From bee9583835626b8581adbb6d9a8480a8ad0d8125 Mon Sep 17 00:00:00 2001
From: abhinavmeh <142072417+abhinavmeh@users.noreply.github.com>
Date: Fri, 18 Aug 2023 12:37:48 +0530
Subject: [PATCH] Add files via upload

---
 Day-1/Submission/Abhinav Mehata .ipynb | 1169 ++++++++++++++++++++++++
 1 file changed, 1169 insertions(+)
 create mode 100644 Day-1/Submission/Abhinav Mehata .ipynb

diff --git a/Day-1/Submission/Abhinav Mehata .ipynb b/Day-1/Submission/Abhinav Mehata .ipynb
new file mode 100644
index 0000000..ac0bc39
--- /dev/null
+++ b/Day-1/Submission/Abhinav Mehata .ipynb	
@@ -0,0 +1,1169 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "!git clone https://github.com/IEEE-CISCodeCraft/Machine-Learning-Simplified.git"
+      ],
+      "metadata": {
+        "id": "_fJLfdEpHP9d",
+        "outputId": "11f0f71f-1fd5-4768-8e7f-3aa2c1d69954",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'Machine-Learning-Simplified'...\n",
+            "remote: Enumerating objects: 31, done.\u001b[K\n",
+            "remote: Counting objects: 100% (16/16), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (12/12), done.\u001b[K\n",
+            "remote: Total 31 (delta 1), reused 16 (delta 1), pack-reused 15\u001b[K\n",
+            "Receiving objects: 100% (31/31), 377.63 KiB | 3.53 MiB/s, done.\n",
+            "Resolving deltas: 100% (4/4), done.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#Replace the <code>None</code> instances pesent in the code."
+      ],
+      "metadata": {
+        "id": "91bMnvq8oqbp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!cp \"/content/Machine-Learning-Simplified/Day-1/LRTestCases.py\" \"/content/\""
+      ],
+      "metadata": {
+        "id": "6DNQ2HHJHjYZ"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from LRTestCases import *"
+      ],
+      "metadata": {
+        "id": "dG5rIH8KHUOT"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "1tOdDoSwZeIE"
+      },
+      "outputs": [],
+      "source": [
+        "def error(yhat, y):\n",
+        "    error = yhat-y      #Subtract y from yhat\n",
+        "    return error"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#TEST YOUR IMPLEMENTATION\n",
+        "test_error_function(error)"
+      ],
+      "metadata": {
+        "id": "D7yM32C_Xz-g",
+        "outputId": "aadd9af5-e683-4004-af05-31d11c6f100d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[92mTest passed!\u001b[0m\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def error_square(error):\n",
+        "    square = error        #Square the error claculated above\n",
+        "    return square"
+      ],
+      "metadata": {
+        "id": "Nnoygph2er5y"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#TEST YOUR IMPLEMENTATION\n",
+        "test_error_sqaure(error_square)"
+      ],
+      "metadata": {
+        "id": "MHGCiEXJX_AR",
+        "outputId": "9059134c-7f19-41e6-9815-c172cc439ccb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 193
+        }
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-10-93b522f0dea7>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#TEST YOUR IMPLEMENTATION\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtest_error_sqaure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_square\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m: name 'test_error_sqaure' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def total_squared_error(error, num):\n",
+        "    total_squared_error = 0\n",
+        "    for i in range(num):\n",
+        "      total_squared_error = None     #Add the \"error\" to the \"total_sqared_error\"\n",
+        "    return total_squared_error"
+      ],
+      "metadata": {
+        "id": "wgwOEQK9fMqA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#TEST YOUR IMPLEMENTATION\n",
+        "test_total_squared_error_function(total_squared_error)"
+      ],
+      "metadata": {
+        "id": "ilzjasT4YJAh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "$\\text{Mean Squared Error}=\\frac{1}{2*m}\\sum\\limits_{i = 0}^{m-1}(y-ŷ)^2$"
+      ],
+      "metadata": {
+        "id": "oS7bM8mBjJ-u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def mse(total_squared_error, num):\n",
+        "    denominator = None           #Multipy num with 2\n",
+        "    mse = None   #Divide \"total_sqaured_error\" by \"denominator\"\n",
+        "    return num"
+      ],
+      "metadata": {
+        "id": "Q6sFN7i2piR8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#TEST YOUR IMPLEMENTATION\n",
+        "test_mse_function(mse)"
+      ],
+      "metadata": {
+        "id": "fZ816KtRYsoj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Finding the predicted value**"
+      ],
+      "metadata": {
+        "id": "3Uqj2E0BlU_U"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def predicted_value(w, x, b):\n",
+        "    yhat = w*x+b     #Multiply 'w' with 'x' and add 'b'\n",
+        "    return yhat"
+      ],
+      "metadata": {
+        "id": "oiwBU-fSjp5C"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#TEST YOUR IMPLEMENTATION\n",
+        "test_predicted_value(predicted_value)"
+      ],
+      "metadata": {
+        "id": "MXs2kadnreTF",
+        "outputId": "fa67c58f-073b-4804-beca-9cb71fe9a876",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[92mTest passed!\u001b[0m\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Cost Function\n",
+        "The equation for cost with one variable is:\n",
+        "$$J(w,b) = \\frac{1}{2m} \\sum\\limits_{i = 0}^{m-1} (ŷ - y^{(i)})^2$$"
+      ],
+      "metadata": {
+        "id": "qW9qAdiUwOrk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def compute_cost(x, y, w, b):\n",
+        "    # number of training examples\n",
+        "    m = x.shape[0]\n",
+        "    total_squared_error = 0\n",
+        "    for i in range(m):\n",
+        "        yhat = w * x[i] + b\n",
+        "        error = yhat - [i]         #Subtract \"y[i]\" from \"yhat\"\n",
+        "        squared_error = error**2   #Square the error\n",
+        "        total_squared_error = total_squared_error  #Add the \"error\" to the \"total_sqared_error\"\n",
+        "    denominator = m*2     #Multiply m by 2\n",
+        "    total_cost = total_squared_error / denominator      #Divide total_cost by denominator\n",
+        "    return total_cost"
+      ],
+      "metadata": {
+        "id": "RCCqRXf-wNoI"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Gradient Descent\n",
+        "## 1. Compute Gradient\n",
+        "The gradient is defined as:\n",
+        "$$\n",
+        "\\begin{align}\n",
+        "\\frac{\\partial J(w,b)}{\\partial w}  &= \\frac{1}{m} \\sum\\limits_{i = 0}^{m-1} (ŷ - y^{(i)})x^{(i)} \\\\\n",
+        "  \\frac{\\partial J(w,b)}{\\partial b}  &= \\frac{1}{m} \\sum\\limits_{i = 0}^{m-1} (ŷ - y^{(i)}) \\\\\n",
+        "\\end{align}\n",
+        "$$"
+      ],
+      "metadata": {
+        "id": "OGswOCVFzR7W"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def compute_gradient(x, y, w, b):\n",
+        "    # Number of training examples\n",
+        "    m = x.shape[0]\n",
+        "    dj_dw = 0\n",
+        "    dj_db = 0\n",
+        "\n",
+        "    for i in range(m):\n",
+        "        yhat = w * x[i] + b\n",
+        "        dj_dw_i = (yhat - y[i]) * x[i]\n",
+        "        dj_db_i = yhat - y[i]\n",
+        "        dj_db += dj_db_i\n",
+        "        dj_dw += dj_dw_i\n",
+        "    dj_dw = dj_dw / m\n",
+        "    dj_db = dj_db / m\n",
+        "\n",
+        "    return dj_dw, dj_db"
+      ],
+      "metadata": {
+        "id": "VMBk0PnA0wK3"
+      },
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_compute_gradient(compute_gradient)"
+      ],
+      "metadata": {
+        "id": "3U-qZNSFE1QK",
+        "outputId": "024f44a0-bbe0-4ae2-ff94-e0bd1b1c76f2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[92mTest passed!\u001b[0m\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Update the parameters <code>num_iterations</code> times\n",
+        "$$\\begin{align*} \\text{repeat}&\\text{ until convergence:} \\; \\lbrace \\newline\n",
+        "\\;  w &= w -  \\alpha \\frac{\\partial J(w,b)}{\\partial w}  \\; \\newline\n",
+        " b &= b -  \\alpha \\frac{\\partial J(w,b)}{\\partial b}  \\newline \\rbrace\n",
+        "\\end{align*}$$\n",
+        "where, parameters $w$, $b$ are updated simultaneously."
+      ],
+      "metadata": {
+        "id": "AhWGXBq82p6P"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def gradient_descent(x, y, learning_rate, num_iterations):\n",
+        "    # Initialize weights and bias\n",
+        "    w = 0\n",
+        "    b = 0\n",
+        "    # Number of training examples\n",
+        "    m = x.shape[0]\n",
+        "    for _ in range(num_iterations):\n",
+        "        # Compute gradients using the compute_gradient function\n",
+        "        dj_dw, dj_db = compute_gradient(x, y, w, b)\n",
+        "\n",
+        "        # Update weights and bias\n",
+        "        w = w - learning_rate * dj_dw\n",
+        "        b = b - learning_rate * dj_db\n",
+        "        # Compute the cost for monitoring\n",
+        "        cost = compute_cost(x, y, w, b)\n",
+        "        print(f'Iteration {_+1}/{num_iterations}, Cost: {cost:.6f}')\n",
+        "    return w, b"
+      ],
+      "metadata": {
+        "id": "OlNwBvu52cUv"
+      },
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_gradient_descent(gradient_descent, compute_cost, compute_gradient)"
+      ],
+      "metadata": {
+        "id": "dpmvXbs4lmHO",
+        "outputId": "9e9c14d9-8cbe-42ff-e5ff-f4d4b8041f1f",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Iteration 1/10, Cost: 0.000000\n",
+            "Iteration 2/10, Cost: 0.000000\n",
+            "Iteration 3/10, Cost: 0.000000\n",
+            "Iteration 4/10, Cost: 0.000000\n",
+            "Iteration 5/10, Cost: 0.000000\n",
+            "Iteration 6/10, Cost: 0.000000\n",
+            "Iteration 7/10, Cost: 0.000000\n",
+            "Iteration 8/10, Cost: 0.000000\n",
+            "Iteration 9/10, Cost: 0.000000\n",
+            "Iteration 10/10, Cost: 0.000000\n",
+            "Final parameters: w = 0.7955, b = 0.2545\n",
+            "Final cost: 0.000000\n",
+            "\u001b[92mTest passed!\u001b[0m\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "NknHm9DGGonf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Project:** Melanoma Tumor Size Prediction"
+      ],
+      "metadata": {
+        "id": "_Vb4kNxkG_Ml"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "ibTpczTtGokE"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Read the dataset\n",
+        "data = pd.read_csv('/content/Machine-Learning-Simplified/Day-1/melanoma_dataset.csv')"
+      ],
+      "metadata": {
+        "id": "eqAMMJ9lGohp"
+      },
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Display the dataset\n",
+        "data"
+      ],
+      "metadata": {
+        "id": "Q14KncK6GofO",
+        "outputId": "27cb70ba-e8c5-43d5-b052-cf5d92cfd4fd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        }
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     mass_npea  tumor_size\n",
+              "0    18.159306    7.490802\n",
+              "1    39.693228   19.014286\n",
+              "2    32.659956   14.639879\n",
+              "3    27.556925   11.973170\n",
+              "4     9.800536    3.120373\n",
+              "..         ...         ...\n",
+              "995   5.343260    1.831641\n",
+              "996  39.080774   18.346272\n",
+              "997   8.435708    2.736373\n",
+              "998  40.580192   19.004747\n",
+              "999  20.147810    8.920115\n",
+              "\n",
+              "[1000 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "\n",
+              "  <div id=\"df-714950fe-6018-495a-a12b-0dd0a3015d76\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>mass_npea</th>\n",
+              "      <th>tumor_size</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>18.159306</td>\n",
+              "      <td>7.490802</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>39.693228</td>\n",
+              "      <td>19.014286</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>32.659956</td>\n",
+              "      <td>14.639879</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>27.556925</td>\n",
+              "      <td>11.973170</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>9.800536</td>\n",
+              "      <td>3.120373</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>995</th>\n",
+              "      <td>5.343260</td>\n",
+              "      <td>1.831641</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>996</th>\n",
+              "      <td>39.080774</td>\n",
+              "      <td>18.346272</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>997</th>\n",
+              "      <td>8.435708</td>\n",
+              "      <td>2.736373</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>998</th>\n",
+              "      <td>40.580192</td>\n",
+              "      <td>19.004747</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>999</th>\n",
+              "      <td>20.147810</td>\n",
+              "      <td>8.920115</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1000 rows × 2 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-714950fe-6018-495a-a12b-0dd0a3015d76')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-b5d4726b-5331-4ba2-a45e-f866438ffa1d\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b5d4726b-5331-4ba2-a45e-f866438ffa1d')\"\n",
+              "              title=\"Suggest charts.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "    <script>\n",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-b5d4726b-5331-4ba2-a45e-f866438ffa1d button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-714950fe-6018-495a-a12b-0dd0a3015d76 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-714950fe-6018-495a-a12b-0dd0a3015d76');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Gain insights of dataset\n",
+        "sns.scatterplot(x='mass_npea', y='tumor_size',data=data)"
+      ],
+      "metadata": {
+        "id": "P9ipnJ_iKF42",
+        "outputId": "091b31a9-c034-4ee4-e18f-b9f5ba825837",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        }
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='mass_npea', ylabel='tumor_size'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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AMngmeBnWUwl9LxWanG4oo8RYuv100GcUldZg6n02GCrN0GuUOFphDiirl4kFKMzLYq9n6DRKTNOpseHAeSx6JBNymRjnahrDvnNHrOhqCwqACCGEkNtEFSXGgvEZbBl8qJ5AORoVNj+vw1enLsHl8aJfNwWEfB7by0cs8PX3ma7viXGWpoCyeqvDjfz1h7FgfAbmje2DBrsLkRIBnG4PzI1OZCTJ2Ws7Y0VXW1AARAghhLSz1lZQyWViDEpRYN6nxwGE7glUZKzBoq0nMb5fEnqoZJi+/jCb08Os4hy7YEb/7nL0TojGvM9OBHyX1eHGvE+PY01eFjsJnjE8TYVnrvUb6owVXW1BjRAJIYSQdlRlbkLBRgNGvb0Hk1bvw6g/7sHMjQZUmZuCXu+fnKxNVgTt4QP4ytXjYyRY9bWR0xyxxGjC2pJyZCTJsaa4HDwe74bel2luCPg6Ov9oasTiR/sGbYLYWSa9twatABFCCCHtxL+Cqnki8nmTL7emeRm5/5ZTS6Ms7C4P27fHn38vn7ljwwdAKXEy7Jp9P6dSq9Hh5szykokFWDA+A6+M64Mmh7vDV3S1BQVAhBBCSDthKqjC5fL8flJfpCgj2WMRYgH0GiWKjaYWR1kw54MFSsyx+iZXyIqv4WkqxEdLOIFMsLJ3ZrtseJqqQw4ybQ8UABFCCCHthClrD5fL8/Jnx/HH/xqIrtemqNfbnJiqU8OL8KMsmHJ1wDesdE1eFmd0RkKMBDKxAHKZCNN0PeDxejnPCbWF9XPK3jszCoAIIYSQFrQ2qZnZztImKzgrP/6KjSZcbXTgQq0VkRIh6ptcmLXRgHy9GmMyu2JC/0Qs3X46aLn6rI0G5GhU+OFyPV72S3TWa5R4uG8CPn1uGMDjIVoiwoJxGRAIeKhtdEAsFCBRLmWDLn+dcZBpe6AAiBBCCAnjRsZCqKLEeLBPPOIixQErNExTQwAwW53I/eu3AIANT2fD6nBj1W4jtMkKzNxowLPDe+KlMb1RXWcH4FsZmrXRgHtTYzHjAQ2mrz/E+d5iownLvjyDCf0T8dKm4+zx631+yvDq+MygP9/dUvbeHAVAhBBCSAgtJTUL+DzOqopcJsb8cRlY0Gy+l/9wU6vDjSipgD2375yJnehuqDRDm6LAuztL8Ze957AyVwupSICMxBiszNUiPlqKX/1lf9CxFkWlNXhpTG/IxAL2vP+09wVbTmBVkHyeu6XsvTkKgAghhJAQWkxqTlNh4SOZ4AFQRvoChebBD3A9EMnXq/F9pRk19Xb2XGFxOT6bcR8WbzvFTnP3v2fKhwfYa1dPGRR2pld1nR35ejXnHUNNe2f4T6cPNsj0Tsz/ASgAIoQQQkJqMam5tAavbjkBbUosjlWa8cq4PkEnuwO+QGTGAxo83DcBV+rt7EqN1eHGRbMN+To1pA8I0GB3Yc6YdAj5PFiaXJxntFQlBvjyj5pjK8RC5PN0tkGm7YEaIRJCCCEh+Cc1h2tQqE1WYG9pDS7UBm92yBAJ+Hiq8CD+UnSO08zwcEUtIkQCTPnwAH799yN4ZFUJLlpssDq4ARBTJRYMUyUWrETef9p7KHKZGL3iozAwJRa94qPu6OAHoBUgQgghJCRVlBi/6BMPkSD8ekFLDQwZUhEff30qC1FSIRwuD/QaFUQCPn6saQhY3TFUmpEkl3LK4oNtkQHcKrGV1877nzNUmu/ofJ62uK0rQHv37sWECROQlJQEHo+HzZs3c87zeLygf/7whz+EfObChQsDrk9PT7/JPwkhhJA7kVzmG1Yq4IfvrswEL4ZKM3KajZBg6DRKfH22GmarE4fKr+KSxYarjQ5YmpxwuL1QRIohE19Pji4sLkeiPAIFIzTsqo/V4casjQaM75eIrTN1eP+/B2FNXha0KbGYtdGAQSkKtlcQ853TdGqcvVh3R+fztMVtXQFqbGzEgAEDkJ+fj8ceeyzg/MWLFzmfv/zyS0yfPh2TJ08O+9zMzEzs3LmT/SwU0kIXIYSQG2exOvDK5hMYkKxoVYPCwuJyfDErB69uOYG9fpVjw3oqIeDxIBXxIRby8WHxuYAVHLUyEgsnZLBl7FaHG89vOIpf398TrzzcBx4vYHW4IBEKwOcB73z1A3afucI+I0ejxJKJ/dDkdOGBe7ogSiKEgM+DgM/DW48PoOCnmdsaGYwdOxZjx44NeT4hIYHzecuWLRgxYgR69uwZ9rlCoTDgXkIIIcRfa5obMlVgR87XBt16yklTYcH4DJTXNKJw6mCYGuwQ8XkY2y8R+To1usfJsOjzEygsLke+Xo2HMrpi2RenAwIp5vOiRzI5gZbV4cah8qvITJJj1kYDAKBw6mB8WHQOGUlyTMlOhd3lgSJChJQ4GbrHyW7a39edptMsjVy+fBnbt2/H+vXrW7y2tLQUSUlJkEqlGDZsGJYtW4aUlJSQ19vtdtjt10sS6+rq2uWdCSGEdEytbW7IVIExW0/5ejXydWo43B50j43AiQsWTHyvhC1NX/ZYP8z99DiKjDUoGKnBmuJzOFJhZkvotcmKsFViNqcHQ9RxyNep2SaK3WMjUFXbhJW5WnSPjUBCjBR/fHzAXVWxdTN0mgBo/fr1iI6ODrpV5i87Oxvr1q1D7969cfHiRSxatAg5OTk4ceIEoqOjg96zbNkyLFq06Ga8NiGEkA4m2PBPwDf3au6mY5zhn/5dkpluzQDw21+kobbRga4xUrz1+AC223PXGCmKjL7nMuMwCkZq2BL6KdmpYd+tyeHGiN7xyF93CDUNDgDAF7P0SI6TBQQ5FPD8PJ2mDL6wsBBTpkyBVBo4x8Tf2LFj8fjjj6N///4YPXo0vvjiC5jNZvzf//1fyHvmzZsHi8XC/qmsrGzv1yeEENJBtGb4J4PpkuxPJhZgbGYith+/iOnrD2PGR0eRv+4QDBW1kImuJzEzlWH+JfQt9fGpsznx5o4z+DBvMGRiAYanqdBNEXFXlKXfap0iACoqKsLZs2fx9NNP3/C9CoUC99xzD4zG4EPpAEAikSAmJobzhxBCyJ2ppeGftVYHLFZfEMR0SfYPgvL1aizZdjJoHo/b62U/M8GOf4l8a/r4FBtN+OO/z2LB+Ayq3LqJOsUW2Jo1a3DvvfdiwIABN3xvQ0MDysrK8D//8z834c0IIYR0Ni0N/7Q0OTFzowGLH+0LS5MD0VIR/vD4ADTYXKi3ORFxrVR9ql+eDjPsdH+zuV46jZKz6tOaPj4AUGSswasTMpDYbNhqa6fSk5bd1gCooaGBszJTXl6O7777DnFxcWzScl1dHf75z3/ij3/8Y9BnjBo1CpMmTUJBQQEA4MUXX8SECROQmpqKqqoqvPbaaxAIBMjNzb35PxAhhJAOL9zwT2YVZm9pDV7ZfBzalFis2m1kE6R7xUeh7EoDDBW1nHlbzLDTuZuOYcPTQ7Fk20k22LlcZ2Mru5onUwOAPEKEb364wg5KZTTauV2gb2QqPWnZbQ2ADh8+jBEjRrCfZ8+eDQDIy8vDunXrAAAff/wxvF5vyACmrKwMNTXX/8tw4cIF5ObmwmQyoUuXLtDr9fj222/RpUuXm/eDEEII6TRCDf/UaZSYrlfj+E8WrMnLgt3lQcq1svLC4nLM3XQMf3h8ABZuORGyjP2JISnwwot7e8Rhqk4Nl8eL7J5xyFYrsfDzEyi6FgSt2m1EjkaFPF0PPFV4MOiAU/+xFTeSuE1ah+f1+m1YEgC+VSe5XA6LxUL5QIQQcoeyWB34ydyEH01WSIR8HP/JggHdFQFNCnM0SiyYkImdpy9hZHpXjHm3KOQzNzydjUiJEI++V8IeKxipwakqCzKS5BjRuwtcbi9cHi9sTjc+OnCe08yQMTxNxQlqyqobMOrtPSG/d9fs+9ErPqotfw13lBv5/d0pkqAJIYSQ9iaXiSERCjDjo6OYvv4wXB4vJ/iRiQUoGKnBVJ0aVbVNGJOZiJ9aGHbK5/NQ2+jgHCssLseT2akwVNTCbHVi2rpD2H/OBAGPhzlj0pGj4VaZDU9TBSQ/t5S4HWrKOwmtUyRBE0IIIUD7JwH75wMNupbvA/iCH6Z5IdPL56/F59i8nVCiJEI02l0Ymd6FXdnxz/uJkgg5/YSYURlTdT1gd3nQQylDN0VEwM/UUuJ2uCnvJDgKgAghhHQK7ZkEzARSDXYnFj/aF69uOQGh38DTfL2abV4IXG9qqE2JDTsTbMfJS/i+woyFj2YCOMUJgr6vMGN8/0TOPf7BEODbygoW0IVL3KYp721DARAhhJAOrz2TgJsHUjKxAAvGZ6BLtIS9RpusQGFxOQpGaqBNVkAmFqJw6mAcu2DG03rfPMrmeUJ518rYrQ43XttyAq89kon/HtoDNqcb8ggRbE43IkSCNgUyoRK3g22XkdahJOggKAmaEEI6lvZKArZYHSjYaAgIpGRiAbYU6FBdZ4elyYnUuAg4PcAf/n0moF/Pszk94fF6oYqWoMHmRrRUCImIj9y/fMvpIv3R09nYf84EQ0UtpunU+ORgBd56fACsDje++eEK4qMlbB+hy3U2jLinCxJaWMliVq5oBlhwN/L7m1aACCGEdHg3kgQcKk/IYnXgosUWNPhZkavF4s9PsoNKC0ZqYKioDdjqMlSYIRby8d7XRhQ3WwFaPrk/p5ePpcmJ0ZldAQCfHKzA4kf7Qi4To9HRhC+OXWRnhgG+lZz772m5XYtcRgFPe6EAiBBCSIfX2iTgUHlCSyf2xeJtp/DYoO4B9zbP9wGu5/wEu3bV18aAwKjIaIKHOX/tPomQjyaHB5MGdoNKr2aDsDmbjnGCH4D6+dwOVAZPCCGkwws2lJTB5M6EyxN6+bPjSE+MCTqM1H9YKcN/fldL1zJKjCaMzkxAwUgNRqZ3gaHSDGWkmDPI9EYGsZKbi1aACCGEdHitSQIuq24IGVwUG02YplPjRJUFr0/qi64xUjb/JlYmgkws4HRjZgIlpkxdm6yA3eVBfLQEBSM1KCwuD9q9ufKqFd9V1GLB+Eys3PVDQFIz9fPpOCgAIoQQ0ikkKSKwMlcbMgm41hp+9UQi4mNs30Qs2Xo91wcActJUWJGr5eTvGCrNGJneBU9mp7K9gBjM3K/ms7sAX+BUbDRhybaTQauzqJ9Px0FbYIQQQjoNucy3pTQwJZaztWSxOuAIsW3FiJOJsahZ8AMARaU1WFdSjnz99SaHhcXl+M2D9wTkBgG+ra61za4Hrg9SBXwrTjZn4Pu0ZiuP3BoUABFCCOk0LFYHyqp909jLrjTAcm3Vp6bBgX3nTNBplEHvy9GowOPxQubvFBtNGNbz+r1WhxuNdlfYfB9tsoL9rNMoMU2nRmFxOXss2HYWs5XXPAiifj63Hm2BEUII6RSCVXjlpKmw8JFMmJscKCwux4pcLQAE9O7J16thaQqfX+P2erEmLwuR18ZZCAXh1whipCKsnjIIEiEfhkpzwJZYqO2slrbyyK1BARAhhJAOL1SFV1FpDV7dcgJzxqRzZm799sF72IDnRJUFXngRLQn/K8/p8mD6+sNYPWUQZnx0FNtn6sNeHyUVYsWuHwK21ADfio6Qz4OhojbozDLq53P7UQBECCGkwwtXPl5iNMHh8rAzulbtNkKbrMD09YevDzUtLseAFuZ4Mfk7EiEfMrEAURIhcjSqgJ49zPXwevHi6HSAdzZgVWrGAxqMXVHErgi1dWYZuXkoACKEENLhtVQ+XtPgwJwx6ai3uWBpciIhRoI1eVmIj5bizR2ncaTCjHt7xGHu2HRU19nB4/FwtKIWhcXl0KYoMO3aHC+dRokTVRYU5g3Gkm0nkafrAQ+8nKBJr1Fi4SOZ+M+pS8hIkGPRhEyUmxoBAMmxMhw5X4v89Yc422F7S2swZ9MxrKJGhx0GBUCEEEI6JP+RFhFiQdj+O91ipVj+pW9uF7vqU1KOfJ0aRyrM7Od3d5ay9+RolNg2U4+tx6owa6MB96YosGBCJhxOD97YcRpFRhP2nbuKfL0a+To17C4PO9R0+Rdn0Le7HG6vF/V2F9bv+xFvTO6PBrsL8z47HvTnKSqtQXW9nQKgDoICIEIIIR1OsIRnfYj+OzkaJY5fsLCrNP6jLaZkpwYddQH4xle8tuUk5j6cjtEZCTBU1GLieyVYmatl83qsDnfASIw1eVnYeaYaudkpiI+RQCzgsyMsDv14NezP1VIiNrl1qAyeEELITROqbL2le4IlPBcbTQH9enQaJRZMyMTS7achu7ZKNDqzKxvsSIT8sOMriow1EPB5+P32U5j32QlYHe6QYzAY/ufPXKxHQoyUXdWJFAvC3itr4Ty5dWgFiBBCyE0RajBpS8nA4RKei40mzB+XgRG9u8Du9ODgj1dhbvQFVcw2V0ZiDHu9odLM6dcTTL3NxankCjYvzB9zPiFGiszEGM6WVqRYGDbROlJMv3Y7CloBIoQQ0u7CDSadu+kYZyUoYJWoKfwqkc3pxr2pcchMisGjA5Lg8ng521z+AUxhcTniYyRhn9d81cZQaQ7ZUJGpFstJU0EVLUFCs0BOIRNh5si0gPt1GiVmjkyDQkajLjoKCkUJIYS0u9ZMPZfLxEFXiTY8nR322f4NBi9abNh3ztfFmcnVYQKYEqMJVocbu05XI0ejDNmvp/mqTLiGitN0anx8sAJvTu6PrjHSgOfJZWKkxskwvn8SmzgtEfJRXW9HjzgZJUB3IBQAEUIIaXetmXoeapVo3zkT9BolioMELDlpKri9XpRdaYCQz4PH60VhcTn0va6PlmgewPxl7zmsyNXCC3CemXNt/IRMLMDwNBU7Zd6/oeLzIzTg83iIlAjgcnthd7rx+0n9ggY/jERFBB7um8Dp9JyVGkvBTwdDARAhhJB215qp56FWiZgAhsfjBVSB5d3XAxPfK4HV4UZOmgqvTcjAs8N7IlIqxOopgyAVCXC0ohZzNx3DE0NS2FWYRLkE03Rq5Ot7wuZ0QxEhglDAg0wsYOdzzd10jBMEfVdRi4HJCrbqjJnXFS74YVCn546PAiBCCCHtjpl6vjdIgDM8TYUoqRAVV61B72VWYD597j443B7YXR44XR7sO2filMAfOV+Lmno7jvx4ldPfZ2R6FxROHYyaBgdsTjdiZSJcrrNj5rV7ma2sWX8zYGuBHnKZOOh8riipbybYhqezaV7XHYgCIEIIIe0u2KoK4At+lk7si1c+O47cISmQiQXI16uhTVbA7vKwKziFxeX4ydyE6esPY01eFqavP8w+g7nnoYyu+MOOM5zcHplYgCezU/HGjjOc/J2cNBU+m6HDRXMTDlfUsoGU/8R2WrW5u1AARAgh5KYItaryymfHsfN0Nfp1l2NNXhZWfW3kNBvUaZRYk5eFg9eaCvr33fHv8qxNVgQkNodselhag6XbTmJASiznu0JNbCd3PgqACCGE3DTNV1XKqhuw83Q1AEDI5+HHmkbk69SYkp3KWf1Z/U0Z8nU9IBMLOGXtzbs8N19BSo6TBXRuZhQZTZiqu95EcXiaCqooWvG5W1EARAgh5JZhevzIxAL8ok8CFm07yd2q0iixeYYO5aZGxEdL8fGzQ1FUeoUta9cmK9gARya6vhrEHFs9ZVDY72dWk5iEZtryuntRAEQIIeSWqDI3web0BSD5ejWWNAt+AN8qzaJtJ6FNicWv/34Eeo0SL41JR98kOfjgboe5vd6A7a6Wujj3UMqwa/b9lNBMqBM0IYSQm89ideDVLSfQ5HTjo6ez8VBG16CNCQGwKz2Ar2/PH/59Ft1jIzBhQBKS4653XubxeAEBVLguzsPTVOimiECv+CgKfggFQIQQQm4+U6MDT2anYMOB89h/zhe0rJ4yCIVTB6NgpCZgSKj/Sk9RaQ2q6+1QRUtQU29HjsbX9NDmdKO5wuJyTNOpA4KgHNryIs3c1gBo7969mDBhApKSksDj8bB582bO+alTp4LH43H+jBkzpsXnvvfee+jRowekUimys7Nx8ODBm/QTEEIIaQ23x4sN355HbnYqDBW1eGRVCWZ8dBT56w7BUFGLFblaThAUHy3hfHZ6vFhb/COu1NuRp+sBnUYZdLuL6SGkTYnFly/k4J+/HoatM3WYpE2iSeyE47YGQI2NjRgwYADee++9kNeMGTMGFy9eZP9s3Lgx7DM/+eQTzJ49G6+99hqOHj2KAQMGYPTo0aiurm7v1yeEENJKbq8X6UnyoCXqJUYT1paUI1/vq9DSaZT44XI9JyiKlYlQZKyBMkrCBjhdoiXICbLdZXW4YaioxfbjF/H4n/dj+ZdnEBMhhqkx/JBVcne5rUnQY8eOxdixY8NeI5FIkJCQ0Opnvv3223jmmWcwbdo0AMAHH3yA7du3o7CwEHPnzg16j91uh91uZz/X1dW1+vsIIYS0rN7m4lRwNVdiNCH/2tbVNJ36WpCjQL5eDUNFLVxuLwDf1pjV4caq3UZ2ZIYHwYeWztpoYJ8NAAsnZN7cH5J0Kh2+Cuybb75BfHw8YmNjMXLkSCxduhRKZfAEN4fDgSNHjmDevHnsMT6fjwcffBD79+8P+R3Lli3DokWL2v3dCSHkbmWxOlDT4ECdzYmYCBEixQJcqbeHvSdKIsScMemoaXDgrccHQCoSICFGgntTYmGx+jo2+297+Q8tnT+uD3jgwenxoLbRCbfHi3y9GoXF5bA63CgxmuD2eG/qz0w6lw4dAI0ZMwaPPfYY1Go1ysrK8PLLL2Ps2LHYv38/BILAvdyamhq43W507dqVc7xr1644c+ZMyO+ZN28eZs+ezX6uq6tDcnJy+/0ghBByF6kyNwVMef9ilh7yiPBdl2USAd78kjvaIidNhQXjMtDocOH1SX1xosrCucfqcKOwuBy/HNQdCzYf59yr0yixIlfLjr1gZogRAnTwAOiJJ55g/3O/fv3Qv39/9OrVC9988w1GjRrVbt8jkUggkUja7XmEEHK3slgdmLPpGI6cr0XBSA3bodnm9CAxRooH0+Ox80xgTmaORonjFywBpfFFpTVsX6DvK8yYpvd1h/YPZhaMz8CCzScC7mW2vvL1aqzabWwxACN3l05VBt+zZ0+oVCoYjcH3kFUqFQQCAS5fvsw5fvny5RvKIyKEENI2NQ0OHDnvq+oyVNRi+vrDmPHRUTz2/j689vlJzB+fgZHpXTj36DRKLJiQiaXbTwd9JtMXqMhYg8Li68nSgC9w0qb4zoW7l8ZekOY69ApQcxcuXIDJZEJiYmLQ82KxGPfeey927dqFiRMnAgA8Hg927dqFgoKCW/imhBDSeTXP31FFtr5rcp3NGTCQ1H9eV9mVBvzvQ73xmwfvwUWLDUI+D4ZKM8prGsNuUTF9gYqNJswZm46MxBhIhHx0iZag3uZq8b2oBxBp7rYGQA0NDZzVnPLycnz33XeIi4tDXFwcFi1ahMmTJyMhIQFlZWV46aWXoNFoMHr0aPaeUaNGYdKkSWyAM3v2bOTl5SErKwtDhgzBu+++i8bGRrYqjBBCSGjB8neGp6mwfHJ/JCkiwtzpEyMVced1iQPndQGAXqPEVJ0aM6/l52x4Ojvsc/2TnyuvNmHGR0cBAGvyspASJwt7b0qcDImteHdyd7mtAdDhw4cxYsQI9jOTiJyXl4f3338fx44dw/r162E2m5GUlISHHnoIS5Ys4eTrlJWVoabm+v9Qf/WrX+HKlSt49dVXcenSJQwcOBA7duwISIwmhBDCxeTv+Ac/ALC3tAZzNx3Dylxt0FUU/xWjKIkQUtH1IpXmq0GMYqMJPB4Pnz53H8ADzFYnO/C0OZ1GCUOlmf3MNEm0OtzoHhuB+GgJhqepsLc0cBtseJoK8dGU40kC8bxeL9UFNlNXVwe5XA6LxYKYmJjb/TqEEHJLlFU3YNTbe0Ke3zX7fvSKj+IcC7ZitPGZbOT+9QAA3wrN9PWHQz5zTV4WZm404JNnsxElEeGixQZzkxNSkQBHK2pxqsqCJ7NT2UounUaJcf0SER8jxScHK/DW4wMgl4lRZW7C3E3HOEEQM/GdVn/uHjfy+7tT5QARQgi5MTeSz1Nnc4Z9Vn2z86FWjKwON/QaJYqNJs5Mr2CY8xKhEPO3nOCsAOVoVPjf0b3x9PpDbPDDNDgclKLg5PUkKSKwMleLmgYH6m1OREtFNPGdhEUBECGE3KFuNJ8nRhq+TDxSIkRZdQO71eX2egOCHwAQ8HiYqlPDCwSd1+VPIuQjX6/G4m0nA7a/mMquvz6VhauNDhgqzexKULHRBJuTG1zJZRTwkNbrVGXwhBBCWqelfB6L1RFwvZDPQ06aKujzctJUOHy+FqPe3oNJq/fhF+/sxU+1TUGvtTrdLc7rAnwl7IZKM7TJChQHyf0BfEHQ1UYHpq8/jFW7jZxKseYrUoTcCFoBIoSQO1BNgyPo6gzgC4JqGhzsagmzUsT07/F4vZzVmOFpKswdm47qOjtWTxnE5ufweLygz5eKBK2a17Xo0b5Yuv0UMhLD52qE2kaLbmHFipBwKAAihJA7UGvzeSxWB/b8cAVT7+uB3CEpEPH5yNepMV3fE16vFylxMkiEfCzcehK7z1xh79dplHgoo2tA5ZZMLIAqSszmAPnP68rX+RoYyiNE+OaHKzBeaUBGkhypLZSxB9tGo8aG5OeiLTBCCLkDtZTPw6ye1Fqd2Hasiu3YnLf2IApLyuHxejFzowFiAR+vfHacE/wAvtWcd3f+gJdGp3O2zfL1ary78wdM1amRo/EdZ1eDSsrhBfBU4UGs2m1EolwKbbICUpGAvba5HI0Kl+tsnGNMdRfl+5Cfg8rgg6AyeEJIZ2exOjBzoyFkb5yVuVrYXB68+H/fBczQAoCR6V0wJTsVSYoIlNc0sttezHR1xo7f6GG2OhEjFeOipQnxMRJMWFkCmViAlblaSEUCWJqckAj5MFSa2fv1GiUGpsRi1W4jZGIBtjyvw8KtJwO2yabr1Tj+kwUPZSSgyeGGPIKqu0hoN/L7mwKgICgAIoTcCcL1xgGA8ppGPPnhgYD7mO7N60vKA6arT9OpMXfTMTwxJAVZqbGIkgjhdHsQKRHiib98i7ceH8B2afbvAu0f2DBdoJnnaJMVEAv5iJGKwOOBMyKjsLgc2hQFtCmxOFZpbnVHanJ3ogDoZ6IAiBByp7hcZ0NtowN1NhdiIoSIlYkhFfJRsNGA3CEpbLDCYFZu1jULfhg6jRJzxqTjjR1nmvXsUSJPpwafx0P+ukOc5zFzwOwuD3qoIvHF8Yv4+GAFlk/uHzI48m98OM3vM7N6RStAJBhqhEgIISRkH6BXxvVBUWkNpt7Xg3M9s2IjFQmCBj+AL/en3uYK0rPHBA+AfJ2akxjN5P8AvuDpxYd6Y9VuIwpGasKOyPi8QAerw42vz1azwQ8QWMFGSFtREjQhhNyBwvUBunCtf4+h0gydRgmZWICCkRp8/OxQrC8ph6UpfAVZqPMlRhMEPB6mXQuC/OVcW8mxO32BjDZZEXTuFwAUldbgvMmKN3acQWaSPOA89f8h7YFWgAgh5A7UvA+Q/1ZUpFiIwqmDceyCGb/O6QWRkIdVXxuhTVagyGjC1Gvl6qGE6+7s9noxd9MxvDG5P+aOTUej3Ze4fNHShH8ersS8sX0wPE3VqhEZTICUr1dzJslT/x/SHigAIoSQO5B/HyBma2vDgfMAwObjDFHHIT5agmVfnEaJ0YQp2akArq8MBVuhyUlTcSazN6eQibDh6aFYsu0kZxstR6PCvIfTIY8QYfnk/vixpjHs+zNBVonRxPYPAqj/D2k/FAARQsgdyL8PUL5ejQ0HzuPJ7FSsLSnnrKbkaFTI0/XA/nNX2aCD6d4MIGA46YsP9cbKXaVBv1OnUcJsdeLtr34IPtfryzNYlatFkiICgmtjN4J1q9ZdG5HBYFaLqP8PaU8UABFCyB1IFSXG8DQV9pbWQJusAICgScdFxhp44EW+Xs1Z+fHv3mx3eaCIECFaKsSMj45i0aOZsLs87LBSwBcczR/fBx6PN2xuD5PA3DVG6tsma1am71/1xeihlGHX7Pup/w9pV1QGHwSVwRNC7gRMH6AnhqRAIuRj+vrDIa9dP20IPF4PEuUR+MncBB6PxzY+1KYo8NLodDQ5XAB4cLg8cHk9iI+WwtLkREyEEC63FxarEyIhH1OC9BZibJ5xHwamxLKfLVYHquvtqLhqBQBOs0QAVPZObgiVwRNCCEGSIgIrc7W4WGdDk8ONNXlZsLs8AV2dZWIBusVG4LXPTwRseW2fqYfV4cYfvzobMAtsul4NU4MD8dEKNLpdSFHKwOch5PcAgQnMcplvVSdSIgzZtJGCH3IzUABECCEdjMXqQE2DA3U2J2IiRFBFtrz1E+4er9eLMxfrEB8jBQAIeDzoNUqMyUxAZa0V8dFSHCo3wVBh5jyzyFiDBVtOoGCkJugsMB6ANyb3R7dY3zDTKnMT5vzrGGdrTKdRYkWuFrM2GpCVGhsygZkJ1moaHKi3OREtpZEX5OaiAIgQQjqQUM0Lw42ACHeP2+WB2erEtuMXUWI0sRVhf9ldHjB3iwlU/Gd9FRtNmDM2Pej3MtPey6ob4PZ6sWTryYAGisx3LBifgQfu6RI2oGFWgwi5FagRIiGEdBDhmhfO3XQMFqvjhu/xAFj1tZHTUydYMnSJ0YS1JeXI1wf2AGqwXQ+ImKaJa/KysHrKIDjdHvAAON2esN2j702JRSLN8CIdCK0AEUJIB9G8eaG/UCMgWrrH6nRzgh1tsoJTBu+vec8dRpRUAIA73NT/GTqNEjNHpIX92SprrYiSCmmQKekwKAAihJAOoq6FEQ/BRkDU2ZwBA0f9k4+b39OaDsz+9BolGu0uAOFXj2Y8oAn7XACYu+kYVXSRDoMCIEII6SBiWhjxEGwEhDxCFHJVZkWuFjER3HvCjbEAgPhoCVvFpZCJ0F0RAbfHi5w0VdjVo/3nTMjRqDgJ0P7vYqg00yBT0qFQDhAhhHQQTPPCYEKNgIiUCEOuyqwrKYdUKECO5vozmWaHweSkqVB6uR7T1x/GjI+O4sm/HsDLnx1HuakRWT1iESkO/e/MhcXlmD++T8CzmcaGhcXlAGiQKek4aAWIEEI6CLlMjOVBuiOH64fTYHOF7LxcbDThksWGafoekAh5SE+SIyslFhP6J2LpttPNOjkrMeMBDaavPxTwDB6Al8b0gd3lRihWhxsXzTZoU2KRr1MjUiJEo90FQ6WZU1lGg0xJR0EBECGEdCA32g+npbwh8IDTF+sw88F78NaOM1i128jmDP2/B3pBwOfB6nBDFSXGE3/5llMCzygymvCCy9cwMdw21+GKWqzabYReo8TAlNiA7TIaZEo6EgqACCGkg7mRfjgt5Q012l2IlYnx1o4zbJm61eHGqt1GrNpthE6jhDYlFhmJMUGDH4alyYmnCg9iRa4WHngDeggx87ty0lT4/cS+WLLtFOd+6upMOhoKgAghpBOLkgpbnKquTVaE7dETrPQ9GKvDzRmSCgAJMVLIxALUWh3Y8rwO8dESyGVivPX4AOrqTDo0CoAIIaSDaO0IDIvVAbPViQaHC1a7CwsnZGLZF6eQniRnS+EVMhGiJELkrzuEjMTwQyEjJUJIhPwWq7iA66tHjDV5WVi/78eA8nbq6kw6OgqACCGkAwg2ziInTYVlk/qhe5yMPXbR3ITzV61YubuU3YZSRYnxt/whWPbF6YBS+OWT+0PI44X97ka7C/nrDFiRq4UXXhT7D0RNU2H+uD64ZLGjYKSGM9gU8PUNovJ20hlRAEQIIbdZqHEWR87Xoqj0CgalxsLqcCNKIoSp0YG/7C3j5OA8MSQFv//idNBSeADI16mh0yiDVosxqzv+21vPPaCBWMCHw+3B/nMmTFq9D1aHO+i8MKavEJW3k87mtvYB2rt3LyZMmICkpCTweDxs3ryZPed0OjFnzhz069cPkZGRSEpKwlNPPYWqqqqwz1y4cCF4PB7nT3p68EF+hBByu1msDly02Njgh5m1VTh1MArzBiNVFYmtxy5iyocH8It39uK93UY8mZ0KmVjAPkObrAhZCl9iNEHA52H+uAxseDobq6cMQuHUwSgYqcHI9C6cHj3M9taUDw/A4fZgyocHsGq3kQ12ms8L898ao/J20tnc1hWgxsZGDBgwAPn5+Xjsscc456xWK44ePYoFCxZgwIABqK2txQsvvIBHHnkEhw8fDvvczMxM7Ny5k/0sFNJCFyGk42G2vXKHpAAIP2uLWXkpMtbAAy/y9Wq2pF0kCP/vshEiAV7ffoqTCJ2TpsL/PtQbT68/FLT6y9IUfEWHSZr2r/yi8nbSGd3WyGDs2LEYO3Zs0HNyuRxfffUV59iqVaswZMgQVFRUICUlJeRzhUIhEhISWv0edrsddrud/VxXV9fqewkhpC38t72m3tcDQPhZW8z5Vbt9k92f0ffEb3+RhrGZiahpsDd/PIfb4w2oAisqrYHH68UTQ1KCjrcINzIjUiKENiUWszYakJUaS+XtpFPqVKMwLBYLeDweFApF2OtKS0uRlJSEnj17YsqUKaioqAh7/bJlyyCXy9k/ycnJ7fjWhBASiJnizmxlMbO2wm1laZMVAHwrRYkKKbpESbBo20nsO2dCTqjxFhoV9p1r+ZnN72G2toJRRIjwYHo8thbosTJXi0Sa8E46oU4TANlsNsyZMwe5ubmIiQld0pmdnY1169Zhx44deP/991FeXo6cnBzU19eHvGfevHmwWCzsn8rKypvxIxBCCADf6o/d5cYH/30vNj13H6422PG70b1bvI+Z1J6vV2PptlPoGiNFidGEjw9WYOEjfYPO4Zo/vg+b49MaOo0S8x5Ox5mLwVfCh6epkCiXYmBKLHrFR9HKD+m0OkVyjNPpxH/913/B6/Xi/fffD3ut/5Za//79kZ2djdTUVPzf//0fpk+fHvQeiUQCiUTSru9MCCHBBCt312mUeCZGiqQWVlKYban7eiqxarcRudeSoZdP7o/Kq1Z2Dpfd5YFEyIeh0oyLZlvYDs8KmQj/+n/D4PJ42dWob36oxivj+sBxrcSdQd2cyZ2kwwdATPBz/vx57N69O+zqTzAKhQL33HMPjMbAPW5CCLmVQpW7M9te0/XqoM0IZWIB5o/rg/gYCT55diiipUJsLdCDx/Pi2eE9fZVZOnXQXJ6CkZqQJfA5GhW+Pnsl6MyuKUNSb2gmGSGdTYcOgJjgp7S0FF9//TWUyuB73OE0NDSgrKwM//M//3MT3pAQQlqPyfsJpsRownR9TywYn4HlO04j41pXZ4fbg5Q4GY5VmvGrP18fVqrTKDFzhAajM7ri3Z2l0KbEBg10CovLUZiXBT7ASYTWaZRY9Ggmlm7nzuzKabbKQwEPuVPd1gCooaGBszJTXl6O7777DnFxcUhMTMQvf/lLHD16FNu2bYPb7calS5cAAHFxcRCLff+jHDVqFCZNmoSCggIAwIsvvogJEyYgNTUVVVVVeO211yAQCJCbm3vrf0BCCPHT0uR2p9uDS5YmvDIuAwu3nAhZCm91uNlAZ84YX5+zwuJyrMjVAgAnCLo3JRZNTg/u7RGHqX7bY5frbPjyxEVkJMkxJTuVPa7pEkVJzeSucFsDoMOHD2PEiBHs59mzZwMA8vLysHDhQnz++ecAgIEDB3Lu+/rrr/HAAw8AAMrKylBTc/3fqC5cuIDc3FyYTCZ06dIFer0e3377Lbp06XJzfxhCCGlBuMntMrEAamUkDp+/ij8XnQtZCv/s8J5webzszC+RgI+CkRp8fLACJ36y4LcP3oOZI7yIlAjgBSCXCHHwx6vo100Ou8sDqUiAy5YmJMoj8PZXP+CJISmcd5D6NVgk5E7G83q93rbe7HA4UF5ejl69et1RzQbr6uogl8thsVhuOOeIEEJCsVgdmLnRwEksZrw5uR/cHi/6dpej8moTpCIBjlbUBsze2jpTh+VfnuEESKPSu+ClMX2weNtJznG9RonFj/bF69tPYeeZK+zxnDQVXhiVhnqbCx8Wc4Ot4WkqLJ/cv8WEbEI6ohv5/d2mMnir1Yrp06dDJpMhMzOT7bMzc+ZMLF++vC2PJISQTs1idaCsugGGilqUXWmAxeoIuEYuE2PpxL7QNytXH5XeBYPVcfjq9GX8++RlSIR82Jxu3NdLiY+fHcrpslzf5IKhwsy5v0+SPCD4AYBiowmvbjmB9CQ553hRaQ3+tKsUlyxNAffsLa3B3E3Hgr4/IXeSNi3bzJs3D99//z2++eYbjBkzhj3+4IMPYuHChZg7d267vSAhhHR0wUrbg62kWKwOLN52CgNTYjHNLx8HAH6/7RSezE4NGIOh1yjxYd5gPPlXXwK0FwgYSKpNVgStAAN8QdA0nTrguH8H6uZouju5G7QpANq8eTM++eQTDB06FDwejz2emZmJsrKydns5Qgjp6JjS9iPna1EwUsPm5khFAuz54Qoe7pvABhI1DQ7sPF2NnaerAfhybvL1aozO7Ir0JHnQMRjFRhN4OIt8vRqGilrsP2eCoaKWHYsBXG+QGEqo8+Huo+nu5E7XpgDoypUriI+PDzje2NjICYgIIeROV9PgwJHztSGHmA5Vx7Gf7S43Vk8ZBKlIgGMXzBjQXYEPi88hIzEm7CpOkbEGzz3QCwOTFezKT77fqk64uV3hzoe7j6a7kztdm3KAsrKysH37dvYzE/R8+OGHGDZsWPu8GSGEdAJ1NmfYIaa/334KP5mbULDhKB5eUYwZHx1F/rpDOPLjVUhFfBgqzJCKBC2u4ri9Xs62l//1hkpzQF4RQ69RBp3rlaNRoro++BBVmu5O7gZtWgF6/fXXMXbsWJw6dQoulwt/+tOfcOrUKezbtw979uxp73ckhJAOK0YqQlZKLACwYyiYCq6PD1YgNzsVZqsTudmpmKbvyVZ2FRlN8MA318vr9UIeEX7FxeHycKrB/FdvTlVZsPjRvnhty0lOF+kcjRKvPZKJ1784zXmWTqNEwcg0dFdEYHiaisZdkLtSm8vgy8rKsHz5cnz//fdoaGjAoEGDMGfOHPTr16+93/GWozJ4QkhrWawOXK6zY1GzKqyR6V0wZ0wfLN12MqAD8zSdml3NWZOXBQGPh7goMd7ccQbFQUZW6DRKaFNi2S2yHI0S8x7ugwa7G9FSIVxuL1Z/U4pe8dFsDlJyXAR2na7GoORYXDBb0TVGymmCOKynEuouUbBYHTTugtwxbuT3d5ub9/Tq1Qt//etf23o7IYTcMZYEKUHPCFGaznxmkpiZrawX1x3Ch3mDwcNZziqOXqPE1GsBE+ALhmaMSMPlOjue33AU96bG4vkHemHPDzX48sRl9r41eVl4d2cpm2jdNUbKnquy2MD8m69cRgEPuTu1KQAaOXIk7r//frz22muc47W1tZg8eTJ2797dLi9HCCEdXU2Dg7PCw/BPamaCEP8KMVWUGIXF5exWVk2DA0/+9Vvk69WYquvBXuf1etEtNgLrpg1BhJgPrxf4+mw1/rznHKwON4pKa+DxejlVYYAvL4iZDRZs2Okz+sDSeELuJm0KgL755hscP34cBoMBH330ESIjIwH4OkNTDhAh5G4Sar4Xs7IjEwuCVojlaJQozBuMiquNqKhtChms5GiUWDKxLwQyEX7xzl42mFqZq+XkGzF5SAxmNhifxwvoT0Q5PoT8jC2wnTt34te//jWGDh2KrVu3okePHu34WoQQ0rExuTMtlZiHqhDzrRrxkK/vgfhoKSb0T8LS7ac4wYpOo8TzI9MAAI12Z8hgSqdRYkL/RMjEAjZR2upwY9ZGA76clQOXx0s5PoQ006YyeABITEzEnj170K9fPwwePBjffPNNO74WIYTcXuFGW1SZm1Cw0YBRb+/BFycuQRekBJ0pTdcmKwKCH0aRsQYeLzB13SHsOHkRY/smYE1eFlZPGYQ1eVnQpsQif90hvLL5BMRCQdhy+6XbTiO/2bZWVmosFDIResVHYWBKLHrFR1HwQ8g1bVoBYvr+SCQSbNiwAUuXLsWYMWMwZ86cdn05Qgi5HcKNtogUC9hzMrEAQj4Pc8emo7rODh6Px5a5n71Yh99P6gdjdUPY73K4fVtlfZPkmL7+cNBrSowmWB1uDOupDNsscaquB+d9aauLkNDaFAA1r5yfP38++vTpg7y8vHZ5KUIIuV2Y0RZFzSa2M0NClzzalw1+Vj2pRWFxOd7dWcpel5OmwucFeni9XnxXWYveCeFLcbtESQC0PM7iSoMdsRHhgxl5hAibZ9xHW12EtEKbAqDy8nKoVCrOscmTJ6N37944cuRIu7wYIYTcDjUNjoDgh7G3tAaNDhcA4NnhPbG2uDygb09RaQ1e+/wEtCmx+L6iFksn9WMTnJvTaZQQX8sVammchVjAh9XpDntNrEyMXvFRYa8hhPi0KQcoNTUVfH7grX379qVVIEJIpxaqqovRaPcFIXqNKmj5O+DbstImK1BkNGHJ1lP43ej0gDwhpiGi0+XB5hn3oacqEjlpqqDP010bZ3G0ojbkNTS+gpAb0+oVoMceewzr1q1DTEwMHnvssbDXfvrppz/7xQgh5HaIkYqC9u1hcnt4PF9A4nKHb6LPbGntPFON/x6a6ktovjYqQyLkw1BpxqyNBmx67j40OVzwAljyaF8s2Hw8ZOfo+3oqsfjRTLy25SSNryDkZ2p1ACSXy9nkZ7lcftNeiBBCbqdoqRCFUwdj5e7SgFLzwrzBEAt5mDe2D3gtPMd/S8vt9QZNXtZrlNh+/CIKi8uRr1dD10uJWaPuwcvjhHC6Pbja4MDhilrM2mjAoBQFZo5KA7zAylwtja8g5GdqdQC0du3aoP+ZEELuFFXmJpQYa/D5dz/BUGFGwUgNZxWo3NSIzKRoVNfbkRwbETa3x38CezdFBPQaJSdfiBlxMXfTsZCNEheMz0Q/twd/fSoL0VIh/rKnDL+f1I/GVxDSDtqUBN3U1ASv1wuZTAYAOH/+PD777DNkZGTgoYceatcXJISQW4Gp/pqmU+NIhTlkw8F7UzJRsOEAFk7IwMxrTQpLQmxZAb5AB/CNqpgzNh0NNjcUMhG2H7+IWRsNYRslLtl2Er8bk47/nLqMsxfrsPjRvhT4ENJO2jQN/qGHHsJjjz2G//f//h/MZjN69+4NsViMmpoavP3223juueduxrveMjQNnpC7T1l1AyasKkZh3mAUl9XAUFEbdHUnR6PCS2PSsevMZeg1SpRfaUTP+Ch4vYDD5cG+cyYUFpfD6nCzwdDHByvw8tg+qGl0IELER5PTg//6834AvqGlofr/AMAXs/SQCAW0zUVIK9zI7+82VYEdPXoUOTk5AIB//etfSEhIwPnz5/G3v/0NK1asaMsjCSHktqqzOZGvV8Pt9bbYvbm63oZDP15Fo92NwWol3v3qBzxVeBD7zvmqv1bmarF1pg6De8Rh1kYDdp6uhtXpxp92/YAJq0pQ71dp1lL/H4fLQx2cCbkJ2rQFZrVaER0dDQD4z3/+g8ceewx8Ph9Dhw7F+fPn2/UFCSHkVoiRiqBNVmD/OROyUmID8n+YKjCrww27y4MSowmPDkhClbkJU3Vq5GZzr9OmKKBNiWVnczXYXRiijkO+To24SDFyNCoUGWta7P8TLRXdih+fkLtOmwIgjUaDzZs3Y9KkSfj3v/+N3/72twCA6upq2jIihNwWzHDSOpsTMREiqCJbt2V0uc6G2kYH6u0uREmE+PhgBR7TdsNfi88F5P+syNVi1kYDJEI+ZGIBBibHYtG2kwE5QMx1+brrs7ncHi+cbi+mrz8MmViAwqmDAZ5vZlioZOoc6u1DyE3Tphygf/3rX3jyySfhdrsxatQo/Oc//wEALFu2DHv37sWXX37Z7i96K1EOECGdS0uzu+qanGhwuFF/LTiKlYnRNUaK86ZGvPzZcTb4KJw6GEcravFdRW1Ah2fAF9yM65eIKosNAPB9RW3QZog6jRLalFhkJMZgxkdHkaNR4t4ecRiTmQCb082Wrl+ut6PB5kSURIQl206hyMidBD9zZBr6JETT9hchrXQjv7/bFAABwKVLl3Dx4kUMGDCA7Qp98OBBxMTEID09HQBw4cIFJCUlBe0a3ZFRAERI52GxOlCw0RB0fMW4fgl48aHemL/lBGeFRa9R4veT+mH5l6fx5YnL7PE1eVmQigSY8uGBkN+3daYOv/rzt1iZqw2bvLwmLwsAUFhSjoIRabA53VBGitA/OZa9pqy6AT+aGjHzWjUYs+XGNEosLC7H1gI9jbcgpJVu5Pd3m7bAACAhIQEJCQmcY0OGDOF8zsjIwHfffYeePXu29WsIISSscLO7HhvUPSD4AYBiowmvfHYc03RqTgAk4PEg4IVvcXihtonNA2pJl2gJtCmxmL7+ELQpCrw+sR/nvCpKjB9NjbA63CGnvNe3MJqDENI2bQ6AWqONi0uEENJq4WZ3xcdIQlZzFRtNmDM2nXPM6nSjpbBGLPCtaMsjwicnx8dI8Ks/f8smQZcYTQFBk1wmRvfYiLDPoSRoQm6OzrU3RQghzcSECRAabOGnp1vtbqyeMgiFUwejYKQGMpEAxy6YkaMJPnA0J02FbooIrMnLgs3pDhhwyl6nUWLX6Wo2+GFcMFtRZW7iHEuIkWI4DTgl5JajAIgQ0qmposQhA4gYafhFbplEgBkfHUXBhqNIkksRHyPFUHUcXnskAzlBprc/P0IDsZCPmRsNmLnRgGk6ddAp73k6Nf6y91zA93m9wNxNx2CxOthjcpkYyyf3D/gZaMApITdXm5OgWyM6Ohrff/99p8sBoiRoQjqXKnMTXt1yAumJMWwisUImQpJcivmbTwSt6MrRKDF/fAbO1TQiOVaGYxfMWLr9NKwON2RiAeaP64N+3eW4UNsEseB6UvKHT2XB6nSzoy78k5fjoyUorW7Akm2nAlZ/mMqwVbuN2DX7/oDEZqaMnwacEtJ2N70TdGvxWkgm3Lt3LyZMmICkpCTweDxs3ryZc97r9eLVV19FYmIiIiIi8OCDD6K0tLTF733vvffQo0cPSKVSZGdn4+DBgz/nxyCEdHBJigi8Mq4Pvq+oxfT1hzHjo6N48q8H8MaOM1g6sV/Aak5OmgrPj0jDpNX78Nw/jmL8ymJsP34RK3K1kIkFsDrcePmzE1j+5RmcrKrD9PWHsWq3EVaHG+YmJ9aV+Ka3M8nLzHf+eU8ZcjQq3Jsay/k+ZiRGYXE5gOCJzXKZGL3iozAwJZY6PxNyC9zWJOjGxkYMGDAA+fn5eOyxxwLOv/nmm1ixYgXWr18PtVqNBQsWYPTo0Th16hSkUmnQZ37yySeYPXs2PvjgA2RnZ+Pdd9/F6NGjcfbsWcTHx7fLz0UI6Vgu19lw0WxDbnYqpul7st2Y9/xQg3ydDQ/3S8RUnZpdpfnhcj3y1x/irNIwydL5ejVbkVViNHGaGcrEAnSPjcA0nRpREiEGpcSy35WVGovFj/ZFoiICSx/tC+OVBk5J+6yNBvb7KLGZkNvvhgMgp9OJiIgIfPfdd+jbt2/Ya0+dOoWkpKSQ58eOHYuxY8cGPef1evHuu+9i/vz5ePTRRwEAf/vb39C1a1ds3rwZTzzxRND73n77bTzzzDOYNm0aAOCDDz7A9u3bUVhYiLlz57bmRySEdDDhujxXmZsw51/fcxoSMt2YT1ZZ8O6uUk4l2Jq8LLz82Ymg39M84AGuz+qSiQUozBuMP+w4w/muHI0S22fqEef3TgqZCOv3/Yi9QcrzKbGZkI7hhrfARCIRUlJS4HaHr64AgOTkZAgEgja9WHl5OS5duoQHH3yQPSaXy5GdnY39+/cHvcfhcODIkSOce/h8Ph588MGQ9wCA3W5HXV0d5w8h5NawWB0oq26AoaIWZVcaOAnCgC/AKdhowKi392DS6n0Y9cc9mLnRgCpzEyxWh68DdLMcnxKjCWtLyjGid3xAGXxL/Xuan2dmdS0Y1wfvfV0a8F1FRhNe/fwk5xglNhPS8bVpC+yVV17Byy+/jL///e+Ii4tr73cC4Os0DQBdu3blHO/atSt7rrmamhq43e6g95w5cybkdy1btgyLFi36mW9MCLlR4UZYJCkirgc4zVZS9pbWYO6mY1gwPiNkE8Rg/X+YLaw1eVlBh5wC4AwnzdGokCCX4qOnsyER8lEUYuWoqLQG1fV2TmCTpIjAylwtJTYT0kG1KQBatWoVjEYjkpKSkJqaisjISM75o0ePtsvL3Srz5s3D7Nmz2c91dXVITk6+jW9EyJ2vpeCGCR5CBTh7S2tgbgrfJdk/x0cmFmBFrhZv7jjDqQrzH16qTVHAUGkG4BuXsejRTFyus2HKhwewesqg8D9PkHeRyyjgIaSjalMANHHixHZ+jUDMmI3Lly8jMTGRPX758mUMHDgw6D0qlQoCgQCXL1/mHL98+XLA2A5/EokEEonk5780IaTVWgpumJyfUGRiAaIl4f8RJhUKkKNRochYg3y9GmtLygNWhpjPC8ZnIFsdB+OVBmydqYPXC+QVHsTvJ/nGV/ivDIV6H0JI59GmAOi1115r7/cIoFarkZCQgF27drEBT11dHQ4cOIDnnnsu6D1isRj33nsvdu3axQZpHo8Hu3btQkFBwU1/Z0JI64ULbgBfqXioLs/Mao6hohY6jTLodldOmgpxkSIUjNTAAy+0yYqQ87ZKjCb87qF0fHniIv7w7x/wr/83DE8VHoTV4YbT7csJMlSaQ36XTqNEpPimFtUSQtrZz/pf7JEjR3D69GkAQGZmJrRa7Q3d39DQAKPx+j+QysvL8d133yEuLg4pKSn4zW9+g6VLlyItLY0tg09KSuKsQI0aNQqTJk1iA5zZs2cjLy8PWVlZGDJkCN599100NjayVWGEkI4h3AgLAGzOzPA0VUA1Vb5ejXUl5ThaYcaKXN8/d/wDk5w0FRaMz8Dh87UY1lOJVx7ugwa7bztMJhZwmhcyeUCX621IT4iBTqPENz9cYbfPDJVm6DVKFBaXB/8ujRLPj0yDQkal7YR0Jm0KgKqrq/HEE0/gm2++gUKhAACYzWaMGDECH3/8Mbp06dKq5xw+fBgjRoxgPzN5OHl5eVi3bh1eeuklNDY24tlnn4XZbIZer8eOHTs4PYDKyspQU3P9H46/+tWvcOXKFbz66qu4dOkSBg4ciB07dgQkRhNCbq9QwQ1wvVScqaZ6bcsJ9Pbr8pyqlAEAjlb4+uvk69XIv9bnRyoSICVOhgtXrZBHiFBvc0EeIYKlycmuHK0tKeesBuk0SjzSPwlV5ibMHJmG/HWH2HNnLtZh6cR+WLDlBOe7AN/A0zMX69EjTka5PoR0Mm0ahfGrX/0K586dw9/+9jf06dMHgK/nT15eHjQaDTZu3NjuL3or0SgMQm6NKnMT5m46xgmCftEnHgsfyYTN6UGdzQlFhAgCPg+vfHY8oNfPNJ2a02CQCXDWl5Rzrh2epsLrk/qhyFiDbceqgm+ZaZRYMrEvYmViXKqz4UKtb2ipodKMjw9W4H8f6o1BKQo02n2jMgR8HgR8HpSRlOhMSEdxI7+/2xQAyeVy7Ny5E4MHD+YcP3jwIB566CGYzeYbfWSHQgEQIbfO5TobahsdqLO5IJcJESES+oKda0FRwUgNDBW1IXNvmPlaLV37iz7xeHF0b4x+tyjku/znN8PRNUaCgo2GoAnaw9NUWJmrpYCHkA7qRn5/t2kLzOPxQCQK3O8WiUTweMI3GSOE3L2Yjs6WJgdkEiF4AMxWB4qMJhQW++ZrNQ9gWkpe9u/cHO7ar05X49nhvcK+X53NCQGf12J1GgVAhHR+bQqARo4ciRdeeAEbN25kR1389NNP+O1vf4tRo0a16wsSQu4MwZoeMttYp6osWJGrhZDHCwhgbrRzczgSUcul7K2pTiOEdH5tboT4yCOPoEePHmzDwMrKSvTt2xf/+Mc/2vUFCSGdX6imh8xKjzYlFmtLyvHiQ70D7m2p/05yXARWTxkEiZAPZWTL/bxaKmWXCMNnBdAgU0LuDG0KgJKTk3H06FHs3LmTHTHRp08fzgwuQghhhGt6yGxjrdptxNyxvIDzhkozcjTKgBlcgC9o+ffJy5wcIL1GiWKjKaDcXSET4WqDA9P1avZ7/Z8z06+UvaXqNEJI59emJOg7HSVBE9I+mJwfU6MDDXYXZ+6Wf4AiEwvR5HQjSS5Fnc2BepsbPB4PNqcbESIBeihlWLL9FHafucI+O1wV2McHKvBEdkpA52edRomn9T3x/QUz+nWTQyYWIlIiwMmqOoy4pwsSFBEAglenMYNME69dQwjpeG56FRgAHDp0CF9//TWqq6sDEp/ffvvttjyyw6AAiJCfL1zOz9xNx7B8cv+AAOXB9Hj8bkw6lmw7yZnXlaNR4X9H94a50QGr043usRH4z6nL+PhgBZ4YksJpanj8JzMe7puIxVtPhlw1YirH1uRloZsiAolyaUBiMxO80SBTQjqPm14F9vrrr2P+/Pno3bs3unbtCh7v+rK1/38mhNydWsr5eeuXA1BlaUK+To0p2alsN2aRgIfF204G5OgUGWt84yz8AhemM3OwpoajMxKCBj/MO+Tr1NBplKiutyMrNTZoYEODTAm5s7UpAPrTn/6EwsJCTJ06tZ1fhxByJwiX82OoMCNFKcNfi88FbE+9Nj4Tf95zLuh9/iXvhkoz5o/rE3K46U/mphbfcebINOrgTMhdLHx5Raib+HzodLr2fhdCyB0iXCl5vl6N17acCBq4LNl2Evn66319ZGIBCkZqsCYvC6unDELXGCkKRmrw8cEK9OsuD1rN1RrdYiPQJyGazfkhhNx92rQC9Nvf/hbvvfce3n333XZ+HULInSDcoNNwzQqLjCZMvbbKE2puV06aCh8/OwxmqyPkd5yosmDZpL6Ij5FyBp4WFpcjKzUWiTGBOT+EkLtLmwKgF198EePGjUOvXr2QkZER0BX6008/bZeXI4R0PharA0I+DzlpqpDbYOEwaYT5enXQLa6i0hq89vkJzBmTHvR+mViAft3kWFtcHjA7rHDqYNr2IoQAaOMW2KxZs/D111/jnnvugVKphFwu5/whhNydqsxNKNhowNgVRVgwLgM6jZJzXqdRIj4mfLPCJEUERqV3gTZZEXKLq8RogsPlCXg+4Auc1jQLfph7Vn9tRIRYcIM/FSHkTtSmFaD169dj06ZNGDduXHu/DyGkk2pe+VVuaoQ2JRb5OjXsLg8kQj4MlWbsOl0dtrGh4bwZLzx4D9vbJ5QrDXZM0wU2NbyvpzLkFhvN8iKEMNoUAMXFxaFXr/BDBQkhdxf/yi+ZWABlpDhoICITC7B5hg6LmpW7+zc2/OTXQ2F3hp/xJRbwMXOjAfl6NfJ1asjEQlgdLohbGJ1Bs7wIIUAbt8AWLlyI1157DVartb3fhxDSSVisDpRVN8BQUYuyKw1we72QiQVs8vIPl+uDblFZHW5UmZugTYllq7vW5GVBmxLLdnWurrMjOc63FRaMTqOEodIMq8ONVbuNmL7+MKwOF9bv+xHyiPCzumiWFyEEaOMK0IoVK1BWVoauXbuiR48eAUnQR48ebZeXI4R0TMG6POekqbAiV4uTVRasLSmHocKMFblaANwtqhyNEsro4KtDjPgYKQ6cM2HRhEw43Sc4Iyn8V4r8j1XX2/Hm5P7ghUnAHp6mQpRUiLLqBtTZnIiJEEEVSQ0PCbkbtSkAmjhxYju/BiGkswjV5bmotAZerxcvjUnHO1+VAgBm+W1RMXlAXaIl2HW6OuxU9n+fvARDRS2G9lJiZa6WHUkRIRbgaIWZM/8rJ02FJY/2RaxMhEaHG69+dhx59/WAx+vlPH94mgpLJ/bFK58dx87T1Zzjyyf3RxL1BCLkrkLDUIOgWWCEhFZW3YBRb+8JeX7rTB0mrCxhPzefyp4SJ8OuM5evlar/iCJj8NUdq8ONHI0Kyyf3Q7dYGXtNqBldFqsDBRsNKCqtCZwEHyFCD1UkXt1yghP8MIanqbAyV0srQYR0cjd9Fhgh5O7BBBzMlhGT6xOqSqvJ4Ubh1ME4WlGLjw9WsENPOc0MNSqM7ZuARY9motHhQuXVJrZKzH91p8hYg/MmK6IkQjY4CTWjyz8Jm8kN8rfjhZygwQ9A1WGE3I3aFADx+fywQ0/d7vDlq4SQziFcro9/oOKv3ubC9PWHodMo8WHeYKzY9UPQ4aaLtp7CnDHpuFJvx4yPQucNmpucrQpOwo3f8J13hT1P1WGE3F3aFAB99tlnnM9OpxMGgwHr16/HokWL2uXFCCG3V0u5Pvl6dcAqC1OdBfgSn/k4iwEpCuw+cyXg+SVGEyyNzhZzbyRCfquCk3DjN3znw//jjqrDCLm7tCkAevTRRwOO/fKXv0RmZiY++eQTTJ8+/We/GCHk9go30b3YaMKMBzScAChYdVaRsQYvPJjG5uL4z+SyOtyod7hw7Iw5ZNUWE1BNGtitxfdVRYkxPE3FqRhjDE9TITYy/HlVFG1/EXI3aVMfoFCGDh2KXbt2tecjCSG3SbhhowAgEfGx4elsbHxmaEAfH3+WJiemrz+MGR8dRf66QzBU1GJFrhYysQASIR/vfV2G/32oN/RBxmZM06lx5mId3F4vyq40wBLmneQyMZZP7o/haSrO8eFpKrwxuT+6xkjDnqf8H0LuLu2WBN3U1IQVK1agW7eW/02NENKxWawOSEXhZ2ZFSoT4nzX7sDJXi+nrD7f62Uw+0Pxxfdhmhk+vP4R//b9h+Mlsg6XJySZEbzxwHrnZqZj4XgmsDneLJetJighO2bx/lVhrzhNC7h5tCoBiY2M5SdBerxf19fWQyWT4xz/+0W4vRwi5cc2rttrS6M9sdeL7C+aQvXpy0lQwVJiRr1fDUBn6Ov+cIH8lRhPmje2D6vrLWJOXBQCotTphc7ohFQkgFPAwrKdvRch/VWlvaQ3mbjoWtmQ9VJVYa88TQu4ObQqA3n33XbjdbggEvn9D5PP56NKlC7Kzs1FfX9+uL0gIab1gVVs30uiPCZ5sLjeWbj8dtJOzTqPEokcyMX5lMVbmajFzoyFkx+e8azlBzfvySEUCCPg8HLtgxrs7SznPnqZTw+UGnlp3MOg7Usk6IaQ9tCkAys/Px8WLFxEfH885bjKZoFarqQyekNsgVNUWs2ryh8cHoMHmCrky5B88ffzMUFgd7qCdnA2VZtQ02NlVGf/r5oxJx4XaJrbj8xN/+RYAsCJXG7QXUJ6uB749d5V9FhNA/Xp4+GHLVLJOCPm52hQAeb3eoH2AGhoaIJVKf/ZLEUJuXLiqrcPna2FqsOOixQa7y4NaqxMHy6/igXu6IFERERA8CQW+/30HaygIAA/c4xtSGh8jgV6jRLHRhFW7jdAmK9iePgUjNdCmKKBNicXakvKgvYAALz5+digu1DZxKsRiI2mgKSHk5rqhAGj27NkAAB6PhwULFkAmu96e3u1248CBAxg4cGC7viAhpHVCNQJkprMv++I0ipptZalVkZCJBQHB04FyE5Y91g/x0ZKA8vV7U2JRbKxBjkaJXaerMTAlFs89oIHb60VshJgtaS8sLseKXC0iRIKQg0+LjCZM9WuEqNMosSJXC48HIfOKqGSdENIebigAMhh8/T28Xi+OHz8Osfj6P4TEYjEGDBiAF198sX3fkBDSKqEaAebr1UFXYJjPr0/sxwmeZGIB+iTGYG1xeUDAVJiXBbvLg7/vP4/fjUnH1QYHrE43XG4PUlUywAPMG5sOr/c0io0mzNpowAf/fW/Y97a7PAHvNG9sOmaOTAOfxwvIZ6KSdUJIe7ihAOjrr78GAEybNg1/+tOfbsmg0B49euD8+fMBx2fMmIH33nsv4Pi6deswbdo0zjGJRAKbzXbT3pGQjiBUI0BtsiLkCkyJ0YRGh4sTPOXr1VhTHDxg4vN4eHV8H8x7uA+q622wXqvaOnS+Fn/f/yOm5/TES/86hvemDMJzNhcsTc4Wt7MkQm47shKjCUIBH30SZFhFJeuEkJukTTlAa9eube/3COnQoUOcpOoTJ07gF7/4BR5//PGQ98TExODs2bPs53Bzywi5UzCNAOduOha023EoVocb3RQRbPAULmAqKq2B2wss3HoioDJsmk6NNUW+bS9TgwMerxcykQAutxc5GhVn6rv/fcHK5G0ON2f4KSGEtLcOPw2+S5cunM/Lly9Hr169cP/994e8h8fjISEh4Wa/GiEdTvNGf5ESITxeb9h7IiVC2FwevDG5P+ZsOsbZkgrmp9qmkNtp2pRYXG10sMNQp+nUeOZvh7F8cn+AB852VrDRGQxKciaE3GwdPgDy53A48I9//AOzZ88Ou6rT0NCA1NRUeDweDBo0CK+//joyMzNDXm+322G329nPdXV17frehNxKTKM/pqz9pdHpYRsVbj9+Ed9V1OL3E/vhrccHwNQYfgRGKCVGE1suz3zmA/jvoamYtdGAT5+7Dz+Zm2B3eRAfLcEPl+uDjs6gJGdCyK3QrrPAbrbNmzfDbDZj6tSpIa/p3bs3CgsLsWXLFvzjH/+Ax+PBfffdhwsXLoS8Z9myZZDL5eyf5OTkm/D2hNw6/mXtVZYmTNerkdNs1laORonpejUKi8tRbDThlc3H8aOpEXyer0dPMDkhtqwYIiEfyXERWD1lEAqnDsaAlFj8IiMeQ3vGwQuwM8GeKjyI+BgptCkKzv2U5EwIuVV4Xm8L6+MdyOjRoyEWi7F169ZW3+N0OtGnTx/k5uZiyZIlQa8JtgKUnJwMi8VySxK9CWkvTCfnq1YH6m0uHK2oRXaPOChkYrg9XticbkRKBPACOHOxDl1jpPjughl9k+SwuzxQqyLBgxc8Hh+Lt50MyPN5bUImO5crmI+ezsaUDw9w7xmfCYfbDZlYiAVbrucO+XeHBoBuiggkyqUU/BBC2qyurg5yubxVv787zRbY+fPnsXPnTnz66ac3dJ9IJIJWq4XRGDypE/BViUkkkp/7ioTcdOHmfAUbgzEyvQsmD+qG+ZuDJy3/ff95jOwTj5nXOjkzFVndYiMwd0w6+HygweaGTCJEdZ0NX526BG2KIuh2ml6jxP5zgblBS7adwssPp0PI42GaTs0eZ5osMu8iFvIp+CGE3DKdJgBau3Yt4uPjMW7cuBu6z+124/jx43j44Ydv0psRcmuEm/MVKRYEHYORkSTHq82CH4CbtJwkjwgxquL6LC+rww2dRomn9T3RJzGG8wzANxw1774eQROai4w18ACQy0T45GAFtCmxAaM1PjlYgbceH/Cz/44IIaS1OkUA5PF4sHbtWuTl5UEo5L7yU089hW7dumHZsmUAgMWLF2Po0KHQaDQwm834wx/+gPPnz+Ppp5++Ha9OSLsIN+drzqZjWPxIJgYkK3DkfC1ne6qlHkD5OjXkMhHe+s/ZIKMqTPDA1xdo1W4jm9Q8tJcS2pRYPP+ABiIhH1IRHyIBH4+t3hdya6zpWln7okf7Yu6mY5x3orwfQsjt0CkCoJ07d6KiogL5+fkB5yoqKsDnX8/lrq2txTPPPINLly4hNjYW9957L/bt24eMjIxb+cqEtBuL1YGLFlvIOV9FpTU4V9MIQ0UtVuRqOZVVLZW0210eCAW8oFtawPUgiXGkwowlj/aFF4DV6Ua9zQWHywtJC/8kkUf4ytqbl+lTc0NCyO3SKQKghx56CKFytb/55hvO53feeQfvvPPOLXgrQm4+Ztvrv4em+oaLJisCZnNZHW7YXR42iGFWbIDALsvNKSJEaLQFX7VhiIR8yMQCAMB7Tw7CpTobVn5tDNgCWzt1MKatOxSwCpSTpkJ89PUcO6ZMnxBCbqdOVQZPyN2E2fY6cr4WamUkDBW1bBl5/rpD7IqPTCxgA50SowmjMxNQMFIDmVgAQ6U5oPydkaNRoUtMy8n/Ah4PK3K1+PX9PXHR0hQQ/AC+VahVXxuxYFwfzvHhaSq8SdtbhJAOqFOsABFyJwhXwRUMM6G9YKQmoCQduJ6EPH9cH05vnsqrVjY42nz0AhZP7Bu0CixP1wPLvjiN3zx4D/QaJYpDNErcf84EQ0Ut5oxJx5V6e8jtsqLSGrwwKg0bns6GRMSHIkJM21uEkA6LAiBCboFwFVxJioig9zAT2kMlMsvEAmhTYjEwJRaxpkYUTh2MoxW1iBYLoU2JhUwkwP8M6wGb0405Y9LRYHPB3ORkK6/8c4VeHJ0OHu9syFEVVocbAh6vxZwiAMhMiqGghxDS4VEARMhNFq6Ca+6mY1iZqw0aMDAT2oMFHTKxAO89OQgXLU24aG4CkyLXTS5Ft7gInNpv4QRNzRsU+tt95gqeGtoDL4/tg6r7mtjy9BNVFpz4yYKVuVrfO/CA7rERkIkFIau9Yim/hxDSSVAARMhNxmxlBbO3tAY1DY6gQYMqSozhaSpIRYKAc7++vyekIj62H78YsLXVQxWJAckK7D5zhT1uaXKGfUer0w2X14Pp6w8D8AVYTG+gd3eWstflaJRYk5eF6esP0wwvQkinRgEQITcZs5UVSn2I83KZGG9M7g9zkxOj0rugT5KcrQK7p2s0Xvs8dIPDOWPS8c5X1wOXlqrB5BEiVNfZ2aGp+Xo11paUB+0NBPAwf1wfvPzZCfZ4DvXyIYR0MhQAEXKTMVtZoUSHOS8TC7B02ym8NKYPFm87yW5rfV6gC9u7pzlDpTnkRHi9RoloqRAvfGzA8sn9AYRvoFhkrMG8h/vg8wId7E4PFDIR4qMlFPwQQjoVKoMn5CZjtrKCGZ6mQpRUiHNXGvDD5XqcuVSHo+evouxKAy7X2XCpzgZ1fFRAFZjVHr53T/PtqcLickzTqQOmvOdoVFj8aF+8/40RNQ0OzNpogDYlFlEtdDb80dSIR1aVIC5SjLSu0RT8EEI6HVoBIuQmk8vEWD65P+ZuOoa9zarAlk7si8VbT2LSoO4BW056jRILxmcgKyU2YDVGKOCF/U4hn3ve6nBjw4HzePWRDDTa3DA12gH4Vob+68/78cSQFKzJSwYAKCMlcHnCV3tJhHzK+SGEdGoUABFyCwQbARElFeKVz44jPTEmaL5NsdGEpdtOY/ZD9wQ8r9hYgxyNCkXGwOTqnDQVzl1p4BzTaZR4YdQ9WPbFaUzJTmWTnRn+AdaavKywW2Y6jRLV9XbK+SGEdGoUABFyizQfAVFW3YCdp6uROyQlbL7N3LHpAcf/svccVj2pBeC9lpjsk6NRYrpeje8vmPHR09kQCXiot7lgqDSjttGB3Weu4Jf3+lZ6ZGIB8vXqgPEaLo8XhcXlWJGrBRA49X3Jo30RKxNR8EMI6dQoACLkNmGqw1pqLtjocAd0arY63CjYYMCCcRl44cF7UF1vh1Qk8M3M8wJp8dGwuzyIj4nAX/acw84z1Vg9ZRAA3/aVf5m7f/Cl0yjxSP8kAMCsjQbk69XI16nZ3kCaLlFIVUW2918FIYTcchQAEXKbMNVhLZWoWx0uX6dmnOVseWlTFBiUqsCk1fsAgA1oSpqtCC2e2BceeNnvMVSaMX9cn6DbbiVGE5ZsO8kOVPUPjoanqbDy2qoQIYR0dlQFRshtwlSHMfk2weg0Shw+X4sn//otBqQo8NHT2fjb9CHYOlMHbUosLpptsDrcYfv2vLr5JKZkp6JLtAQ5GiUKi8vRr7s89Ewvown39eS+z3Dq80MIucPQChAhtwlTHfbalhOYplMDQEAV2FS/WVyGiloMTFZg1kYDfn1/T3RTRCBKKsSavCx0jZECAAwV5oAS+CJjDf7fA73wxF++xYpcLTwox4XaprDvJhUJsGv2/WzCNg01JYTcaSgAIuQ2SlJEYOEjmai8asWLD/XG3LE81FqdEPP5SJBLcKnOhrceH8AOMJ276RieHd4TD6Z3xaU6G+quJTgXFpdDm6LAilwtZ8gpQ8DnYVCKgs3r6R4bfAArQx4hQq/4qJv5oxNCyG1FARAht5DF6kBNgwN1NidiIkSIkgjx2ucnsa/MN34iKyUWcpkIQgEfP5ltiIsU49yVRizdfhqAL89nfbP5XDqNkg18gHI2f8efQibCH/9rIOqanKi4aoXX68sPKgqyDUb9fQghdwMKgAi5RarMTQFT4XPSVJim64Ep2an4+7c/YmCyAm/952xAIvPmGTo4XB68seN0QNDCXMskKEeKhRiUEoujFbUoLC7HvamxSIyRQi4To2uMFJESIV7bcgJ5OjU84G67Ua4PIeRuwfN6vd7b/RIdTV1dHeRyOSwWC2JiYm7365A7wOU6G8qqG2BucrL9dgqLy2F1uJGjUeL5kRoI+XxYmpzg8Xic84BvZMUr4/tgzLtFIb/jo6ezMeXDA+xnnUaJghEaCPg89G42rsJidcDU6IDb44Xb44XV4YY8gnJ9CCGd2438/qYVIEJusipzE+b863vOyo3/tlWR0YTnHtDgl3/Zz57PSVNh47NDMX3dIdQ0OFBkrEFdkyvs91iauFPlS4wm8AHc2yMOykjusNLmTRkJIeRuQ2XwhNxEFqvDt+0VZNtqbYkvXwcAzM2Cl6LSGry14yzWTh0CmVgAAOz/DSVYP6Eiown9uslRb3MGuYMQQu5eFAARcpNYrA5ctNg4OT/+SowmaJMVAEIFLzXg84Bf398TACAV8cP2CzJUmoOes7s8iL7WdJEQQogPbYER0k6YvBqXxwuPxwtTowMCfvip7XaXJ2zwcqnOhrGZifi+0oxGuwvTdGrwweN0hM5JUyHvvh7XqsACKa7l9hBCCLmOAiBC2kGVuQmvbjmBJ4akcDoyr8nLCnufIkKEaTo15m46hoKRmoDBpAIeD0u2ncLvxvSG1e7CrI0GfPR0Nl4a2xuVV5vYlaOPDpwP6P0D+JoppipllO9DCCHNUABEyM/E5PkMSFZgbUk5DBVmNpgRCfjY8Ew29pWZOFVdgG/lJjk2ApfqmvDR09lYuu0Up3+PXqPEg3264khFLRrtLqTEyZCVGourjQ7YXR7M+OgoALCDTe0uT8Dk9mWT+qFbrOzW/WUQQkgnQWXwQVAZPLkRZdUNGPX2HqzJy8LMjQaselKLwuJyzvT25mMtdBolpunU2HjgPH7z4D14Y8cZzvUMnUYJbUosRvaOx6DUWPxUa8WVBgfsTjcaHW62XB4A8vVqdgWppyoSiXIprfwQQu4qVAZPyC1Ud63Cyu7y4NnhPbG2WfADAMVGE3jg4aOns3G10QFDpZkNhqbrewYNfgBfonS+To1oqRBV5ibM/fQ4J6nav5yeWT1iprZT8EMIIaFRAETIzxRzrcJKIuRDr1FxxlT4KzLWYKquB6avP8w53rwEHvBtazErOpFiITxeL/b8cAVHztdyrmO2vJjxF9TJmRBCWocCIEJagVPh5fXCandBLhNDFSmGKkqM4WkqGCrNyNGowj7H7vIEHGteAs/k9KwtKefkBPmv9vjnEpUYTXjl4T6YNLAbdXImhJBWogCIkBaEqvACfNtNSyf2RcFIDT7YU4axmQlhnxWs3w8TODGl7fl6dcD3AIGrPf4cLg8ykuRt+vkIIeRuRI0QyV3DYnWgrLoBhopalF1pgMXqaPG60sv12HP2Cvp2kwcNSvaW1uDlz46jwe7CjAc0kIoEIZsV6kP0+zlVZcH/ju7N3qdNVgR8D8O/eaI/anRICCE3psMHQAsXLgSPx+P8SU9PD3vPP//5T6Snp0MqlaJfv3744osvbtHbko6qytyEgo0GjHp7Dyat3odRf9yDmRsNqDI3hb3uF+/sxbbjVRjVJz5kUFJsNEEiFOCXH+zHtuNVKBihCQiCdBolFj6SiTNVFs7xnDQVnsnphafXH4I2JRZr8rIgE4dfmG2+jTY8TUWNDgkh5AZ1ii2wzMxM7Ny5k/0sFIZ+7X379iE3NxfLli3D+PHjsWHDBkycOBFHjx5F3759b8Xrkg6GncfVbCTF3tIazN10jK2YCnVdidGE6jp72O9osLtQMFKDft0UEPB4eOXhPrA63LjSYIdYwMflOhuqam3o212B3OxU2F0eSIR8JMmlEAr46JMQzW5rtdQ80X8bjZKeCSGkbTpFACQUCpGQED63gvGnP/0JY8aMwe9+9zsAwJIlS/DVV19h1apV+OCDD27ma5IOqqbBEXIe197SGtQ0OCCXicNeF44qSoy0+Cj8Y/+PnNycnDQVXpuQiQtXraiy2LB0++mAbs3//k0O3txxBvf2iMNLY9NRXWdHl2gJcjTKgAGqzDN7dYnE5hn3IVoqoqRnQghpo04RAJWWliIpKQlSqRTDhg3DsmXLkJKSEvTa/fv3Y/bs2Zxjo0ePxubNm0M+3263w26//m/4dXV17fLepGOoa2ESuqXJibLqBpgag+cEAUyicmBQIhML8Pf8bCzYciJgi6yotAaLPj+Bsf0SA5KWAd/qTUKMFG89PgBmqxPzNx9HkdHEVoF5AM4zdRolZjygAR88DEyJbcVPTgghJJQOnwOUnZ2NdevWYceOHXj//fdRXl6OnJwc1NfXB73+0qVL6Nq1K+dY165dcenSpZDfsWzZMsjlcvZPcnJyu/4M5PaKaSFBWCLk4+zlekSIBQHnZGIBCkZqkJUSi9ce6RtQ5r5gXB/UWh0h84OKjCb06y4PyAnK8du6ksvEcHm8bHBldbgxa6OBzQlaPWUQts7UQZsSi+nrD6HB4bqRH58QQkgQHX4FaOzYsex/7t+/P7Kzs5Gamor/+7//w/Tp09vlO+bNm8dZNaqrq6Mg6A7C9OnZG2R7S69R4suTl7BqtxEFIzXQa5RsV+bm/XiY5oTPPdALIiEfTrcHYgEf1fXh84Mu1DZBmxJ7raOzCDERQiTGcMdU1DarSLM63JxVo9VTBrGfgw09JYQQcmM6/ApQcwqFAvfccw+MxsAtBQBISEjA5cuXOccuX74cNodIIpEgJiaG84fcOeQyMZZP7o/hadzVG2Y+FzNLq7C4HFN1auivrdY8O7wn1vmVvjNByZMfHsCKnaXYV2ZCdb09aG8ff2IBH6t2GzF9/WF4vV7Eyrh5OxarA44gDRL9+X+HPIJK3gkh5OfqdAFQQ0MDysrKkJiYGPT8sGHDsGvXLs6xr776CsOGDbsVr0c6qCRFBFbmarFr9v3YPOM+7HghBwNTYjldlZmtp4Epsdg+S4cxmQkhZ3QVGWugTVZAIuTDUGkO2fsnR6Pi9P5psLvwu39+zym/r2lwYN85U8hn6Pz6B+WkqRAfLWnD3wAhhBB/HT4AevHFF7Fnzx78+OOP2LdvHyZNmgSBQIDc3FwAwFNPPYV58+ax17/wwgvYsWMH/vjHP+LMmTNYuHAhDh8+jIKCgtv1I5AOQi4To1d8FAamxKLJ6VvNab6dxKzyOFxe/NSsR1BzdpcHhkozTlVZME2nDghg9BolXhzdm11hYgIZpvyeacRYZ3OisLg86DOYqfGFxeUYnqbCm1TyTggh7aLD5wBduHABubm5MJlM6NKlC/R6Pb799lt06dIFAFBRUQE+/3ocd99992HDhg2YP38+Xn75ZaSlpWHz5s3UA4hwhEuMlokFiJGKIBTwwj5DESFCYXE5VuRq8fGB82yej93lgTxChGipEPnrDsHqcLOBzKyNBgDc8vsYqYhdfcrXq5GvU8Ph9qBLlARiIR8utwdbC/RU8k4IIe2I5/V6vbf7JTqauro6yOVyWCwWyge6Q1msDszcaAhIjJaJBb7Kq6+NGJASC0NFbdAKrxyNCr8b0xtv7DgDQ4UZHz87FDUNDggFPMTJxBAL+XC7PSg3WdltssLics6K06fP3YdBqbEh3wXwlcozjRoJIYSEdyO/vzv8ChAhN4NcJsbrk/ph7qfHOHk+88f1weqvjSgymnCkwowVuVoAgf14Fj6SiaraJswflwGvF2i0O+HxevHnPefYa9fkZWHGR0dDvoPsWtk9k6Q9d9MxThBEXZ4JIeTmoRWgIGgFqHOxWB2oaXCgzuZETIQIqsjWbRWVX2nAuZpGxMdI0GBzI0oqBODFhJUl7DVM6bs2WQG7y4PkuAgcv2DBlQY73vmqFGvysiCPECFOJsaCz7nNEAtGakKuIOk0SiyckIm0rtEBP0e9zUldngkhpA1oBYjcNarMTQHzu4anqfD6pH5wuD2wNIUOivh8HqQiASqvNkEqEuBAuQlDesRxrmnej2dNXhbiY6RYuv00m9SckRgDmVgYEOgw+UFA4ArSNJ0aAj43x0guo4CHEEJuFQqASKcVdsjpp8cwMCWWDV6Y7SSZWICaBgdqrQ443B7sP2dic3NyNCr8ok/XgFUfqUiAoxW1KCwuR3yMBNPWHoI2RcEmNf/1qSyYGgObIfonNs8Zk44LtU1sPtAnByvw1uMDbsnfEyGEkEC0BRYEbYF1DmXVDRj19p6Q5//1/4bB0uSE3eVBhEiABLkUf91ThnEDkthtr5gIIcRCPvLXHkJlbRPenNwPKXEyrPzaGLBqUzBCgwtXrRiQHIudZy7B4fJiSI84iIV8REqEGPunopDvsiYvC9PXHwZwPRhLVES0318GIYQQ2gIjd4fWDDllgg7A15dnycS+WLLtFHafucI5/vfp2ai8aoXL48F7zYIfwLeFxQcP9/aIxebvT2LOmHS8seMM3t1ZCgABYzT8DU9TQdMliia4E0JIB9LhGyESEkpLQ06bKzaasGDzCWQkyQOOz998HA63B2KhIGDiO6PIWIN+3eQoMZrQYHNxgqTmYzQYzGpPqioSA1Ni0Ss+ioIfQgjpAGgFiHRa4Yac+o+P8FdsNGGaTh30+Jyx6ai82nL3ZwAwN3FXn/zzfeaPy4DN6abVHkII6cBoBYh0Wkz/nJxmQ05z/MZHBGMPMXi0weZucbApcz7YdUzFmM3pptUeQgjp4GgFiHRqkWIBHu6XiKn39WArtroppJi0el/AnC9GqCAnSipASVkNdBplyN49hkpzwIDT5qJvcGuOEELIrUcrQKRTq2lwYN6nxzF9/WG8+M/vwQPg8XgxKEUR9Hp9iK0xvUaJ6jo7O5Q02KrSdL0ap6oseHF0b5yqsoR8vq+hIiGEkI6M/klNOh3/zs9SER9r8rIg4PEgl4lw5mId/v7tj5iqU8MLbgNCvUaJpZP6YcnWk5zn6TVKvDohE0/+9VtYHW58fKACz4/oxa4qSYR8VNfb0VMZiQHJCkxffwjLJ/eH3eXhPD9Ho0SeTo1Gu+tW/VUQQghpI+oDFAT1Aeo4/IOdaKkQYj4fJWU1iI+Rsv19IsR8REuFqLe5ESkRYsLK4oBmhkwDwnH9ElBvcyFSIkSj3YVoqQgRIgFqrXZcqrNDESGCIkKM/yk8gJoGB+ddctJUGJCswKrdxqDP7xItwRN/+RYbns7GwJTY2/Q3Rgghdy/qA0TuCM3HXPz2F2kY0iMO245fDFjZmXqtK/M7vxqIgpEaNjDh8Xg4dbGO7fZ8f1oX1Nlc+GBPGQYkKzA2MxELNh/nlL7rNEosn9wfszYaOHlERaU1eO7+Xli12xgwIkOnUUKbEgurw005QIQQ0glQAEQ6pGBjLkb0jscbO84EJCgXG03wAvhgyiCkqiIRJRHC0uRkR1icqrJgRa4WszYaECUV4v09RjyZnYrqOhsWbTsZtOkhAOTr1ZwgBwBEQt+WG4/HY8dj+I/FGJ6mgiqKKr8IIaSjowCI3BYtTXCvaXAEzPgCELQ6CwAMFWZ0i5Ph5c+OBx08uuHAeSwY1wcSIR8ZSXKsLSlHvk4d8nklRhPyg/QLqvPrLp2jUWHzDB2+PHkRszYakJUaizcm96fSd0II6QQoACK3XKgJ7ssn90fStflYwcZcWO3ukINKhXweFm45EXI1R5sSC21qLK42OqC9lsczJTs17Hs27xfUvLlikbEGS7afwqvjMzC+XxI1PSSEkE6EyuDJLRV2gvumY7BYfYnHwcZciIU8rMjVwlBRi+nrD2PGR0eRv+4QDBW1GNUnPuQIC0OFGQ9ldIXT7YHHC8RHS1AwUgOZSBD2Xf37BelCNFcsKq0Bn8ejpoeEENLJ0AoQuaVCbW3JxAL0T1bgosWGczWNiJIIseyxfliy7RSbiGxucmJtSXnQVZ7qOnvQ75OJBViRq8UfdpwJSHR+sE9XjEzvwhmMysjRKJEol+LT5+6DVMTHFycuBSRFM+pbGMpKCCGk46EAiNxS/ltbzHZWVkosEhVSLN12ipN0nJOmQuHUwchfd+ha4MELmbMTSr5eHTJoAs5gzpj0wH4+aSo8P0KDOJkYCYoIlFU3BCRD+6OqL0II6XwoACK3FLO1xazMrC3xbSkZimsDgpSi0hrA68X2mXpcaXDA4wndsso3okIZsA3G5PsEU2I0oabBAW1KLJvwnBAjRbRUCHmEiN3SCjd0laq+CCGkc6IcIHJLMcGE/8rMoJTYkCs7py/Vw+sFoqVCRIcZMVFYXI5XJ2RCp1He0PvIxAJkJPqaZRkqzXj8z/thd3k4+TzM0NXhzcZjDE9TUdUXIYR0UrQCRG4pJpj4saYRq3YboYoSI0oS/L+GqigxPnp6KBZ87qvuKhipCTmoVJuigN3lxqJHMmF3emB1uBElFaKlPueNdhdmfHQ04FhzSYoIrMzVoqbBgXqbE9FSEVV9EUJIJ0YBELnlkhQRuGRpgkwsQOHUwai3BZ+d9cbk/ljs16iwsLgcK3K1ABAwg2vBhEws//I0vj13ld1aayloytH4VnRkYgEnuTlUTo9cRgEPIYTcKSgAIreFPEKMfL0a9TYX9p8zBQ1S4mMknGNWhxuzNhqQr1cjX6dGtFSEepsTkRIh3vzyNHafuYKCkRpO0nOooEmnUSJP1wMfHTjPdom2OtyU00MIIXcJCoDITWWxOmC2OtHocKHR4YYiQoT4aAlUUWLc11MJc5MzZJDSaAssOfefwbXxmaGYvv4w1uRlYee1UvbmSc9M0LQyV4sZD2hgaXKyg1GZoMfu8iBfr8axSjPl9BBCyF2CpsEHQdPg28dFcxPOX7Vi5e7SgDLzNyf3x0VLE4QCPq7U2+FwexAfLYVIwMNFiw0SAR/JcTL8aGrkdHxmhpoCwJcv6OF0eyHg8VBvd0Ek4EPI5yH3r98G9OtZk5fFjrAIZscLOUiUSyn4IYSQToymwZPbzmJ14JsfrmDbsaqAra0j52tRbKxBv+5y/FTbBB6Phx8u1+PspXoM7hEHeYQIURIhDpabsGT7aTaY0WmU7HbVsJ5xEAn4WLr9ZEBwtepJLQo2cJsWNh9r0ZzN6abghxBC7iIUAJGboqbBgfhobg6PTCzAr+/vibF9E7Fk60n87l/H2ONr8rKw+msj3t1Zyl7vH/BYHW72WQvG9cGwXiq8svl4yN5BzSe5+4+1CIaaGRJCyN2F+gCRm6LO5uSsujCNDxNjpFiy9SSnYWG+Xo1VXxsDmhiWGE2+qe16NeeYNiUWP5oaQ/YOKjKaMKwntx9Qdb0dOc36+DAo8ZkQQu4+tAJEbgpVpBgSkQAbnxnqa2DoBX64XIe+3RR4adNxzrUtdWtmujQzzE3OoDO5/EmEfHz12+FotLvYnj3339MFczcd43R0pmaGhBByd6IAiLS7ClMj5n12vFmvHhWeH9GLMwuM0VJ+TvPz0VJh0GaF/qIkQqR1jeYck8tAzQwJIYQA6ARbYMuWLcPgwYMRHR2N+Ph4TJw4EWfPng17z7p168Dj8Th/pFLpLXrju9vlOltA8AMARcYarPzaiJgg4yxays/xP6/TKHHJYsPlOlvIsRc5GiViI4MHNXKZGL3iozAwJRa94qMo+CGEkLtUhw+A9uzZg+effx7ffvstvvrqKzidTjz00ENobGwMe19MTAwuXrzI/jl//vwteuO7j8XqQFl1AwwVtbja6AiZm1NiNEEs5AcELoZKc8hgRqdRwlBpBuBbRSoYkYY5m44hUR6BghGagPtyNCr8flI/dI2hgJcQQkhoHX4LbMeOHZzP69atQ3x8PI4cOYLhw4eHvI/H4yEhIaFV32G322G329nPdXV1bXvZTspidaCmwYE6mxMxESKoIlu/LVRlbsKcTcd81VcAVk8ZFPb6y3U2vPJwHyz74gyKjL57CovLsSYvC3wej30O4AtmXp2QgVqrA9pkBY7/ZMHBH014Y3J/8AAoo8R4dXwGHG4PbE4PYqRCKGRiCn4IIYS0qMMHQM1ZLBYAQFxcXNjrGhoakJqaCo/Hg0GDBuH1119HZmZm0GuXLVuGRYsWtfu7dgbNAxjAlxi8fHJ/JCkiwt5rsTow51/HcKSiFgUjNdAmK9AlWhL0WplYgHy9GrGRElRbbHhpTDpecLlxpcGO5FgZAC8m9E/E1Pt6wO7yQCLk43KdDRdqm+Bwe/Drvx8JeKZ/c0O9Rok3Jven4IcQQkirdKpO0B6PB4888gjMZjOKi4tDXrd//36Ulpaif//+sFgseOutt7B3716cPHkS3bt3D7g+2ApQcnLyHd8J2mJ1oGCjgRP8MIanqbAyVxt2JejspTpMWr0vYPiooaI2oP+P/zUMnUaJaTo1ukZLsXzH6aBbZzqNEnPGpOORVSUB51ZPGcSZ5L7h6WxkJsVQXg8hhNyl7thO0M8//zxOnDgRNvgBgGHDhmHYsGHs5/vuuw99+vTBn//8ZyxZsiTgeolEAokk+MrFnaymwRE0+AGAvaU1qGlwhAwmLFYHfqptQr5eHTB8dNWTWvDBw5GKWuTr1Xjgni6wNDkxXd8T2pRYdpwFc8/cselh84YcIarEmidPm5ucYd+ZEEIIYXSaAKigoADbtm3D3r17g67ihCMSiaDVamE0Bu81c7cKVpLurz7M+ZpGO1KUMnSJkSAjMQbT9T3ZWV3Hf7JgwoBELBifgSXbTnJ6/ATr7txoD9/T50qDPeCYf3I0QyLkh31nQgghhNHhAyCv14uZM2fis88+wzfffAO1Wt3yTc243W4cP34cDz/88E14w84rpoXxD/7jIfwTpeURIvB5PLy65QSnezMT3Ah5PByuqMWW7wPngDGf/UdVyMSCG3pPZuts1kYD55ih0oxJA7uFfRYhhBACdIIA6Pnnn8eGDRuwZcsWREdH49KlSwAAuVyOiAhfku5TTz2Fbt26YdmyZQCAxYsXY+jQodBoNDCbzfjDH/6A8+fP4+mnn75tP0dHpIoSY3iaitMZmeE/HqJ5onTBSA2+r6gNOroCAF58qHeL3Z3njk1HRmIMpCIBIsQC6DVKFIfIARLwfdPaz1+1IjlWhu8ra9kVJOaaaTo1PjlYAZX+xgNkQgghd58OHwC9//77AIAHHniAc3zt2rWYOnUqAKCiogJ8/vV8kNraWjzzzDO4dOkSYmNjce+992Lfvn3IyMi4Va/dKchlYiyf3D/seAiL1RFQJdZycMNrsbtz5dUmNoH5wfQuWDKxL+ZvPhEw2X3B+Ax8eeIivq80IyNJjlc+O46/T8/Gh09lwdzkhETIh6HSjE8OVmDxo30p/4cQQkirdPgAqDVFat988w3n8zvvvIN33nnnJr3RncNidcDmdGP++Ax4vF5Y7W7II7jjIZhEaaaMXZusgEwc/r82tVbfNlk4/gnMO89cgQenkK9TI1+nht3lgTxCBJvTjYnvlbArPVOyU3Gs0ozYCBGS5FJ2pMWkgd2g0qsp+CGEENJqHT4AIjdH820tmViABeMzMChFgXM1jYiS2CEW8FFrdWDt1MFQRonx7s4fsGq3EWvyssI+WykTI0oqDLut1TyBefeZK5iSnYrp6w9zcnz8h57GRIg4pfkU8BBCCGmrDj8Kg7S/5ttaTJ+ebceqMPrdIkxavQ+/eGcvXt58HHU2J479ZEa9zYVnc3ph7dTBABBmDpcKUpEA31XUYunEfsjRqDjnmeCmsLg84F6ZWIgvX8iBNiU2IPgBgDgZDS4lhBDSPmgF6C7k3/9HJhZgZa4W65o1KQR8+Tx88PDyuHS4PF402NzoEi2B1eHC0kf7Yv6WEwGNDfN0PTBhVTG0KQr0TojGw/0SMFXn6+6cHBeBf5+8HDS4AQAvvIgQ8aFNVuCtxwdAKhKwpfVZqbFsUjYhhBDyc1EAdBdi+v8wKz9SkSCgootRZKzB1UYnpnx4gM0DGtZTCVOjA6+Oz4CQz8fVRjt4PB6KjTWc/j5v/vss5o/LwIJrgRLTJTpY8KPXKNFdEYFXPjseUFpfOHUwesTJaPWHEEJIu6EtsLsQ01eH6eJsaQrfPNDS5GSDJUNFLaZ8eAC//GA/Rr9bhFc/PwGLzYXV3xjRt5ucc9/uM1dgd7rx4kO9sXWmDkN7xmHRI32Rk8bdFsvRqLB0Yj8s3HoyaGn9e18bEdFCryBCCCHkRtAK0F2g+bT3KKkQv+gTz5az5+vC986RCPkBIy8YzGdtSizWlpRzGhwCgMXmwhN/+Zb9LBMLsLVAh3qbGzaXGzKxrw/QJUsTdp+5EvT7i1oYy0EIIYTcKAqA7nChpr0vndgX5640AgAMlWboNMqQw0gNleYWe//k69RBgymRgIeCkRpkpcRCLhNBKODjaqMTMRFC8PnARYsN3WMjcNXa9rEchBBCyI2iLbA7WLAmhoBv0On8zSeQqpQB8A0wnaZTB1R25aSp2IqtlhobMuf9r8vRqCAWCHCqygKX14u3/nMWE1YW4/E/+7bP3txxBgI+Dz+ZmwIGmzYX3cLYDkIIIeRG0ArQHaylae9WhxsbnsnGvjIT5m46hieGpHAaEabERmDh1pOwOtwtBijMeeb/5miU+N2Y3nhn51lkJMmDbp8xPYJeGpOO/5y6HHIVKsdvLAchhBDSHmgF6A7W4rR3uwtPrz+M7ypqsXxyfxQWl2P6+sP46MB5OFweNNhdWDA+E/pr22Chev8w22Q5GhUS5FJsnanHyw/3gdXuwu4zV6BNVgQNbABfECQW8nHmYl3QVSi9Rollk/pR/g8hhJB2RStAdyAm6bmlVRu3x8smLfPAw0dPZ+NqowMnqizwwovXvziN05fqUTh1MKx2Fyb0T8TSbadRZLy+qqTXKPG70elYsesHLJiQgT/t/AHPj9DgP6cvY1BKLAC0uH1mc7ix+NG+eG3LCWhTYtlVKEWECKlKGbrFyn7+XwohhBDihwKgO4x/0nPBSA1yNCpOwMLQaZTYf84EbbICgK/fz1RdD0xffxgFIzVYU3x9y+pKvR3T1x9m+wAxjQ2ZQaQSER9Thqbi3a/OomDUPbhsseFw+VX0TfKVxbcmvydJEYG3Hh/AzveKlnJnkhFCCCHtiQKgO0jzpOfC4nJ8NuM+LN52KqBjMzNr64//NQAFIzXskNPCqYOhihJzRlUwKzhWhztoJZg2WYHUOBnmje2DSe/vg9XhxsfPDgXgywUKV2U23C+/R06jLgghhNwiFADdQZonPVsdblw02zjbSsyqDdOxuXtsBP7x7XlOYJOjUWFFrpa9RiYSsEGS3eXhjKhgujov2nYKix7JxBNDUlBYXI4LtU148Z/fY0WuFhsOnMe0a+Xx/kHQ8DQV3pjcn4IeQgghtxwFQHeQYEnPhytqYaioDbpyk6NR4vgFS8DKTJGxBh748oMK/397dx8VdZ3vAfw9A8zAwDAwPAyQoBSoWIKIoaMolRS62oZ6tg7XCpDaSjCR4+6GN7OubXir3fWJ1XY3idOGeq2De3tUDwquiCkgZeYTygo3BYTiaXgYmPndP2h+MTAgtMWMzvt1Duc4v993fnx+56PHz/k+HquGh6ts0DPmhHiJxc3p2mb881Ijqht1OF3zHbYmRsJRIkGH3oDnd5/GiphgOEokWPvQJLywUILvOnrgq5TD392ZxQ8REVkFC6DbREuHHi5ODvjz8ulmPTS7jlVja2IkpMCgM7bWP3w3EnJKLD7PtLnhiphgvHHgvMUdoCXoW8Ju2um5u9cotlvx/YqukqqmQcXXvFBvbEuMZPFDRERWwwLIhg08wsLb1fIcGUu7PZt6aJ7ffRrP7z6N95/VIrmlC3qDET5ucsgcpfhOp7d4MGl/w+0AfayqCSvaf3iGabJzSVUTUmPu5LAXERHZLBZANmqoIyw2LQtHgIeLeG2o3Z7FnpiYYJyu+Q66bgNS88rM2rydNGPYGLxc5ejqGb5AcnFywK7ke1HX0omvrrWI192dHSFzkOC1hKnQG4zQdfdyZRcREdkMFkA2aLgjLH73wZf472Xh6OoxoNcowGAUhtztuaSqCesWhuH+Sb5QOA0+Tf1mZ4DJHSXo0AvDxtqh70VqXhnmhngjJWYCFDIHdOgN8FTIcJev2yjemoiIaOxwJ2gbNNwRFv+81IirTTpcadThlQ/PorpRN+yzrn7bgWU7jqOy9jvMDfE2u2c6AyzGwhlgGx6+G0cuNqCr1zDoeyamHaCBvonTbx/rOw1+Ho+uICIiG8ceIBt0syMsnJ0csP1IlThR+WZtAWDjx+eQmzwDvwj3h69SLi5nb2zrwgsLJ6OhTY+unr4zv4K9XbF427G+JfAyB2xNjIQRwpB7CZmUVDUh7b4QJEwLgK67l0NdRERks1gA2SB3Cyefm3Zhjgz0QI9BQGrMnYgM8sSZb1qGHMaKCfGCIPwwhKXTG/DJl9fMVoPNDfGCt1KO9PzT4mTm/3lmFmaM9xQPTDUtZV8xJxiuckfounvN9hLqr1cQ8MvtJZge5IFNS8MxTs1jLIiIyPawALJB3m4yzAv1xtHvh8FMvTC5JdWD9uJ5KuZOhI/rO3KixKyw8caLi8MgAPjf9DkQBODNA+fNih+gb2m8ERDPBAP6jqbIXjoVL//vWRw61yDuAD0v1Bv/uShMXPZuSU+vER16A45VNSGr4Ay2c7k7ERHZIBZANkilkGHTsnBxIvSKmGDkllRb3ItH7ijFk7Mm4HcLJgMAunuMcHKQouhiA5b8ue9YijkhXnhh4eRBxU//55iG0uaEeOHjM9fxZW0zXlsyFVm/CENrZ98yfFe5I7r0BswN9bY4R6n/nCCgb75SY7ueBRAREdkcFkBjqKVDj+aOHuj0vdDpDfBwcYKvUm6xQDAdDnq5oR1uzo4W9+JRyBzwHzPH4+1jV8TiRiFzwIuLwjA/zBeT/JSQO/ZtitjQ2j1sbN29RrN5PR16A9YVnMG2xEjc6eOGa819R1uUX+3b6dko3HxOEAC03WQ+ExERkTWwABoj15s7cfXbDmw7fMl8qOr7jQH77+1jonF3hsEo4Nz1VovPHNgz1H+obF3BV2K7OSFeiL9bM2x8gWoXRAZ5ms3rOfp9Dw4As2X5/ecEAYDKxQlFF29YnBOktDCfiYiIyNq4DH4MtHToUXTxxqDiB+gbJnrhgy/R0qG3+N0ADxcEDTGRODLQw+x5ww2Vnfm/FswNHXo5+4Gz9dh+uGpQAdPcqbd4yOr2w1VIzStDal4ZunuNFr/L5fBERGSrWACNgcZ2PXyVcosrtYC+npbrLV04XfMdLt9oH1QM+SrlmGeheOnuNZp9HlgQmShkDrjR3o3/+uXdmDtgz5+YEC+8mjAV0wI9sCv5XqQ/EAKF7IdNE7t7jGjptFycmcgdpYOKKx55QUREtoxDYGOgtatnULEy0JVGHVa+VwFg8JEXAydFm6hczIeXLP2O/sNibxVfwYqYYCR/P3Tl5+6MhrYubPzoLKYEqLD9cJXZGWKRQR44fqUJi6f6Dxu7p0KG7YmRaGzXo62rh0deEBGRzWMBNAbcnZ3wre7mvSgmpiMv1i+eAgepBN6uMjhKJfjFPX5Inj0B3b1GBKpd0NDabbYHUP9nmAwcFhu4jP53Cybj8PkbWD5zPIAfltK/uCgMvu7OeH73aTwSEWC2LL8/0zCXSsGCh4iIbh0cAhsD3m4yNLT1FSuWzB2wfBzomxtU+20HHt52DOfq2lDV0I6sgq+QmleGle9VoPbbTqzafRopc4LF55rO9upvqGExwHzfoP69RyVVTZioUYqTmh2kEmxaFj5oGI7DXEREdKu6JQqgnJwcTJgwAc7Ozpg5cyZOnjw5bPt9+/Zh8uTJcHZ2xtSpU/HJJ5+MUaSWqRQy3DfRB6seCB1UoPQdIhqMXceqB32vu9eIFTHB2Hb4Epo7zZeTyx2l4i7NkUGeeDtpBqbeocKGxebzfG429GY67X1g71FDWzc69AbMC/WGl6sMAR4u2JYYicLMWOxfORuFmbHYlhgJfwur14iIiGydzQ+B7d27F5mZmdi5cydmzpyJzZs3Iz4+HhcuXICvr++g9sePH0diYiKys7OxePFi5OfnIyEhARUVFbjnnnus8AZ9/D1coJA54LWEqdDpe9GhN8DN2RGffVVndgxFf3JHKSIDPbD9cNWgM7/6n+Tef1jLtA/QbxdORke3Ae4uwy9Dd5M7DdrA0PS7B/bwcJiLiIhuFzbfA/THP/4RTz/9NFJSUjBlyhTs3LkTCoUCu3btsth+y5YtWLBgAX7zm98gLCwMGzduxPTp07F9+/Yhf0d3dzdaW1vNfn4OKoUM471dMSVAhRkT1PB3d0bF1e8sFj+mosTUgzNweMt0kvugIa8gD/i6O2PLoYtQu8oglWDI09znhnjDaBSQMse8B2puqDdCfNzYw0NERLctmy6A9Ho9ysvLERcXJ16TSqWIi4tDaWmpxe+UlpaatQeA+Pj4IdsDQHZ2NlQqlfgTGBj407zATZhWdw2cW2PaVXnXsWpxaGpgwWMa/locHoADGXPxwXNafLwqBmsfmgRHiQSTA1R4JKcEdS3dSImZMGj5+9wQL6TETIARgtkGhvNCvfH6snCM93Zlbw8REd22bHoIrLGxEQaDARqN+S7GGo0G58+ft/iduro6i+3r6uqG/D1ZWVnIzMwUP7e2to5ZEWSaW9PYrkdLZw+6egw4fqVJLEr6D3X134G5u9cIDxcn3OXrBo27MwDgWnMnXvjgS7PVWtdaOlF4rh4RQZ5I/v57ckcpTtc2Y/fnNXh1yVR8mB7D5etERGRXbLoAGityuRxyudxqv7//3JprzZ3YWXxZ7JHZdawau5LvhVQiwT8vNYrzfUzzc0zFD2BeTPUvaGIn+uCFD740myvU//sa9zF8WSIiIhtg0wWQt7c3HBwcUF9fb3a9vr4efn5+Fr/j5+c3qva2ZqgiZqQbDVqaqKxSwOIz2dNDRET2yqYLIJlMhqioKBQWFiIhIQEAYDQaUVhYiPT0dIvf0Wq1KCwsREZGhnjt0KFD0Gq1YxDxT2Oo1Vb/TsHCFVxEREQ/sOkCCAAyMzORlJSEGTNmIDo6Gps3b4ZOp0NKSgoA4Mknn8Qdd9yB7OxsAMDq1asRGxuLP/zhD1i0aBH27NmDsrIy/OUvf7HmaxAREZENsfkC6LHHHsONGzfw0ksvoa6uDtOmTcNnn30mTnSuqamBVPrDYrbZs2cjPz8fL774ItatW4fQ0FDs37/fqnsAERERkW2RCIIgWDsIW9Pa2gqVSoWWlha4u3OGMBER0a1gNP9/2/Q+QEREREQ/BxZAREREZHdYABEREZHdYQFEREREdocFEBEREdkdFkBERERkd1gAERERkd2x+Y0QrcG0NVJra6uVIyEiIqKRMv2/PZItDlkAWdDW1gYACAwMtHIkRERENFptbW1QqVTDtuFO0BYYjUZcu3YNSqUSEolEvN7a2orAwEDU1tZyh2gbxjzZPubo1sA83RqYpx8IgoC2tjYEBASYHZNlCXuALJBKpRg3btyQ993d3e3+L9mtgHmyfczRrYF5ujUwT31u1vNjwknQREREZHdYABEREZHdYQE0CnK5HBs2bIBcLrd2KDQM5sn2MUe3Bubp1sA8/TicBE1ERER2hz1AREREZHdYABEREZHdYQFEREREdocFEBEREdkdFkCjkJOTgwkTJsDZ2RkzZ87EyZMnrR2S3Tp69CgefvhhBAQEQCKRYP/+/Wb3BUHASy+9BH9/f7i4uCAuLg6XLl2yTrB2LDs7G/feey+USiV8fX2RkJCACxcumLXp6upCWloavLy84ObmhmXLlqG+vt5KEdufHTt2IDw8XNxET6vV4tNPPxXvMz+2adOmTZBIJMjIyBCvMVejwwJohPbu3YvMzExs2LABFRUViIiIQHx8PBoaGqwdml3S6XSIiIhATk6Oxfuvv/46tm7dip07d+Lzzz+Hq6sr4uPj0dXVNcaR2rfi4mKkpaXhxIkTOHToEHp6evDQQw9Bp9OJbdasWYMPP/wQ+/btQ3FxMa5du4alS5daMWr7Mm7cOGzatAnl5eUoKyvDAw88gEceeQRnz54FwPzYolOnTuGtt95CeHi42XXmapQEGpHo6GghLS1N/GwwGISAgAAhOzvbilGRIAgCAKGgoED8bDQaBT8/P+GNN94QrzU3NwtyuVzYvXu3FSIkk4aGBgGAUFxcLAhCX16cnJyEffv2iW3OnTsnABBKS0utFabd8/T0FP72t78xPzaora1NCA0NFQ4dOiTExsYKq1evFgSB/5Z+DPYAjYBer0d5eTni4uLEa1KpFHFxcSgtLbViZGRJdXU16urqzPKlUqkwc+ZM5svKWlpaAABqtRoAUF5ejp6eHrNcTZ48GUFBQcyVFRgMBuzZswc6nQ5arZb5sUFpaWlYtGiRWU4A/lv6MXgY6gg0NjbCYDBAo9GYXddoNDh//ryVoqKh1NXVAYDFfJnu0dgzGo3IyMjAnDlzcM899wDoy5VMJoOHh4dZW+ZqbJ05cwZarRZdXV1wc3NDQUEBpkyZgsrKSubHhuzZswcVFRU4derUoHv8tzR6LICIaEykpaXhq6++wrFjx6wdCg0wadIkVFZWoqWlBe+//z6SkpJQXFxs7bCon9raWqxevRqHDh2Cs7OztcO5LXAIbAS8vb3h4OAwaDZ9fX09/Pz8rBQVDcWUE+bLdqSnp+Ojjz7CkSNHMG7cOPG6n58f9Ho9mpubzdozV2NLJpMhJCQEUVFRyM7ORkREBLZs2cL82JDy8nI0NDRg+vTpcHR0hKOjI4qLi7F161Y4OjpCo9EwV6PEAmgEZDIZoqKiUFhYKF4zGo0oLCyEVqu1YmRkSXBwMPz8/Mzy1drais8//5z5GmOCICA9PR0FBQU4fPgwgoODze5HRUXBycnJLFcXLlxATU0Nc2VFRqMR3d3dzI8NmT9/Ps6cOYPKykrxZ8aMGVi+fLn4Z+ZqdDgENkKZmZlISkrCjBkzEB0djc2bN0On0yElJcXaodml9vZ2VFVViZ+rq6tRWVkJtVqNoKAgZGRk4NVXX0VoaCiCg4Oxfv16BAQEICEhwXpB26G0tDTk5+fjH//4B5RKpTgXQaVSwcXFBSqVCqmpqcjMzIRarYa7uztWrVoFrVaLWbNmWTl6+5CVlYWFCxciKCgIbW1tyM/PR1FREQ4cOMD82BClUinOnTNxdXWFl5eXeJ25GiVrL0O7lWzbtk0ICgoSZDKZEB0dLZw4ccLaIdmtI0eOCAAG/SQlJQmC0LcUfv369YJGoxHkcrkwf/584cKFC9YN2g5ZyhEAITc3V2zT2dkprFy5UvD09BQUCoWwZMkS4fr169YL2s6sWLFCGD9+vCCTyQQfHx9h/vz5wsGDB8X7zI/t6r8MXhCYq9GSCIIgWKn2IiIiIrIKzgEiIiIiu8MCiIiIiOwOCyAiIiKyOyyAiIiIyO6wACIiIiK7wwKIiIiI7A4LICIiIrI7LICIiIjI7rAAIiIiIrvDAoiIiIjsDgsgIiIisjssgIjoZ3Pfffdh1apVyMjIgKenJzQaDf76179Cp9MhJSUFSqUSISEh+PTTTwEABoMBqampCA4OhouLCyZNmoQtW7aYPbOoqAjR0dFwdXWFh4cH5syZg6tXrwIAvvjiC9x///1QKpVwd3dHVFQUysrKbhrnO++8Aw8PDxw4cABhYWFwc3PDggULcP36dbFNcnIyEhIS8Morr8DHxwfu7u549tlnodfrxTZGoxHZ2dli/BEREXj//ffF+yN5PyIaG47WDoCIbm95eXn47W9/i5MnT2Lv3r147rnnUFBQgCVLlmDdunX405/+hCeeeAI1NTVwcnLCuHHjsG/fPnh5eeH48eP49a9/DX9/fzz66KPo7e1FQkICnn76aezevRt6vR4nT56ERCIBACxfvhyRkZHYsWMHHBwcUFlZCScnpxHF2dHRgTfffBPvvvsupFIpHn/8caxduxbvvfee2KawsBDOzs4oKirCv/71L6SkpMDLywu///3vAQDZ2dn4+9//jp07dyI0NBRHjx7F448/Dh8fH8TGxsJoNA77fkQ0hqx9HD0R3b5iY2OFmJgY8XNvb6/g6uoqPPHEE+K169evCwCE0tJSi89IS0sTli1bJgiCIDQ1NQkAhKKiIottlUql8M4774w6ztzcXAGAUFVVJV7LyckRNBqN+DkpKUlQq9WCTqcTr+3YsUNwc3MTDAaD0NXVJSgUCuH48eNmz05NTRUSExOH/N3934+Ixg57gIjoZxUeHi7+2cHBAV5eXpg6dap4TaPRAAAaGhoAADk5Odi1axdqamrQ2dkJvV6PadOmAQDUajWSk5MRHx+PBx98EHFxcXj00Ufh7+8PAMjMzMRTTz2Fd999F3FxcfjVr36Fu+66a0RxKhQKs7b+/v5iTCYRERFQKBTiZ61Wi/b2dtTW1qK9vR0dHR148MEHzb6j1+sRGRkpfh7u/Yho7HAOEBH9rAYOQUkkErNrpuEro9GIPXv2YO3atUhNTcXBgwdRWVmJlJQUs3k2ubm5KC0txezZs7F3715MnDgRJ06cAAC8/PLLOHv2LBYtWoTDhw9jypQpKCgo+NFxCoIw4vdsb28HAHz88ceorKwUf77++mtxHtBI3o+IxgZ7gIjIZpSUlGD27NlYuXKleO3y5cuD2kVGRiIyMhJZWVnQarXIz8/HrFmzAAATJ07ExIkTsWbNGiQmJiI3NxdLliz5SeL74osv0NnZCRcXFwDAiRMn4ObmhsDAQKjVasjlctTU1CA2Nvbfej8i+vmxB4iIbEZoaCjKyspw4MABXLx4EevXr8epU6fE+9XV1cjKykJpaSmuXr2KgwcP4tKlSwgLC0NnZyfS09NRVFSEq1evoqSkBKdOnUJYWNhPFp9er0dqaiq+/vprfPLJJ9iwYQPS09MhlUqhVCqxdu1arFmzBnl5ebh8+TIqKiqwbds25OXljej9iGjssAeIiGzGM888g9OnT+Oxxx6DRCJBYmIiVq5cKS6TVygUOH/+PPLy8tDU1AR/f3+kpaXhmWeeQW9vL5qamvDkk0+ivr4e3t7eWLp0KV555ZWfLL758+cjNDQU8+bNQ3d3NxITE/Hyyy+L9zdu3AgfHx9kZ2fjypUr8PDwwPTp07Fu3boRvR8RjR2JMJpBbiIiO5WcnIzm5mbs37/f2qEQ0U+AQ2BERERkd1gAEdFtb+HChXBzc7P489prr1k7PCKyAg6BEdFt75tvvkFnZ6fFe2q1Gmq1eowjIiJrYwFEREREdodDYERERGR3WAARERGR3WEBRERERHaHBRARERHZHRZAREREZHdYABEREZHdYQFEREREduf/Ad0da9NqimMzAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Plot a graph to check linearity\n",
+        "data.describe()"
+      ],
+      "metadata": {
+        "id": "ItVOltUuLE-T",
+        "outputId": "39136272-8858-4cc1-fc3f-cb749d8bc4f3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        }
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         mass_npea   tumor_size\n",
+              "count  1000.000000  1000.000000\n",
+              "mean     22.709158     9.805131\n",
+              "std      11.682122     5.842747\n",
+              "min       1.575483     0.092640\n",
+              "25%      12.290811     4.719465\n",
+              "50%      22.968280     9.936148\n",
+              "75%      32.664439    14.886392\n",
+              "max      44.255681    19.994353"
+            ],
+            "text/html": [
+              "\n",
+              "\n",
+              "  <div id=\"df-58b9ec23-1241-4c52-95a6-f9b74af4b543\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>mass_npea</th>\n",
+              "      <th>tumor_size</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1000.000000</td>\n",
+              "      <td>1000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>22.709158</td>\n",
+              "      <td>9.805131</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>11.682122</td>\n",
+              "      <td>5.842747</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.575483</td>\n",
+              "      <td>0.092640</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>12.290811</td>\n",
+              "      <td>4.719465</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>22.968280</td>\n",
+              "      <td>9.936148</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>32.664439</td>\n",
+              "      <td>14.886392</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>44.255681</td>\n",
+              "      <td>19.994353</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-58b9ec23-1241-4c52-95a6-f9b74af4b543')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-688ccfcb-a638-41b9-981d-c17d1fa1e877\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-688ccfcb-a638-41b9-981d-c17d1fa1e877')\"\n",
+              "              title=\"Suggest charts.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "    <script>\n",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-688ccfcb-a638-41b9-981d-c17d1fa1e877 button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-58b9ec23-1241-4c52-95a6-f9b74af4b543 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-58b9ec23-1241-4c52-95a6-f9b74af4b543');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Extract X and Y from data\n",
+        "X= data[['mass_npea']]\n",
+        "Y= data['tumor_size']"
+      ],
+      "metadata": {
+        "id": "mQ5YOGJFLE63"
+      },
+      "execution_count": 34,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Train a Linear Regression Model\n",
+        "model =LinearRegression()\n",
+        "model.fit(X, Y)"
+      ],
+      "metadata": {
+        "id": "M1Jpn6TCLE4s",
+        "outputId": "62eaba7a-fcf4-4c59-b18e-603e178d509e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        }
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Evaluate the Model\n",
+        "y_pred=model.predict(x)\n",
+        "mse = mean_squared_error(Y, y_pred)\n",
+        "mse"
+      ],
+      "metadata": {
+        "id": "E4sxQ9G4LE2v",
+        "outputId": "8241bf27-4b39-4d81-810a-37b2805680f8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.24388273258319765"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the data and regression line\n",
+        "plt.scatter(X, Y,lebel='Data')\n",
+        "plt.plot(X,y_pred, color='red')\n",
+        "plt.xlabel('Mass Npea')\n",
+        "plt.ylabel('Tumor Size')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "F5p0Zr1_LSIP",
+        "outputId": "8acbfdb5-a04e-495e-e895-4738b7e331e3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 825
+        }
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "AttributeError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-42-53e7a8fc19c5>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Plot the data and regression line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlebel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'red'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Mass Npea'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Tumor Size'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m   2860\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2861\u001b[0m         edgecolors=None, plotnonfinite=False, data=None, **kwargs):\n\u001b[0;32m-> 2862\u001b[0;31m     __ret = gca().scatter(\n\u001b[0m\u001b[1;32m   2863\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmarker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2864\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1440\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4696\u001b[0m                     \u001b[0;34m\"No data for colormapping provided via 'c'. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4697\u001b[0m                     f\"Parameters {keys_str} will be ignored\")\n\u001b[0;32m-> 4698\u001b[0;31m         \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_internal_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4699\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4700\u001b[0m         \u001b[0;31m# Classic mode only:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m_internal_update\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m   1221\u001b[0m         \u001b[0mThe\u001b[0m \u001b[0mlack\u001b[0m \u001b[0mof\u001b[0m \u001b[0mprenormalization\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mto\u001b[0m \u001b[0mmaintain\u001b[0m \u001b[0mbackcompatibility\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1222\u001b[0m         \"\"\"\n\u001b[0;32m-> 1223\u001b[0;31m         return self._update_props(\n\u001b[0m\u001b[1;32m   1224\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"{cls.__name__}.set() got an unexpected keyword argument \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1225\u001b[0m             \"{prop_name!r}\")\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m_update_props\u001b[0;34m(self, props, errfmt)\u001b[0m\n\u001b[1;32m   1195\u001b[0m                     \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"set_{k}\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1196\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1197\u001b[0;31m                         raise AttributeError(\n\u001b[0m\u001b[1;32m   1198\u001b[0m                             errfmt.format(cls=type(self), prop_name=k))\n\u001b[1;32m   1199\u001b[0m                     \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mAttributeError\u001b[0m: PathCollection.set() got an unexpected keyword argument 'lebel'"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file