpbqSu9yIIQQmZmZYvz48aJ27drCzs5O+Pn5iXfffVfk5+cb1AEQr732mtG8/fz8xKhRo6TLt27dEmPHjhWenp7CxcVFdOjQQezdu9dobDm7HOzatUsAEElJSWXWlFS0A8CPP/5o8vo2bdoINzc3aU2Sk5PFmDFjhI+Pj7C3txceHh6iffv24pNPPjG43enTp0W3bt2Ek5OTqFmzphg7dqxYvXq1ACBOnDgh1ZW1y4GcdRNC/zh26dJFVKlSRTg7O4u2bduKbdu2mbyPRbscXLt2TURFRYmgoCBRpUoV4erqKkJCQsSSJUuk51FZSu5yUFr79u0FAINdDoSQ/3zJzs4WY8aMETVq1BAuLi6ie/fuIiEhwWiXAyHkPw5EZD0qIYSonChNRCRfjx49kJKSUuZm9kowYcIEHDp0CMeOHavsqRh5+eWXsWHDBmRmZsLBwaGyp0NEZBGeckBEj53JkycjLCwMdevWRVZWFtatW4fffvtNeqOQUkVHR1f2FAAAH330EerUqYOAgADcvXsX27dvx4oVKzBjxgyGWSJSJAZaInrsaLVafPDBB8jIyIBKpUJwcDC+/fZbjBgxorKn9kSwt7fHwoULkZaWBo1Gg8DAQCxevBhvvPFGZU+NiOiB8JQDIiIiIlI0bttFRERERIrGQEtEREREisZAS0RERESKxkBLRERERIrGQEtEREREisZAS0RERESKxkBLRERERIr2/xs9sc/gHHFLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.scatter(df_new['Regional Node Examined'], df_new['Status'], s=60, c='gold')\n",
    "plt.scatter(df_new['Reginol Node Positive'], df_new['Status'], s=20, c='indigo')\n",
    "\n",
    "plt.legend (['Regional Node Examined', 'Reginol Node Positive'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Regional / Reginols Node', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "plt.ylabel('Status', fontsize=15)\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/6-Regional_OR_Reginols_Node&Status.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd37d72f",
   "metadata": {},
   "source": [
    "Histogram can also get plotted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "605f8b91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['Age'], \n",
    "         bins=35, \n",
    "         #range=None, \n",
    "         #density=False, \n",
    "         #weights=None, \n",
    "         #cumulative=False, \n",
    "         #bottom=None, \n",
    "         histtype='stepfilled', \n",
    "         align='mid', \n",
    "         orientation='vertical', \n",
    "         #rwidth=None, \n",
    "         #log=False, \n",
    "         color='navy', \n",
    "         #label=None, \n",
    "         #stacked=False, \n",
    "         #data=None\n",
    "        )\n",
    "\n",
    "plt.legend (['Age'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Age', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/7-Age_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "bd95f1c9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['Race'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['Race'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Race', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/8-Race_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "4a101fe2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['Marital Status'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['Marital Status'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Marital Status', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/9-Marital_Status_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "fbfcb06a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['N Stage'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['N Stage'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('N Stage', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/10-N_Stage_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "4e3a31b6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['6th Stage'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['6th Stage'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('6th Stage', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/11-6th_Stage_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "ff7b0289",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['differentiate'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['differentiate'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('differentiate', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/12-differentiate_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "646759f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['Grade'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['Grade'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Grade', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/13-Grade_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "4d0672d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['A Stage'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['A Stage'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('A Stage', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/14-A_Stage_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "f0f6ed72",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['Tumor Size'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['Tumor Size'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Tumor Size', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/15-Tumor_Size_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "13da0413",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['Estrogen Status'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['Estrogen Status'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Estrogen Status', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/16-Estrogen_Status_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "5ebd16be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['Progesterone Status'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['Progesterone Status'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Progesterone Status', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/17-Progesterone_Status_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "f19cef35",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['Regional Node Examined'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['Regional Node Examined'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Regional Node Examined', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/18-Regional_Node_Examined_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "cb25be3c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['Reginol Node Positive'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['Reginol Node Positive'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Reginol Node Positive', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/19-Reginol_Node_Positive_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "43a73e76",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IBG5MLmOMKY4ZUgAAANy8OCcTAAAA1hEyAQAAYB0hEwAAANYRMgEAAGAdIRMAAADW+ZR0AQXx8/NT1apVS7oMAAAAXMKRI0fcHojxWx4ZMqtWrar09PSSLgMAAACXEBISctl2DpcDAADAOkImAAAArCNkAgAAwDqPPCfzSnJzc8XTMG8uLpdLXl78PxEAAKVFqQqZ58+fV1pamrKyskq6FJSAMmXKKDQ0VL6+viVdCgAAuIJSFTLT0tJ0yy23KCgoSC6Xq6TLwXVkjFFmZqbS0tJUv379ki4HAABcQakJmbm5ucrKylJQUJB8fEpN2bAoKChIx44dU25uLofOAQDwcKXmv9R552Ayg3nzyht7zscFAMDzleopQZfr2WLZrzETi2W/AAAAN4tSM5PpiRYtWqRWrVopOjpajRo1UufOnZWbm2u1j7fffluvvfZakfbhcrl08uTJfMtXrlwpl8ulRx991G35vffeK5fLpS1bthSp3ylTpujw4cPO+2eeeUZ//vOfi7RPAABQOpTqmcySlJGRoQcffFDr169XWFiYJOnHH3+86sP52dnZlz3H9MEHHyxSnVfSoEEDffTRR5o0aZJ8fX3166+/avXq1apVq1aR9z1lyhR16dJF1apVs1ApAAAoTZjJvEYHDx6Uj4+PgoKCnGUtW7Z0QmZ4eLjbTGDr1q21cuVKSVLHjh315JNPqnPnzurWrZvuu+8+vfrqq866KSkpql69urKystxm/yIjI/XDDz84682cOVN33nmnJCkxMVFt2rRRixYt1LZtW61du7ZQnyMwMFC/+93vtHjxYknShx9+qLvuusst+O7atUtdunRRs2bNFB0drY8++shpc7lcevnll3XrrbeqTp06mjlzpiTpueee04EDBzRgwABFR0dr48aNkqQDBw7ojjvuUOPGjfW73/1Ox44dkyStWbPGmRWOiorS9OnTC1U/AADwTITMa9S8eXO1a9dOoaGh6t+/v1555RXt37+/0Ntv3LhRn3/+ub766islJCQoKSnJaUtKStLQoUNVpkwZt23i4+OdEJe33siRIyVJw4cP1/r167VhwwZNnTpVo0aNKnQtCQkJmjFjhiRpxowZSkhIcGsfOnSoBg0apM2bN2v+/PkaNWqU9u3b57T7+/tr7dq1+vTTTzVmzBhlZ2fr6aefVs2aNbVgwQJt3LhR0dHRkqS1a9fqvffe09atW1WtWjX97W9/kyS9+OKLeuyxx7Rx40Zt2bJFgwcPLnT9AADA8xAyr5GXl5cWLlyo1atXq3v37vr222/VpEkT7dq1q1DbDx8+3AmR7du3V1ZWlr7//nsZY/Tee+854fFiI0aM0Lx583T+/Hnt3r1bO3bsUI8ePSRJGzZsUIcOHRQVFaUHH3xQW7du1fnz5wtVS2xsrFJTU7V06VL5+PioQYMGTtuJEye0ceNGJ7RGREQoNjZWycnJzjpDhw6VJDVq1Eg+Pj7KyMi4ZF89evRQ5cqVJUnt2rXT7t27JUmdOnXSX//6Vz333HNKTk5WpUqVClU7AADwTIUKmWfPnlW/fv0UGRmp6Ohode/eXXv37pUkHT58WN27d1dERISioqLcwsfp06c1ZMgQ1a9fX5GRkVq0aFGxfIiS1LBhQz3wwAP66KOPFBMToyVLlkiSfHx8lJOT46x39uxZt+0CAwPd3sfHxyspKUnLly9XtWrVFBUVla+vWrVqqWXLllqyZImSkpI0fPhw+fj46Pz587rrrruUmJioLVu26Ouvv5YxptAhU7pwsc+wYcPyhdtL3Trq4vf+/v7Oz97e3srOzr5kP5da99FHH9XHH3+sGjVq6IknntBDDz1U6NoBAIDnKfSFP/fff7969Oghl8ulN998U/fff7+WLVumCRMmKCYmRp9//rnWr1+vAQMGaPfu3fLx8dHkyZPl5+enXbt2KSUlRe3atVOnTp1uiFmq/fv3a+/evbrtttskST///LNSUlJUr149SVK9evW0du1aNW/eXOvWrdP27dsvu78RI0YoOjpahw4dKnAWM0/eoe2ffvpJn332maQLATYrK0u1a9eWJL3xxhtX/XnyDpHffffdbsvLly+v6OhoZ3Z19+7d+vbbb/Xmm29ecZ/ly5fXL7/8Uqj+t2/frgYNGqhu3bqqXbu2nnjiiav+DAAAwHMUKmT6+/urZ8+ezvuYmBhNmTJFkjRv3jylpKRIktq0aaPg4GAlJyerY8eOmjt3rnOuYZ06dRQXF6fFixcrPj7eSvEleT/L7OxsPffcc0pJSVFAQICys7M1YsQI9e3bV5L0/PPPa8SIEXr33XfVsmVLNWnS5LL7q1Gjhlq3bq2PP/5Yf//73y+5Xt++ffXHP/5RERERaty4saQLYe65555T27ZtFRoaqj59+lz156lWrZomTJhQYNucOXP0wAMPaMqUKXK5XPrHP/7hBNrLGTNmjEaOHKmAgAC3c04L8sYbb2jFihXy9fWVt7e324VQAADcDIp6/29Pu8+3y1zD41PuvfdeBQUF6amnnlLt2rV1+vRpp23QoEHq3bu37r33Xt1yyy3avXu3cwub8ePHKzAwUE8//fRl9x8SEqL09HS3ZTk5OdqxY4ciIyPl7e19tSXjBsDvAADgRlbaQmZBee1iV33hzwsvvKCdO3fq+eefl5T/XL3fZtaL2y+VZxMTExUSEuK8CrpxOAAAAEqPqwqZkydP1qJFi/TZZ58pICDAuUfkkSNHnHVSU1MVGhoqSQoNDXUuEPpt28XGjh2r9PR05/Xbi2IAAABQuhQ6ZCYmJuqDDz7QF198oYoVKzrLBw4cqGnTpkmS1q9fr4yMDMXGxuZrS0lJ0apVq67pfEEp/4wpbl78LgAA4PkKdeFPenq6HnvsMdWtW1edOnWSJPn5+Wnt2rV6+eWXNXz4cEVERMjX11ezZ892nhYzbtw4JSQkqH79+vLy8tK0adOceyReLZfLJZfLpaysLM7Hu0llZWU5vwcAAMCzXdOFP8XtUieSZmRk6Ny5c6pVqxZB4yZjjNH+/fvl5+en6tWrl3Q5AABYd6Nd+FPo+2R6gmrVqik1NVU7d+4s6VJQAvz9/Z07FQDAjaa0BQzgSkpVyPTy8lKdOnWUm5t7ySvVcWNyuVzy8uIpqAAAlBalKmTmIWwAAAB4NtIaAAAArCNkAgAAwDpCJgAAAKwjZAIAAMC6UnnhDwDgxsMtfFDS+B20i5lMAAAAWEfIBAAAgHWETAAAAFhHyAQAAIB1hEwAAABYR8gEAACAdYRMAAAAWEfIBAAAgHWETAAAAFjHE38AwAOU9JNGito/APwWM5kAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOq8sBQCV/dXdRcXU4AE9DyAQACwh5AOCOw+UAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOq8sBAFDpv40V4GmYyQQAAIB1hEwAAABYx+FyAABwQ+ChCJ6FmUwAAABYV6iZzDFjxmjJkiVKTU3Vf/7zH0VFRUmS2rdvr9OnT0uSsrOz9dNPP2nTpk1q1qyZ4uPj9eWXX6pKlSqSpK5du+qVV14ppo8BAABQsphJdVeokDlgwACNHz9esbGxbstXr17t/LxgwQI9++yzatasmbNswoQJevjhhy2VCgAAgNKiUCEzLi7uiuvMmDFDo0aNKnJBAADg6nELJngaKxf+7N+/XytXrtSsWbPclicmJuqdd95RaGio/vrXvyo6OrrA7RMTE5WYmOi8P3nypI2yAAC4bjhUCrizcuFPUlKSevfu7Zx/KUnPP/+8du3apc2bN2vUqFHq0aPHJcPj2LFjlZ6e7rwCAwNtlAUAAIASUuSQaYzRzJkz8x0qr1Wrlry8Luy+f//+Kl++vLZv317U7gAAAFAKFDlkrlq1SufPn1fXrl3dlqenpzs/r1mzRpmZmapfv35RuwMAAEApUKhzMkePHq3FixcrIyNDXbp0UWBgoHbt2iVJevfddzVy5Ehn1jJPfHy8Dh06JG9vb5UtW1bz589XhQoV7H8CAABQZFw4BNtcxhhT0kX8VkhIiNtMKAAUNy7aKP2KGnL4HSgaTwiZN/sYXu8xuFJe44k/AAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsK5QN2MHAAAoTjf7PS5vRMxkAgAAwDpCJgAAAKwjZAIAAMA6QiYAAACsI2QCAADAOkImAAAArCNkAgAAwDpCJgAAAKwjZAIAAMA6QiYAAACsI2QCAADAOkImAAAArCNkAgAAwDqfki4AAACUfi7XsyVdAjwMM5kAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOZ5cDAG4IPDsb8CzMZAIAAMC6QoXMMWPGKDw8XC6XS1u2bHGWd+zYUXXr1lV0dLSio6P12muvOW2nT5/WkCFDVL9+fUVGRmrRokX2qwcAAIBHKtTh8gEDBmj8+PGKjY3N1zZ16lT17t073/LJkyfLz89Pu3btUkpKitq1a6dOnTqpUqVKRa8aAAAAHq1QM5lxcXEKCQm5qh3PnTtXo0ePliTVqVNHcXFxWrx48dVXCAAAgFKnyOdkjhs3Tk2bNtXdd9+tPXv2OMvT0tIUFhbmvA8PD1daWlqB+0hMTFRISIjzOnnyZFHLAgAAQAkqUsicPXu2tm3bps2bN+v222/Pd9jc5XI5PxtjLrmfsWPHKj093XkFBgYWpSwAAACUsCKFzNq1a0u6ECYffvhh7dmzR5mZmZKk0NBQ7d2711k3NTVVoaGhRekOAAAApcQ1h8zs7GwdOnTIeb9w4UIFBwcrKChIkjRw4EBNmzZNkpSSkqJVq1apT58+RSwXAAAApUGhri4fPXq0Fi9erIyMDHXp0kWBgYHatGmTevXqpXPnzsnLy0tVqlTRkiVLnG3GjRunhIQE1a9fX15eXpo2bZoqV65cbB8EAAAAnsNlLneyZAkJCQlRenp6SZcB4CbC02IAlHbGTLyu/V0pr/HEHwAAAFhHyAQAAIB1hEwAAABYR8gEAACAdYRMAAAAWEfIBAAAgHWETAAAAFhHyAQAAIB1hEwAAABYR8gEAACAdYV6djkAeDoeCwkAnoWZTAAAAFhHyAQAAIB1hEwAAABYR8gEAACAdYRMAAAAWEfIBAAAgHWETAAAAFhHyAQAAIB1hEwAAABYR8gEAACAdYRMAAAAWEfIBAAAgHWETAAAAFhHyAQAAIB1hEwAAABYR8gEAACAdYRMAAAAWEfIBAAAgHWETAAAAFhHyAQAAIB1hEwAAABYR8gEAACAdYRMAAAAWFeokDlmzBiFh4fL5XJpy5YtzvKEhAQ1aNBA0dHRiouL08aNG522+Ph4hYSEKDo6WtHR0Ro3bpz14gEAAOCZfAqz0oABAzR+/HjFxsa6Le/Xr5/eeecd+fj46OOPP9agQYO0Y8cOp33ChAl6+OGH7VYMAAAAj1eokBkXF1fg8j59+jg/x8TEKDU1Vbm5ufLy4ig8AADAzcxaGnz99dfVs2dPt4CZmJioZs2aqXfv3m6H0gEAAHBjK9RM5pW8//77mjdvnr755htn2fPPP68aNWrIy8tL//rXv9SjRw/t3LlTgYGB+bZPTExUYmKi8/7kyZM2ygIAAEAJKfJM5ty5c/Xss8/qiy++ULVq1ZzltWrVcmY1+/fvr/Lly2v79u0F7mPs2LFKT093XgUFUQAAAJQeRQqZ8+bN01NPPaUvv/xSoaGhbm3p6enOz2vWrFFmZqbq169flO4AAABQShTqcPno0aO1ePFiZWRkqEuXLgoMDNSuXbs0dOhQVa9eXX379nXW/eqrrxQUFKT4+HgdOnRI3t7eKlu2rObPn68KFSoU2wcBAACA53AZY0xJF/FbISEhbjOhAHAlLtezJV0CAJQoYyZe1/6ulNe41xAAAACsI2QCAADAOkImAAAArCNkAgAAwDpCJgAAAKwjZAIAAMA6QiYAAACsI2QCAADAOkImAAAArCNkAgAAwDpCJgAAAKwjZAIAAMA6QiYAAACsI2QCAADAOkImAAAArCNkAgAAwDpCJgAAAKwjZAIAAMA6QiYAAACsI2QCAADAOkImAAAArCNkAgAAwDpCJgAAAKwjZAIAAMA6QiYAAACsI2QCAADAOkImAAAArCNkAgAAwDpCJgAAAKwjZAIAAMA6QiYAAACsI2QCAADAOkImAAAArCNkAgAAwDpCJgAAAKwrVMgcM2aMwsPD5XK5tGXLFmf54cOH1b17d0VERCgqKkrJyclO2+nTpzVkyBDVr19fkZGRWrRokf3qAQAA4JEKFTIHDBig5ORkhYWFuS2fMGGCYmJitHPnTs2cOVNDhw5Vdna2JGny5Mny8/PTrl27tHTpUj300EP6+eef7X8CAAAAeJxChcy4uDiFhITkWz5v3jyNHj1aktSmTRsFBwc7s5lz58512urUqaO4uDgtXrzYVt0AAADwYNd8TmZmZqZyc3NVtWpVZ1l4eLjS0tIkSWlpaW4znxe3AQAA4MZWpAt/XC6X23tjzCXbf9t2scTERIWEhDivkydPFqUsAAAAlLBrDplBQUGSpCNHjjjLUlNTFRoaKkkKDQ3V3r17C2z7rbFjxyo9Pd15BQYGXmtZAAAA8ABFmskcOHCgpk2bJklav369MjIyFBsbm68tJSVFq1atUp8+fYpYLgAAAEqDQoXM0aNHKyQkROnp6erSpYvq168vSXr55Ze1evVqRUREKD4+XrNnz5aPj48kady4cTpz5ozq16+vbt26adq0aapcuXLxfRIAAAB4DJe53MmSJSQv0AJAYblcz5Z0CQBQooyZeF37u1Je44k/AAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwzqeoOzh+/Lg6duzovD99+rT27Nmjw4cP684771RaWprKly8vSRoxYoT+9Kc/FbVLAAAAeLgih8yKFStq48aNzvvJkydr1apVqly5siRp6tSp6t27d1G7AQAAQCli/XD5zJkzNWrUKNu7BQAAQCliNWR+9913yszMdJu5HDdunJo2baq7775be/bsKXC7xMREhYSEOK+TJ0/aLAsAAADXmdWQOWPGDN17773y8blwFH727Nnatm2bNm/erNtvv/2Sh83Hjh2r9PR05xUYGGizLAAAAFxnLmOMsbGjU6dOqUaNGlq3bp0aNmxY4Dr+/v7av3+/goKCLruvkJAQpaen2ygLwE3C5Xq2pEsAgBJlzMTr2t+V8pq1mcz58+erWbNmTsDMzs7WoUOHnPaFCxcqODj4igETAAAApV+Rry7P8+6777pd8HPu3Dn16tVL586dk5eXl6pUqaIlS5bY6g4AAAAezFrI/Oabb9zelytXTt9//72t3QMAAKAU4Yk/AAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwzkrIDA8PV8OGDRUdHa3o6GjNnTtXknT48GF1795dERERioqKUnJyso3uAAAA4OF8bO1owYIFioqKcls2YcIExcTE6PPPP9f69es1YMAA7d69Wz4+1roFAACAByrWtDdv3jylpKRIktq0aaPg4GAlJyerY8eOxdktAAAASpi1czKHDh2qpk2b6r777tORI0eUmZmp3NxcVa1a1VknPDxcaWlp+bZNTExUSEiI8zp58qStsgAAAFACrITMr7/+Wps2bdKPP/6ooKAgjRgxQpLkcrnc1jPGFLj92LFjlZ6e7rwCAwNtlAUAAIASYuVweWhoqCSpTJkyevTRRxUZGamgoCBJ0pEjR5zZzNTUVGddAAAA3LiKPJN56tQpHT9+3Hn/wQcfqEWLFpKkgQMHatq0aZKk9evXKyMjQ7GxsUXtEgAAAB6uyDOZhw4d0l133aWcnBwZY1S3bl3NmjVLkvTyyy9r+PDhioiIkK+vr2bPns2V5cANyuV6tkjbGzPRUiUAAE9Q5MRXt25dbdiwocC24OBgLVu2rKhdAAAAoJThiT8AAACwjmPXADxCUQ+3AwA8CzOZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADreKwkbghFfSShMRMtVVJ68VhHAIBNzGQCAADAOkImAAAArCNkAgAAwDpCJgAAAKwjZAIAAMA6QiYAAACsI2QCAADAOkImAAAArCNkAgAAwDpCJgAAAKwjZAIAAMA6nl0O3AB47jgAwNMwkwkAAADrmMkELCjqTKIxEy1VAgCAZ2AmEwAAANYRMgEAAGAdIRMAAADWETIBAABgHSETAAAA1hEyAQAAYB23MIJH4GbiAADcWIo8k3n27Fn169dPkZGRio6OVvfu3bV3715JUseOHVW3bl1FR0crOjpar732WlG7AwAAQClgZSbz/vvvV48ePeRyufTmm2/q/vvv17JlyyRJU6dOVe/evW10AwAAgFKiyCHT399fPXv2dN7HxMRoypQpRd0tcF1xuB4AALusX/gzdepU3XHHHc77cePGqWnTprr77ru1Z88e290BAADAA1m98OeFF17Qzp079fbbb0uSZs+erdq1a8sYo2nTpql3797aunVrvu0SExOVmJjovD958qTNsgCPx0wqAOBG4zLGGBs7mjx5sj788EN9+eWXqlixYoHr+Pv7a//+/QoKCrrsvkJCQpSenm6jLJQShCwAAIrGmInXtb8r5TUrM5mJiYn64IMP3AJmdna2MjMzFRwcLElauHChgoODrxgwb1ZFDVlF/cUq6f4BAMCNpcghMz09XY899pjq1q2rTp06SZL8/Py0fPly9erVS+fOnZOXl5eqVKmiJUuWFLlgAAAAeL4ih8yQkBBd6oj7999/X9TdAwAAoBTiiT83iJI+p7Gk+wcAAJ6FZ5cDAADAOkImAAAArCNkAgAAwDpCJgAAAKwjZAIAAMA6ri63gCurAQAA3BEyRUgEAACwjcPlAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCu2EPmzp071b59e0VGRqpt27baunVrcXcJAACAElbsIfOBBx7Q/fffrx07dmj8+PEaNWpUcXcJAACAElasIfPw4cP68ccfNWzYMEnSXXfdpZSUFO3du7c4uwUAAEAJK9aQuW/fPtWsWVM+Pj6SJJfLpdDQUKWlpRVntwAAAChhPsXdgcvlcntvjMm3TmJiohITE533GRkZCgkJKZZ6Tp48qcDAQLdltWoVS1cogoLGCZ6JsSodGKfSgXEqPTxxrEJC/n5d+zty5Mhl212moNRnyeHDhxUREaHMzEz5+PjIGKMaNWpozZo1Cg8PL65uLyskJETp6ekl0jcKj3EqPRir0oFxKh0Yp9KDsbqyYj1cXq1aNbVo0ULvv/++JGnhwoUKDw8vsYAJAACA66PYD5f/7W9/U3x8vF544QWVL19e7733XnF3CQAAgBJW7CGzQYMG+u6774q7m0IbO3ZsSZeAQmCcSg/GqnRgnEoHxqn0YKyurFjPyQQAAMDNicdKAgAAwDpCJgAAAKy7aUImz1D3TGfPnlW/fv0UGRmp6Ohode/e3Xki1OHDh9W9e3dFREQoKipKycnJJVssJEnPPvusXC6XtmzZIolx8kTnzp3Tww8/rIiICDVp0sR56hpj5VmWLl2qVq1aqUWLFoqKinIujGWcStaYMWMUHh7u9ndOuvy4nD59WkOGDFH9+vUVGRmpRYsWlUTpnsfcJDp16mRmzpxpjDFm/vz5JiYmpmQLgjHGmDNnzphPPvnE5ObmGmOMeeONN0zXrl2NMcaMHDnSTJw40RhjzLp160xoaKjJysoqqVJhjPnhhx9M9+7dTWhoqPnPf/5jjGGcPNGjjz5q/ud//sf5d3XgwAFjDGPlSXJzc03lypXNpk2bjDHGpKSkGD8/P/Prr78yTiVs1apVZt++fSYsLMz5O2fM5f/9PPvss2bEiBHGGGP27NljgoODzbFjx6536R7npgiZhw4dMhUqVHB+GXJzc01wcLBJSUkp2cKQz/r16029evWMMcaUK1fOHD582Glr06aNWbFiRQlVhrNnz5qYmBizZ88etz++jJNnOXnypKlQoYI5ceJEvjbGynPkhcxVq1YZY4zZtGmTqVmzpjl37hzj5CF+GzIvNy6NGzc269atc9oGDhzoTGzdzG6Kw+U8Q730mDp1qu644w5lZmYqNzdXVatWddrCw8MZsxL09NNPa9iwYapTp46zjHHyPLt371ZQUJD++te/qnXr1rr99tv11VdfMVYexuVyad68ebrzzjsVFham2NhYvffeezpx4gTj5IGu9O8nLS1NYWFhBbbdzG6KkCkV7hnqKFkvvPCCdu7cqeeff14SY+ZJvvvuO61fv14PPfRQvjbGybNkZWVpz549aty4sb7//nu9+eabGjx4sLKzsxkrD5Kdna0XX3xRixcvVmpqqr766iuNGDFCEv+mPNWVxuXidsbsgpsiZNauXVvp6enKzs6WdGHw9+3bp9DQ0BKuDHkmT56sRYsW6bPPPlNAQICCgoIkSUeOHHHWSU1NZcxKyKpVq/Tf//5XderUUXh4uNLT09WtWzetW7dOEuPkScLCwuTl5aWhQ4dKkpo3b646depo27ZtkhgrT7Fx40YdOHBAt912mySpTZs2qlmzpjZv3iyJcfI0V/pvUmhoqHPR6m/bbmY3RcjkGeqeLTExUR988IG++OILVaxY0Vk+cOBATZs2TZK0fv16ZWRkKDY2toSqvLlNmDBBBw4c0N69e7V3716FhIRo6dKl6tGjB+PkYapUqaLOnTtr6dKlki78xy4lJUUNGjRgrDxI3uTH9u3bJUm7du3S7t27FRkZyTh5qMuNy8VtKSkpWrVqlfr06VNitXqMkjsd9Pr673//a2JiYkxERIRp1aqV2bJlS0mXBGPMvn37jCRTt25d07x5c9O8eXPTtm1bY4wxGRkZpmvXrqZ+/fqmcePGZuXKlSVcLfJcfEI84+R5du/ebTp06GCioqJM8+bNzaJFi4wxjJWn+ec//2mioqJMs2bNTNOmTc0HH3xgjGGcStpDDz1katWqZby9vU1wcLBzMerlxuXkyZNm0KBBpl69eiYiIsLMnz+/pMr3KDxWEgAAANbdFIfLAQAAcH0RMgEAAGAdIRMAAADWETIBAABgHSETAAAA1hEyARS7tWvXqn///goNDZWfn5+Cg4PVrl07PfbYYyVW0zPPPJPvCR62xcfHF+p+vPHx8XK5XLrlllt08uTJfO2pqany8vKSy+XSM888Y7/Qi/zzn//UlClT8i3fu3evXC6XJk+eXKz9A7hxEDIBFKtPPvlE7du316+//qpJkyZp2bJlev3113Xbbbdp7ty5JVbXfffdp++++67E+v+tMmXKKDs7u8DvZObMmbrllluuSx2XCpkAcLUImQCK1aRJk1SnTh0tXbpUgwcPVocOHTR48GBNnjxZaWlp1vo5c+bMVT0vOCQkRDExMdb6LypfX1/169dPM2bMcFtujFFSUpLuvvvuEqoMAK4NIRNAscrMzFSVKlXk4+OTr83Ly/1P0KUOB4eHhys+Pt55n5SUJJfLpWXLlikhIUFVq1ZVQECA5s6dK5fLpa+++irfPqZPny6Xy+U8G/q3h8v79eunsLAw5ebm5tv21ltvVcuWLZ3306ZNU1xcnKpVq6Zy5cqpadOmmjRpkrKysq74fVxOQkKCVq9e7TxqUJK+/PJLpaamauTIkQVus2XLFvXt21eVKlWSv7+/oqOj9d5777mts3LlSrlcLn3wwQd68sknVbNmTZUvX15dunRx66tjx4765JNPlJqaKpfL5bx+KzExUXXq1FFgYKDatWunNWvWuLXv2bNHgwcPVs2aNZ3TIzp37qyNGzcW4dsBUNoQMgEUq3bt2mnt2rUaM2aM1q5dW+QgdrGEhASVKVNGs2fP1oIFC9S/f39Vq1ZNM2fOzLduUlKSWrZsqWbNml1yX2lpaVq+fLnb8v/+979at26dW8jbvXu37rnnHs2ePVsff/yxRo0apVdeeUUPPPBAkT5Ply5dFBYW5jab+e677youLk4RERH51t++fbvat2+vn376SVOnTtWiRYvUuHFjxcfHa9KkSfnWf+KJJ5Samqp//OMfeuedd7Rz507dcccdysnJkSS99dZbuu2221S9enV99913zuti06ZN0xdffKEpU6Zozpw5OnXqlHr27KlffvnFWadnz5764YcfNGnSJH3xxReaPn26WrRooePHjxfp+wFQypTsUy0B3OiOHj1qYmNjjSQjyZQpU8a0b9/evPjii+bEiRNu60oyEydOzLePsLAwM2LECOf9zJkzjSRz77335lt37NixpmzZsub48ePOsq1btxpJ5o033nCWTZw40Vz8JzArK8sEBwebe+65x21/48ePN76+vubo0aMFfr6cnByTlZVlZs2aZby9vc2xY8ecthEjRpiwsLACt7vYiBEjTLly5Zy6qlevbrKyskxmZqbx8/MzSUlJ5siRI/m+n8GDBxs/Pz+Tlpbmtr8ePXqYgIAA5ztYsWKFkWR69uzptt68efOMJPPdd985y3r16lVgzSkpKUaSadq0qcnOznaWr1u3zkhynrt99OhRI8lMmTLlip8bwI2NmUwAxSooKEjffPON1q9fr5deekl9+/bVjh079Je//EVNmzbV0aNHr3nfd911V75lCQkJOnPmjNsFNDNnzpSfn5/uueeeS+7Lx8dHw4YN06JFi5xZuZycHM2ePVt9+/ZVUFCQs+6GDRvUp08fBQUFydvbW2XKlNG9996rnJwc7dix45o/jySNHDlShw4d0meffaY5c+bI19dXAwcOLHDd5cuXq3Pnzqpdu7bb8vj4eJ0+fTrfLGSfPn3c3ufN6qampha6vl69esnb2/uS+6hcubLq1aunV155RYmJidqwYUOBpyAAuPERMgFcF61bt9bjjz+u+fPn68CBA/rTn/6kvXv3FnhYt7Bq1KiRb1mTJk3Upk0b55B5Tk6O3n//ffXt21eVK1e+7P4SEhJ09uxZffjhh5KkpUuX6uDBg26HytPS0nT77bdr//79ev31150APW3aNEkXLkAqirCwMHXu3FkzZszQjBkzNHjwYAUEBBS4bmZmZoHfQc2aNZ32i10clCXJz8/vqmu+0j7yzont1q2bJk2apJYtW6pq1aoaM2aMTpw4Ueh+AJR+hEwA112ZMmU0ceJESRcuXMnj5+enc+fO5Vv/t2Epz6Xuczly5EitWbNG27Zt0+eff54vKF5K48aN1bZtWyegzpw5UzVr1tTvf/97Z52PPvpIp06d0qJFizRs2DDFxsaqdevW8vX1veL+CyshIUFLlizRxo0blZCQcMn1goKCdPDgwXzLDxw4IEmqUqWKtZquRlhYmN59911lZGRo+/bt+tOf/qS33npL48aNK5F6AJQMQiaAYlVQCJKkbdu2Sfr/s27ShavI867+zrN8+fICb1B+OUOGDJG/v7+SkpKUlJSkWrVquQXFyxk5cqTWrl2r5ORk/fvf/9aIESPcDg/nBdu8GTzpwm2G/v73v19VjZfTv39/9e/fXwkJCZe9zVLnzp21fPlyJ1TmmTVrlgICAq7pFk1+fn5Fno29WGRkpJ566ik1bdpUP/74o7X9AvB8+e8pAgAWdevWTSEhIbrjjjvUsGFD5ebmauPGjXr11VcVGBioRx55xFl3+PDh+t///V89/fTT6tChg7Zu3ao333xTFSpUuKo+K1asqP79+yspKUnHjx/Xn//853y3S7qUIUOGaOzYsRoyZIjOnTvnduskSeratat8fX01ZMgQjR8/XmfPntX06dP1888/X1WNl+Pv768FCxZccb2JEyfq448/VqdOnfT000+rcuXKmjNnjj755BNNmjTpqr83SWratKkWLVqk6dOnq1WrVvLy8lLr1q0Lvf3mzZv18MMPa+DAgYqIiJCvr6+WL1+uzZs3a8KECVddD4DSi5AJoFg99dRTWrx4sV577TUdPHhQ586dU40aNdSlSxf95S9/UaNGjZx1x40bp19//VVJSUmaPHmy2rZtq3nz5qlv375X3e/IkSP1wQcfSFK+oHg5FSpUUP/+/fXPf/5Tt912myIjI93aGzZsqIULF+qpp57SnXfeqaCgIN1zzz0aO3asevTocdV1FkWDBg20evVqPfHEExo9erTOnDmjRo0aaebMmVf1mS/2yCOP6KefftITTzyhX375RcaYq7rJffXq1VWvXj299dZb2rdvn1wul+rWratXX31V//M//3NNNQEonVzmav56AAAAAIXAOZkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOsImQAAALCOkAkAAADrCJkAAACwjpAJAAAA6wiZAAAAsI6QCQAAAOv+H5No4ZQ0haLAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['Survival Months'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['Survival Months'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Survival Months', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/20-Survival_Months_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "45422997",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80) # Maximize the plot\n",
    "\n",
    "plt.hist(df_new['Status'], bins=35, histtype='stepfilled', align='mid', orientation='vertical', color='navy')\n",
    "\n",
    "plt.legend (['Status'], loc = 'best')\n",
    "plt.title (\"Breast Cancer\", fontsize=20)\n",
    "\n",
    "plt.xlabel('Status', fontsize=15)\n",
    "#plt.xticks(rotation=90) # Rotating the x labels\n",
    "\n",
    "#plt.savefig ('D:/IMT/3- Data Science/3- Data pre-processing (2)/21-Status_hist.png')\n",
    "\n",
    "#plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c31bc1e",
   "metadata": {},
   "source": [
    "End of the excercise!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}