04-Price-and Elapsed-Time-Regression.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# A Linear Regression Analysis of PBSB Artwork Data\n",
    "This analysis uses linear regression to answer the following questions: 1. Can we estimate the price of a portrait and, 2. Can we estimate how long a portrait will take to complete?\n",
    "\n",
    "Data wrangling performed in the basic and advanced analysis is repeated here with minimal explanation. Some additional pre-processing of the input data was done in Excel. This dataset uses data at the order line level of detail which gives better visibility into individual pieces of art."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e729f13-bda8-4273-99c8-dd89532746fc",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "fb267bac-ec19-4e5d-b44e-85af230f5ab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "import squarify\n",
    "\n",
    "# Machine learning and logistic regression libraries\n",
    "from sklearn import preprocessing\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dee2753-5c61-4034-9401-3e402e1c693b",
   "metadata": {},
   "source": [
    "## Import Excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "df27f97a-3e98-4aa0-997d-0078efe6c1e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>PortraitID</th>\n",
       "      <th>ServiceType</th>\n",
       "      <th>PortraitSubject</th>\n",
       "      <th>NumSubjects</th>\n",
       "      <th>Medium</th>\n",
       "      <th>SaleDate</th>\n",
       "      <th>Size</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Frame</th>\n",
       "      <th>CompletedDate</th>\n",
       "      <th>ActualTime</th>\n",
       "      <th>Price</th>\n",
       "      <th>PromisedBy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Commission</td>\n",
       "      <td>Dog</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Pastel</td>\n",
       "      <td>2003-08-06</td>\n",
       "      <td>16x22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unframed</td>\n",
       "      <td>2003-08-15</td>\n",
       "      <td>22</td>\n",
       "      <td>800.0</td>\n",
       "      <td>2003-08-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Commission</td>\n",
       "      <td>House/dwelling</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pen &amp; Ink/Watercolor</td>\n",
       "      <td>2003-08-07</td>\n",
       "      <td>11x14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-08-10</td>\n",
       "      <td>8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2003-08-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>Commission</td>\n",
       "      <td>House/dwelling</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pen &amp; Ink / Watercolor</td>\n",
       "      <td>2003-05-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-05-31</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>Commission</td>\n",
       "      <td>People</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Graphite</td>\n",
       "      <td>2003-03-01</td>\n",
       "      <td>18x24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-03-31</td>\n",
       "      <td>25</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Commission</td>\n",
       "      <td>People</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Graphite</td>\n",
       "      <td>2003-04-01</td>\n",
       "      <td>11x14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-04-15</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2003-04-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  PortraitID ServiceType PortraitSubject  NumSubjects  \\\n",
       "0           1           1  Commission             Dog          3.0   \n",
       "1           2           2  Commission  House/dwelling          1.0   \n",
       "2           6           8  Commission  House/dwelling          0.0   \n",
       "3           7           9  Commission          People          3.0   \n",
       "4           8          10  Commission          People          1.0   \n",
       "\n",
       "                   Medium   SaleDate   Size  Unnamed: 8     Frame  \\\n",
       "0                  Pastel 2003-08-06  16x22         NaN  Unframed   \n",
       "1    Pen & Ink/Watercolor 2003-08-07  11x14         NaN       NaN   \n",
       "2  Pen & Ink / Watercolor 2003-05-01    NaN         NaN       NaN   \n",
       "3                Graphite 2003-03-01  18x24         NaN       NaN   \n",
       "4                Graphite 2003-04-01  11x14         NaN       NaN   \n",
       "\n",
       "  CompletedDate  ActualTime   Price PromisedBy  \n",
       "0    2003-08-15          22   800.0 2003-08-22  \n",
       "1    2003-08-10           8   150.0 2003-08-15  \n",
       "2    2003-05-31           6   160.0        NaT  \n",
       "3    2003-03-31          25  1000.0        NaT  \n",
       "4    2003-04-15          16     0.0 2003-04-15  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the URL of the dataset - in this case we are using a dataset that has been uploaded to OneDrive and shared\n",
    "#file_path = 'C:\\\\Users\\\\simon\\\\OneDrive\\\\Documents\\\\Data Analysis\\\\Art\\\\Artwork-Items.xlsx'\n",
    "\n",
    "# Load the dataset into a DataFrame\n",
    "df = pd.read_excel('OneDrive/Documents/Data Analysis/Art/Artwork-Items-Reg.xlsx')\n",
    "\n",
    "# Display the first few rows to ensure it loaded correctly\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "784d0d43-03fe-41fd-aa9b-fff51bf57d66",
   "metadata": {},
   "source": [
    "## Basic Data Cleanup\n",
    "Unlike the price data used in previous analyses, the item price data is already formatted as a number and doesn't need further manipulation. There are still some rows with missing values which must be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "52206125-923d-46ca-9d95-3497e6824bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the rows with no order date and no sale type\n",
    "df = df.dropna(subset=['SaleDate'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "24ed41f2-70ff-4ac8-a544-6c200db3bdb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out rows with 'Price' == 0 or 'ActualTime' == 0\n",
    "df = df[(df['Price'] > 100)]\n",
    "df = df[(df['ActualTime'] > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "8892daef-38e7-44d1-87e7-12b130d4d044",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out any records which dont have 'ServiceType' == 'Commission' or 'ServiceType' == 'Artwork sale'\n",
    "df = df[(df['ServiceType'] == 'Commission') | (df['ServiceType'] == 'Artwork sale')] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "0ed091c2-0859-4352-a215-64e0d08100fd",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>PortraitID</th>\n",
       "      <th>ServiceType</th>\n",
       "      <th>PortraitSubject</th>\n",
       "      <th>NumSubjects</th>\n",
       "      <th>Medium</th>\n",
       "      <th>SaleDate</th>\n",
       "      <th>Size</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Frame</th>\n",
       "      <th>CompletedDate</th>\n",
       "      <th>ActualTime</th>\n",
       "      <th>Price</th>\n",
       "      <th>PromisedBy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Commission</td>\n",
       "      <td>Dog</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Pastel</td>\n",
       "      <td>2003-08-06</td>\n",
       "      <td>16x22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unframed</td>\n",
       "      <td>2003-08-15</td>\n",
       "      <td>22</td>\n",
       "      <td>800.0</td>\n",
       "      <td>2003-08-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Commission</td>\n",
       "      <td>House/dwelling</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pen &amp; Ink/Watercolor</td>\n",
       "      <td>2003-08-07</td>\n",
       "      <td>11x14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-08-10</td>\n",
       "      <td>8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2003-08-15</td>\n",
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       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>Commission</td>\n",
       "      <td>House/dwelling</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pen &amp; Ink / Watercolor</td>\n",
       "      <td>2003-05-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-05-31</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>Commission</td>\n",
       "      <td>People</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Graphite</td>\n",
       "      <td>2003-03-01</td>\n",
       "      <td>18x24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-03-31</td>\n",
       "      <td>25</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>Commission</td>\n",
       "      <td>Dog</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graphite</td>\n",
       "      <td>2003-09-06</td>\n",
       "      <td>Custom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-09-25</td>\n",
       "      <td>24</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2003-10-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  PortraitID ServiceType PortraitSubject  NumSubjects  \\\n",
       "0           1           1  Commission             Dog          3.0   \n",
       "1           2           2  Commission  House/dwelling          1.0   \n",
       "2           6           8  Commission  House/dwelling          0.0   \n",
       "3           7           9  Commission          People          3.0   \n",
       "8          12          30  Commission             Dog          2.0   \n",
       "\n",
       "                   Medium   SaleDate    Size  Unnamed: 8     Frame  \\\n",
       "0                  Pastel 2003-08-06   16x22         NaN  Unframed   \n",
       "1    Pen & Ink/Watercolor 2003-08-07   11x14         NaN       NaN   \n",
       "2  Pen & Ink / Watercolor 2003-05-01     NaN         NaN       NaN   \n",
       "3                Graphite 2003-03-01   18x24         NaN       NaN   \n",
       "8                Graphite 2003-09-06  Custom         NaN       NaN   \n",
       "\n",
       "  CompletedDate  ActualTime   Price PromisedBy  \n",
       "0    2003-08-15          22   800.0 2003-08-22  \n",
       "1    2003-08-10           8   150.0 2003-08-15  \n",
       "2    2003-05-31           6   160.0        NaT  \n",
       "3    2003-03-31          25  1000.0        NaT  \n",
       "8    2003-09-25          24   650.0 2003-10-10  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cbf1167-1225-4da6-858f-efae2c11ecf9",
   "metadata": {},
   "source": [
    "## Data Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f215893a-efc7-4b2c-be03-9478af898d35",
   "metadata": {},
   "source": [
    "The source data has been cleaned up since previous analyses and obvious outliers were corrected."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79aac43-3838-410e-b9ff-f20ddb83038f",
   "metadata": {},
   "source": [
    "### Extract 'Year'\n",
    "Since the SaleDate column is already in date/time format, the Year can be extracted directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "788fb6be-0cf3-4c06-a4eb-230fc038c3bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Year column\n",
    "df['Year'] = df['SaleDate'].dt.year.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0db19770-e0d9-4f4e-a0e0-3921920d845c",
   "metadata": {},
   "source": [
    "### Calculate 'Area' for each item where possible\n",
    "Use data in the 'Size' column to calculate the area of each painting in square incheas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "42677163-aa19-402b-8a88-8d5064935e38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    352.0\n",
      "1    154.0\n",
      "2      NaN\n",
      "3    432.0\n",
      "8      NaN\n",
      "Name: Area, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Function to calculate area from 'Size' column\n",
    "def calculate_area(size):\n",
    "    if pd.isna(size):\n",
    "        return None  \n",
    "    try:\n",
    "        # Convert the value to a string\n",
    "        size_str = str(size)\n",
    "        # Use regular expression to find the first pair of numbers (including decimals)\n",
    "        match = re.search(r'(\\d+(\\.\\d+)?)\\s*[xX*]\\s*(\\d+(\\.\\d+)?)', size_str)\n",
    "        if match:\n",
    "            height, width = map(float, match.groups()[::2])  # Extract only the numeric parts\n",
    "            return height * width\n",
    "        else:\n",
    "            return None  # Handle cases where the size format is incorrect\n",
    "    except ValueError:\n",
    "        return None  # Handle cases where the size format is incorrect\n",
    "\n",
    "# Apply the function to the 'Size' column and create a new 'Area' column\n",
    "df['Area'] = df['Size'].apply(calculate_area)\n",
    "\n",
    "# Display the DataFrame with the new 'Area' column\n",
    "print(df['Area'].head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "705952df-f304-4e23-98f3-16c945c97f17",
   "metadata": {},
   "source": [
    "### Remove Records With 'Area' == null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "61dacbe8-ac0b-49fb-a34f-c96c0da91904",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop any records that don't have a useable 'Area'\n",
    "df = df.dropna(subset=['Area'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97055e20-92d3-4d42-99c2-52b0dd6ff283",
   "metadata": {},
   "source": [
    "### Create a Price Per Hour Feature and a Price Per Area Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "fd4e058d-92c9-4a74-a7f8-0a90ff363a1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Price_Per_Hour'] = df['Price']/df['ActualTime']\n",
    "df['Price_Per_Area'] = df['Price']/df['Area']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7df2b71-5064-481a-b340-f6b2a4218e0e",
   "metadata": {},
   "source": [
    "### Separate Out Commissioned Portraits\n",
    "Create a dataframe that only contains commissioned paintings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "163f5cf6-0e9b-4331-862e-15a6ffbcb396",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_commission = df[df['ServiceType'] == 'Commission']\n",
    "df_retail = df[df['ServiceType'] == 'Artwork sale']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70f0786-28bf-4e5b-8cad-72acfc73a93b",
   "metadata": {},
   "source": [
    "### Drop Commission Records with Missing Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "2fc25959-1c11-412c-8ce8-5bffaea54592",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop any commission records with missing dates\n",
    "df_commission = df_commission.dropna(subset=['CompletedDate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebfb3c8a-02d1-49cb-b84e-bc30db5e3e3d",
   "metadata": {},
   "source": [
    "### Filter Out Mediums Other Than Oil and Pastel\n",
    "The choice of medium has an impact on pricing and time taken to complete. We're just interested in oil and pastel portraits for this analysis as they make up the bulk of the studio output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "83102270-03a0-4135-9402-90cd1d657052",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out values that don't start with 'Oil' or 'Pastel'\n",
    "df_commission = df_commission[df_commission['Medium'].str.match(r'^(Oil|Pastel)', na=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb47d478-2268-4eaa-acea-d94a2324418c",
   "metadata": {},
   "source": [
    "### Remove Commission Records With no Subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "93218ef1-4808-42d2-806d-e34279c2399a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_commission = df[df['NumSubjects'] > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "858ec2a7-18bd-45d0-b24c-f0d0be275d06",
   "metadata": {},
   "source": [
    "## Linear Multi Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a29055-e5bb-4b14-a340-382525b2721b",
   "metadata": {},
   "source": [
    "### Can We Predict the Price of a Person's Portrait?\n",
    "I tend to price portraits based on a combination of my historical data and a rough assessment of how difficult I assess they will be to complete. In the absence of a 'difficulty' rating, let's examine the correlation between year, area, number of subjects and price. This requires a multi-valued linear regression.\n",
    "Portraits of people are the most time-consuming and difficult to complete, so we'll focus on those for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "6510154a-030a-4c9b-aabc-8a082d68dabf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter all portraits out of the dataframe except for those of people\n",
    "df_people = df_commission[(df_commission['PortraitSubject'] == 'Person') | (df_commission['PortraitSubject'] == 'People')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba2cd34-3c64-4495-9add-49adddd7876d",
   "metadata": {},
   "source": [
    "Price vs year isn't a meaningful regression in the absence of any other factors. Let's calculate price per subject and see if there's any variation in that over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "4c2c369e-4b82-4285-8652-8513d41efe95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate price per subject. We already know that there are no zeros remaining in the NumSubjects column.\n",
    "df_people.loc[:,'Price_Per_Head'] = df_people['Price']/df_people['NumSubjects']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00109f25-e43a-4377-895f-e8cb2a194ab0",
   "metadata": {},
   "source": [
    "Before we start regression, let's plot the relationships between some of the variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "1c7b6a67-bac5-4f50-bf06-689cefd05fef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ox44dUcc//fRT9e/fX5I0duxYOZ1OrVu3LnL9/v379dFHH2nChAmSpPHjx6u0tFSbN2+OnPP222+rtLQ0ck5jEhISlJqaGvUBAAAAAADQHL6Aqdz0HspNT9KhCp98gaCCdbqgLMvS0Sq/BmemaERO180g4ro0r6KiQgUFBZHLu3bt0tatW5Wenq5+/frprrvu0mWXXaaJEyfq/PPP19q1a7VmzRqtX79ekuTxeHTddddp/vz5ysjIUHp6uhYsWKBRo0ZpypQpkkIzqC644AJdf/31evLJJyVJN9xwg2bMmNHkHfMAAAAAAABi4QuYqgkEVeMLqsZvKlBbQTTz9L56eN2nOlThk6eHU3bDkDdo6mjtrnk3TRosm+34VUKdnWFZVtyq2NevX6/zzz+/wfHZs2dr5cqVkqSnn35aS5Ys0RdffKGhQ4dq0aJF+uY3vxk5t6amRnfddZeee+45VVdXa/LkyVq2bJlyc3Mj5xw5ckS33367Xn75ZUnSxRdfrMcee0w9e/Zs8lhj2YoQAAAAAAB0L/6gqWp/UDX+oGp8x4Knxry/p0TPbd6rL45UyZTktBkanJmimyYN1oS8Xu036FYSS2YS1yCqMyGIAgAAAAAAYYHa4KnaH5TXb8ofPH7w1BjTsrSvpEaWEeqOGpGT2mlnQsWSmXTYXfMAAAAAAAA6ikBkxpOpGn8w5uCpPpth6Cs5bmW6E1tphJ0DQRQAAAAAAEA9gaCpmoCpal+wVYInhBBEAQAAAACAbi9oWpGOp2ofwVNbIYgCAAAAAADdTtC0QqFTbfjkCxA8tQeCKAAAAAAA0OWFg6dw+ETwFB8EUQAAAAAAoMsxTUs1gdAyO4KnjoMgCgAAAAAAdHp1g6eagCmvPxjvIaERBFEAAAAAAKDTCQdPNX5T1f4gwVMnQRAFAAAAAAA6vPrBky9gyrKseA8LMSKIAgAAAAAAHU4gaKomYEYKxul46hoIogAAAAAAQNz5g6GZTjX+oLx+U/4gwVNXRBAFAAAAAADanbd2mZ3XH/ocMAmeugOCKAAAAAAA0KYsy5I3sswu9Nmk36lbIogCAAAAAACtyjTrBE+1M58oFodEEAUAAAAAAFooaFqRUvGagMmOdjgugigAAAAAABCT8I521b5Q+ESxOJqKIAoAAAAAAJyQL2DWLrFjRzu0DEEUAAAAAACIEg6cwuFT0GSZHVoHQRQAAAAAAN0YO9qhPRFEAQAAAADQjZimFdnJrsYflJdicbQjgigAAAAAALqw+jvaef3BeA8J3RhBFAAAAAAAXUggaKq6zjI7isXRkRBEAQAAAADQidXd0a7GZypgEjyh4yKIAgAAAACgE2FHO3RmBFEAAAAAAHRQ7GiHroYgCgAAAACADsI0Q8FTdW25ODvaoashiAIAAAAAIE7Y0Q7dDUEUAAAAAADtJBA0VRMwVe0LsqMduiVbPB/8jTfe0EUXXaScnBwZhqGXXnrpuOfOmTNHhmHokUceiTru9Xp12223qVevXkpOTtbFF1+sL774IuqckpISXXXVVfJ4PPJ4PLrqqqt09OjR1n9CAAAAAADU4QuYKqvxq7i8RnuPVGnPkSoVl9WovMZPCIVuKa5BVGVlpUaPHq3HHnvshOe99NJLevvtt5WTk9Pgurlz52r16tV64YUX9Oabb6qiokIzZsxQMHhsOuOsWbO0detWrV27VmvXrtXWrVt11VVXtfrzAQAAAAB0b95AUKVVfh0oq9Huw5X6oqRKh8q9qqgJEDwBivPSvAsvvFAXXnjhCc/58ssvdeutt+of//iHvvGNb0RdV1paqt/+9rdatWqVpkyZIkl69tlnlZubq3/+85+aPn26PvnkE61du1ZvvfWWxo0bJ0n6zW9+o/Hjx2vHjh0aOnRo2zw5AAAAAECXxo52QOziOiPqZEzT1FVXXaW77rpLI0aMaHD9li1b5Pf7NW3atMixnJwcjRw5Uhs3bpQkbdq0SR6PJxJCSdJZZ50lj8cTOQcAAAAAgJMxTUvVvqCOVPq072i1Pj9cpX1Hq3Wk0qcqX4AQCmiCDl1W/tBDD8nhcOj2229v9PqioiK5XC6lpaVFHc/KylJRUVHknMzMzAa3zczMjJzTGK/XK6/XG7lcVlbWnKcAAAAAAOik2NEOaH0dNojasmWLfvWrX+m9996TYRgx3dayrKjbNHb7+ufUt2TJEi1atCimxwUAAAAAdF6BoKnqOsvs6HQCWl+HXZr373//W8XFxerXr58cDoccDod2796t+fPna8CAAZKk7Oxs+Xw+lZSURN22uLhYWVlZkXMOHDjQ4P4PHjwYOacxCxcuVGlpaeRj7969rffkAAAAAABx19iOdgfLvexoB7ShDhtEXXXVVfrwww+1devWyEdOTo7uuusu/eMf/5AkjR07Vk6nU+vWrYvcbv/+/froo480YcIESdL48eNVWlqqzZs3R855++23VVpaGjmnMQkJCUpNTY36AAAAAAB0XuxoB8RfXJfmVVRUqKCgIHJ5165d2rp1q9LT09WvXz9lZGREne90OpWdnR3Z6c7j8ei6667T/PnzlZGRofT0dC1YsECjRo2K7KI3bNgwXXDBBbr++uv15JNPSpJuuOEGzZgxgx3zAAAAAKCLYkc7oGOKaxD17rvv6vzzz49cnjdvniRp9uzZWrlyZZPu45e//KUcDodmzpyp6upqTZ48WStXrpTdbo+c8/vf/1633357ZHe9iy++WI899ljrPREAAAAAQFyZZih4qq4tF/cGTFkET0CHY1j8zWySsrIyeTwelZaWskwPAAAAAOKMHe3QFaQkOpTpToz3MFoslsykw+6aBwAAAABAGDvaAV0DQRQAAAAAoMPxBUzVBGpnPPlMBUyCJ6ArIIgCAAAAAMSdNxAKnMLhU9CkRQboigiiAAAAAADtih3tgO6LIAoAAAAA0KZM06qd6WSyox3QzRFEAQAAAABaFTvaATgegigAAAAAQIv4g9HL7NjRDsDxEEQBAAAAAGLCjnYAmosgCgAAAABwQjX+oLx+drQD0HIEUQAAAACAiLo72lXXBlDsaAegtRBEAQAAAEA3xo52ANoTQRQAAAAAdCNB01J1eEc7f1C+AP1OANoPQRQAAAAAdGHsaAd0PJZl6VCFT+/vKdHR6oCuO2dgvIfUbgiiAAAAAKALiexo5wuFT+xoB8SXaVnad7RaOw9UaGdxhQqKQ59Lq/2Rc74ztq88PZxxHGX7IYgCAAAAgE7MW9vv5PUTPAHxFgia2n24SjuLw6FTuQoPVqrKFzzh7T7eV6bxgzPaaZTxRRAFAAAAAJ2It06xeI0/qKBJsTgQD9X+oD47eGyG084DFfr8cKX8wab/ncx0J+jUvj3lctjacKQdC0EUAAAAAHRgdZfaVRM8AXFRVu1XwcFQ2FRQu7xub0mVmvrX0ZB0SloP5WemKD8zRXmZKcrPdOuU9B7KdCe26dg7GoIoAAAAAOhA/EHz2K52PpbaAe0pXCK+s7g8MtOpoLhCB8q8Tb4Ph83QgIxk5dUGTkOyUjSod7KSXEQwEkEUAAAAAMRVIBI8sasd0J7CJeIFxRX6tM5Mp6N1SsRPJtFh0+DIDKfQ5wEZyd1qqV2sCKIAAAAAoB0FTUvV/qCqfUGCJ6Cd1C0RD810alqJeF2piY46gZNb+ZkpOiWth+w2ow1H3vUQRAEAAABAGzLDwVPtcjtfgOAJaEs1/qA+O1ipncXlkeBp16HYSsR7pyREQqf8rNBMp0x3ggyD0KmlCKIAAAAAoBWZpqWaQGjGUzXBE9Cmymv8x2Y5HWhZiXhenSV2PZNcbTru7owgCgAAAABawLIs1fjNyKwnX8CUZbGzHdCawiXiBZGldaHldS0pEc/PTNHgTErE2xtfbQAAAACIgWVZ8gbMUMdTIFQyTvAEtJ66JeJ1ZzvFVCLutGlw72OBU35mivpTIt4hEEQBAAAAwEnU1PY7hXe3I3gCWkfDEvEKFR6siLlE/NjSOkrEOzqCKAAAAACoxxsIqsZnRgrGTYInoMUoEYdEEAUAAAAA8gWOhU41/qCCTW06BtColpaIS1JfSsS7JIIoAAAAAN2OP1gbPPlCS+0CJjvbAc3RGiXidpuhARlJdfqc3JSId2Fx/a6+8cYb+sUvfqEtW7Zo//79Wr16tS655BJJkt/v17333qtXXnlFn332mTwej6ZMmaIHH3xQOTk5kfvwer1asGCBnn/+eVVXV2vy5MlatmyZ+vbtGzmnpKREt99+u15++WVJ0sUXX6xHH31UPXv2bM+nCwAAACBOAsFju9rV+AiegOYwLUv7j9ZELa0rKK5QSVUMJeIOmwb1TonMdMrPStEASsS7lbgGUZWVlRo9erSuueYaffvb3466rqqqSu+9955+/OMfa/To0SopKdHcuXN18cUX6913342cN3fuXK1Zs0YvvPCCMjIyNH/+fM2YMUNbtmyR3W6XJM2aNUtffPGF1q5dK0m64YYbdNVVV2nNmjXt92QBAAAAtJugaYWCJ19oqZ0/SPAExCIQNLX7SNWxWU4HmlciXndZXV5mivqmJVEi3s0ZVgfZ7sEwjKgZUY155513dOaZZ2r37t3q16+fSktL1bt3b61atUqXXXaZJGnfvn3Kzc3VK6+8ounTp+uTTz7R8OHD9dZbb2ncuHGSpLfeekvjx4/Xf//7Xw0dOrRJ4ysrK5PH41FpaalSU1Nb/HwBAAAAtJ6gaUV2tav2ETwBsThWIn5sltNnhyqaXSKel5mivKwUZVEiflIpiQ5luhPjPYwWiyUz6VQLLktLS2UYRmRJ3ZYtW+T3+zVt2rTIOTk5ORo5cqQ2btyo6dOna9OmTfJ4PJEQSpLOOusseTwebdy4sclBFAAAAICOw6yd8RQOn3wBgiegKeqWiIdnO+09Qok42k+nCaJqamr0ox/9SLNmzYqka0VFRXK5XEpLS4s6NysrS0VFRZFzMjMzG9xfZmZm5JzGeL1eeb3HytXKyspa42kAAAAAaAbTtFQTCM12IngCTs6yLB2u9EV2rAuHT0VlNU2+j3CJeH6mOxI4USKOluoUrx6/36/LL79cpmlq2bJlJz3fsqyo6X+NTQWsf059S5Ys0aJFi5o3YAAAAAAtEjQteWuDp5qAKa+/6b00QHdDiTg6kw4fRPn9fs2cOVO7du3Sa6+9FrXWMDs7Wz6fTyUlJVGzooqLizVhwoTIOQcOHGhwvwcPHlRWVtZxH3fhwoWaN29e5HJZWZlyc3Nb4ykBAAAAqCcQNFUTMCkXB04iqkT8QGimU8tKxN3Kz0zRKWk9KBFHu+jQQVQ4hNq5c6def/11ZWRkRF0/duxYOZ1OrVu3TjNnzpQk7d+/Xx999JGWLl0qSRo/frxKS0u1efNmnXnmmZKkt99+W6WlpZGwqjEJCQlKSEhoo2cGAAAAdG++gKmaQCh0qvGZCpgET0B99UvEdxaXa9ehyhaViOdnpSiTEnHEUVyDqIqKChUUFEQu79q1S1u3blV6erpycnL0ne98R++9957+7//+T8FgMNLplJ6eLpfLJY/Ho+uuu07z589XRkaG0tPTtWDBAo0aNUpTpkyRJA0bNkwXXHCBrr/+ej355JOSpBtuuEEzZsygqBwAAABoB5ZlyRsw5fUfC5+CsTQjA91AeY0/qsuJEnF0VYZlWXH7F2D9+vU6//zzGxyfPXu27r//fg0cOLDR273++us677zzJIVKzO+66y4999xzqq6u1uTJk7Vs2bKoZXRHjhzR7bffrpdfflmSdPHFF+uxxx6L7L7XFLFsRQgAAAB0Z6YZCp5q/MHa4MlUHH/sADqUcIl43aV1zS0RrzvTKS8zhRLxTigl0aFMd2K8h9FisWQmcQ2iOhOCKAAAAKBxQdMKhU7+ULG4L0DwBEh1S8QrVFCnSJwScYR1xyCKuBQAAABATPzB2tlOfpNicaBWIGhqz5Eq7ayzvK6gOLYScXei41jgVPu5b1oSJeLoUgiiAAAAAJyQt3Z5nbc2fKJYHN1djT+oXYcq9emBY4HTZ4cqYioR75XiUl5mioZkukNL67JSlEWJOLoBgigAAAAAEeFi8fCMJ2+AYnF0bxU1Ae0sLo8UiDe3RDyv97GldXmZKUqjRBzdFEEUAAAA0I2ZphUpFK/xB+Wl3wnd2OEKb9TSup0HWlYinp/p1uDMZErEgTr42wAAAAB0I3WLxav9QfkCLLND92NZlvaV1tSGTcdmO8VSIp7gsGlw72Tl1y6to0QcaBqCKAAAAKALo1gc3V1jJeKFxRWqjKFEPCXBEVpS1zsUOOVTIg40G0EUAAAA0IVQLI7urDVKxDNSXLXL6lKUl+lWfmaKslIpEQdaC0EUAAAA0EnVLxav8Qdl0u+EbqI1SsRP6dmjNnCiRBxoLwRRAAAAQCdBsTi6q/ol4gXFFdpfGluJeP+MpDoznVI0uHeKkhP4kRhob/ytAwAAADqocPBU7QuqJhBabgd0Za1ZIp5XWyI+hBJxoEOJKYh6+eWXm3zuxRdfHPNgAAAAgO6M4AndSWuUiLsTHcrLpEQc6ExiCqIuueSSqMuGYURNBa5b3hYM8o8mAAAAcCKWZanGb6raH1S1PygfS+3QRXn9QX12qDISOO08QIk40F3FFESZdXbc+Oc//6m7775bixcv1vjx42UYhjZu3Kh7771XixcvbvWBAgAAAJ1duFy82hcKnuh4QldUURNQwcHaAvHa5XV7KBEHUKvZHVFz587VE088oXPOOSdybPr06UpKStINN9ygTz75pFUGCAAAAHRmoR3tQsFTjZ/gCV1LuES8oM7yuuaWiOfVznaiRBzo2pr9t7uwsFAej6fBcY/Ho88//7wlYwIAAAA6LW8gqBqfWRs8BWUSPKELqFsiHg6ddh4ob1GJeH5migb2okQc6G6aHUSdccYZmjt3rp599ln16dNHklRUVKT58+frzDPPbLUBAgAAAB2ZL3AsdKrxBxWMZf0R0AEFTUu7D1ceC5yaUSKekuCIhE3hpXW5lIgDUAuCqKefflqXXnqp+vfvr379+kmS9uzZoyFDhuill15qrfEBAAAAHYo/WBs8+UJL7QJ1elSBzoYScQDtrdlBVF5enj788EOtW7dO//3vf2VZloYPH64pU6bwhgMAAIAuIxA8tqud12/KHyR4QucUKRE/UB4JnppTIl5/phMl4gBiYVi0JTZJWVmZPB6PSktLlZqaGu/hAAAAoI0ETSuy1K7aFyR4QqdUt0Q8vMQulhJxmyEN6JVMiTjQxlISHcp0J8Z7GC0WS2bSoneRyspKbdiwQXv27JHP54u67vbbb2/JXQMAAADtImhadXa1C8oXIHhC59FYiXhBcYWOVPpOfuNaCQ6bBvVOrg2c3JSIA2hTzQ6i3n//fX39619XVVWVKisrlZ6erkOHDikpKUmZmZkEUQAAAOiQAkFTNQFT1b5Q8MSMJ3QWQdPSniNVUUvrCg5WqNJLiTiAzqPZQdSdd96piy66SMuXL1fPnj311ltvyel06sorr9Qdd9zRmmMEAAAAms0fNCMznuh4QmcRLhEPz3T6tLhCuw5VxjRjL1wiXnemEyXiAOKt2UHU1q1b9eSTT8put8tut8vr9WrQoEFaunSpZs+erW9961utOU4AAADgpCzLkjdgyus3VRMIzXgKxtLEDMRBpES8TqfT7sOVMZWI5/RMjIRNebUf6cmUiAPoeJodRDmdzkiSnpWVpT179mjYsGHyeDzas2dPqw0QAAAAOB7TtGoDp9CsJ2/AFHvxoCNrlRLxjORI2JSfFSoRT6FEHEAn0ex3qzFjxujdd9/VkCFDdP755+snP/mJDh06pFWrVmnUqFGtOUYAAABA0rFldjV+U94AxeLouCzL0v7Smkjo1PIS8dDyOkrEAXR2zQ6iFi9erPLycknSAw88oNmzZ+umm25SXl6eVqxY0WoDBAAAQPdUd5mdt3bWU8AkeELHEykRL67QzgPlLS4RD890okQcQFdkWMxdbpKysjJ5PB6VlpYqNTU13sMBAADocsLL7ML9Tl6/KZP/qqKDae0S8bzMFA3JdFMiDnRTKYkOZboT4z2MFoslM2nRQuJAIKD169ersLBQs2bNktvt1r59+5SamqqUlJSW3DUAAAC6uEDQVE0gvNSOZXboeCq8ARXWhk2UiANA62h2ELV7925dcMEF2rNnj7xer6ZOnSq3262lS5eqpqZGTzzxxEnv44033tAvfvELbdmyRfv379fq1at1ySWXRK63LEuLFi3SU089pZKSEo0bN06PP/64RowYETnH6/VqwYIFev7551VdXa3Jkydr2bJl6tu3b+SckpIS3X777Xr55ZclSRdffLEeffRR9ezZs7lPHwAAADEKL6/z+llmh47nSKVPO4vLtfMAJeIA0Jaa/a54xx136PTTT9cHH3ygjIyMyPFLL71UP/jBD5p0H5WVlRo9erSuueYaffvb325w/dKlS/Xwww9r5cqVGjJkiH72s59p6tSp2rFjh9xutyRp7ty5WrNmjV544QVlZGRo/vz5mjFjhrZs2SK73S5JmjVrlr744gutXbtWknTDDTfoqquu0po1a5r79AEAAHACphnqd6rxB1lmhw4lXCIeDpuaUyLuctg0uE6JeF5migb1SqFEHACaoNkdUb169dJ//vMfDR06VG63Wx988IEGDRqkzz//XMOHD1dVVVVsAzGMqBlRlmUpJydHc+fO1d133y0pNPspKytLDz30kObMmaPS0lL17t1bq1at0mWXXSZJ2rdvn3Jzc/XKK69o+vTp+uSTTzR8+HC99dZbGjdunCTprbfe0vjx4/Xf//5XQ4cObdL46IgCAAA4vrrL7LwBU76AKapIEW+tUSKenGA/ViCe6VZeZor6pVMiDqB10BEVA9M0FQw2fAP/4osvIrOVWmLXrl0qKirStGnTIscSEhI0adIkbdy4UXPmzNGWLVvk9/ujzsnJydHIkSO1ceNGTZ8+XZs2bZLH44mEUJJ01llnyePxaOPGjU0OogAAAHBMIGiqunaJXY0/KH+QZXaIr1YpEU92HVtaV7u8Ljs1kRJxAGhFzQ6ipk6dqkceeURPPfWUpNCMpoqKCt133336+te/3uKBFRUVSZKysrKijmdlZWn37t2Rc1wul9LS0hqcE759UVGRMjMzG9x/ZmZm5JzGeL1eeb3eyOWysrLmPREAAIAuIGhaqvYHVe0LEjwh7ipqAio4eGxZXXNLxOsurcvPdFMiDgDtoNlB1C9/+Uudf/75Gj58uGpqajRr1izt3LlTvXr10vPPP99qA6z/2wfLsk76G4n65zR2/snuZ8mSJVq0aFGMowUAAOgagqalGn8wEj4RPCFeWqNEvH9Gcp3AKUWDMykRB4B4afa7b05OjrZu3arnn39e7733nkzT1HXXXacrrrhCPXr0aPHAsrOzJYVmNPXp0ydyvLi4ODJLKjs7Wz6fTyUlJVGzooqLizVhwoTIOQcOHGhw/wcPHmww26quhQsXat68eZHLZWVlys3NbdmTAgAA6KAsy1KNP7TcrtoflNff9A4doDXULxEPf461RHxQr2TlZx2b6TQwI1kJTnsbjhwAEIsW/RqgR48euvbaa3Xttde21ngiBg4cqOzsbK1bt05jxoyRJPl8Pm3YsEEPPfSQJGns2LFyOp1at26dZs6cKUnav3+/PvroIy1dulSSNH78eJWWlmrz5s0688wzJUlvv/22SktLI2FVYxISEpSQkNDqzwsAAKCjqPEHI7OeavyUi6P9hEvEQ2FTbYl4caUqvIEm3wcl4gDQOcUcRL388stNOu/iiy8+6TkVFRUqKCiIXN61a5e2bt2q9PR09evXT3PnztXixYuVn5+v/Px8LV68WElJSZo1a5YkyePx6LrrrtP8+fOVkZGh9PR0LViwQKNGjdKUKVMkScOGDdMFF1yg66+/Xk8++aQk6YYbbtCMGTMoKgcAAN2KLxAuGA8ttzMJntAOfAFTnx06NsNp54EKfUaJOAB0WzEHUZdccknUZcMwGvz2zDCMRnfUq+/dd9/V+eefH7kcXgo3e/ZsrVy5Uj/84Q9VXV2tm2++WSUlJRo3bpxeffXVqF35fvnLX8rhcGjmzJmqrq7W5MmTtXLlStntx6bf/v73v9ftt98e2V3v4osv1mOPPRbrUwcAAOhU/OGd7XyhGU8Bk54ntK1Kb22J+IFjJeKfx1gi3seTGLW0jhJxAOhaDKuFc7Ddbrc++OADDRo0qLXG1CGVlZXJ4/GotLRUqamp8R4OAABAA+xsh/YULhGv2+m072jsJeKRWU6UiAPohlISHcp0J8Z7GC0WS2bCuzwAAEAnFQiaqgmYkaV2BE9oC5ZlqaisRjsPHAucCoordLg5JeLhWU5ZlIgDQHdFEAUAANBJBMJL7fwmM57QJlq7RDwv0618SsQBAHUQRAEAAHRQvoCpmkBomZ3XbxI8oVVRIg4AiIcWB1GGYfAPDQAAQAtZliVvwJTXfyx8CsbS8AycACXiAICOIuYgKi0tLSp4qqio0JgxY2Sz2aLOO3LkSMtHBwAA0EWZZih4qvEHa4Mns8FOxEBzHKn0RcKmT2uX11EiDgDoKGL+1+SRRx5pg2EAAAB0bb6AKW9t4OQNBGNa/gQ0JlIiXhs6hWc7xVoiPrBXsoZEOp1SNKgXJeIAgLYTcxA1e/bsmM5//vnndfHFFys5OTnWhwIAAOiUwrOd6gZPLLNDS9QtEQ93OhUUV1AiDgDodNp8fu2cOXM0btw4DRo0qK0fCgAAIC68gWCk34nZTmgpX8DUrkOV2llcHgmcPjtYKW8Mr6v0ZFedLqfQ5z4eSsQBAPHX5kEUXQcAAKArCQTN2tlOodDJ6zdl8v8dNFO4RLzu0rpmlYiHQ6csSsQBAB0bjYMAAAAn4AvU7mLnCy2zC5jMdkLz1C0R31lcoZ3F5TGXiPdLT1J+lvvYTKfeKUpJ5L/0AIDOg3+1AAAA6gj3OtX4g6rx0+2E2FmWpQNl3kjYFA6eDldQIg4AAEEUAADotizLinQ7VfuDlIojZo2ViBcerFB5TWwl4nm9Q8vqKBEHAHR1BFEAAKDbsCzr2Gwn+p0QI1/A1GeHoneta06JeHhZHSXiAIDuqFlBVDAY1JtvvqlTTz1VaWlpJzy3f//+cjqdzRocAKDtmaal7fvKdKTKp/Qkl0bkpMrGb+HRRQRNK7LEriZgyhcw2UgFTRIuEQ8XiLe0RDwcPmWkJLTdoAEA6ASaFUTZ7XZNnz5dn3zyyUmDqI8++qhZAwMAtL2NBYe0fEOhCosr5A9actoNDc5M0U2TBmtCXq94Dw+IWTh4qvYHVe0Lyh+kWBwnV7dE/NPaTqfmlIiHdq1zUyIOAMAJNPtfx1GjRumzzz7TwIEDW3M8AIB2srHgkO5ZvU0V3oDSklxy2W3yBU19sr9c96zepsWXjiKMQodnmpaqa2c8VfuD8sWwRArdj2VZKiqriSyrKygOzXg6XNn0EnGn3dCg3seW1eVTIg4AQEyaHUT9/Oc/14IFC/TAAw9o7NixSk5Ojro+NTW1xYMDALQN07S0fEOhKrwBZace6yZJtNmVnWpTUZlXyzcU6qxBGSzTQ4cSNC15A6HZTgRPOJGgaWlvSVUkbAqHTxXeGErEXfaoZXX5WW5KxAEAaKFmB1EXXHCBJOniiy+OKle0LEuGYSgYDLZ8dACANrF9X5kKiyuUluRqUJBrGIZ6JjlVWFyh7fvKNKqvJ06jBCR/sLZYvLZgnKV2aIwvYGrXocpI2LSzuDzmEvG0JOexZXV1SsRtlIgDANCqmh1Evf766605DgBAOzpS5ZM/aMlltzV6fYLdplLT0pGqpi9XAVqDNxAKnby14VPAJHhCtEpvQIUHK+qEThXafbhKwRhaxPt4EiOznCgRBwCgfTU7iJo0aVJrjgMA0I7Sk1xy2g35gqYSbQ17TbxBU06bofQkVxxGh+7Csix5A9Eznkx2tEMdJVW+Bkvrvjxa3eTbR5WI1y6to0QcAID4atG/wv/+97/15JNP6rPPPtOf/vQnnXLKKVq1apUGDhyoc845p7XGCABoZSNyUjU4M0Wf7C9XdqqtwRLro1V+Devj1ogc+v7QesI72tX4g6oJmPIFTFkET1DofedAmbc2bCqPhE6HKlpWIj6wV7ISKREHAKBDaXYQ9Ze//EVXXXWVrrjiCr333nvyer2SpPLyci1evFivvPJKqw0SANC6bDZDN00arHtWb1NRmVc9k5xKsNvkDZo6WuVXSoJdN00aTFE5WoR+JzQmaFr6oqRKO2tnOhUcDIVO5TWxlYgPDs9yqg2e+qUnyXGc5cYAAKDjMKxm/ipyzJgxuvPOO3X11VfL7Xbrgw8+0KBBg7R161ZdcMEFKioqau2xxlVZWZk8Ho9KS0vZERBAl7Gx4JCWbyhUYXGF/KYlp83Q4MwU3TRpsCbk9Yr38NDJ1PiD8vrNSM8T/U7wBUx9friyztK6UIl4Tawl4uFZTlluSsQBAF1KSqJDme7EeA+jxWLJTJo9I2rHjh2aOHFig+Opqak6evRoc+8WANCOJuT10lmDMrR9X5mOVPmUnuTSiJxUZkLhpEyzTr9TIBRA0e/UvYVLxMMF4i0pEc+rM9uJEnEAALqWZgdRffr0UUFBgQYMGBB1/M0339SgQYNaOi4AQDux2QyN6uuJ9zDQwdHvhLpau0Q8HD65E51tOGoAANARNDuImjNnju644w49/fTTMgxD+/bt06ZNm7RgwQL95Cc/ac0xAgCAduYLHFtiR79T99VYifjO4godjrVEvFeK8rMoEQcAAC0Ion74wx+qtLRU559/vmpqajRx4kQlJCRowYIFuvXWW1tzjAAAoI2F+53Cy+zod+p+KBEHAADtodll5WFVVVX6+OOPZZqmhg8frpSUlNYaW4dCWTkAoKug3wl1S8RDnU4tKxHPy3QrPzNFfXpSIg4AQCwoK2+Cqqoq3XXXXXrppZfk9/s1ZcoU/frXv1avXm2zu1IgEND999+v3//+9yoqKlKfPn30/e9/X/fee69sttBv1yzL0qJFi/TUU0+ppKRE48aN0+OPP64RI0ZE7sfr9WrBggV6/vnnVV1drcmTJ2vZsmXq27dvm4wbAICOwjQtVdPv1G1V+QIqKD5WIl5QXKHPYywRz05NjFpal5eZooxklwxCJwAAEKOYg6j77rtPK1eu1BVXXKHExEQ9//zzuummm/SnP/2pLcanhx56SE888YSeeeYZjRgxQu+++66uueYaeTwe3XHHHZKkpUuX6uGHH9bKlSs1ZMgQ/exnP9PUqVO1Y8cOud1uSdLcuXO1Zs0avfDCC8rIyND8+fM1Y8YMbdmyRXY7HQUAgK7DNC3VBIKq9gVV7Q/KF8MsF3RudUvEC4pDy+u+KImtRDw3PSmqQDyfEnEAANCKYl6aN3jwYP385z/X5ZdfLknavHmzzj77bNXU1LRJoDNjxgxlZWXpt7/9beTYt7/9bSUlJWnVqlWyLEs5OTmaO3eu7r77bkmh2U9ZWVl66KGHNGfOHJWWlqp3795atWqVLrvsMknSvn37lJubq1deeUXTp08/6ThYmgcA6Kgsy1KN31S1/1jwxIynrs2yLB0o96rgQGhZXXim06FmlIjnZYaKxCkRBwCg/bE0rwn27t2rc889N3L5zDPPlMPhiAQ7re2cc87RE088oU8//VRDhgzRBx98oDfffFOPPPKIJGnXrl0qKirStGnTIrdJSEjQpEmTtHHjRs2ZM0dbtmyR3++POicnJ0cjR47Uxo0bmxREAQDQUVjWsY6n0JI7gqeuLFwiHl5at7O4QoXFFSprRol4XmaKhlAiDgAA4ijmICoYDMrlckXficOhQKDp/xmKxd13363S0lJ95Stfkd1uVzAY1M9//nN973vfkyQVFRVJkrKysqJul5WVpd27d0fOcblcSktLa3BO+Pb1eb1eeb3eyOWysrJWe04AAMTKGwiqxmdGup4oF++aWrtEPD/LrbzMFPXxUCIOAAA6hpiDKMuy9P3vf18JCQmRYzU1NbrxxhuVnJwcOfbiiy+2ygD/8Ic/6Nlnn9Vzzz2nESNGaOvWrZo7d65ycnI0e/bsyHn1yzItyzppgeaJzlmyZIkWLVrU8icAAEAz+IO1oVNtz1MsxdLoHOqXiO8srtDuGEvE+3gSo7qc8jJT1Csl4eQ3BAAAiJOYg6i64U/YlVde2SqDacxdd92lH/3oR5FOqlGjRmn37t1asmSJZs+erezsbEmK7KgXVlxcHJkllZ2dLZ/Pp5KSkqhZUcXFxZowYUKjj7tw4ULNmzcvcrmsrKxNlh4CACDVznjym/LWLrULmBSMdyX1S8R3Flfoy6PNKxGvWyROiTgAAOhsYg6iVqxYEdP5X3zxhXJycmSzNa+DoKqqqsFt7Xa7zNr/oA8cOFDZ2dlat26dxowZI0ny+XzasGGDHnroIUnS2LFj5XQ6tW7dOs2cOVOStH//fn300UdaunRpo4+bkJAQNesLAIDWUrfjqcZvstSuC2ntEvG8zBQNyaJEHAAAdB0xB1GxGj58uLZu3apBgwY16/YXXXSRfv7zn6tfv34aMWKE3n//fT388MO69tprJYWW5M2dO1eLFy9Wfn6+8vPztXjxYiUlJWnWrFmSJI/Ho+uuu07z589XRkaG0tPTtWDBAo0aNUpTpkxptecKAEBjTNNSTeBY6ORlV7suoW6J+KcHKlRwMBQ6lcdQIp7ksmtw72O71uVlpqg/JeIAAKALa/MgqqX/0X700Uf14x//WDfffLOKi4uVk5OjOXPm6Cc/+UnknB/+8Ieqrq7WzTffrJKSEo0bN06vvvqq3G535Jxf/vKXcjgcmjlzpqqrqzV58mStXLlSdju/XQTakmla2r6vTEeqfEpPcmlETqpsNgpz0bUFgqZqIjOegvLFUDSNjqm1SsSP9Tm5lZ+Zoj49KREHAADdi2G18a9k3W63Pvjgg2bPiOooysrK5PF4VFpaqtTU1HgPB+gUNhYc0vINhSosrpA/aMlpNzQ4M0U3TRqsCXm94j08oNX4g6HQqdoflNdvyh8keOrMqnwBFRZXRi2t+zzGEvGs1IRI2BTavS5FGcmuk26kAgAAupeURIcy3YnxHkaLxZKZtPmMKADd08aCQ7pn9TZVeANKS3LJZbfJFzT1yf5y3bN6mxZfOoowCp0WxeJdx9EqXyRsipSIl1SrqZFT3RLx8GynvN4pSu1BiTgAAEBjCKIAtDrTtLR8Q6EqvAFlpyZGZgAk2uzKTrWpqMyr5RsKddagDJbpocOjWLxrsCxLxeXeqF3rCoordLDC2+T7cNoNDeyVrPxMd+3yuhQN6k2JOAAAQCzaPIhiCjrQ/WzfV6bC4gqlJTVchmIYhnomOVVYXKHt+8o0qq8nTqMEGhcImseCp4ApH8XinU7QtPRlSbV21nY5hWc7lTWnRLx2WR0l4gAAAK2jw5eVA+h8jlT55A9ach3nB7YEu02lpqUjVU3fzhxoK5FldgH6nTqjhiXiFfrsYEVMJeI9ezgjYVN+bZE4JeIAAABto8VBVEFBgQoLCzVx4kT16NFDlmVFzYD4+OOPlZOT09KHAdCJpCe55LQb8gVNJdoaLlnxBk05bYbSk1xxGB26s/Ayu2pfUDW1wRPL7DqPtigRz8tMUa8USsQBAADaS7ODqMOHD+uyyy7Ta6+9JsMwtHPnTg0aNEg/+MEP1LNnT/2///f/JEm5ubmtNlgAncOInFQNzkzRJ/vLlZ1qi/oBz7IsHa3ya1gft0bksAMl2lb94KnGzzK7zqJuifjOAxUqONiMEvG0pEjYlJ9FiTgAAEBH0Owg6s4775TD4dCePXs0bNiwyPHLLrtMd955ZySIAtD92GyGbpo0WPes3qaiMq96JjmVYLfJGzR1tMqvlAS7bpo0mKJytDqCp84nXCIeDpxaUiJed2ndwN7J6kGJOAAAQIfT7CDq1Vdf1T/+8Q/17ds36nh+fr52797d4oEB6Nwm5PXS4ktHafmGQhUWV6jUtOS0GRrWx62bJg3WhLxe8R4iugDTtCKBU40/KC/F4h1a3RLxgjrL6ygRBwAA6D6aHURVVlYqKSmpwfFDhw4pISGhRYMC0DVMyOulswZlaPu+Mh2p8ik9yaUROanMhEKzBU1L1f5gaEc7f1C+GAqp0b7CJeLhAvGC4goVHqxQjb/p37O0JOexpXW1vU6UiAMAAHRuzQ6iJk6cqN/97nd64IEHJIW2ZDdNU7/4xS90/vnnt9oAAXRuNpuhUX098R4GOil/MDTTqdrPjnYd2bES8VDgFCoRr1QghhLx7NTEyNK6cKdTRjIl4gAAAF1Ns4OoX/ziFzrvvPP07rvvyufz6Yc//KG2b9+uI0eO6D//+U9rjhEA0E14a5fZef2hzwGT4KmjqVsiHp7t1JwS8fCyurxMSsQBAAC6k2YHUcOHD9eHH36o5cuXy263q7KyUt/61rd0yy23qE+fPq05RgBAFxQuFq/xH+t4Mul36jDaqkR8UO9kJXbREnHTslRwoFKlNT55El3Ky0pmGSEAAEA9hkWra5OUlZXJ4/GotLRUqalsOQ8AsaJYvOOqXyIenukUS4l4D6c9emldZor6Z3SfEvH395Touc17tfdwpfy1mzPkZiRr1pm5GtMvLd7DAwAAHVRKokOZ7sR4D6PFYslMmj0jasWKFUpJSdF3v/vdqON/+tOfVFVVpdmzZzf3rgEAXUAgaKomMuOJYvGOojVKxHv2OFYiPqR2iV1Ozx7ddvbP+3tK9PC6T1XlCyo10alUuyF/0NJnByv08LpPNW/qEMIoAACAWs0Ooh588EE98cQTDY5nZmbqhhtuIIgCgG4mXCwenvFEsXj8VfuCKjxYoU8PNL9EPCs1od5MJ7d6pVAiHmZalp7bvFdVvmDo66LQ1yXBYahXikuHKnx6bvNejc7t2W2DOgAAgLqaHUTt3r1bAwcObHC8f//+2rNnT4sGBQDo+CgW71hKq/zaWWdZXawl4oak3PSkYzOdMlM0ODNFHkrET6jgQKX2Hq5UaqIzEkKFGTLkTnRq7+FKFRyo1JDslDiNEgAAoONodhCVmZmpDz/8UAMGDIg6/sEHHygjI6Ol4wIAdCAUi3ccUSXi4Z3rDsReIj4gI1n5WcdmOg3qnaIeXbREvC2V1vjkNy2l2huf7eSyGyq3LJXW+Np5ZAAAAB1Ts4Ooyy+/XLfffrvcbrcmTpwoSdqwYYPuuOMOXX755a02QABA+wuaVqTbqSZgykexeFyYlqUvSqq180BLS8STlZfpjsx06peRJGc3KRFva55El5y2UCdUgqNhGOULWnIahjyJrjiMDgAAoONpdhD1s5/9TLt379bkyZPlcITuxjRNXX311Vq8eHGrDRAA0PZ8AbN2R7ugvH6Tfqc48AdNfX4oVCL+aTNLxD09nFG71uVnde8S8faQl5Ws3IxkfXawIqojSpIsWSqv8WtQ7xTlZSXHcZQAAAAdh2G18Ffcn376qT744AP16NFDo0aNUv/+/VtrbB1KLFsRAkBHFp7t5A2Y8gZCwRPL7NpXa5SIZ7oTImETJeLxVXfXPHeiUy67IV8wFEIluezsmgcAAI4rJdGhTHdivIfRYrFkJi0OoroLgigAnVG428nrrw2dAsx2am+tUSLer06JeD4l4h3S+3tK9Nzmvdp7uFJ+K7QcLzcjWbPOzCWEAgAAx9Udg6iYlubNmzdPDzzwgJKTkzVv3rwTnvvwww/HctcAgFZgmlbtEjszMuuJ3ze0D0rEu7cx/dI0OrenCg5UqrTGJ0+iS3lZySyLBAAAqCemIOr999+X3++XJL333nvHnf7PsgAAaB91g6dqf5BS8XYSNC19WVJdGzi1vEQ8v3amEyXinZvNMDQkOyXewwAAAOjQYgqiXn/99cif169f39pjAQCcRP3d7Lz+YLyH1OWFS8R31pnpRIk4AAAA0DzN2jUvEAgoMTFRW7du1ciRI1t7TACAWnWDp/CMJ7SdcIn4ztpldZSIAwAAAK2rWUGUw+FQ//79FQzym3gAaE1B01J1eMYTwVObao0S8dx6JeJ5lIgDAAAAJ9SsIEqS7r33Xi1cuFDPPvus0tPTW3NMANBt+AJmbcdTUF4/O9q1hcZKxAuKK1Rc3owS8dqwKS8zRYN7p6iHixJxAAAAIBbNDqJ+/etfq6CgQDk5Oerfv7+Sk5Ojrn/vvfdaPDgA6Eosy5I3cGw3uxp/UMEYlnzh5EzL0hcl1bXL6lpeIh6e6dSfEnEAAACgVTQ7iLrkkktkGEa77M705Zdf6u6779bf//53VVdXa8iQIfrtb3+rsWPHSgr9cLdo0SI99dRTKikp0bhx4/T4449rxIgRkfvwer1asGCBnn/+eVVXV2vy5MlatmyZ+vbt2+bjB9A9BU1L3tod7cLhEzvatZ5wiXhBcYU+bUGJeKRAvHam0ylplIgDAAAAbSXmIKqqqkp33XWXXnrpJfn9fk2ePFmPPvqoevXq1RbjU0lJic4++2ydf/75+vvf/67MzEwVFhaqZ8+ekXOWLl2qhx9+WCtXrtSQIUP0s5/9TFOnTtWOHTvkdrslSXPnztWaNWv0wgsvKCMjQ/Pnz9eMGTO0ZcsW2e0srQDQcv6gWdvtFPrMMrvWU7dEPDzL6fNDzSsRr9vp1NudQIk4AAAA0I4MK8Zfz991111atmyZrrjiCvXo0UPPPfeczjvvPP3pT39qkwH+6Ec/0n/+8x/9+9//bvR6y7KUk5OjuXPn6u6775YUmv2UlZWlhx56SHPmzFFpaal69+6tVatW6bLLLpMk7du3T7m5uXrllVc0ffr0k46jrKxMHo9HpaWlSk1Nbb0nCKBTsixLvqCpGr8pb234FDAJnlpDabU/FDYdKI8ET1/EWCLeN62H8rPclIgDAACgQ0tJdCjTnRjvYbRYLJlJzDOiXnzxRf32t7/V5ZdfLkm64oordPbZZysYDLbJzKKXX35Z06dP13e/+11t2LBBp5xyim6++WZdf/31kqRdu3apqKhI06ZNi9wmISFBkyZN0saNGzVnzhxt2bJFfr8/6pycnByNHDlSGzdubFIQBaB7C5pWVLcTy+xazrIsHSz3Rs1yirVE3GEzNKBXctTSOkrEAQAAgI4r5iBq7969OvfccyOXzzzzTDkcjsgMo9b22Wefafny5Zo3b57uuecebd68WbfffrsSEhJ09dVXq6ioSJKUlZUVdbusrCzt3r1bklRUVCSXy6W0tLQG54RvX5/X65XXe+yHobKystZ8WgA6uHC3kzfAbnatIVwiXlAndNp5oDymEvFEp015vY8tq8vPclMiDgAAAHQyMQdRwWBQLpcr+k4cDgUCTf9hIhamaer000/X4sWLJUljxozR9u3btXz5cl199dWR8+p3fFiWddLejxOds2TJEi1atKiFowdiY5qWtu8r05Eqn9KTXBqRkyqbjf6atmaalmpqA6fwZ5PZTs3mD5rafbgqamld4cFKVfuDTb6PuiXi4c+UiAMAAACdX8xBlGVZ+v73v6+EhITIsZqaGt14441KTk6OHHvxxRdbZYB9+vTR8OHDo44NGzZMf/nLXyRJ2dnZkkKznvr06RM5p7i4ODJLKjs7Wz6fTyUlJVGzooqLizVhwoRGH3fhwoWaN29e5HJZWVmbzPgCwjYWHNLyDYUqLK6QP2jJaTc0ODNFN00arAl5bbMZQHdVt1TcGwjKF2C2U3NV+4MqrDvLiRJxAAAAACcQcxA1e/bsBseuvPLKVhlMY84++2zt2LEj6tinn36q/v37S5IGDhyo7OxsrVu3TmPGjJEk+Xw+bdiwQQ899JAkaezYsXI6nVq3bp1mzpwpSdq/f78++ugjLV26tNHHTUhIiArbgLa0seCQ7lm9TRXegNKSXHLZbfIFTX2yv1z3rN6mxZeOIoxqgcgyO0rFWyRSIl4bPBUUV2jvkSpKxAEAAAA0WcxB1IoVK9piHMd15513asKECVq8eLFmzpypzZs366mnntJTTz0lKbQkb+7cuVq8eLHy8/OVn5+vxYsXKykpSbNmzZIkeTweXXfddZo/f74yMjKUnp6uBQsWaNSoUZoyZUq7Ph+gPtO0tHxDoSq8AWWnJkZmgSTa7MpOtamozKvlGwp11qAMluk1gWVZkULxGn/oM8vsYlO/RDwcPjW3RDwcOlEiDgAAACDmIKq9nXHGGVq9erUWLlyon/70pxo4cKAeeeQRXXHFFZFzfvjDH6q6ulo333yzSkpKNG7cOL366qtyu92Rc375y1/K4XBo5syZqq6u1uTJk7Vy5co22ekPiMX2fWUqLK5QWpKrwVIkwzDUM8mpwuIKbd9XplF9PXEaZfMFAqbWfLhfXx6t0ik9k3TRqX3kcLReuXTQtCKF4jW1M5/Yza7pTMvSlyXVDXauK632N/k+6peI52WmaECvZErEAQAAADRgWPzE1iRlZWXyeDwqLS1VampqvIeDLmTDpwe14I8fKNOd0OiMJ9O0VFzh1f9+d7QmDekdhxE232/eKNTj6wtVXu2XKckmyd3DqVvOG6zrJw6O+f5M05IvaMob3s0uwG52sYiUiNfuWEeJOAAAABBfKYkOZboT4z2MFoslM+nwM6KAri49ySWn3ZAvaCrR1nCGnjdoymkzlJ7kauTWHddv3ijUQ2t3KGhactgNOQzJtKTSKr8eWhvqfTtRGBVeYhf6CM14InRqurol4gXFFfq0uEK7D1fKH2x+ifiQLLd6pTScuQcAAAAATUUQBcTZiJxUDc5M0Sf7y5Wdaov6Id+yLB2t8mtYH7dG5HSemXiBgKnH1xcqaFpyOQzZjNASLZsh2QxTvoClx9cX6poJAyPL9PzB2tDJH1RNwJQvwBK7pqpfIr7zQLm+KKmOqUT8lLQeyq+d4ZSf5VZe7xR5kigRBwAAANC6CKKAOLPZDN00abDuWb1NRWVe9UxyKsFukzdo6miVXykJdt00aXCnKipf8+F+lVf75bAfC6HCbIZNdltQZVV+vfDOXk0ZniVvIKigSeh0MpZl6VCFT5/WLqujRBwAAABAZ0MQBXQAE/J6afGlo7R8Q6EKiytUalpy2gwN6+PWTZMGa0Jer3gPMSZfHq2SKclhqMGsJkuSYYQ+f3aoQlW+jHgMscMLl4iHwyZKxAEAAAB0BQRRQAcxIa+XzhqUoe37ynSkyqf0JJdG5KR2qplQphnqdUpLcsmwQjva2RsZv2WFloNlpXb+Ur7WEKgtEf800ulUroLi2ErEUxMdys9yR3U69aVEHAAAAEAHQxAFdCA2m6FRfT3xHkaThXeuq/FHl4mfOSBdyYkOldcEZBhm1PI80zIVMCV3okNfG5oZr6HHTbU/qM8OVmjngWNL6z5vRol4Xp2ldfmZKertTqBEHAAAAECHRxAFoEkCtWXiNf5Q+OQLmDKPUyZutxu64sx++s2/P5M/KDlsZmg5niUFTMluSFec2U92e9cOTspqS8Q/rbN73d4jVTGViPdN6xEKnMKznSgRBwAAANCJEUQBaMCyrNod7EzVBEKznQKmGdN9zDwjV5L0+817VFkTCHVDKTQT6ooz+0Wu7wrCJeI7i8trd63rXCXipmWp4EClSmt88iS6lJeVzJI+AAAAAG2CIAqAfAEzapmdP2g1KBlvjpln5OrbX+2r13YU60BZjbJSE/W1oZmdeiZU/RLx8OdYS8QH9z62rC6eJeLv7ynRc5v3au/hSvlrS/JzM5I168xcjemX1u7jAQAAANC1EUQB3YhpWvIFQ11OvoApf9CSNxBU0Gx56HQ8druhqcOz2uz+21KrlYhHCsRDy+tOSevRaIl7e3t/T4keXvepqnxBpSY6lWo35A9a+uxghR5e96nmTR1CGAUAAACgVRFEAV1QOHDyBU35A+HPVszL67qTcIl43aV1LS0Rz8tMUWYHLRE3LUvPbd6rKl9QvVJcMhQaY4LDUK8Ulw5V+PTc5r0anduTZXoAAAAAWg1BFNDJBU0ramldaKYTgdOJhEvE6y6t+6KkSk2dGGZIOiWtR9TSuvxMd6cqES84UKm9hyuVmuiMhFBhhgy5E53ae7hSBQcqNSQ7JU6jBAAAANDVEEQBnYhp1paIB4K14ROh04k0KBGvDZ4OlMVYIp6RXLtzXfuWiLel0hqf/Kal1OP0dbnshsotS6U1vnYeGQAAAICujCAK6KDCy+u8/mOznQidjq+xEvGC4godbUaJeN2ldQMykuVytH+JeFvzJLrktIU6oRIcDcMoX9CS0zDkSXTFYXQAAAAAuiqCKKADsKzamU5+U95gUF4/odOJ+GtLxHe2Zol4VopO6dkxSsTbQ15WsnIzkvXZwYqojihJsmSpvMavQb1TlJeVHMdRAgAAAOhqCKKAdhbudPIFCJ2aIlwivvPAsT6nlpaI52emqHcHLRFvLzbD0Kwzc/Xwuk91qMInd6JTLrshXzAUQiW57Jp1Zi5F5QAAAABaFUEU0Ib8QfNY6FT7mZ3rjo8S8fY1pl+a5k0douc279Xew5Uqt0LL8Qb1TtGsM3M1pl9avIcIAAAAoIshiAJaKNzl5A+aCgStUPhU+2fTavqsne4kXCIeCpvKW1wiHp7pNDgzWUku3tZiMaZfmkbn9lTBgUqV1vjkSXQpLyuZmVAAAAAA2gQ/sXUjgYCpNR/u15dHq3RKzyRddGofObpgCXNrM01LAdNS0LQUME0FTUv+2sDJHwxdxvGZlqV9R4+ViIeX2FEi3nHYDENDslPiPQwAAAAA3QBBVDfxmzcK9fj6QpVX+2VKskla9H/bdct5g3X9xMHxHl67Mc3QLCXTCgUkliUFrdAxy1QkaAoHT0GTWU2xCNQrEd9ZXK7Cg5Wq8sVWIn4scHIrPzNFp6R1nxJxAAAAAOjKCKK6gd+8UaiH1u5Q0LTksBuyKxTEHK3y68G//1emaen6iYNlGIpbebNpWrJ0LByq+9m0LJlmnT9boaVdphXa3UuSLEsKx0WWdezYsessZi61snCJeN1Op12HYisR752ScKxAPCs00ymzm5eIAwAAAEBXRhDVxQUCph5fX6igacnlMGTIkCVDdkMyDFP+oPTY+kJ97StZsttDP/zbDCMUSin0OSz853BIYNQ7HnaiCUThgCkcDoVDJXRsZdV+FdTZua6guEJ7YygRl6S+tSXidTudeia52m7QAAAAAIAOhyCqi1vz4X6VV/vlsBuyGbao0Mdm2OSwmaqsCei1HcWaOjxLUigsCk0vIiDqbuqWiEc6nYrLYyoRt9sMDchIiuxYR4k4AAAAACCMnwy7uC+PVsmU5DDCM5BCM5JkqHYpXihuOlBWE+eRor2ZlqX9R2uidq0rKK5QSVUMJeIOmwb1PlYgnp9FiTgAAAAA4PgIorq4U3omySZFSrfrLqUyJNmM0Oes1MQ4jRDtIRA0tftIVWiW04HQTKfCgxWUiAMAAAAA2hVBVBd30al99OO/blOFt2HgYEkKWlKS06avDc1s/8GhTdT4g/rsYGVkWR0l4gAAAACAjoIgqouz2Qz1dieowlt13HPSU1wyWEnVKZXX+CPL6sJF4pSIAwAAAAA6KoKoLm77vjJ5/aZ6JTtVUuVX3UkxNkmpPRzy+U0VHKjUkOyUuI0TJ1a/RPzT2plOsZaID8xIjgqcKBEHAAAAALSnTvUT6JIlS3TPPffojjvu0COPPCIp9AP6okWL9NRTT6mkpETjxo3T448/rhEjRkRu5/V6tWDBAj3//POqrq7W5MmTtWzZMvXt2zdOz6T9HKnyyR+0lJXaQ1mpiTpa5VdNICiHzSZ3Yujbf7jKp9IaX5xHirDGSsR3HqjQ0epmlIhnHSsSp0QcAAAAABBvnSaIeuedd/TUU0/p1FNPjTq+dOlSPfzww1q5cqWGDBmin/3sZ5o6dap27Nght9stSZo7d67WrFmjF154QRkZGZo/f75mzJihLVu2yG63x+PptJv0JJecdkO+oKlEp11pyS75Ambk+pqAKadhyJPIUqx4aM0S8dAsJ0rEAQAAAAAdV6cIoioqKnTFFVfoN7/5jX72s59FjluWpUceeUT/8z//o29961uSpGeeeUZZWVl67rnnNGfOHJWWluq3v/2tVq1apSlTpkiSnn32WeXm5uqf//ynpk+fHpfn1F5G5KRqcGaKPtlfruzU6NkwliyV1/g1qHeK8rKS4zTC7qNuiXhBbZF4rCXivVJckbApr7ZInBJxAAAAAEBn0SmCqFtuuUXf+MY3NGXKlKggateuXSoqKtK0adMixxISEjRp0iRt3LhRc+bM0ZYtW+T3+6POycnJ0ciRI7Vx48YuH0TZbIZumjRY96zepqIyrzw9HDIk+YKhECrJZdesM3NlI8hoVeU1/tqwqSLyee8RSsQBAAAAAN1bhw+iXnjhBb333nt65513GlxXVFQkScrKyoo6npWVpd27d0fOcblcSktLa3BO+PaN8Xq98nqPFUGXlZU1+znE24S8Xlp86Sgt31CoggPlqgmGluMN6p2iWWfmaky/tJPfCRplWZYOV/qiltYVFFeoqKymyfdhtxkakJGk/Ew3JeIAAAAAgC6tQ/+ku3fvXt1xxx169dVXlZiYeNzz6i9LsizrpEuVTnbOkiVLtGjRotgG3IFNyOulswZlaNuXpfpvUZk8iS7lZSUzEyoGx0rEK1RQp0i8pKoZJeK1y+q6Q4m4aVkqOFCp0hofrzsAXRrvdwAAACfXoYOoLVu2qLi4WGPHjo0cCwaDeuONN/TYY49px44dkkKznvr06RM5p7i4ODJLKjs7Wz6fTyUlJVGzooqLizVhwoTjPvbChQs1b968yOWysjLl5ua22nOLB5vN0KhTPJHd8nB8USXitbOdYi0Rd9eWiOdnHtu5rm9aUrcqEX9/T4me27xXew9Xym9actoM5WYkMxMPQJfD+x0AAEDTdOhEYvLkydq2bVvUsWuuuUZf+cpXdPfdd2vQoEHKzs7WunXrNGbMGEmSz+fThg0b9NBDD0mSxo4dK6fTqXXr1mnmzJmSpP379+ujjz7S0qVLj/vYCQkJSkhIaKNnho6kbon4zuJyFRRXNKtEPC8zRUNql9flZaUoq5uXiL+/p0QPr/tUVb6gUhOdSrUb8gctfXawQg+v+1Tzpg7p1D+cGYYhQ5LNMGQYoaDXZtS5bBi1H6HrDEXP3jSi7qvOn2XIUui1Z1lS+FVoWbXH6o3DZoTvO3Tb8H2FxxH9OA1fj429Qo/3sg3f3rKsBmOzIuMNXRcef2jMdY7VOb/uOcd77MaGEnXbOo8RPna8ccmKvm14XJEx1RlL/evrfo3rjtMwjn1dDDX8XtZ/XuEj9b9edcfQ2HF0bF39/Q4AAKA1deggyu12a+TIkVHHkpOTlZGRETk+d+5cLV68WPn5+crPz9fixYuVlJSkWbNmSZI8Ho+uu+46zZ8/XxkZGUpPT9eCBQs0atSoyC566D7Ka/yRJXXhXqe9JbGXiOf1Pra0jhLxhkzL0nOb96rKF1SvFFfkB/IEh6FeKS4dqvDpuc17NTq3Z6ssW4klFIqEBlGBQfR9hY9FwpzaAKLufXbnkNEwjHqBUff9WrSXuuFf3YDNtI4FcFIjwVb4unqBXdMeswnn1B2f6oR+tVc2FrjVfT46zvXhczqD9n6/AwAA6Ow6dBDVFD/84Q9VXV2tm2++WSUlJRo3bpxeffVVud3uyDm//OUv5XA4NHPmTFVXV2vy5MlauXKl7HZ7HEeOthRVIl4bODW3RDwUNrkpEY9BwYFK7T1cqdREZ9SsECkU6LgTndp7uFIFByo1tI9bDpshp90mh92Qw2bIYbdFjtVfydidAyB0X9HhX/f8O1A/mDrRjMGoQEyNh2pWM8K5+vdlSdr+Zam+LKlSepJLzkb+X9EzyaUvS6p0sNyn4TmpUWlb/Zl8lmXJrDeDr8FMw6ixRAeRsYy9vljeWk90P8f7unfGkBEAALSNTvcT9fr166MuG4ah+++/X/fff/9xb5OYmKhHH31Ujz76aNsODnFRv0Q8HD7FXiKefGznuqyuXyLelkprfAqYkstui/oBOrw8LckwVOULyOW0aWCv5LiOFUDnUD+E7iiz8moCpoKmlOi0y9ZIB2CS064Kb0C+oClPD2ccRtjx1Q2mjhdeNTgv6njd25w4gGzq+c157MZCxhPdb4NwtbH7rHdfx/njcZ9T4/dX73IjIe/xnHBszXgsAED31OmCKHRvgaCpPUeqavucQrOcCosrVEmJeJuyGYbsNiOy5M1uhP5sDy95s4VmkIXPK68JKNFpkww1Gub5gqZcdpt6p9DDBqBzC82EMuQLmkq0NZwR5Q2actoMpbOE+7ii+vNO+E8x/053NScLwU4amjW4vv7tmx+ynez2LR1b/RNa47EazPSsdx/H6yCsv1Q6+r6O/7gA0FwEUeiwavxB7TpUGbW07rNDFc0qEQ8FTm7lUyJ+Qvba5XBOu00uu00uh01Oe2ipXCxGneLR4MwUfbK/XNmptqivt2VZOlrl17A+bo3ISW3tpwAA7WpETirvd0AznXimY6O3aLOxIDaNLQ0+XmjV1MCw7oYg4e90uAc0crzee2zTxtr4GBu7j7YKNxubJdnoUuv6t60XLta/bf2vc6OP2eA2TbvPBs+9kfs+0XiAEyGI6kZM09K2L0v136IyeRJdystK7jDFqeU1/mMF4rUfe480r0Q8vLQuLzNFafwGulHhPqb6oVNrzQqz2QzdNGmw7lm9TUVlXvVMcirBbpM3aOpolV8pCXbdNGlwo8tYAKAz4f0OQHfU2OYv9c5otzGc/LwTXtsqY0G0+kuumxK61Q+3dJLrjxfGRd1H/VmCjdz+ePdRfyzNXSZd9/EaDXCt0GqT7sawiC2bpKysTB6PR6WlpUpN7Xy/1dxYcEjLNxSq4EC5agKhZQK5GcmadWZuu24pXb9EPLxzXfNLxEOB0+DeKUpOIFety2Gzyekw5LCFgqZjwZPRbjPCwq+7wuIK+U1LTpuhwZkpumnSYE3I69UuYwCA9sD7HQAA6M5iyUwIopqoMwdRGwsO6Z7V21ThDahnbVGqP2iprMavJJdd86YOaZMwqjVKxBMcNg2mRPyEbIYhl8N27MNuU4LD1mGWH5qmpe37ynSkyqf0JJdG5KQyMwBAl8T7HQAA6K5iyUyYQtLFmaal5RsKVeENKDs1UZLkC5hKcBjqleLSoQqfntu8V6Nze7ZomV7QtLT7cKUKiiv0aQtLxPN6hwKn/CaUiJuWpYIDlSqt8XW45YZtwWk/FjaFgydnjP1NAIC2YbMZGtXXE+9hAAAAdGgEUV3c9n1lKiyuUFqSS4ZhRK1/NWTInejU3sOVKjhQqSHZKU26T68/qM9qS8TDs5w+O9j+JeLv7ynRc5v3au/hysgyiHgsN2xLLodNiU67Ep129XDaO93OflFLVYKWnHaWqgAAAABAd0YQ1cUdqfLJH7TkOs6sGZfdULllqbTG1+j1FTUB7ayzrK6guEJ7OkCJ+Pt7SvTwuk9V5QsqNdGpVLshf9DSZwcr9PC6T9tsuWFbS3DalVgnfOpswVNddZeEpiW55LLb5Aua+mR/ue5ZvU2LLx1FGAUAAAAA3QxBVBeXnuSS027IFzSVaLM3uN4XtOQ0DHkSXTpc4Y3sWBfewW5/aWwl4v0zkpTfxiXipmXpuc17VeULqleKK7LVa2svN2xrztoup/ASu0RH6PvTFfpF6i8JDc90S7TZlZ1qU1GZV8s3FOqsQRmd8vkBAAAAAJqHIKqLG5GTqsGZKfpkf7myU0OzoizLkj9oqSYQ1NEqv1wOmxau/rBZJeJ5tSXiQ9qxRLzgQKX2Hq5UaqIzEkKFNXe5YVuy24yoXifnccrEu9IytvpLQusyDEM9k5wqLK7Q9n1l9KkAAAAAQDdCENXF2WyGbpo0WPes3qbPDlXKtCz5AmbU0rqagHnC+0hJcET6nMJL63JPUiLelkprfPKbllLtjT/+yZYbtiW7zVCCw64Eh00JzlD45GhCmXhXW8Z2siWhCXabSk1LR6ra/3sEAAAAAIgfgqhuYEJeLy2+dJRuff59Hak88Q/+GSmuyLK6vMwUDcl0Kys1thLxtuZJdMlpC3VCuRyS128paJmyGzYlOI2o5YZtyTBCM50Saj8SnfZm7WDXFZexnWxJqDdoymkzlN7CrjAAAAAAQOdCENVNTMjrpSvH9dOvXyuIHDulZ2Jox7o6wVN6cscPBvKykpWbkaz/7i+XaZnyB01ZlmQYod4lm2HTV/q4lZeV3GqP6bTbaj8MOWuX2TW2vK45uuIytvpLQus+L8uydLTKr2F93BqRkxrHUQIAAAAA2htBVDcybUS2PD2cSk9xaXDvFKW0col4e7EZhs7on6ate0oUtCSHTbLZJMuSqv2m7IapM/qnxVxUbrcZkcAptKTOiIRPbTkjrCsuY6u7JLSozKueSU4l2G3yBk0drfIrJcGumyYN7jQzvAAAAAAAraNzJhFolpGneDS8T6o+P1wZ76G0iGlZemd3iXq47AqalvxBU6YZmhHVw2mT3Wbond0l+s7pfY8bRhmGEVlSl+AMdTo1Z1lda+iqy9jCS0LDBeylpiWnzdCwPu5OWcAOAAAAAGg5gih0OuFd8zKSE+RyGA06orwBq8GueU57qDw8sTZ0ctlbZ1lda+jKy9gm5PXSWYMytH1fmY5U+ZSe5NKInFRmQgEAAABAN0UQhU6n7q55hgwlOg1JodlMhmEo0WGp0itZhqWcnj3ksts6dPDR1Zex2WxGp+m2AgAAAAC0rfisRQJaILxrXsC0ZLcZcthtoQLx2g9TUoLDpr49k5TotHeKACe8jG1YH7eqvAEVV3hV5Q1oWB+3Fl86imVsAAAAAIAugRlR6PActmMhU4LDplN69tDQzan6ZH+5UhIcXWYpG8vYAAAAAABdHUEUOhSHLdTllBAJnuyyNxLEdNWlbCxjAwAAAAB0ZQRRiKtw2JRYWyTe1J3r2JENAAAAAIDOhyAK7cYwDCU66wRPjpb1N7GUDQAAAACAzoUgCm3GbjOU6LQr0WGPLLer2+fUGljKBgAAAABA50EQhVbjsNlCM56c9sjMJwAAAAAAgDCCKDSLYRiRXewSHLH1OwEAAAAAgO6JIAonVT90cjlsctlbf5kdAAAAAADo2gii0ICrdoZTOHRiiR0AAAAAAGgNBFHdnN1mRHaxS3CEwid2nQMAAAAAAG2hw5f6LFmyRGeccYbcbrcyMzN1ySWXaMeOHVHnWJal+++/Xzk5OerRo4fOO+88bd++Peocr9er2267Tb169VJycrIuvvhiffHFF+35VDoEw5DciU71dieob1qS+mckK9uTqJ5JLvVw2QmhAKCDMk1L274o1YZPD2rbF6UyTSveQwIAAABi1uGDqA0bNuiWW27RW2+9pXXr1ikQCGjatGmqrKyMnLN06VI9/PDDeuyxx/TOO+8oOztbU6dOVXl5eeScuXPnavXq1XrhhRf05ptvqqKiQjNmzFAwGIzH04obwzDU250gd6JTLkeH//YDACRtLDik2Ss2a86qd7Xgjx9ozqp3NXvFZm0sOBTvoQEAAAAxMSzL6lS/Uj148KAyMzO1YcMGTZw4UZZlKScnR3PnztXdd98tKTT7KSsrSw899JDmzJmj0tJS9e7dW6tWrdJll10mSdq3b59yc3P1yiuvaPr06Sd93LKyMnk8HpWWlio1NbVNnyMAAGEbCw7pntXbVOENKC3JJZfdJl/QVEmVXykJdi2+dJQm5PWK9zABAADQjcWSmXS6KTGlpaWSpPT0dEnSrl27VFRUpGnTpkXOSUhI0KRJk7Rx40ZJ0pYtW+T3+6POycnJ0ciRIyPnAADQ0ZimpeUbClXhDSg7NVGJztAS6kSnXdmpCarwBrV8QyHL9AAAANBpdKogyrIszZs3T+ecc45GjhwpSSoqKpIkZWVlRZ2blZUVua6oqEgul0tpaWnHPac+r9ersrKyqA8AANrT9n1lKiyuUFqSS4YR3eFnGIZ6JjlVWFyh7fv4NwoAAACdQ6cKom6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      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 1, figsize=(12, 12))\n",
    "\n",
    "sns.regplot(\n",
    "    x='Area',\n",
    "    y='Price',\n",
    "    data=df_people,\n",
    "    ax=axs[0]\n",
    ")\n",
    "axs[0].set_title('Portrait Price (Person) vs. Canvas Area')\n",
    "\n",
    "sns.regplot(\n",
    "    x='NumSubjects',\n",
    "    y='Price',\n",
    "    data=df_people,\n",
    "    ax=axs[1]\n",
    ")\n",
    "axs[1].set_title('Portrait Price (Person) vs. Number of Subjects')\n",
    "\n",
    "sns.regplot(\n",
    "    x='Year',\n",
    "    y='Price_Per_Head',\n",
    "    data= df_people,\n",
    "    ax=axs[2]\n",
    ")\n",
    "axs[2].set_title('Portrait Price per Head vs. Year')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "fffd0f26-bb02-4201-81fc-47a44eb7ec44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R2 value is:  0.8150900489428188\n",
      "The intercept is: -79272.5862260555\n",
      "The coefficient for Year is 39.445110110312974\n",
      "The coefficient for Area is 1.0524866390030698\n",
      "The coefficient for NumSubjects is 288.76765780229414\n"
     ]
    }
   ],
   "source": [
    "#Fit linear regression to predict price using list of features\n",
    "x1=df_people[['Year', 'Area', 'NumSubjects']]\n",
    "y1=df_people['Price']\n",
    "lre_multi=LinearRegression()\n",
    "lre_multi.fit(x1, y1)\n",
    "print('The R2 value is: ', lre_multi.score(x1,y1))\n",
    "\n",
    "# Get the intercept\n",
    "intercept = lre_multi.intercept_\n",
    "print(f\"The intercept is: {intercept}\")\n",
    "\n",
    "# Get the coefficients\n",
    "coefficients = lre_multi.coef_\n",
    "\n",
    "# Display the coefficients\n",
    "for feature, coef in zip(x1.columns, coefficients):\n",
    "    print(f\"The coefficient for {feature} is {coef}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2991911-ec7e-431b-89a4-897a26b55a32",
   "metadata": {},
   "source": [
    "Despite having a handful of outliers, there appears to be a reasonably strong correlation between Year, Canvas Area, Number of Subjects and Price.\n",
    "Although, I might feel tempted to use this model to price out a proposal to a new customer, a quick check shows that it undervalues a portrait I would want to take on in 2025."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5130afad-30ff-4878-9b3e-6ff7a41cad06",
   "metadata": {},
   "source": [
    "### Can We Predict Time to Complete?\n",
    "Let's assess the correlation between year, area, number of subjects and the time to complete each portrait. This requires another multi-valued linear regression.\n",
    "\n",
    "As with price, there's no point looking at the relationship between actual time taken and year. Let's look instead at whether the amount of time taken per subject changes over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "db0748fe-470a-4554-9a07-72861195d587",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate time per subject. We already know that there are no zeros remaining in the NumSubjects column.\n",
    "df_people.loc[:,'Time_Per_Head'] = df_people['ActualTime']/df_people['NumSubjects']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8281ea9e-689f-4555-8a33-84287aa11de6",
   "metadata": {},
   "source": [
    "Before we start regression, let's plot the relationships between some of the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "32b1ec86-27ab-4ad7-a9a5-d01d804bb1c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 1, figsize=(12, 12))\n",
    "\n",
    "sns.regplot(\n",
    "    x='Year',\n",
    "    y='Time_Per_Head',\n",
    "    data=df_people,\n",
    "    color = 'orange',\n",
    "    ax=axs[0]\n",
    ")\n",
    "axs[0].set_title('Completion Time Per Subject vs. Year for Portraits of People')\n",
    "\n",
    "sns.regplot(\n",
    "    x='Area',\n",
    "    y='ActualTime',\n",
    "    data=df_people,\n",
    "    color = 'orange',\n",
    "    ax=axs[1]\n",
    ")\n",
    "axs[1].set_title('Completion Time vs. Canvas Area for Portraits of People')\n",
    "\n",
    "sns.regplot(\n",
    "    x='NumSubjects',\n",
    "    y='ActualTime',\n",
    "    data=df_people,\n",
    "    color = 'orange',\n",
    "    ax=axs[2]\n",
    ")\n",
    "axs[2].set_title('Completion Time vs. Num Subjects for Portraits of People')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a25ddae-c001-4999-b8d1-202a5047e68e",
   "metadata": {},
   "source": [
    "The big outlier here (200 hrs actual time), just happened to be an extremely difficult portrait to complete and took twice as long as the regression line predicts (see the last plot).\n",
    "\n",
    "It captures an aspect of the portrait creation process that the price regression analysis misses completely—some painting projects take longer than others and it can be difficult to predict which ones those will be in advance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "2aa99018-ce36-4390-bb48-7fb4abba8963",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R2 value is:  0.6059059359108159\n",
      "The intercept is: -5714.259688343057\n",
      "The coefficient for Year is 2.846720892057733\n",
      "The coefficient for Area is 0.056146721153824325\n",
      "The coefficient for NumSubjects is 9.431313897337585\n"
     ]
    }
   ],
   "source": [
    "#Fit linear regression to predict price using list of features\n",
    "x2=df_people[['Year', 'Area', 'NumSubjects']]\n",
    "y2=df_people['ActualTime']\n",
    "lre_multi=LinearRegression()\n",
    "lre_multi.fit(x2, y2)\n",
    "print('The R2 value is: ', lre_multi.score(x2,y2))\n",
    "\n",
    "# Get the intercept\n",
    "intercept = lre_multi.intercept_\n",
    "print(f\"The intercept is: {intercept}\")\n",
    "\n",
    "# Get the coefficients\n",
    "coefficients = lre_multi.coef_\n",
    "\n",
    "# Display the coefficients\n",
    "for feature, coef in zip(x2.columns, coefficients):\n",
    "    print(f\"The coefficient for {feature} is {coef}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589c5390-0e04-4dc3-9183-34a266872679",
   "metadata": {},
   "source": [
    "We end up with a reasonably good fit for the regression but in this case, a quick check of the data for a current portrait reveals a spurious answer of negative time required. Even with the fairly wide standard deviation, the plot of actual time per subject vs. year provides a more convincing result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ae7182f-bc07-4732-86b5-4e1d2f5920b0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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