Youtube Revenue Prediction.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["**Youtube Revenue Prediction**\n","\n","\n","> by: Noordeep Kaur (102017145) and Ankita Sharma (102017120)\n","\n","\n","\n"],"metadata":{"id":"pEoULszuzCGR"}},{"cell_type":"code","execution_count":3,"metadata":{"id":"v0-lBITZWVX9","executionInfo":{"status":"ok","timestamp":1668958884299,"user_tz":-330,"elapsed":1194,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import pandas_profiling\n","from IPython.display import display\n","%matplotlib inline\n","plt.style.use('ggplot')"]},{"cell_type":"markdown","source":["## **Import Data**"],"metadata":{"id":"-jwNHxj5YReW"}},{"cell_type":"code","source":["#import 2018data\n","from google.colab import files\n","uploaded1 = files.upload()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"id":"ym_b_mA-fJoh","executionInfo":{"status":"ok","timestamp":1668958458995,"user_tz":-330,"elapsed":11804,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"e5f8bf8f-90f2-4cd2-ccde-eed29243594b"},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-eb985322-ac7c-4787-8825-a730fac9a7e2\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-eb985322-ac7c-4787-8825-a730fac9a7e2\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving Table data 2018.csv to Table data 2018.csv\n"]}]},{"cell_type":"code","source":["#import 2019data\n","from google.colab import files\n","uploaded2 = files.upload()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"id":"dIfEmrN2fKOL","executionInfo":{"status":"ok","timestamp":1668958469821,"user_tz":-330,"elapsed":10852,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"d68106dd-31f0-4269-e326-56a202fc90b0"},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-5a138fc3-f68b-457e-ad70-e5d3ca38ce19\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-5a138fc3-f68b-457e-ad70-e5d3ca38ce19\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving Table data 2019.csv to Table data 2019.csv\n"]}]},{"cell_type":"code","source":["#import 2020data\n","from google.colab import files\n","uploaded3 = files.upload()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"id":"UPwd6IF8fKsR","executionInfo":{"status":"ok","timestamp":1668958481180,"user_tz":-330,"elapsed":10599,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"12f07486-8949-42f0-b229-dddf3c96329f"},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-39ce0b66-c9d9-4eb6-9edd-536307b98eba\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-39ce0b66-c9d9-4eb6-9edd-536307b98eba\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving Table data 2020.csv to Table data 2020.csv\n"]}]},{"cell_type":"code","source":["df = pd.concat((pd.read_csv(\"Table data 2018.csv\")[1:-1], \n","                pd.read_csv(\"Table data 2019.csv\")[1:-1],\n","               pd.read_csv(\"Table data 2020.csv\")[1:-1])).reset_index(drop=True)"],"metadata":{"id":"y3S7RWXwcSsi","executionInfo":{"status":"ok","timestamp":1668958889360,"user_tz":-330,"elapsed":501,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":745},"id":"eMlzxMrXf2Pi","executionInfo":{"status":"ok","timestamp":1668958892148,"user_tz":-330,"elapsed":837,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"4f8730b9-5271-4fe5-c38a-409b086a8022"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Date  Av­er­age views per view­er  Unique view­ers  \\\n","0     2018-08-21                       1.1538             13.0   \n","1     2018-08-22                       1.1935             31.0   \n","2     2018-08-23                       1.3684             19.0   \n","3     2018-08-24                       1.0000              8.0   \n","4     2018-08-25                       1.0625             16.0   \n","...          ...                          ...              ...   \n","1127  2021-09-22                       2.2721         105872.0   \n","1128  2021-09-23                       1.8941         215712.0   \n","1129  2021-09-24                       2.1319         305881.0   \n","1130  2021-09-25                       2.3468         152920.0   \n","1131  2021-09-26                       2.3480         132573.0   \n","\n","      Im­pres­sions click-through rate (%)  Im­pres­sions  Com­ments ad­ded  \\\n","0                                     7.38          122.0               2.0   \n","1                                    23.14          121.0               3.0   \n","2                                    20.65           92.0               1.0   \n","3                                    13.64           44.0               0.0   \n","4                                    13.89           72.0               0.0   \n","...                                    ...            ...               ...   \n","1127                                  4.04      4376635.0             546.0   \n","1128                                  5.83      4854638.0            2741.0   \n","1129                                  6.08      7768271.0            4741.0   \n","1130                                  4.51      5788014.0             671.0   \n","1131                                  4.24      5375782.0             527.0   \n","\n","      Shares  Likes (vs. dis­likes) (%)  Dis­likes  Sub­scribers lost  \\\n","0        1.0                     100.00        0.0                0.0   \n","1        0.0                     100.00        0.0                2.0   \n","2        0.0                     100.00        0.0                0.0   \n","3        0.0                        NaN        0.0                0.0   \n","4        0.0                        NaN        0.0                0.0   \n","...      ...                        ...        ...                ...   \n","1127   399.0                      98.19      195.0              972.0   \n","1128   764.0                      99.10      312.0             1221.0   \n","1129  1055.0                      99.09      521.0             1520.0   \n","1130   633.0                      98.53      276.0             1112.0   \n","1131   555.0                      98.73      191.0             1042.0   \n","\n","      Sub­scribers gained    Likes  Av­er­age per­cent­age viewed (%)  \\\n","0                     5.0      5.0                              53.59   \n","1                     6.0      4.0                              44.58   \n","2                     2.0      5.0                              38.75   \n","3                     0.0      0.0                              24.81   \n","4                     1.0      0.0                              45.50   \n","...                   ...      ...                                ...   \n","1127               2781.0  10594.0                              25.75   \n","1128               2953.0  34422.0                              30.35   \n","1129               4804.0  56677.0                              47.81   \n","1130               3555.0  18458.0                              41.24   \n","1131               3194.0  14857.0                              39.27   \n","\n","      Videos pub­lished  Videos ad­ded  Sub­scribers     Views  \\\n","0                   1.0            1.0           5.0      15.0   \n","1                   NaN            NaN           4.0      37.0   \n","2                   NaN            NaN           2.0      26.0   \n","3                   NaN            NaN           0.0       8.0   \n","4                   NaN            NaN           1.0      17.0   \n","...                 ...            ...           ...       ...   \n","1127                0.0            0.0        1809.0  240554.0   \n","1128                1.0            4.0        1732.0  408582.0   \n","1129                1.0            0.0        3284.0  652105.0   \n","1130                0.0            0.0        2443.0  358868.0   \n","1131                0.0            0.0        2152.0  311277.0   \n","\n","      Watch time (hours) Av­er­age view dur­a­tion  \\\n","0                 0.4666                   0:01:51   \n","1                 0.9575                   0:01:33   \n","2                 0.5849                   0:01:20   \n","3                 0.1152                   0:00:51   \n","4                 0.4491                   0:01:35   \n","...                  ...                       ...   \n","1127          19512.3516                   0:04:52   \n","1128          19855.7704                   0:02:54   \n","1129          54463.2654                   0:05:00   \n","1130          26757.4077                   0:04:28   \n","1131          22117.2441                   0:04:15   \n","\n","      Your es­tim­ated rev­en­ue (USD)  \n","0                                0.000  \n","1                                0.000  \n","2                                0.000  \n","3                                0.000  \n","4                                0.000  \n","...                                ...  \n","1127                           126.600  \n","1128                           163.558  \n","1129                           397.066  \n","1130                           178.664  \n","1131                           135.841  \n","\n","[1132 rows x 20 columns]"],"text/html":["\n","  <div id=\"df-2a2ed37a-77b0-4df1-928c-fa438ae3c61e\">\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>Date</th>\n","      <th>Av­er­age views per view­er</th>\n","      <th>Unique view­ers</th>\n","      <th>Im­pres­sions click-through rate (%)</th>\n","      <th>Im­pres­sions</th>\n","      <th>Com­ments ad­ded</th>\n","      <th>Shares</th>\n","      <th>Likes (vs. dis­likes) (%)</th>\n","      <th>Dis­likes</th>\n","      <th>Sub­scribers lost</th>\n","      <th>Sub­scribers gained</th>\n","      <th>Likes</th>\n","      <th>Av­er­age per­cent­age viewed (%)</th>\n","      <th>Videos pub­lished</th>\n","      <th>Videos ad­ded</th>\n","      <th>Sub­scribers</th>\n","      <th>Views</th>\n","      <th>Watch time (hours)</th>\n","      <th>Av­er­age view dur­a­tion</th>\n","      <th>Your es­tim­ated rev­en­ue (USD)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2018-08-21</td>\n","      <td>1.1538</td>\n","      <td>13.0</td>\n","      <td>7.38</td>\n","      <td>122.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>100.00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>5.0</td>\n","      <td>5.0</td>\n","      <td>53.59</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>15.0</td>\n","      <td>0.4666</td>\n","      <td>0:01:51</td>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2018-08-22</td>\n","      <td>1.1935</td>\n","      <td>31.0</td>\n","      <td>23.14</td>\n","      <td>121.0</td>\n","      <td>3.0</td>\n","      <td>0.0</td>\n","      <td>100.00</td>\n","      <td>0.0</td>\n","      <td>2.0</td>\n","      <td>6.0</td>\n","      <td>4.0</td>\n","      <td>44.58</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>4.0</td>\n","      <td>37.0</td>\n","      <td>0.9575</td>\n","      <td>0:01:33</td>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2018-08-23</td>\n","      <td>1.3684</td>\n","      <td>19.0</td>\n","      <td>20.65</td>\n","      <td>92.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>100.00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>2.0</td>\n","      <td>5.0</td>\n","      <td>38.75</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2.0</td>\n","      <td>26.0</td>\n","      <td>0.5849</td>\n","      <td>0:01:20</td>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2018-08-24</td>\n","      <td>1.0000</td>\n","      <td>8.0</td>\n","      <td>13.64</td>\n","      <td>44.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>24.81</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>8.0</td>\n","      <td>0.1152</td>\n","      <td>0:00:51</td>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2018-08-25</td>\n","      <td>1.0625</td>\n","      <td>16.0</td>\n","      <td>13.89</td>\n","      <td>72.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>45.50</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>17.0</td>\n","      <td>0.4491</td>\n","      <td>0:01:35</td>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1127</th>\n","      <td>2021-09-22</td>\n","      <td>2.2721</td>\n","      <td>105872.0</td>\n","      <td>4.04</td>\n","      <td>4376635.0</td>\n","      <td>546.0</td>\n","      <td>399.0</td>\n","      <td>98.19</td>\n","      <td>195.0</td>\n","      <td>972.0</td>\n","      <td>2781.0</td>\n","      <td>10594.0</td>\n","      <td>25.75</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1809.0</td>\n","      <td>240554.0</td>\n","      <td>19512.3516</td>\n","      <td>0:04:52</td>\n","      <td>126.600</td>\n","    </tr>\n","    <tr>\n","      <th>1128</th>\n","      <td>2021-09-23</td>\n","      <td>1.8941</td>\n","      <td>215712.0</td>\n","      <td>5.83</td>\n","      <td>4854638.0</td>\n","      <td>2741.0</td>\n","      <td>764.0</td>\n","      <td>99.10</td>\n","      <td>312.0</td>\n","      <td>1221.0</td>\n","      <td>2953.0</td>\n","      <td>34422.0</td>\n","      <td>30.35</td>\n","      <td>1.0</td>\n","      <td>4.0</td>\n","      <td>1732.0</td>\n","      <td>408582.0</td>\n","      <td>19855.7704</td>\n","      <td>0:02:54</td>\n","      <td>163.558</td>\n","    </tr>\n","    <tr>\n","      <th>1129</th>\n","      <td>2021-09-24</td>\n","      <td>2.1319</td>\n","      <td>305881.0</td>\n","      <td>6.08</td>\n","      <td>7768271.0</td>\n","      <td>4741.0</td>\n","      <td>1055.0</td>\n","      <td>99.09</td>\n","      <td>521.0</td>\n","      <td>1520.0</td>\n","      <td>4804.0</td>\n","      <td>56677.0</td>\n","      <td>47.81</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>3284.0</td>\n","      <td>652105.0</td>\n","      <td>54463.2654</td>\n","      <td>0:05:00</td>\n","      <td>397.066</td>\n","    </tr>\n","    <tr>\n","      <th>1130</th>\n","      <td>2021-09-25</td>\n","      <td>2.3468</td>\n","      <td>152920.0</td>\n","      <td>4.51</td>\n","      <td>5788014.0</td>\n","      <td>671.0</td>\n","      <td>633.0</td>\n","      <td>98.53</td>\n","      <td>276.0</td>\n","      <td>1112.0</td>\n","      <td>3555.0</td>\n","      <td>18458.0</td>\n","      <td>41.24</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>2443.0</td>\n","      <td>358868.0</td>\n","      <td>26757.4077</td>\n","      <td>0:04:28</td>\n","      <td>178.664</td>\n","    </tr>\n","    <tr>\n","      <th>1131</th>\n","      <td>2021-09-26</td>\n","      <td>2.3480</td>\n","      <td>132573.0</td>\n","      <td>4.24</td>\n","      <td>5375782.0</td>\n","      <td>527.0</td>\n","      <td>555.0</td>\n","      <td>98.73</td>\n","      <td>191.0</td>\n","      <td>1042.0</td>\n","      <td>3194.0</td>\n","      <td>14857.0</td>\n","      <td>39.27</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>2152.0</td>\n","      <td>311277.0</td>\n","      <td>22117.2441</td>\n","      <td>0:04:15</td>\n","      <td>135.841</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1132 rows × 20 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a2ed37a-77b0-4df1-928c-fa438ae3c61e')\"\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 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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":5}]},{"cell_type":"code","source":["df.columns = df.columns.str.replace(' ', '_')\n","df.columns"],"metadata":{"id":"mKtHJCrIf41y","executionInfo":{"status":"ok","timestamp":1668958894002,"user_tz":-330,"elapsed":10,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"5ab069d5-7aa2-4abb-91be-8455309fd0f1"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['Date', 'Av­er­age_views_per_view­er', 'Unique_view­ers',\n","       'Im­pres­sions_click-through_rate_(%)', 'Im­pres­sions',\n","       'Com­ments_ad­ded', 'Shares', 'Likes_(vs._dis­likes)_(%)', 'Dis­likes',\n","       'Sub­scribers_lost', 'Sub­scribers_gained', 'Likes',\n","       'Av­er­age_per­cent­age_viewed_(%)', 'Videos_pub­lished',\n","       'Videos_ad­ded', 'Sub­scribers', 'Views', 'Watch_time_(hours)',\n","       'Av­er­age_view_dur­a­tion', 'Your_es­tim­ated_rev­en­ue_(USD)'],\n","      dtype='object')"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["col_map ={'Av­er­age_views_per_view­er':'Average_views_per_viewer',\n","          'Unique_view­ers':'Unique_viewers',\n","          'Av­er­age_per­cent­age_viewed_(%)':'Average_viewed',\n","          'Im­pres­sions':'Impressions',\n","          'Dis\\xadlikes':'Dislikes',\n","          'Sub­scribers_lost':'Subscribers_lost',\n","          'Sub­scribers_gained':'Subscribers_gained',\n","          'Videos_pub­lished':'Videos_added',\n","          'Videos_ad­ded':'Videos_published', \n","          'Sub­scribers':'Subscribers',\n","          'Im­pres­sions_click-through_rate_(%)':'Click_rate',\n","          'Com­ments_ad­ded':'Comments',\n","          'Watch_time_(hours)':'Watch_hours',\n","          'Av­er­age_view_dur­a­tion':'Average_view_sec',\n","          'Your_es­tim­ated_rev­en­ue_(USD)':'Revenue'}"],"metadata":{"id":"d53tdO5Ug_F1","executionInfo":{"status":"ok","timestamp":1668958895590,"user_tz":-330,"elapsed":3,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["df = df.rename(columns=col_map)"],"metadata":{"id":"_vFC3bd4hF-Y","executionInfo":{"status":"ok","timestamp":1668958898731,"user_tz":-330,"elapsed":549,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["df = df.drop(['Likes_(vs._dis­likes)_(%)', \n","              'Videos_added', \n","              'Subscribers_lost',\n","              'Subscribers_gained','Comments',], axis=1)"],"metadata":{"id":"sHhFPa7yhJHK","executionInfo":{"status":"ok","timestamp":1668958900725,"user_tz":-330,"elapsed":5,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["print('Data Size: ', df.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6dr3z7W0iSvW","executionInfo":{"status":"ok","timestamp":1668958902763,"user_tz":-330,"elapsed":11,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"ccb90f66-216a-436d-f2b9-c8edbe4d447a"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Data Size:  (1132, 15)\n"]}]},{"cell_type":"code","source":["print('Null Values: ', df.isna().sum().sum())\n","print('Data types: ', df.dtypes.reset_index().groupby(0).count().reset_index().values.tolist())\n","df.head(5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":392},"id":"yk1xuqm9iV8y","executionInfo":{"status":"ok","timestamp":1668958904505,"user_tz":-330,"elapsed":9,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"1c61620a-93de-454b-bf6a-ee2d5e934908"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Null Values:  141\n","Data types:  [[dtype('float64'), 13], [dtype('O'), 2]]\n"]},{"output_type":"execute_result","data":{"text/plain":["         Date  Average_views_per_viewer  Unique_viewers  Click_rate  \\\n","0  2018-08-21                    1.1538            13.0        7.38   \n","1  2018-08-22                    1.1935            31.0       23.14   \n","2  2018-08-23                    1.3684            19.0       20.65   \n","3  2018-08-24                    1.0000             8.0       13.64   \n","4  2018-08-25                    1.0625            16.0       13.89   \n","\n","   Impressions  Shares  Dislikes  Likes  Average_viewed  Videos_published  \\\n","0        122.0     1.0       0.0    5.0           53.59               1.0   \n","1        121.0     0.0       0.0    4.0           44.58               NaN   \n","2         92.0     0.0       0.0    5.0           38.75               NaN   \n","3         44.0     0.0       0.0    0.0           24.81               NaN   \n","4         72.0     0.0       0.0    0.0           45.50               NaN   \n","\n","   Subscribers  Views  Watch_hours Average_view_sec  Revenue  \n","0          5.0   15.0       0.4666          0:01:51      0.0  \n","1          4.0   37.0       0.9575          0:01:33      0.0  \n","2          2.0   26.0       0.5849          0:01:20      0.0  \n","3          0.0    8.0       0.1152          0:00:51      0.0  \n","4          1.0   17.0       0.4491          0:01:35      0.0  "],"text/html":["\n","  <div id=\"df-6e140851-caec-4754-9025-b37fe68c1627\">\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>Date</th>\n","      <th>Average_views_per_viewer</th>\n","      <th>Unique_viewers</th>\n","      <th>Click_rate</th>\n","      <th>Impressions</th>\n","      <th>Shares</th>\n","  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<td>NaN</td>\n","      <td>4.0</td>\n","      <td>37.0</td>\n","      <td>0.9575</td>\n","      <td>0:01:33</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2018-08-23</td>\n","      <td>1.3684</td>\n","      <td>19.0</td>\n","      <td>20.65</td>\n","      <td>92.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>5.0</td>\n","      <td>38.75</td>\n","      <td>NaN</td>\n","      <td>2.0</td>\n","      <td>26.0</td>\n","      <td>0.5849</td>\n","      <td>0:01:20</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2018-08-24</td>\n","      <td>1.0000</td>\n","      <td>8.0</td>\n","      <td>13.64</td>\n","      <td>44.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>24.81</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>8.0</td>\n","      <td>0.1152</td>\n","      <td>0:00:51</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2018-08-25</td>\n","      <td>1.0625</td>\n","      <td>16.0</td>\n","      <td>13.89</td>\n","      <td>72.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>45.50</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>17.0</td>\n","      <td>0.4491</td>\n","      <td>0:01:35</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e140851-caec-4754-9025-b37fe68c1627')\"\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 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Kaur","userId":"00197144500844905503"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":661},"id":"uvdwssA_lE5D","executionInfo":{"status":"ok","timestamp":1668958910276,"user_tz":-330,"elapsed":6,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"a69745d2-9801-4f88-9060-ebb4a912e971"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           Date  Average_views_per_viewer  Unique_viewers  Click_rate  \\\n","0    2018-08-21                    1.1538            13.0        7.38   \n","1    2018-08-22                    1.1935            31.0       23.14   \n","2    2018-08-23                    1.3684            19.0       20.65   \n","3    2018-08-24                    1.0000             8.0       13.64   \n","4    2018-08-25                    1.0625            16.0       13.89   \n","...         ...                       ...             ...         ...   \n","1127 2021-09-22                    2.2721        105872.0        4.04   \n","1128 2021-09-23                    1.8941        215712.0        5.83   \n","1129 2021-09-24                    2.1319        305881.0        6.08   \n","1130 2021-09-25                    2.3468        152920.0        4.51   \n","1131 2021-09-26                    2.3480        132573.0        4.24   \n","\n","      Impressions  Shares  Dislikes    Likes  Average_viewed  \\\n","0           122.0     1.0       0.0      5.0           53.59   \n","1           121.0     0.0       0.0      4.0           44.58   \n","2            92.0     0.0       0.0      5.0           38.75   \n","3            44.0     0.0       0.0      0.0           24.81   \n","4            72.0     0.0       0.0      0.0           45.50   \n","...           ...     ...       ...      ...             ...   \n","1127    4376635.0   399.0     195.0  10594.0           25.75   \n","1128    4854638.0   764.0  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<tr>\n","      <th>1</th>\n","      <td>2018-08-22</td>\n","      <td>1.1935</td>\n","      <td>31.0</td>\n","      <td>23.14</td>\n","      <td>121.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>44.58</td>\n","      <td>NaN</td>\n","      <td>4.0</td>\n","      <td>37.0</td>\n","      <td>0.9575</td>\n","      <td>93</td>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2018-08-23</td>\n","      <td>1.3684</td>\n","      <td>19.0</td>\n","      <td>20.65</td>\n","      <td>92.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>5.0</td>\n","      <td>38.75</td>\n","      <td>NaN</td>\n","      <td>2.0</td>\n","      <td>26.0</td>\n","      <td>0.5849</td>\n","      <td>80</td>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2018-08-24</td>\n","      <td>1.0000</td>\n","      <td>8.0</td>\n","      <td>13.64</td>\n","      <td>44.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>24.81</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>8.0</td>\n","      <td>0.1152</td>\n","      <td>51</td>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2018-08-25</td>\n","      <td>1.0625</td>\n","      <td>16.0</td>\n","      <td>13.89</td>\n","      <td>72.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>45.50</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>17.0</td>\n","      <td>0.4491</td>\n","      <td>95</td>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1127</th>\n","      <td>2021-09-22</td>\n","      <td>2.2721</td>\n","      <td>105872.0</td>\n","      <td>4.04</td>\n","      <td>4376635.0</td>\n","      <td>399.0</td>\n","      <td>195.0</td>\n","      <td>10594.0</td>\n","      <td>25.75</td>\n","      <td>0.0</td>\n","      <td>1809.0</td>\n","      <td>240554.0</td>\n","      <td>19512.3516</td>\n","      <td>292</td>\n","      <td>126.600</td>\n","    </tr>\n","    <tr>\n","      <th>1128</th>\n","      <td>2021-09-23</td>\n","      <td>1.8941</td>\n","      <td>215712.0</td>\n","      <td>5.83</td>\n","      <td>4854638.0</td>\n","      <td>764.0</td>\n","      <td>312.0</td>\n","      <td>34422.0</td>\n","      <td>30.35</td>\n","      <td>4.0</td>\n","      <td>1732.0</td>\n","      <td>408582.0</td>\n","      <td>19855.7704</td>\n","      <td>174</td>\n","      <td>163.558</td>\n","    </tr>\n","    <tr>\n","      <th>1129</th>\n","      <td>2021-09-24</td>\n","      <td>2.1319</td>\n","      <td>305881.0</td>\n","      <td>6.08</td>\n","      <td>7768271.0</td>\n","      <td>1055.0</td>\n","      <td>521.0</td>\n","      <td>56677.0</td>\n","      <td>47.81</td>\n","      <td>0.0</td>\n","      <td>3284.0</td>\n","      <td>652105.0</td>\n","      <td>54463.2654</td>\n","      <td>300</td>\n","      <td>397.066</td>\n","    </tr>\n","    <tr>\n","      <th>1130</th>\n","      <td>2021-09-25</td>\n","      <td>2.3468</td>\n","      <td>152920.0</td>\n","      <td>4.51</td>\n","      <td>5788014.0</td>\n","      <td>633.0</td>\n","      <td>276.0</td>\n","      <td>18458.0</td>\n","      <td>41.24</td>\n","      <td>0.0</td>\n","      <td>2443.0</td>\n","      <td>358868.0</td>\n","      <td>26757.4077</td>\n","      <td>268</td>\n","      <td>178.664</td>\n","    </tr>\n","    <tr>\n","      <th>1131</th>\n","      <td>2021-09-26</td>\n","      <td>2.3480</td>\n","      <td>132573.0</td>\n","      <td>4.24</td>\n","      <td>5375782.0</td>\n","      <td>555.0</td>\n","      <td>191.0</td>\n","      <td>14857.0</td>\n","      <td>39.27</td>\n","      <td>0.0</td>\n","      <td>2152.0</td>\n","      <td>311277.0</td>\n","      <td>22117.2441</td>\n","      <td>255</td>\n","      <td>135.841</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1132 rows × 15 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-326b2f2f-17d4-46f8-889d-d84df8097bd2')\"\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 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object"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["df.isna().sum().reset_index().style.highlight_min()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":520},"id":"zvb6IiGtwSo7","executionInfo":{"status":"ok","timestamp":1668958913750,"user_tz":-330,"elapsed":8,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"9c0580c9-47af-4d4d-9a9d-c6f0cb5019d3"},"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7fc0cc815410>"],"text/html":["<style type=\"text/css\">\n","#T_c3070_row0_col1, #T_c3070_row1_col1, #T_c3070_row2_col1, #T_c3070_row3_col1, #T_c3070_row4_col1, #T_c3070_row5_col1, #T_c3070_row6_col1, #T_c3070_row7_col1, #T_c3070_row8_col1, #T_c3070_row10_col1, #T_c3070_row11_col1, #T_c3070_row12_col1, #T_c3070_row13_col0, #T_c3070_row13_col1, #T_c3070_row14_col1 {\n","  background-color: yellow;\n","}\n","</style>\n","<table id=\"T_c3070_\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th class=\"col_heading level0 col0\" >index</th>\n","      <th class=\"col_heading level0 col1\" >0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_c3070_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_c3070_row0_col0\" class=\"data row0 col0\" >Date</td>\n","      <td id=\"T_c3070_row0_col1\" class=\"data row0 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n","      <td id=\"T_c3070_row1_col0\" class=\"data row1 col0\" >Average_views_per_viewer</td>\n","      <td id=\"T_c3070_row1_col1\" class=\"data row1 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n","      <td id=\"T_c3070_row2_col0\" class=\"data row2 col0\" >Unique_viewers</td>\n","      <td id=\"T_c3070_row2_col1\" class=\"data row2 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n","      <td id=\"T_c3070_row3_col0\" class=\"data row3 col0\" >Click_rate</td>\n","      <td id=\"T_c3070_row3_col1\" class=\"data row3 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n","      <td id=\"T_c3070_row4_col0\" class=\"data row4 col0\" >Impressions</td>\n","      <td id=\"T_c3070_row4_col1\" class=\"data row4 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n","      <td id=\"T_c3070_row5_col0\" class=\"data row5 col0\" >Shares</td>\n","      <td id=\"T_c3070_row5_col1\" class=\"data row5 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n","      <td id=\"T_c3070_row6_col0\" class=\"data row6 col0\" >Dislikes</td>\n","      <td id=\"T_c3070_row6_col1\" class=\"data row6 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n","      <td id=\"T_c3070_row7_col0\" class=\"data row7 col0\" >Likes</td>\n","      <td id=\"T_c3070_row7_col1\" class=\"data row7 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n","      <td id=\"T_c3070_row8_col0\" class=\"data row8 col0\" >Average_viewed</td>\n","      <td id=\"T_c3070_row8_col1\" class=\"data row8 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n","      <td id=\"T_c3070_row9_col0\" class=\"data row9 col0\" >Videos_published</td>\n","      <td id=\"T_c3070_row9_col1\" class=\"data row9 col1\" >141</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n","      <td id=\"T_c3070_row10_col0\" class=\"data row10 col0\" >Subscribers</td>\n","      <td id=\"T_c3070_row10_col1\" class=\"data row10 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n","      <td id=\"T_c3070_row11_col0\" class=\"data row11 col0\" >Views</td>\n","      <td id=\"T_c3070_row11_col1\" class=\"data row11 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n","      <td id=\"T_c3070_row12_col0\" class=\"data row12 col0\" >Watch_hours</td>\n","      <td id=\"T_c3070_row12_col1\" class=\"data row12 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n","      <td id=\"T_c3070_row13_col0\" class=\"data row13 col0\" >Average_view_sec</td>\n","      <td id=\"T_c3070_row13_col1\" class=\"data row13 col1\" >0</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_c3070_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n","      <td id=\"T_c3070_row14_col0\" class=\"data row14 col0\" >Revenue</td>\n","      <td id=\"T_c3070_row14_col1\" class=\"data row14 col1\" >0</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["df.Videos_published.value_counts(dropna=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jYAbyriSxhWB","executionInfo":{"status":"ok","timestamp":1668958915388,"user_tz":-330,"elapsed":3,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"988ae90a-e19f-487e-c7ec-5be04478d8c8"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.0    854\n","NaN    141\n","1.0    113\n","2.0     17\n","3.0      4\n","4.0      3\n","Name: Videos_published, dtype: int64"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["df.Videos_published = df.Videos_published.fillna(0.0, axis=0)\n","df.isna().sum().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JuKgU84nxsvp","executionInfo":{"status":"ok","timestamp":1668958917231,"user_tz":-330,"elapsed":8,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"e0026687-3d45-4414-8500-717d38a04939"},"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["df.columns"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"v9aKoIufx2mQ","executionInfo":{"status":"ok","timestamp":1668958919027,"user_tz":-330,"elapsed":7,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"69968ae7-bf8c-43fe-afe6-3e8503981240"},"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['Date', 'Average_views_per_viewer', 'Unique_viewers', 'Click_rate',\n","       'Impressions', 'Shares', 'Dislikes', 'Likes', 'Average_viewed',\n","       'Videos_published', 'Subscribers', 'Views', 'Watch_hours',\n","       'Average_view_sec', 'Revenue'],\n","      dtype='object')"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["df.head(2)"],"metadata":{"id":"9ecG_u0Fx7SD","executionInfo":{"status":"ok","timestamp":1668958920962,"user_tz":-330,"elapsed":11,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"1934a14e-be4c-405a-923a-86ac00323a8a","colab":{"base_uri":"https://localhost:8080/","height":211}},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Date  Average_views_per_viewer  Unique_viewers  Click_rate  \\\n","0 2018-08-21                    1.1538            13.0        7.38   \n","1 2018-08-22                    1.1935            31.0       23.14   \n","\n","   Impressions  Shares  Dislikes  Likes  Average_viewed  Videos_published  \\\n","0        122.0     1.0       0.0    5.0           53.59               1.0   \n","1        121.0     0.0       0.0    4.0           44.58               0.0   \n","\n","   Subscribers  Views  Watch_hours  Average_view_sec  Revenue  \n","0          5.0   15.0       0.4666               111      0.0  \n","1          4.0   37.0       0.9575                93      0.0  "],"text/html":["\n","  <div id=\"df-4e3c8749-7816-4a1c-a849-cc0fcada5529\">\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","        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    <td>0.4666</td>\n","      <td>111</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2018-08-22</td>\n","      <td>1.1935</td>\n","      <td>31.0</td>\n","      <td>23.14</td>\n","      <td>121.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>44.58</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>37.0</td>\n","      <td>0.9575</td>\n","      <td>93</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e3c8749-7816-4a1c-a849-cc0fcada5529')\"\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 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color='c')\n","plt.title('YouTube Revenue by Date', fontsize=20)\n","plt.ylabel('Revenue (USD, $)')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":412},"id":"NB7jir1jTbUS","executionInfo":{"status":"ok","timestamp":1668958923302,"user_tz":-330,"elapsed":8,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"e6a1eb58-e747-432b-b95f-2dbb3cfc8e3c"},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x432 with 1 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fy2XMTmFTNKGS5XKdkHHRUymsWbNm6fjjj9e4ceOUmZkZ9titW7dq7dq1OuaYY3T77bdLkq666iq98847+vLLL+XxeNS9e3fdcMMNkqRevXrpzDPP1K233qqEhAT98Ic/ZOU3AAAAAADag8YadRuXip3gYCdsg+xwq79FUAUUcoxzIGUiWYnO6TpOlUpBq951BE0OlU466STt27dP9957r+6///6wx55wwglasWJFyPZTTz3V9ZxLL71Ul156aVOHBwAAAAAAWkPYUCloOpllhYZIVgSVSk4VRA3XMcbIbc0412omtwojY0UWUoVcn0olqRmh0hVXXNGS4wAAAAAAAPEgXKjk9QZ+N4d6I5ngwCdcmOMYKjncL/RE5zH6twdttozkiSIACjMFzwT0VIr8kvEsolCpvLxcn3/+ucrLy5WZmal+/fo1OuUNAAAAAAAcjsKESsGVP8Y6dHxwo+5oK5VcA6Og85yOCb63/ZqtUanUQRp1Nxoq/fnPf9YLL7wgy7LUuXNnlZWVKTExURdffDHVSgAAAAAAdDTBYVCj09+CprtF0qjbaVpapNPmfOcHbA/aH3DNKAKg4Gdwurf9foe5sKHSu+++q7///e+aOnWqTjvtNCUmJsrr9Wr9+vV66qmnlJeXp6FDh8ZqrAAAAAAAoL0xjU1/OxTEGHsFUthwSM2rVNrxlcyWj+Q58TuBYwxpoB1lpZKPY6Nu+88do1Ip7JJqb7zxhq677joVFhYqMTFRkpSYmKjCwkJdd911WrlyZUwGCQAAAAAA2omQCqLgSiXLeV/wlLawPZWs0OAmitXfzLpVspYttG0IM/ZoVmpznUZnv0nwz4evsKHSl19+6bpC26BBg/TVV1+1yqAAAAAAAEA7FVLsYw+RvKHBkT2ICZ4aF+4eIVPYoqhUkqS6WocxmqDDnXo3hRGuWXjAsxEqqa6uzrUhd2Zmpurq6lplUAAAAAAAoJ1yq1TyeBoqf+whk7H1QjIKaert+3Hft7Keni/jyxnsDb6Dj29s1Tin6/vDIIfqJ8vrfr2Q60fYU6mDVCqF7alkjNHevXsPLfvnsB8AAAAAAHQgwVGALyhKTAoNlYwJDINcpsaZrf+Ueesf8px3qdSjZ/09QgIg33kRVio5BViOq8J53K8Xcv2g67ndu4PEJWFDperqak2dOjVWYwEAAAAAAO2dW6VScrLz6m/+80z4fdKhwMhYkgmaXBWuSujQQeGv77SiXBSZUsSVSh2kUXfYUOmPf/xjrMYBAAAAAADiQdhKpeDV32yVS/aqJd/34GsY69CsqJCeSnLeHnBMY6GSw+pvUTXq9l3b6ZyOV6kUtqeSk4qKCm3fvl3V1dWtMR4AAAAAANCeuVUqJTlUKtmrkyxvYBjjVqkULgBy3G4/JMpQyTLRVRWF6+tEpVKgF198UUceeaTOOOMMSdLmzZv10EMPqbq6WhkZGZo+fbr69+8fk4ECAAAAAID2wKWBdmJiQ6gU1DfJHug0Gvp4A443xsjjaZifZrlUMAWMxSXYcQmVTNThT5hgKzhM6wDCViqtXr1avXr18n9ftGiRvv/972vJkiUaN26cli9f3uoDBAAAAAAA7Ujw1K+Ankre0GAnIFRybtTt326ZMOFMUN+lRscZQU8lE7QiXaPXNA2nNVKp1EGEDZVKSkrUs2dPSdKePXv07bff6pJLLlFaWpouuOACff311zEZJAAAAAAAaC9cQqXUTlJNjfvqbyFNvL2Bx0mBIVTwvVynxSn0GPu47NcJWf0tyulv/mCrkXM6yPS3sKFSamqqKisrJUn/+c9/lJ+fr7S0NEmSx+OR1+sNdzoAAAAAADjchAQzDQFKWiepptph+pvtPNdG3fYqJIft9uOb0qjbslVCBY89mkqlcMFWQJgW+SXjWdhQadCgQfrd736nDRs26OWXX/b3VpKkr776Sjk5Oa0+QAAAAAAA0I6ENLu2hUreOqmuznasrceSCVppzXH6m9c5SJJtylmTKpX8B4QeH820tUiqpSQqlSRpwoQJSklJ0fLly3Xcccdp7Nix/n2bN2/Wd7/73VYfIAAAAAAAaEdcQiVPWqf671WVgfvcpra5NtJ2Oybwfo2OzVgywdVNjtPfzKHjGuNfpa6x1d8iu1y8C7v6W3p6uqZMmeK477LLLmuVAQEAAAAAgHYsXKWSJFN1MPBYexDj1qjbPz3Ncg6bJFswFM10NUvyJLoHUpYVeFyj1wu3+ptDD6fDXNhQadWqVaEnJCUpNzdX/fv3V1JS2NMBAAAAAMDhxrWnUnr9Z3XVoX0BlUrBK7s5TH8LniLnWKkUbvqbQ2iUkOhYqRRQnWQZ17lc5uMPpczO8hzbP3xfJ/uwIl2hLs6FTYXeeuutkG11dXUqKipSUlKSpk+frry8vFYbHAAAAAAAaGeCQiUTVKmkgEolW6hkWc7VSfZrWl65Nup2W8EtzNgOVSLZxuN0bJhrWvNmSZISn3ipkZ5KLqvWHcbChkozZsxw3ffiiy9qyZIluuuuu1p8UAAAAAAAoJ1qZPpbQKhkGblWKjkFTCGNs6Nd/S3ouxU0Zc64HBzpCnAR91SiUXdYY8eO1bZt21pyLAAAAAAAoL1rLFSyT38zJnDqmVuoFNB3yaWCKKKV16KoVHKbZhdWuJ5KHa9Rd5NDpbq6OiUkNPl0AAAAAAAQj1x6KnkcK5WCV39rZPpbSPAUbaWSS78np9XfIpz+FsAKM4amXC/ONSkVqq6u1rPPPqsTTjihpccDAAAAAADaNbdKpYZG3W49lUIadTtUCoVUKjlVMzWhp5JjhVETpr8pgjE4jeMwFban0k9+8pOQbV6vVwcOHFC/fv10yy23tNrAAAAAAABAOxQcwNTV1n+mZ9R/1tYc2mcPkiwrIIwxliWP/bj6jY0HT01p1G2ZwO/2bfZrNyZctVTA74VQSVOnTg3ZlpCQoNzcXOXm5rbaoAAAAAAAQJwo21//md09dF/Y6W9e28/2nkr26iT7zw7bGhNUqWTkEiRFWqlkRRBsSY1XMh0mwoZKJ510UqzGAQAAAAAA4oEtjDHGSAdKpczOUqcM52ONrVrIdWqbvZm37XynaqKmVCo5NvluSg+kRhp1ezwN+zpGqOTaU2nJkiUqLS0Ne3JpaamWLFnS4oMCAAAAAADtVEDoY8mUlUqdu0opKaGH2kMi+0pwkmMTbhNSqdTIKmuN7fNVQ/krodyagEdaqeQwjc5+Dd+CZhH3aIpvrpVKPXv21J133qmjjz5aJ554onr27KlOnTrp4MGD2r17t/79739r165duvTSSxu9SVFRkRYsWKDS0lJ5PB6NGjVK559/vsrLy/Xwww/r22+/Vffu3XXLLbcoMzNTxhgtWrRImzZtUmpqqqZMmaI+ffq06IMDAAAAAIAmCK4w2l8iZXWVx+ORUlKlmmopMVHyegOns4X0S3KYfmasoCIfh2qicIGN2+pv/gojl8Aq2kbdjqu/SfIkSPKG7jtMuYZK5557rkaMGKENGzZo06ZNWr9+vSorK5WRkaFjjjlG5557rgYPHqzExMRGb5KYmKhrr71Wffr00cGDBzV9+nQNHDhQa9as0SmnnKKLL75Yf/nLX/SXv/xF11xzjTZt2qQ9e/Zo3rx5+uyzz/Tkk0/qN7/5TYs+OAAAAAAAiIw5sF8qLpInv29IpZLKSuU5tn/999S0+lApoSFUCl79zXIJcgJWf3PpdeQ4hS1kpIFfLSvws7nT38KuQGekBE9014tzYXsqJSUlqbCwUIWFhc26Sbdu3dStWzdJUqdOnZSXl6fi4mKtX79eM2fOlCQNGzZMM2fO1DXXXKMNGzboe9/7njwej/r376+KigqVlJT4rwEAAAAAAGLH/OMvMu++ocQHnw6t9inbL2V1rf+eklr/mZgo1Sqwj1JIo26XwMh19bcwVUJOxwffI3i/W8AVTrgxGCN5Ep3HcZhy7anUWvbu3avt27erX79+2r9/vz8o6tq1q/bvr+8YX1xcHLC6XE5OjoqLi2M9VAAAAAAAIEnVVfX/pMBioIOVUvVBh1CpoYbFXqkUtlG3ffU3t1ApqOrIiWujbodKJbdV5sIJVy1lzKGeSh0kVApbqdTSqqqq9OCDD2rixIlKT08P2OfxeOrnX0Zh5cqVWrlypSRp9uzZAUFUR5CUlNThnjne8c7iC+8rPvHe4g/vLP7wzuIT7y3+8M7iy+H+vspSU3XQWMrNzVVV50ztb9je1WNULKlzXi91ys3VvowM1UlKSEqSJalzRoYOqKHdkIy6ZGWppOHczPR0pTf8zspSU3Ww4fjkrl21r+GYbl27KKnhmOKkZNVKSk1NVReX3/W3CQmyx0Ndu2QpOTdX5Z3SVSEpJTlJ3RrO9SYlqMh3ny5d/fcJ9t+Gz9zcXJWmpKhaUmJCQsj7LkpMlJWYKCOpS1aWUg7jvwefmIVKdXV1evDBB3X22WfrjDPOkCR16dLFP62tpKREWVlZkqTs7GwVFRX5z923b5+ys7NDrjlq1CiNGjXK/91+TkeQm5vb4Z453vHO4gvvKz7x3uIP7yz+8M7iE+8t/vDO4svh/r6sykrJa6moqEimbL9/e+mX2yRJ5Z4kVRQVyZtQP/3L8tRX7Bwo21+/opsk47W0v+TQLKTysjJVNvzOrMpK//Ee20ylkuJieVIzJEnemmpJUvXBStffteUNbJJdWlwsT+ciWRXlkqSa6mr/uabUfp998qQGFr8EKyoqklVVX63lrasNGYO3ttb/8/79pfIcJn8PPXv2dN0Xk+lvxhgtXLhQeXl5GjdunH/7kCFD9Oabb0qS3nzzTZ122mn+7WvXrpUxRp9++qnS09PppwQAAAAAQFsxlq3p9aGpXaasoe6oc32RiFI71X/6FvWyjPvqb06NuoN7Kjk1xHZsku27jtv0N4f9jd0n3PVdG3Uz/c3Rzp07tW7dOpWWlur666/Xzp07VVdXp/z8/EbP3bp1q9auXatjjjlGt99+uyTpqquu0sUXX6yHH35Yq1atUvfu3XXLLbdIkgYNGqSNGzdq2rRpSklJ0ZQpU5r4eAAAAAAAoNlMfThkjFFAU6XKivrP9PpqIk9mVv3eRFvD6oDV31z6GNl7KgU0bXJq1N2UUMmpp5JLf6UwTNhG3SJUcrJu3To99dRTOv300/XOO+/o+uuvV1VVlZ599lndfffdjZ5/wgknaMWKFY777rnnnpBtHo9H119/fSRDAwAAAAAArc2t2qjiQP1nWsPUsa4Ns4wSfJVKlvu5llNgZAVtd1ohrimNuh32u1VNhWUPv4J3WYRKTlasWKFf/OIX6t27t9atWydJys/P15dfftmaYwMAAAAAAG3AVJRLnTrJk2CbxiaFrs5WWd+rSJ3qK5WU1RAqVVU2XMgKDHQCghxb/yPLXknUWPAULrBprFLJcj420hDICjMGYyRPxwqVIuqptH///pBpbk1ZrQ0AAAAAALRvprpK1s1Xy/zxKdvGQ6GSCahUKpeSkuVJTq7/3qVhka3yskPnGVsvJhPB9Den7fafw05/C/ruD64ceiG5TcULq5ExdLBKpYhCpT59+mjt2rUB29555x3169evVQYFAAAAAADaSMMqa+YDWw7gb9IdWKlkKsulTodWTfN06Vr/Q11d6PHhGnXbK4oam/4WbqpacDhkD7TcrhfmmiY4HArbU8lWqRSSbh2eIpr+NmnSJP3qV7/SqlWrVF1drV//+tfatWuXfvGLX7T2+AAAAAAAQCz5ZiU5NbIOnv5WUX5o6pt0qFLJfp79eK9typvb9d2mpTUcExL0BNzPZfqbY8gTwfS3kJCqkelvHaxSKaJQKS8vT3PnztWHH36owYMHKycnR4MHD1ZaWlprjw8AAAAAALQFy6GqJzgkqiyX0jMPfe/SLfAakfRRCrh+8PH2Y3yfkU5VU1CvpqDruVVEBZzvUqnk2KjbOAdyh7GIQiVJSk1N1dChQ1tzLAAAAAAAoK0FN7eWZMJVKuX08H/1pHUKvZYxUmKi5PXKeB2mvNXfwPn6diYoIHI8xq1Rt0OFUSSrv7lOf3OpfKJSKdQ999zj2pR71qxZLTogAAAAAADQhvxBjG2bLyTxekMrlWw9lULU1dZ/JtSHSq6VSvYgy2lanF2TVn9zCoMimP4WHDaFC7YsQiVHI0eODPheWlqq1atX6+yzz26VQQEAAAAAgDbiD2AcAiCHSiKPvaeSJM8Pb5Uno7OshfdJVVX1GxMS6z+9zqGScatUshyCn3CNukOmqwWHSi7T31wrlWxjtKxDOZTj8bZG3YRKhwwfPjxkW2FhoR577DFdfvnlLT0mAAAAAAAQI+ZAmbR3lzx9T6jf4A+QHAIdE9RIWwqpVEooHF7/Q3KqVNMQKiU2hC32SqWAldhs4Y/bFLVwK68dOijwm2XJE3Cu28pyEUx/syzbOJ0qqESlUqSys7P11VdfteRYAAAAAABAjFkP3Cnt/kaJT7zUsKEh+HHqOWRZoVU6btPfUlKlal+lkkOoFElPJeN0TLieSkHfg/tDuU1/c7tmQGWTy8p19mN9z+m42tzhJ6JQadWqVQHfa2pq9P7776t///6tMigAAAAAABAju7+RVD8FzePxOFcEhZt65hYqJafIVFfX/+w0/c3p+iE9lZpXqRTSH8p1ap3b6m9BfZ8cmpgHjM8fnkWxQl0ciyhUeuuttwK+p6am6vjjj9fYsWNbZVAAAAAAACDGLKt+lbbg5tbSoRDFPgXMJ6inkl+KbfpbUnL9Z21t4P2Cfw6+vmPwFKYKKLjiKDgEcqqOcjrP8RiXfkx2ySn1p9XVyXm5s8NLRKHSjBkzWnscAAAAAACgLXnr6kMlp/AmIPQJPM3jOv0t5dD0t9S0+k9fyBR8ffs9XWaohV15zfEE2abyOdxTEVQqBU9/CzcGYx0Kz+pqQ/cfhiLuqVRZWaldu3apqqoqYPvJJ5/c4oMCAAAAAAAx5pua5jR1y74iXHAAk9bJ+XopqVJ52aGfJck3HU5y7q8UrlIp3LbgcQYf6z/HpTrJbbpa8DG+726NulPqK5UIlWzWrFmjp556SmlpaUrx/YIkeTwezZ8/v9UGBwAAeeiOAgAAIABJREFUAAAAYsRf1eMN3RfQSDtoX6pLqJRsq1TyZQk1DaGSJyFoKloEq785rUoXOtDAr8FhkVsfJbfqp4D7ew9d3yV48ySn1B9BqHTI8uXLdeutt2rQoEGtPR4AAAAAANAWvHX1n06hTbieSr6pbUE8KakyvhDJd4wvZEoMDpV8YY238V5H4SqV3HoqOTXYdltlzul838+2c/yNze3Xa+ippNo69zEeRhIaP0SyLEvf+c53WnssAAAAAAAg1nzBSJ2vUskhYLHCVCqlOYdKAT2VfNPffCFTYpJzqGSMXHsdRdKo27VSyeFce0DkNv3NuIdKob8nY2tIXhNmjIePiEKliy66SM8995ysDrIkHgAAAAAAHYYvVPJNf4u6UilMT6WGHkqehlDJX7mUnCzjtVXz2K9vuUx/iyRUCt4VPGXObWpdpNPfwlVRGVPf6Dwxkelvdn/9619VWlqql156SZmZmQH7/u///q9VBgYAAAAAAGLAkyDJsjXqduip5A9nbH2FfFymvyk59VBYlBI0/S0lTaqxVfPYQx+3aiB7XydXLo26G6lUcr1m2Eql4HupPqBLSiZUsps6dWprjwMAAAAAALQyY4ysR+9Vwojz5TllSP1GX6WSr3LIcWUzW6ATXKHjm9oWzL49ePpbalrgFDHjElpFW6nk1lPJ6dxm9lQKOcdYhEpOTjrppNYeBwAAAAAAaG2WJX28QSa/36FQKcEXKoWb/mavEgrc70lw6axjWz3eHyrZeyz5AqaA6xv36W/+aqMoKpVCQiXL+dimTH8LOcdI8oVKHaNRd0ShUm1trf785z/rnXfe0YEDB7RkyRJ99NFH2r17t84777zWHiMAAAAAAGgJARVBDTwJgdscK5XsPZXCNcq2sVcqpTpUKlWW265vD32CVljzfQkOiJwE7wtXqRRcheTECjo+YDpecKWSJI+kpCSptmNUKkXUqHvJkiX65ptvNG3aNP9yeb169dI//vGPVh0cAAAAAABoQZYV+ClJvkojX3WNU8Bi2UKfSEOlZIdKJXuoZK9UsjfUDu5jFMzl/sZpe3Co5LTinORe/RTSUyn02t4Fv5F34ez6319SspTM9LcAH3zwgebNm6e0tDR/qJSdna3i4uJWHRwAAAAAAGhBllOlUtD0t9aoVAqa/uZJSZWxN+q2VyoFzHhzCH5cm2pHECoFnmA7LoLpb16vc+Pwze8d2pacIiWlyNj7RR3GIqpUSkpKkhX00srKytS5c+dWGRQAAAAAAGgFviDEG2b6m2OlUvShksfeUykxqf6fr6dSWlCj7oDru1UT+T7deio59YLyNlzGFy7ZK4+ibNQdfIxTEJXcsSqVIgqVCgsLNX/+fO3du1eSVFJSoqeeekpDhw5t1cEBAAAAAIAW5DT9LaRSqZFG3U2pVPKovnG379yUNKnW3qjbarhNmBXW7M28nTht9lcq+b67NNp2vabt/l4rfMgkScmp9T2VOkij7ohCpauvvlo9evTQz372M1VWVmratGnq1q2brrjiitYeHwAAAAAAaClhp7+F6anUEMCYaEIle08lT0J9vyGf1DSppuZQHyR7YOS2wprjCm6hYwxgBVUoOa0mJ/krmsJe0/K67/NJTm5Y/a1jVCpF1FMpKSlJEydO1MSJE/3T3ny9lQAAAAAAQJxw6kvka9QdUU8lbxMrlTzOq8HV1daHT7bAyLiFSjIO2xS63y64Uslt+ptbpVJwZVK41d+khp5KyVJFeei+w1BElUpz5szRunXrVFtbq6ysLAIlAAAAAADiUbhKpUh6KgVPT7MHRcGCQ6WA1eDS6j99zbrdGoE7TVFzC5UiqlSyH+/Su0kux3iDV38LPceTnNKheipFVKl00kkn6aWXXtLChQt12mmn6ayzztLAgQOVkBBRJqXHHntMGzduVJcuXfTggw9KklasWKE33nhDWVlZkqSrrrpKp556qiTphRde0KpVq5SQkKBJkyapoKCgKc8GAAAAAADs/I26QyuVTF2dPFLY6W/1wYo9VEoJPda/LzhUCpr+Jkk11VJGpi0wCtNTSU2Y/hZ8rNP1JPcV5aygYxoLopJT5ElKliFUOmTcuHEaN26cdu/erbfffltLlixReXm5zjzzTE2ePLnR84cPH67zzjtPCxYsCNg+duxYXXjhhQHbduzYoXfffVcPPfSQSkpKdO+99+qRRx6JOMACAAAAAAAunBp1K7BRtwkXzlheBYQx4SqVOnWy3SLh0LH2qXC+Zt3+6xvn6qTgY5yEa9Ttr1hq5Noh1wzquxSux5J0aPW32o4RKkWV1Bx11FG64oordNNNNyk/P1+vvfZaROeddNJJyszMjOjY9evXa+jQoUpOTlaPHj105JFH6vPPP49mmAAAAAAAwIl/iltk09+MvYH2/2fvzePkqOv8/+enunt67rMzue8E5AhnUAQlKFlWVhQExRuWn4qIrIIrK17AKiIICAIKX7zdBUVUgou7ihEFBJEQ7gTJfR9z333X5/fHp6qruqZ6MkkmmZnM+/l48Oiuqk9VfT5dE9J5zev9eoMRX/xiTEOi9L2qa4tv4Za/KYVyHU6F8rdSTiX/+5B9RQT2W9ZgB1Ipp9Gwy9/2cI6bqSROpWJ27drFU089xVNPPUVPTw8nn3wy733ve/fr5n/4wx944oknmDdvHhdeeCHV1dV0dHSwcOHCwpjGxkY6OjpCz1++fDnLly8H4MYbbySRGOKH+RAkGo1OuDWPd+SZjS/keY1P5LmNP+SZjT/kmY1P5LmNP+SZjS/Gy/PKZVO0A2XRKPXOfNtiMfJAdUUFlYkEyapKepzxiYYGVDRKq1LYQHVlJfZAOf2RCDUfu4L4m99GpL6x5P12O681dXUkq6rIAFgRapsSdAH1lZXEEonC9WORCBVVVYX7V1dVUunM072WpQj9rO1kP63+HbEy4mUx6hIJOqNRc2+tC+emaqrpdoZWlpdTHXLNdG0tXe4aqqvotSxsKwJ2nob6eqKJRGFeAPXNzaRqaknmc+Pi52F/GZao9MUvfpEdO3awePFiPvrRj3LMMccQiUT268ZnnnlmQZR64IEH+NnPfsZll122V9dYunQpS5cuLWy3tbXt15zGG4lEYsKtebwjz2x8Ic9rfCLPbfwhz2z8Ic9sfCLPbfwhz2x8MV6el25vByCdShbmm3ecR33dXQy0tWH39BTGt+3ejYrHsR3nTV9PDwz0A4qBk5YwkLNhGOvu7e3D1o4jSil6kikAulpbUI3N2PkcANlMhlxvb+G8vt5eBtzrO64jO58P/ax1cqB4RyRCesCsM59xy+x04Vy721vnQH8fqbBrdnYW3vd0daFzOaNq2dDZ3g6x8qLxXX0D6GwOncmMi5+H4TBt2rSSx4YlKr3rXe9i8eLFlA0VwLWX1NfXF96fccYZ3HTTTYBxJrU7P+QAHR0dNDaWVj0FQRAEQRAEQRAEQRgmbjlX3lf+Zg3R/S2fA+JeqZftdEAbblf4mjro7S6UvGkIz1TyZx+VDMPey6DuSHRwllKpEPDhlL/ZtplDrAxyOchniz9HMMHlTvc3rTVquJ/TOGVYmUqnnHIK6XSaJ554gocffhgwYo9f/NlbOn1q37PPPsvMmTMBWLx4MU8//TTZbJaWlhZ27tzJggUL9vk+giAIgiAIgiAIgiA4uHlIRZlKjjTgCiRhYdRFopI9fFGp1jGUKOXLVLK89z4HkXkdIrfIDuQ7DSKwPxrzRCGfqKTDBKZS3d+CY2wNZW7nuowjuvmIxcx9YfCxQ5BhOZVWr17Nrbfeyrx583j99dc555xz2LVrF7/97W+5+uqr93j+7bffzurVq+nt7eXSSy/lggsuYNWqVWzatAmlFJMmTeKSSy4BYObMmbz5zW/mc5/7HJZl8bGPfUw6vwmCIAiCIAiCIAjCSKBD3EiuQJTLDT5WEJps73VvnUruPVwhyVLG0QPoTMb0nityKpXqyhYiBvkJ7o9G0UFRzH2v1BD38WEHBDatC3MnGyIqRcs8USmb9d4fogxLVPrJT37CFVdcwaJFi7j44osBWLBgAevXrx/WTa644opB+97+9reXHH/eeedx3nnnDevagiAIgiAIgiAIgiAME7944+IKRPlS5W/ss1NJ1dShcfKO3JI3/OVvwe5vmiLHUZhraX/K34reD8eplA+M0RD3OZVyAVHJLX+DCdEBblgWoNbWVhYtWlS0LxqNkg/WDgqCIAiCIAiCIAiCMHYpiEMh/563A64k8AlN+5ip5Ja/9XZ771MDEHNEpUxAVHJLzApz0s7h4QhAge3oHkQlu4R45ccO3Ne2i/Og/KKSUkbI8juVDnGGJSrNmDGDF198sWjfK6+8wqxZsw7IpARBEARBEARBEARBOACEOZXc96FOJVdo8h3bm0ylRLN3Xl2Dcy1/CZmbqeQvrwspSxtOqHZQVbIivrWEOZ5C8poGXTIkX8oRlXQwUykWM8Hc0YnjVBpW+dtHP/pRbrrpJo4//ngymQz33nsvK1eu5KqrrjrQ8xMEQRAEQRAEQRAEYaQICkjg6wgXKHXzj/M7lWD45W+nvxNyOdTb3wkb1njSTiGoO8SpFCoA+S5asvwtsN+yvH12iFBF2L4hrukKam75WzBTyXVfTaDyt2GJSocddhg333wzTz75JOXl5SQSCW644QaampoO9PwEQRAEQRAEQRAEYUKie7pg51bU4Yv2PHjYFw1zKgUEo6Jjwe5veef4MEWlaBT1jvPNJeobvf2WZcrT3O5vts89FFaWVqojnJ/gbssa2qkU5tYKXjJYdqdBlcXN1bKBTCVHTFLRmDk+AUSlYbdVa2xs5JxzzuHjH/845557Lv39/Xz7298+kHMTBEEQBEEQBEEQhAmL/sv/Yd9+3V6dk//W1dh/+3PpAQXhaAinUmhQt19w0qaD297ilr+5lJV7olLw+oW5BUQlZQ2vVA2KRaXQkrrhZCoFBDZtF4eM+x1frvtqAmUqDelUSqfTPPTQQ2zatImpU6fyvve9j97eXn72s5/x8ssvs2TJkoM1T0EQBEEQBEEQBEGYWGRSkMui7TzKigzvnPX/gLmHAW8LPx5W/hbcFxbUXdT9TTNcp5IfVV5RvKO83KzRf32tA/lJAadRLAaZNDqbRbllZt5gc5/zLkIdcxL2/fd44plfHNKD3gwR/h0sf8MTlTKZYjeSKyrNWYD1mWthyozwax5CDCkq/fCHP2Tjxo0ce+yxvPjii2zZsoUdO3awZMkSPvnJT1JbW3uw5ikIgiAIgiAIgiAIE4u8I2jkclC2Z1FJa22Ej6E6tRfEmxDhJhfmVMqb6wZFpeEGdQ9FWTmkXFHJ56Aqyk8KdGpraoadW6GjFSZPK76eO6aqCjV9lnEMJfsH33fInKUAwe5v2oZIxHR5y6YDmUpGVFI1dbDoxPDrHWIMKSq99NJLfOtb36Kuro6zzjqLyy67jOuuu44jjjjiYM1PEARBEARBEARBECYmrssml/PcMUOOd8WhIcquhnIqBfOTwIgmQQFqpESleDk6nSqeQ9CpVBB1nNfEZCMqte8eLCoV1ChnbrEY9GaLr+/ew/9aVlb6Mwt2f9OYtZeVGadSUflb0Dl16DNkplIqlaKurg6ApqYmysvLRVASBEEQBEEQBEEQhIOBK/L43TDDGj+UUykkjNsVTvIhpWL5fMCtk3dEpWFHNBcT9XlbyisgnSyeQ6G8LjBf51VNmmI223YPvnZBUzKikoqVhecaBYWqiip0Khk+XzsgvmnbXD9WFhLUXRZ+jUOYIZ1K+XyeV199tWhfcPvoo48e+VkJgiAIgiAIgiAIwkQn73MqjdT40A5v5r0OC+p2RST/WK33JVIJAOtbP4HUgNmIl0NXR3F5nf+9uw2eYNSYMOVnbS0hV3fDvH1OpWzGORRS7ueKSxWVUEpUCq4d7YlKmQzkQzKVJhBDikp1dXXcfffdhe3q6uqibaUUd91114GbnSAIgiAIgiAIgiBMVArlb6XL2fT2zejN67BOOWN4zqahur/Zxa4gc618SFj1vjuVVE0t1Jh8ZuWWv4WJVoW5BTKgrAg0ToL2EFFJB0UlX1lbUXe3wDUrqiA5ED7hYPmb7YSUu06lCV7+NqSo9N3vfvdgzUMQBEEQBEEQBEEQBD/5PYtE+q9/RD/5KJxyRiHYWw8lKvnLzFwKOUthQd25wWP3w6lURLzclL8Fy93cbcsa7FRSCpqaS5S/BTOVynxOJb84NLj8jY7W8DkGBTU0WCZTSfvL3yqrUQ2Thl7vIcg+FkEKgiAIgiAIgiAIgnBAGU45Wz7nHXdFoUCmkv3EH8jf/U1nY4ig7vzgoG6dL1X+NgJyQrwc0qlwJxQ4opJ7zHMhqeapsGs72i92+eftOpWiMS9TKcz95IhLqqLSK8kL4j8vl3O2B2cqWVfdgDrvwuGt+xBCRCVBEARBEARBEARBGIPoQDmbtvPkb/g8+pXnvEH5PORzJpeoVLncprXw+qvONcLK39yg7hJOpbDubyOBKyoFQ8PDnEq2z4W08EgY6IOtGwIXDCl/y2aKM5vcNbj3AqisgmSpoG5nbLzCEcAcQa0sDpm095lV1aAmYPmbiEqCIAiCIAiCIAiCMBZxytkKTqRMGjauQW/xiSl5X8e3fInub47wBIR3fxvkVPIdy2WLBZl8HlMCNkJOJdv2StTcuRREpYjv3u4+hTriOLNn9YvF1wtUvxUyjgoOI3dcSPlbOjnY+QS+3KVKL/9J4ctUcj7XSGR4az7EEFFJEARBEARBEARBEMYidqD8zRWZ/JlJ/twlOyBCFcbkvHFDBXUHx7jXsgPlb/ZIOZUqzKs/JFsHRKVB4eEKVdcAM+agX3up+Hq+MYDXjS2bGbL8jYpK85pODZ6jf4yb/6QslNv9zf2so0NGVh+yiKgkCIIgCIIgCIIgCGORQkaSU84WFJn8Y3K50t3fbHtwaVs+zKnkczNFXZdPtsi5pLUTVj0SmUrl5ebVzTNSVkimUkhQN0DzNOjqKL5ecExhDRnfQcKdShDeAc4dW14BKadETgFlQafSxCt9AxGVBEEQBEEQBEEQBGFsEnQehQRxe7lL2ZLlb9pxMWm/YKNtkzUEgzvC2bYRTdx7h3Z/G4H2b2WuqOSINRGn3E3b5vpK+fKPnFfnvqqyCpL9gQsGM5UcoSebLXZXBUsAKx1RKSys2/bK3zxRyYKYk6mUk/I3QRAEQRAEQRAEQRDGALq9xScU7UX521BOJb/YZBd3fSsKsS44lbRXOhbMVBpBUUm5TiXXIRRxyt00gCsqOYODLqTKKhgIiErB7m+F8rcsRU4lu1hMU5VDOZWcz7y8stipFIuZz8b9bEVUEgRBEARBEARBEARhtNAdbdhXfxy97L/NDkcA0oXStRDRyC8qhYlOUFzepgOuo6JOb26mUt7kGUWiTsh18TnadRLtL3EjKmm/qKRt85+ljCOohFOJiirIpNH+UsCC+OW4mWK+8jetPeFnUPmbm6kU0gHOFZ6CTqUyJ1Mpn4VoFDUSn8c4REQlQRAEQRAEQRAEQRgL9PUAoF953mwPcirlirf9Y/K58Mwl/3n5fCB0O18sGLld2Gynu1s0VsKpxAiJSk5Qt1t2Vgjm1nhOpRKZSmHuIlcAskKcStqfAxUM6navFSYq+cvffPeKxc3nmskY8W2CIqKSIAiCIAiCIAiCIIwFXCeNHejClnOCukPL33yCU0jmUtF20KmUzxc7ldJp8+o6kaLRwZlKrpNoRESluHktZCo54oztXD8S8QlkgdK2gqjU57tgsVOpENTtdn+zHAkkUP7mOpV0aKaSG9Rd6X2OluW5rNp2i6gkCIIgCIIgCIIgCMIo4womwTK3YPmb34nk31dwNmWLr1uUqRQUiBzRJBKBTMq5pu04laLhJXMj7FTSr73kzaFwfwXlFZ7QU5i3U9pWUW02/blKAU2p2Klke6JSYT1ONpRb/pYawqlUXuHtUwp1/MlGtHrpWfM5TVBEVBIEQRAEQRAEQRCEsUCwe1uw/K2QsZQffI7fURR0KtnDcCrFKyCfR7vlbm75W7BzWqFkbgREJVfMedUp93NFpXzeXL+ianB4dtCpVCQqFQtPnqiUMYKTFchUsp2SOLcMb1A3ObxSQMeZ5M5BTZqC+qd3m+2ermEs9tBERCVBEARBEARBEARBGAsEy9fsQLlbMFvJP9afqVSq+1uwlM32iUquwJNJm+5zyjJlXfnc4EwlME6i/USVV2Bd+gVvhyvuDPSb+1dUekJPMKi7UP4W4lQqZCqZ8jddyqmkbRPdFI2ae/X1Dp6kts1cyotFJQB1+jv3csWHHiIqCYIgCIIgCIIgCMJYwN99zf9acCqFZSqFlL+VEpWC5W+2Pbi8K532nEqxmONcCusYN0Ldzo44tvBWTZsJgN651Yg9fqdSMKjbzUHyO5WC9W+DnEpW8TB84d21DdDdOXh+2pTIqbiv/M0twWtMQF2DJ4ZNQCZu4Z8gCIIgCIIgCIIgjCWCTqUS5W/F3d98Libfca211+a+qPzN5zryi0wFUSnlBWVbTlB2mFNpJDKVoFiQmTbLvO7YYkrvKofhVCoqfwuEeTtOJXKZcKeSrT1XU109uidEVHLznfzlbz6XlnXDvd7nOwERp5IgCIIgCIIgCIIgjAWCYlJBDHK7v4WUvxXcS1l03h68P3jdUkHdrqiUSXlB3bGYCf0OlsyNVPc3QLk5SgBNk4x4k814AdrJAbTWg51K8QrjMgoTlQY5ldycqECmktaFsaquEbpDspHc8je/qORzaamyOKq8cm+XfchwUJxK3/ve93j++eepq6vj1ltvBaCvr4/bbruN1tZWJk2axJVXXkl1dTVaa3784x/zwgsvEI/Hueyyy5g3b97BmKYgCIIgCIIgCIIgjB7BLm9Bp5IrGoU4lXRQMMrnfN3USgV12yFOJaf8zc1UyoV1f9Mj51TyY0VgygzYvM4RlarMZ5DJDHIqKaWMWynZ553vikWukyjqOpUcUcnfXc6cgHLdS7X10N0xeE5uKWBR9zfx57gclE/i9NNP50tf+lLRvmXLlrFo0SLuuOMOFi1axLJlywB44YUX2LVrF3fccQeXXHIJP/jBDw7GFAVBEARBEARBEARhdBnkUCpR/laq+5vfwZQLC/MOZip52wW3TSZt9lsWRKOeIANm+0CKSspCTZ3pvFdeeHhqwJeD5LtvZdXQTiVXVMpmKMpPKur+5pa/NUAqib3iSfI3fB7t5iu5ay3q/rZ/yzyUOCii0pFHHkl1dXXRvhUrVrBkyRIAlixZwooVKwB47rnnOO2001BKcdhhh9Hf309nZ0hdoyAIgiAIgiAIgiAcSuQDjqRgN7ewIO5CjlI2XGwqOi9YyhYW1J3y3DnRmJPV5IgwZU5p2kiLSo5bSFnKiDvgOZXAyVXS3hiX2np0Z9vg6zlDVCRi3EnZrFmDm7Hkfn7+Mr5ac199782wcQ36lefMftstfxOnUhijFtTd3d1NQ4N5aPX19XR3dwPQ0dFBIpEojGtqaqKjo6Mw1s/y5ctZvnw5ADfeeGPReROBaDQ64dY83pFnNr6Q5zU+kec2/pBnNv6QZzY+kec2/pBnNr4YieeVqqykGyCfI5FIsNsRgOLRCHWJBKkqczyCLtyrxbbRQHVFOSiLXudajXU1RBqdMdqMqa2qIltejuvtqa+tQVVW0w5UNiboB6rLYqQiEXSsDKuyinxXO7V1tXQAkZpa7OQAsWgUrTWNI/Tz2VJegR7op6a2jnxiEn2AyuepmzyFLqC+rAzKyugAamprKXfu27PwSFJP/IGmxkaUZZFpq6MTqK2rI+5+PrE4FdEISaWIVteQBWrjZcQTCXrKy0kpi0QiQXrmbPyJSvHNa6k794P0xMtIRSIkZsygxTlWXVNNpfzZBMZI9zellJdKvxcsXbqUpUuXFrbb2kIUykOYRCIx4dY83pFnNr6Q5zU+kec2/pBnNv6QZzY+kec2/pBnNr4Yiedld3lVOm1tbQWHUbqvn7a2NuwuI3vk0+nCvbRT5tbX1eVlBgEdra0oWzljTNB3T2cHut8rF+tqb4dUBoABpySst70NnU4DGmXb6FSKLqd6KF8Wh/YWMpkM2PkR+/nUsTjQT29fn5ehnRygJ2vW37VrO9TUm/n19tHn3NdunoYe6KftH6+imqehnXn29Pai3M8nGiXZ04O282QjxqnU3dqC1daGPTAAynzW2lfIpU48ldTLz5FpbUUPJNFAe3dP4Xhf/wADE+jP5rRp00oeGzXPVl1dXaGsrbOzk9raWgAaGxuLfjDb29tpbGwclTkKgiAIgiAIgiAIwkHDn4MEhdI2Xej+Fpap5OsI599flKlke+cFy98KQd1OflE65XU8i0TNdd0xldXmurnsyJa/xePm1V/ypm2ocErOkgODgroB1Oz5ZhlfvxK9ed3gTCUwHeDckj1/iZ+5iVfK1tRszjzvIlh4JHS0QXen91kA1NQNuvxEZ9REpcWLF/P4448D8Pjjj3PSSScV9j/xxBNorVmzZg2VlZWhpW+CIAiCIAiCIAiCcEjhE4W0nfdEkpxPOAIj6gTPyeW8fCX/WPDEqUGd3HwikxtEnUmZfUVB3c4YNzg7OcCIKitlzr2VhXJFJSgITHqg3wvq9otZ02aZ11QS/djvKAzyjymLm/BxrVFBUcmXDaWqqrHuehDrrPNRicnmeGd7cX5UQ1NhnoLhoJS/3X777axevZre3l4uvfRSLrjgAs4991xuu+02HnvsMSZNmsSVV14JwPHHH8/zzz/PZz7zGcrKyrjssssOxhQFQRAEQRAEQRAEYXTJh7iL/PtdASgsqDufA8srfyt2KvmCvksEdatYDB2JFgQYlC+o2xG3VEWlkW2SA55rZyRwnUqWKg7E9otYYU6laAzrU1djf/9W9O7tKM1gaurQPV3hTiVbF0XxKHce9Y4l1BEhAAAgAElEQVR41NXuCGzK279lw34s9NDjoIhKV1xxRej+a665ZtA+pRQf//jHD/SUBEEQBEEQBEEQBGFs4S9fy2a8965AZBeXvw1yM0V8/8R3x2hdLEbZJZxKyjLiTjrtCSmuU8lf/gamG9tIlr+VOWIOCiorvf3lFeY+yRJOJUCdcArqra+gn37M+3wsXz5SXQN6+ybzOcViZp3ptHNUF40t0GAieHRXe1H5m6pvMtPwP5sJjni2BEEQBEEQBEEQBGEs4HcgpZLe+0Hlb+52wM1UlLUUyGFyx2ifnSdve9uWZcrQ0k75W4hTqZB3lEqOcKaSU/6WzXj3AJQVgaoa6O0mtLTNZeZcSCfRbbvdM71j9Y3Q1WHWYFlQXg5p57P1l7b5qa4zAp1b/uYKTw0+B5MAjJHub4IgCIIgCIIgCIIw4fELQGmfqJQPiEj5nHEg+UWoXA6ifgHJeW8H9pUK6laWEXcyaePOcTOV8r6spgqfi2gERSVVVo4GdCaF8t8DoLYe3dOFKsx78H3VzLlGctq6YfCQugafQKeMcJZxnEq2HboOZVnmvK52sEMylTo79n6RhyjiVBIEQRAEQRAEQRCEsYBfJEoOeO/dYO6gQBTs9hYW1B3cFwzqdrctU/6m/U4lt5wu69y/0heifSC6v2XSXu6RS10D9HQNfd/J0wHQu3e4g4rPL5zrlPgVRCZf97cg9Y3oro7i8rf5R5hjhx+95zVNEMSpJAiCIAiCIAiCIAhjAb9IFFr+FnQdBcrd7PwwzimVqeRz8bglX7EYANrJEFIVVYVoowOSqZRJm5I3H6qmHr3x9dCg7sKYikpTJte6y+ywfOHbdY2+OQPxcnTBqVSi/A1MKPeOLSZHyu0QN2U61p2/KA4Tn+CIU0kQBEEQBEEQBEEQRglt59F5Xwc3l5TPqZQPE4iyg8vfggJS8JxccVC39pe/WZYpbxvoA9s2XdEiRlQqBFMXlb+NnJygjjjWvE6fM/hgXT10d5YM6i7Q1AxtLe4g3/lBp1K51/2tVKYSoBqaoLMd7ZYCuvvLK4s6xk10RFQSBEEQBEEQBEEQhFHC/vrnsD91ntnwCUDadSpFo74MoCHK3wLOJR3qVAqUv+XzRUHdqq7BCDi2I6TE3PI3V1Tylb+NIOrYN2Ld+rOCuFREXYNZvyuylRJ0EpPD3Ux1jb4bUSwqoUsLRJOnmVyr9taRdWUdYoioJAiCIAiCIAiCIAijxbaNnrBTlKnkiEoNCejtRtshGUr5gIgU7AYHIUHdPneObXvHlRNO3dNtzrUsz6mUcUSlWBTKysx7a2TlBFVbH36gxuzXXU44dilnUaI5/PzqGi8bKuhUGqL8Tc2ab95sXjeirqxDDflkBEEQBEEQBEEQBGEs4BeNkn3mtanZ7O/pHuw6Cm6HBXX7hSrXqRSN+bY9pxJ1jeZ4T5cRUqIBp5KyDnqekHLL17o7nR1DlL+5+MvVlDIldADKdJrzyt/s0oLRjLneMUucSqUQUUkQBEEQBEEQBEEQxgJ+AcjpeKZcsaSzfXAQd9F21ohMkUjxtfKBcjfbhppa7x6+oO6CgJPNmHK4aMCppJSXq3QA3TvWpVejPvRJs+E6mFxRiVJOpcn+reKDbgmcsqDcX/5GScFIxeMwdYZ3nhCKfDKCIAiCIAiCIAiCMBbI5wsiiu42ohJuWVdnW0imUsCFlM97ndRyYeVvORM8XV5pupvt3u7lEFlWINRaeU6lnM+pVO44lQ6geUedeArW295pNhqaQCn05nXevMJoTPguEDjmX5ffqWTbIYN9l5l3uHnTvnvYc59oiKgkCIIgCIIgCIIgCAcY+9kn0Lt3lDyu3eDtiiqTW9TjOHOajANHd7aHdH8LcS5VVpvtgX5nXKBDnBvCPWU6etd2kysEZl+9L9TasrwyOb9TqarGeX9w5ARVXQvHvgk2rvHmEEZ9k++k4jHKXZflZCplM6brntZDZkOps99v3qTT+zr9Q57oaE9AEARBEARBEARBEA519PdvQUejRO7+TfiATBqdz5nytaraQrmXqmtAR6PGqRTs9uZuR6KQy5kw73i5Eaac8rlB5W+OkKKmTEc/+wRoX1B3rc/RY1kQd1xPrkBlWaiGBBoOqFMpiPVP52C/+IwzzxI3dsWusDGuU0kp8/mA01HPdH/TJe6rmpqxvnp7ccc8oQhxKgmCIAiCIAiCIAgHFfu+e7CffWK0p7Ff6FwO7c/mGWqsG4ady5UelEl7mUjVNZ4oFImaDnBhmUquqFQW98rfrIgpoSuISoESOdsJp548HQb60V2OI8qyULFYsROp0ZTe6bZdzj7llZnpUlLMAWDeYd77Ut3aivaXylRSxULZEN3fCleaNQ81e8FeTnjiIKKSIAiCIAiCIAiCcFDRz/0VVr842tPYL+xvfwX78guGN9jvMCpFOmVEn0gUqmu93J+IBQ1N6K5g+ZsvqLui0oy3bSNK1dahe7udiXrn6HzebDtOJQB2bTOvrrji7Nc9XaYcLhqFVkdUsiwjcIEvOPvAUwgM989zyBMC5W8+p5KaMQeUwv7uDbBlPZY/i0nYa0RUEgRBEARBEARBEA4u+ZzpVjaeWbt6+GPzQziUXDKpglNJVdd6+60Iqr4JOtuMKOSSy3nXTTRDV7v5TCMRqPE7lQJClJsjlJgCgPYLRoB10WcAUHMWoCzLZDq5ApVSKFeE6Wwf/vpHAjczaShRKeIk/AwK6nazohRqwZFYl34BtqyHjlYqlr5rpGc6oRBRSRAEQRAEQRAEQTi45HPo8S4q7Q3DWWs67XMq+fKBIhHTAa2zPVDK5mUqqcQUIzJ1dRgRqraUqJR3yt+U5zhqbzGvjqikps7A+t6vUO843+x3u8+BKYlrmGTedx1kUWnOQvPa0116TKHLW0BVcoO6IxFz9IRTUKe9AyZNIf7G00Z2nhMMCeoWBEEQBEEQBEEQDi75/ND5QgcBvX0zaG3KofbnOnYeZUWGHjSctbqZSmVxU/7mEokYASiXNUJRJOIISlkK4skk4zqibbfJU6qth4E+I9zZvjDvvNf9TcXjJj/JFZV83dxUrMx7P2mKF2Ttz1SyD254tXXhp9F19XDksaUH1TdCRyukBop2q7oG1MWfRR19grfvw5eibBsVFVlkfxCnkiAIgiAIgiAIgnDQ0FobUSQ7uk4l+4EfYP/i+/t/ocww2s0Px6mUSRvxyc1UcrEiqAan9Ku9xYhOmHykQjmcKyplM8ZxVFtvtnu6B4d5a9sTkBoSkEo69ykhDyQm++ZioSoq97yWA4CqqcP6yGVFgtegMa7ryN8JzsE65QyUr7udsiwRlEYA+QQFQRAEQRAEQRCEg4dbwjXa5W+p5MjMIZ2G8j0ILSWcStrXQU2nU77ub8WiUqFUrafLOIWSA+a/eDkAqnmq5yaKmPI3DSYLyfaLSs57N/i6MQHbNpr3pbqqNU/zOZWcErl3fQA1c97Qax4F1NvPRp14ismgEg4K4lQSBEEQBEEQBEEQDh650RGVtJ1Hr1nl7chmIZPZ/wtn0mit6fv5D9Dbt4SPKbVWf0ZSxstUUtNmevvdTCWXpmbjZmrd5eUmVddCeYV5b0V8TqVOz81UVmY+e9tzKil/57NSTiV/eaBlhCfr3R9CHX9y+PhRRCklgtJBRkQlQRAEQRAEQRAE4eDhihwHO1PplZXYN38RvWu7c//M8ErX9kQ6BZk0/b/8EXrlU+FjSq3VLzalTaaSikRg+hxvvysSuaJPNAbNU9F/XIb+5Q/NvkjEiE3ue0dY0R1t3uddWQ3ppNf9DTwHFAyv/G2ozmvChEREJUEQBEEQBEEQBOHgMUrlb7q/17xxX7NZIwgBOpPGvvdmdNvuQefZP78X++nHSl84kzZZRlC43iB8a9W2rxtbroRTyS/eKGWCwOt8HcwmTyvu6haJouYs8K7Z0GjcTG27vfK3+kbo7fG6vwE0TvLdJ1weKJ6LSAhCMfITIQiCIAiCIAiCIBw8Rqn8rVDqlk5693edSi070CueRK9dPeg0/dxfYfULpa+bTg2+dpCgeBS635epBFiXfQmap0FNnTnulsBZEdTkacXXtyKoU84w7195zohQTZOMqOSIeKqu0QhquWzBlaSOOsF3jSHkAfeYGJWEACIqCYIgCIIgCIIgCPuNzuXQ2WFkFI1WUHfBTZT2tnNZ4xxyRaGwcrhcFp0eokwukzGldFDopKa1Lv4s/GtN+dxMJTKVANTxJxP5xj2omBOq7YpKEV9mkkvEgoVHmfNOXWr2JSajW3dB3jbb9Y7TqafLy1SqqcW68mtw3MmmPK4E1jV3oM46H+IVpT8HYUIi3d8EQRAEQRAEQRCE/Ub/6sforRuJXHXD0ANdISU7OqKSTqeM4ca9fybtiUnZcFFpqOwlnUmhnGtpt5xuxZPo++7BuvnHqLJ4sSPJXyLn+wz0nx5xRKVI6H1UQwINKCuCmr3QvD/nQ9DZARVVKKWw7v5N4XyVmILe8lRx+RtAX0+RK0kdeRyRI48ruT4ANX0W6ryLhhwjTExEVBIEQRAEQRAEQRD2G922G1p27nngaAV1+3KPtNaee6goE6lYPNJaO13ihnIqpT2nk+NUonUXDPSZDKOmSYFA7hJOJfd9pMQ/031OJXX40Vi3/gwVcCypqO/cxGTo6y1kSKn6JrQ7bqhSN0HYC+QnSRAEQRAEQRAEQdgn9Ka15K/+OLq/z4grqYE9nzTqmUopI+BoR2JJ+0ShTKB8L5834zIlArjd6wWDut3XgT4AdClRKUxYS/aH38ft1GY5TqRgCVwQt2vbjq3m1XUqgXRxE0YMEZUEQRAEQRAEQRCEfUJv3wztLdC+2xGVkmjbHvokX6aSdoWdA4R9393YK/5qNtzStnSquPQuk0ZnS5S/+d1MpfA7nVynUkFUcgSiovI3X5h3iLCmt24IvY3yZyoNA3XYURCJoFc+ZXb4RSVxKgkjxKiXv33605+mvLwcy7KIRCLceOON9PX1cdttt9Ha2sqkSZO48sorqa4uHRomCIIgCIIgCIIgjAKu2JJKFr+vrCp9jlv+pjXYNnqgH1VTe0Cmp599wuQdnfSWYqdSrlhUKhnUnR1CVFIWaNs4nbKB7m8BpxJ5v1NpcPc361NfhCOORT/0M9QbTwtfTP1eikp1DXD4MbD6BdSJp0J1rTk3nxdRSRgxRl1UArj22muprfX+J7Js2TIWLVrEueeey7Jly1i2bBkf+chHRnGGgiAIgiAIgiAIwiD8Qk0hV2hgaFHJL+hs34x9/ZVYX7wFNXfhyM8vm/XcQ65A5C9XC24HxSN3rsGsJds2gpJzjnav7XZ2c66jB/qdUHDPqaTTSQrFZ67YVFuHqqhEfejS0mtxnUbW8EQlAOt9/4p+pBL14U+hlIKaOujqKHR/E4T9ZUz+JK1YsYIlS5YAsGTJElasWDHKMxIEQRAEQRAEQRBc9KvPo/t7PfHE71RKJoc4E8+pBCbQWmtTPjfSc9QashmvI9sQ5W+FuQczlXzlb9q2sf/6RyMg+QO2M6ni0jq8LnAkw5xKIZlKkdge16OiMTj2jTBn+OKbmjEX69IveE4w1+2UzZQ+SRD2gjEhKn3jG9/gC1/4AsuXLwegu7ubhoYGAOrr6+nu7h7N6QmCIAiCIAiCIAgOOp3CvuNr6CcfLVH+toewbp8go90g69QehKh9wRVs3JI0R0jRAaeS9gV160HlbxnvdfM69E/vhFeeC2QkpT2RKp8zopMrHPU7mUp+Eau3xzdHZ390eEVEkcu/gnXqGcMaG4Z1rqkA0vmD3HlPOGQZ9fK3r3/96zQ2NtLd3c3111/PtGnTio4rpYxNL4Tly5cXhKgbb7yRRCJxwOc7lohGoxNuzeMdeWbjC3le4xN5buMPeWbjD3lm4xN5buOPsfrM8h2ttGmbCjuPjlgkgapohD7HrVNbFiXuzDvfspOum79Mw5dvwXLKt1KVlbi2gSoFfUB1NELlPq41t2UDkakzUbFit4/d30srEM3naEok6NCaLFCmbaqrq+hwxtXEY+SiEQaAMjQNvnlke9q9cfks3UC1BfHaGlqd/WVoYmUxHE8STVWVdOZz5IBynac2kaAvXkY/YNU1EE/2UevcI1VRQTfQkJhE9GA86yX/RGZSM5HmKUTG4M/WaDBW/5yNF0ZdVGpsNP9jqaur46STTmLdunXU1dXR2dlJQ0MDnZ2dRXlLfpYuXcrSpUsL221tbQdlzmOFRCIx4dY83pFnNr6Q5zU+kec2/pBnNv6QZzY+kec2/hiLz8z+xfcL7ppkZ0fBddTf2lJw+3Tv2oXlzFu/vBJ73T9oX/US6vBF5hpdnYXr9beasre+9jYG9mGtursT+/MXod56JtaFlw86BpDr66OtrY2804kt09tDV2trYVxvezt0d5lj/X1Fn7n2jevZutmMb9lFX0tLYX+6r5eMb03tO7dj9xuJKdXeRqatDbunGyJR7Pomkru2k3HuYXea8zr7+lAH61k3TzevY+xna7QYi3/OxhpB84+fUS1/S6VSJJ1621Qqxcsvv8ysWbNYvHgxjz/+OACPP/44J5100mhOUxAEQRAEQRAEQQD0yyvQK582G6kBL8C61xdZ4it/0+7xVEiOEIAj9BRK1PYW5zy9+sXBx7K+EHH/diYQ1J0ZRlA3QE+nN+dcICPJn8XkKwXUSWd9uZwpcWtIQGe7N9YtQ4uMut9DEPaJUf3J7e7u5pZbbgEgn8/zlre8heOOO4758+dz22238dhjjzFp0iSuvPLK0ZymIAiCIAiCIAiCAEZA6TOZQDqVBCeqRPd0eWOSvkwlV1zJpHwdz/yiklM0tq+ZSrbTgS0seLrQ7c25dqE7XSCoOz1EULd/XLdfVPKtIdlffP9U0run41gil4VoDNXQhF7zijd2LzOVBGGsMao/uZMnT+bmm28etL+mpoZrrrlmFGYkCIIgCIIgCIIglMTfuSw54IkhvT5RyR/U7YotftEo5w/qdpw8fifT3uCKQH7xJ3jvdKrQCc6ckyp2GmVS6MyenUq6y0lXSvZ73dxq66GjLeBcSnoOrjCn0kC/EeR270A/8ENzXEQlYZwyJrq/CYIgCMJ4YEUyyakbN7Jw7Vp+39e35xMmCA90d/P0wB46/QiCIAjjHq11seiSSnpCjb/8LZlEt+4yXdbc8X4xKp/3jTWii97X8jd/d7ZSx7Q24lPWm4suKn9Le8cC19F+scpxY2m/U2nKdOjvLRwDjNjmXifgVKKhyWx3tmNff6Xn2ooWh4wLwnhB5FBBEARh1HjThg3szuV4fv58GiORwv4zN29mVTrNM3PnMjM2dr5k3dzWxsX19Xy8oSH0+Js2bKAtn8cCqiyL06uq+EZzM1WW/A4nozWf3rmTl1MptuVyPDhjBqdUVhaOd+fzXNPayp+d1ssX1dXx7yU6sWzNZjl540Yqfd1hL2ts5Mom80X9a62t/KGvj9ZcjinRKP/W1MT7nKYfHfk8F2/fzrpMBhtYUFbGNZMmcVJFBWD+wfSt9nZ+2d1Nv9YcHY/zjeZmDo/HD8THIgiCML7IZIxA4+J3HxWJKv3Y138O9U/vBtsZXyQq+UrHXNFlX51KrkMoN4RTCYx7KOPLWHKPKeWISsPIVOp2nEoDfYX9avJ09JpV6F3bIBKBfB7dstOMi0ShrwedzxsRKhJFNSbQgP7rH4vvI04lYZwi33IFQRCEUWVmLMaynp7C9mvpNEk3H2GMsT2X47CysiHH/HjaNNYuXMijs2fzairFnR0dQ44fz6zLZHjf1q1c09rKxTt2cNbmzWwJKz9weGNFBXdOnUqzT0B0ua61laRt8/e5c/ndrFn8ureXB7q7Q67i8dqCBaxduJC1CxcWBCWASqX46fTp/GPBAm6fMoVrW1pY4TQGqVSKW6dM4ZX581k9fz6fbmzkX7dvJ+f8I+l/+vp4oLub38ycyar58zmxvJzP7Nq1Lx+PIAjCoUfQTZQa8EQYt4wN0F3tRnjp7CjhVArJVApxKtk/uQP9/N+GnpMvA0m/+Az5T7wb7WQ+FZXEua4qyzI5TG7uU3Utuq/XV0aXQfu/h+T2kKk02emKtXMbkakzYepM9OO/N/vmv8Gcv20jOpeFWAxmzIHGBPrRh4rcScoa/HejIIwHRFQSBEEQRpXza2v5lU9UerCnh/c6rhKXtG3ztdZWTtqwgWPXr+cLu3cXhKeufJ4Lt29n0fr1HLluHee+9ho7fF8i37t1K99qa+OcLVs4bO1aPrhtGx1+232A+7q6OHXjRo5at45/3b6dXc6XxlM2bmRzNsvFO3awcO1a0nsQvpqjUU6vqmJV2vuN58pkkndv2cIR69axdNOmQsnYw729nLV5c9H593Z28q/bt+9x/U8PDHDihg3c09HBMevXc/z69UVizHu3buV+3/YD3d2cu2VLYXtdJsMHtm3jqHXreOvGjfy2t3fIdfm5pqWFI+Jxrkkk+P7UqVzf3EyFzz3kp0wpPtHQwBsrKrBCxvyxr4/LGhupsCxmxmJ8oLaWX/h+LvaGzycSLCgrw1KKEyoqeGNFBSsdUancsgrHNOaLUJdt0+X8TGzNZjmpooLZZWVElOK82lrWBkNbBUEQJirpgJvIX+blohS0tTjH+4chKrmZSsWiktYa/cyf0ateGHpOvvvbf/ytebN5/aBjDPQZl1Vdo9nuaDWvM+fCzm3FDqVsFp1Jm/Bx/zVcl5ZPVFKTp5t9uSyqLI5665nQbtavjjzOnLbuNc+pVFmN9bW7sT73dazv3D/02gRhHCCikiAIgjCqnFBeTq9tszadJq81D/f2cn5AVLqhrY0NmQyPzp7NU3PnsiuX4/Z2047XBt5fW8uzc+fy7Lx5VFgWX2lpKTp/WW8v354yhZfmzyerNfeUcA/9dWCAb7a1cc/UqTw/bx4zYjEu22ks7E/Pncv0aLTgRIrvoaRtRzbLn/v7meuU7+3MZrlw+3Y+29jIqvnzuWbSJD6xYwftuRxnVlWxPpNhg0+8WNbTw3tqava4foDWXI5e22blvHncMnkyX2ppKYgkQzFg23xg2zbeU1PDS/Pnc/fUqXxp927WOELYQz09LN20qeT5rfk8x5WXE1WKqFKcWFHBpP2w72tfSYUGXk+nSw/GlBueuGEDV+7aVVIoTNo2L6VSg8rXlm7axLy1a7l4xw4+VFtLwpn3OTU1bM5mWZ/JkNWaB3t6ON1XpicIgjChCYpKmfTgrm11DdBmHJ7an7mUDg/qLjiBtm/GvvdmUyrm7s/nITWAzmawf/fL4nwjh6JspKpqs88RjIqO9ZpfVKj5bzDH1r1mtmfPh9ZdnmPKWZf+3YPY37wqvKwu2e/tb2iCMvN3jCorQ53w5sIwNXUGNCZg3WtmvPOdQMXjqCOORZVJabUw/hFRSRAEQRh1XLfSEwMDLCwrY4pPmNBac193N9dNmkRDJEK1ZfFvjY087DhqGiMR3llTQ4VlUW1ZXD1jBs8ki7/gXlBby/yyMiosi7NraorcQ34e6unhA3V1LCovJ25ZfDGRYGUyydYhSrqCfGzHDg5bu5aTNm6kKRLh352yrN/09vL2qirOqK7GUorTqqo4trycP/X3U2FZ/HN1dWFNGzIZ1mUynFldvcf1A8SU4sqmJmJKcUZ1NVWWxfphuGv+2N/PzFiM99fVEVWKo8vL+ZeaGh5xQsjfU1vL8jlzSp5/SUMDX21p4UddXSzv79+rzynI26qq+G5nJ322zcZMhgd6ekj6czt8NEYi/O+sWfx93jx+P2sWfbbN5Y74F+TqlhaOjMcHCUPL58zhHwsW8N0pUwp5SmAcZm+sqOC0TZuYv3Ytj/T2cl1z8z6vSxAE4ZAiKCoB9AVcpU3NXhC336mUKhHU7UOveBJ2bEH3dhfK03RyAF5/Bb3sv2HNq4NP8glHKuJ8f9hlnL5FndvcIPF5h5uso83rjKtq5nzQNnR1gHL+eZxJQ+tO4zgK+86QTpkQcjAlbE3m7wkVK4PGSd64snLUG45Fv/AMvPaSuW8Qx80kCOMVSQMTBEEQRp331tZy3tatbMnlBpW+tefzJLXmLF/JlgbyjuCQtG2ubW3lL/39dNs2Sil6bZu81kScMqtmn0hVoRT9JUrXdudyLCovL2xXWRYNkQi7crlhB4b/cNo0Tquq4m8DA1y+cycd+Tx1kQjbsll+19fH8nXrCmOzWhfCqs+tqeFrbW1c2dTEst5e/rm6mgrLoi2XG3L9APWRCFFfSdlQa/SzPZvlhWSSI3xzymk9yClWivfV1nJqRQV3dnTwcirFGZs2cefUqfxzdfWwzvfzteZmvtrSwls2bqQhEuGcmpoi4cxPlWVxrPOcJkWjfKO5meM3bKDPtqn2Oci+3trK6+k0D86YgQopuSu3LM6trWXJpk0cVV7OUfE4t7W382IqxYq5c2mORvl1Tw8XbN3Kn+fMoUIC1wVBmKDoXNaIPMEQ6xBUYjJ6/T/MRnIA7fySQ5cqfwtgf+2zAFjf+H9mR2oA7biMdF8Pg/5v7hOV3IBsvXv7oGP0OaJSZZUpedu0FmIx1LRZFP5GraqCvl7IptH9vabczR9A7scV06Ix1PTZ6J1boSxu/r6JlZl7x8tRF3wMvWUDbNsY2uHN+sy1xc4tQRhniKgkCIIgjDozYjFmxWI81t/PrZMnFx1rjEQoV4rHZs9maoiwc09nJxsyGR6ZNYvmaJTtFRW88aWXCPe4DM3kaJRtPrfNgG3Tmc8XOaeGy5srK3lfXR1fb23lR9OnMy0W4/yaGm6eMiV0/GlVVbTv2sWrqRTLenoK7pg9rX9PVFhWUfB5q++3w9OiUU6urOQXM2bs9XUL14jFOK68nHfV1LAyleK+7u59EpUaIhHumjq1sP3NtjaO8wl8Q+H+A8P2CW23tLXx5/5+fjVzJjUhweB+clqzJZPhqHicVek0766pYZrzWb+/rmRNMl0AACAASURBVI7rWltZk8kUhCxBEISJhv7JHei/P471qS+GD4hXeOVtCd/f436nUiaF7u+D/l4joriB2aVIOUHayaQn4PhL1Fz8wpErJu3c5tzTd6zHEZViZai5h6E3rTX3nzy10LWNqlojKmXShXvqrnYjElXVQFc7VNeaY67YFI0akeq5v3pzmTbLOKG0jaqqRr35begHN0LvYIFKRSLm/oIwTpFfuQmCIAhjglsmT+aXM2ZQGXCDWErxYecf9m3Ob/J2ZrP8xWk932/blCtFrWXRmc9z/dat+zyHc2pqeKCnh1dTKdK2zY1tbRxfXj5sl1KQT9TX88TAAKvSac6rqeGP/f38pb+fvNakbJunBwYKoeIxpTi7pobr29rosm1OcxxMe1r/njgqHuf/+vpIOmVlP/eFdi+trmZDJsOvenrIak1Wa15MpVi7hywjl192d9Pn/IMgD6xNp5k0xBfjtG2TcsZnnc/AzVHalMnQkc+T15rH+vu5r6uLzzY2hl7n+WSSdZkMttZ05PN8taWFN1dUUOvc+86ODh7q7eUXM2bQGJjPymSSZ5NJMlqTtG2+29FBay7H8U4J3HHl5TzS20trLoetdeGzmbOPPwOCIAiHAvrZJ8xrm68bpt8B6uQUAQFRaaCo/E3/z8+xb/2yEXDiXulxKH2OWzU1AG7pWl+Ig9Vfeu26odp2oe18wKnkZCqVlaEWn2r25XKoaAyOcErQKpxS6XTau1dXh3EjHXW82XZK3WjbbV4jUdTMeeZyO8x3EOvjn4NFi2H2AnPPI441Y7duHHrNgjAOEVFJEARBGBPMKSsr6QT5UiLBnLIy3rV1K4evW8cHtm8vZAZ9vKGBlNYsWr+ed23Zwpn19fs8h9OqqriqqYlLdu7khA0b2JTN8j2fe2ZvaYpGeW9tLbe3tzM9FuNH06Zxp9Ol7aSNG7m7s7PIUfWemhqeHBjg7OrqonK2oda/Jy5paCCmFMeuX88Vu3ZxnhP+DVBtWdw/YwYP9/ZywoYNHLd+Pd9obSXtCD2/6enhbUMEda/PZvnnzZu5vq2NS3fsoDWf56pEouT40zZtYv66dezK5fjQ9u3MX7eObY5Q9nI6zdJNmzhs3Tq+2dbGXVOnFoVrv23TJn7jdIPbks3ykW3bOGzdOs7YtIkypYqe041tbezI5Th140YWrl3LwrVrucMJNs9ozZd27+bodetYvGEDj/X387Pp0wtutMsaGjgyHufMzZs5Yv16vt/ZyfenTaNOfossCMJEptwRW1wHEEBNXeGtOvoE732TL4culYSMI/SkUyZAu6PN7I+XG5dPCbTTQY3UgOdU6g8RlYJ/H0ZjxoGUTBZnKrnd3uIVsPCoolPUyaebN8543bbbu1dXu5mne06szIxZu9rZjsHMOQDYjtCkpswg8plrUHHne8302ea1vqnkegVhvKK0LpGCOQ7ZsWPHaE/hoJJIJGhraxvtaQh7gTyz8cXePC+dHID2FtSMOQd2UsIekT9nB58HuruZGYsV8qH2Fnlm4w95ZuOTfX1uumUn7N6BWnTiAZiVMBRj5c9a/gv/nxGD5r8B3LykWfNhy3oA1MWfRf/4OwBY192Ffd3l3sn1TUaYqW+E5qmwZhUsONLssyxoCW+0oP7lfej/fdCEWy9aDC8+gzr5dKyPfa5onP3gj9CPLvN2TJ8N2zdj3XAv+vH/Qz/2OyivgIF+yOewbv0pqrYBvfoFdHsr1lvPROfz6F98H/WWpdjfvAr1trPRyx/2rtk4CevaO7Bv/A+sCz+N/ePvFOZt3fkAxMuxLzmH8re/k+wHPxm6Hr19C1RWoRpEWBprjJU/Z2OZadOmlTwmmUqCIAgjgH74PvSTf8C6/X7T+UMQBEEQDhHsO78Ou7Zhffu/UD53inDoo7U2wdNBp1J1DWr2fLQjKhGLY113F3rreqgNOIZ7Os2rv6SsvQXicdMprWUn6tQzYNos9EsrvA5vbnlZPmcEKDCZTEEyGaiuMWVw6RRMmQ7bNxsRKZs1zqI5C+GV56CuEVXbAIA68vhCJp+KRFAfvtRsNE9Dr3+t+B7RGKqyisjXvmvGzzu8EApONIZSCuvuX1M7eQrtjjM2iJo+K3S/IIx3pPxNEARhBNCrX4RMBvvem7F/89PRno4gHFTeX1e3zy4lQRDGAU44sl7x5ChPRDiY6O2bsS85B/3q815+0kAfRKNYV30Tdf5FUOcINNEoavosrJPfBlXVxoHk4oZxp5NeSVlnm8kicvOXauqxznwPqnGSd39XVAJodbKc3DI4P9kMxOIwy+QauaIRA33OMRPMDRTGDMnUGbBxTfG+YK6ev3zOKY9WjrgkCBMNEZUEQRD2E93dCTudcOgX/45+dBm6VPtZQRAEQRhvlJtAZTesWZgYaEdY0X9/3HRxc4lXoKbNQlXVoN5xvrPPy8BTlmXcSpWBTqC2Dd2d3nYkYpxKANoRnvxuHjdTCTwxKsyplM1CLIb13ouhshp1zGKzf6DfEZViqDkLzdxmzt3jutWUmYN3RotFJfWWpVifuRbrs9eKkCRMeERUEgRB2E/066+YN8r5X2o+j/7bY6M3IUEQBEEYIbRtmywdgE1r0W53LeHQxxFSdDppuri5+AWkM96F9eVbve5pDtaFl6POu9DbURHiZo1EvVK5HtPdTS09B+vmn5h9fgHKJSSoW2fTxo0073Ai37kfphlhSg/0oR2nEvPfALMXoI45aQ+LBuV3M7mOq4BTSVkR1KITUUdLzpggiKgkCIKwv7g19YcdZb64TJmBXvXC6M5JEARBEEaC3m7TEWvRYtMGPlgWJAyJfvEZ7KfH6S+aXCEpOVAsKjkCEIBSCjVn4SC3jlq0GLXwSG9HWNezSBTliEq61zi8VTSKqm+Esvjg8RWVMNBnhE4/bm6SS2WVeV2zCl5/1TiVKquIfOXbqPlvGHLJAPiFpwano2nAqSQIgsch1f1N/eUvoftvam7mI06L6f/u6uILLS2h4wC2H3ZY4f07Nm/mlXQ6dNyH6+r41mRTA/xyKsVZW7aUvOb/zZrFMU6b7P/YvZv7urtDxy2Kx/n97NmF7elrSv+lfVNzM1csWEBbW9shtaZD8TnJmgwTbU0ffO15vlVVAVs38co738+/dIVkAIyzNR2Kz0nWNDbWlEgkWLxy5SG1Jjj0nhPImibimhbt2Iz9zau4+t/+k/vLqw6JNY2n55RIJHhs27ZRWdOH/vp79MP3c99b38EXT16632v64EvPcNOjvwLg5cnTOfvCK0te85FlP+GYtSaw+wtnvpefH3tyyTX97sF7QdtErvrmiD2nrS88gV7+W9SblnDWYcfz6pQZoeMO5Z89kDXJmrw16dNPL3l96f4mCIJwIMhk0I/cD4CefyRMmb2HEwRBEARh7KHbW82bqmrIHzK/ix419CsrUYvGScmUm18UVoa2Pxx5fHFeUhiu2yiYyxRGNuN1pxsh1AUfM0HkuRxqxw7I5Uf0+oJwKHFIOZV27Ngx2lM4qCQSCdra2kZ7GsJeIM9sfDHU89Jaw5YNMHMO9s1fhkiEyOe/YY6tegH79msLY9Xb/gXrQ5celDkL8udsPCLPbPwhz2x8si/Pzf6/X6N/81Os7/wcff896LWriNz0owM0w0OP/CfeDYC65D/Q934L9dYzsS68fNjnj+afNfuHt6Gf+XNh2/rU1agTTtmra+Rv+TK8/grqo5eh/+t75jpfvR3761fAsW8kcvlXws/77g3w4jPQmChkell3PYh93eWm+9x/3oWyTNe1/Nc+C42Tiq6V//xFRWJY5Pu/3at57yvy/8bxiTy3PTNt2rSSx8SpJAiCsJfYD/wAvXkdrF2N+uAl0NOJmr3AG+DvXDJ7AXrX9oM/SUEQBEEYCTavg6ZmVGUVetos+Pvj6OQAKix4WSjNyqcA0DtKl6iMNXQwFLsivPxxKNQpbzcNTfw2hoYE1p2/MEHdJbDOfj/2i89AJg0NCdTZ70fF46jzLkLf+y1Y/RIcfYIZnM2gghlMfc7c5yxEnXH2Xs9bEIThI6KSIAjCXqDX/wO93Pttl35pBXR3ed1LAOoaAVBvPRMyafTa1Qd7moIgCIIwIuiNr6PmHwGAmjbLaAM7tphuWsKQ6IyXhaKf/5t5s2MLWusx1YZe93bDxjWDO6P195oytIF+s125D6LSm9+OqqmHI46B1l3oP/wGqqoKLqOS582ej/rY51BNzUWB3+q4N6GratBP/wlVEJWyg4O08znA6UI3c+5ez1sQhOEj3d8EQRD2Ar38t1BZjXXnA6i3nw2rX4B0EuoaCmOUUlh3/wb1kctgynToaEWHhOzpNavQfaUDvAVBEARhNNGd7ab0aN7hZofbqn0cuW1GlT6f00c7HcuSA9A5tsps9KPLsO/8uhGX/PT3oY483tveB3eaUgq16ERUNIY6/yKsex7ao6DkYp18enEHOUDFYqiTT0c//zfv5zCThrKykCsAzVP3es6CIOwdIioJgiDsBXrjGtTRJ6LKK1BH+b5o1TYUjVPRKMqyYLLTLWTXtuLrZLPY3/4q+uf3HugpC4IgCMK+sdF0/lFznW5EicnmH+/bN4/ipECvfBr7kQfQ/jb3YxG3fOywo4v3H+DPT+ey2A/+GP3qyuGN37zOvNm8Dvt3v8T+0/+Y7f4eqK5BnfNhs+13Ze8DSilUZHiC0pDXeecFUF6B/ZM70C/+HXq7BzuVjn2jGRsv3+/7CYIwNCIqCYKwz9hPPor99GOjPY0Dgs5l6bv/XvT6f3j77Dx0tUOi2ew48rjCMVXii5ZaeAREoti3X4v9o9vROWPHpm0X5HPolU+je7oO2DoEQRAEYV/RLU4THMehpCwL5h+BfmUlo9nrx77/HvTD92H/v5tGbQ7DwnEjW+84DwD1piVGDPnv75G/9Svk77oenc2O/H3XrkY/+hD2d/4T+w8PobVG5/PoVLIwRL+6Evu+u9Fd7bB5vdn3j1fQjzyAfui/0P190N8PlTVYZ78f6+5fo0a4w9q+omrqsD56GWxcg/1d0yQlWJpnfeqLWN99cBRmJwgTD8lUEgRhWOiBPvQvf4Q6/yJUTR161Qvon91ljp18uvmiOYbRXe2Qt1FNk4Z3wppV9D/4EwCsa75j6vG7OiGfB+caKhpDnfEu9J/+BxqaQi+j6ptMSOWTj6L/9hgcdbz5UrnbCe/O59BPLUed9d79XaIgCIIgjCztLVBZXRTKrU48Ff3f34Ntm+AgZtXo/l70qhdQhy+Cni6oqYNVL6BXPo06ce86ko3ovNJpUAwOigYY6DOvbjB1LA6PPIB+5BeFjma8vAJGeP56zSrz5qjj0b/6MfrpP0EuCyis/7wLOtuwv/OfZkw2U5in/sNvzL5cFv27B0zJXnUNYL7zjCXUiaeiLr7ChHTXN3klmu7xSARGwBUlCMKeGdv/ChQEYcygX34O/dTyQtCk/b+/9A469vixiu7pxL7+37FvvAqdTg3vnK0bvPcrnjBvOloBUI2eMKUu+BjWl25BzZhT8lrq/ItQH/gENE5C//7X6Ewavdv57e+s+ejHf49+7SXy1/0burcbveF19D9e3rtFCoIgCMIIo9tbPXeugzr+ZIhEsG/+Ivm7v4n95KPGyXug5/Lrn6K/f0uhbNz65H9A81Tse24kf8+N6DWrsJ/4PfnvXIfu7kS74dIh2H9/HNtpb7+/2Hd+DfumLxSFchfm7GYqVdWgyitRkQjqn9+DOufDWDf9EOobsf/6x/12fel0Gr36RXTrLrO95lWYvQDr8q+iLrwctIaWndCyA/23x9CvPm9OrK5BP/Un877ZaRfe1AzHnIT+48OgLNQYDmS3Tnk71pJ3oI49CVVTO9rTEYQJS+S66667brQnMVL09vbuedAhRGVlJQMDY7yWXCjiYDwz/fzf0E/8AQ47CjVEq9ZB52XS5ovF9NmhAYr68d/DprWoqho47Cj0z+9FLTkLtqyHmjrUEceO5DLMPTtaoXUXyummVti/djX2jf8B1bV77Oihtca+92bTqWagH1CoI47Z873//L9Y6RR6zkJY/zrqbWeh170Gz/8N9S/vQ9XUAU4+QAmXkosqi6PmHY5KTEY/9gh68zpUKgXdnVjnfdS4mJ7/G3S2g7LQf/7d/9/evcdHVd17H/+smcmVhJALd4IgEBAFBLFaFRVQtEqrUIu22uppPT6IVM/x2KOoR6ynfV56FG+tFnv0oKYHre0jUqutN1CkgCAIIogSbmKAhNzIPZmZvZ4/VjKAJCQDgczo9/168SKZ7D2z1/7t2Xvt314X7IdL8U38bpvbeTzZ+jrwB2JqxpqW6NwYfxSz+KOYxaeW4mbXrcT+4204adQh53f72kvQvTe+b42LvGaSkjF5I8DzYPMGWPEudEnDnHjskg/WWuyf/gdqqmH3TggkYK6ejjn3IkhMgiV/xy59y7X6Kd6NfedV7PLFkJGJXfm+a3Hj80FKKsYYvGcegfUfYkaOda1cjnS79pVjX/w97Ct34/oAdsViSEqGwu2uRVcoiJny48hYQiaQgMk7BZPSBerrYGlTHWDHFjgxD8pKoXwvpHfDGNPqd81uXIv9cCm2ohT7+H3Yd193cczMgXf/hjntbHwjx7pZ1M67GHPRFOyn62DlEvhoOXTv5epxn62H7r3w/fxu6NEH35QfY844D1u4A/OdK/CNOv2Qz5bW6dwYnxS3tqWnp7f6N3V/E/ma8V5+HooKsWUl+H7wT5DaBZOa1uZ69h/vYOfPhfISzGVXYz0Pu+QNTEYmZvSZ2K1ubCG7fJHrxgWYb4/HFhViVy7BXnZ1q13gvH+8jcnqHkk82WAjbNkEmTmYnn1a3p5PVrum2caHb/bj0LM3lJdiP/8E+8YCqCjFznsUr2QP5rs/bDXRYVe86yqO034GO7dh//Zn7NCTD57NpKX1dm4lYeAQgqechp33GN7//cX+qXaz2tmF7ivM6DMxP5mJfe43bkrmfgNh1BmQlOJmkAPsW6+4LnaAra3BHMH0vceCrSjFu3sG5rIfYS68rLM3R0SiYION2DXLMX36a2ptAVyShn3lLqH0B9dix5w0Eg64NlprobT44Ekpmpi8kzF5J7sHN7/5T+zL+XjBIOaiqR3eHd421LvrfvFud/2xQO4A1x0rkIC5dBr23Itg62euZVBVBfbDf8CX27H//ZB7j+bW1cNG4pvyY/hyG+DqTL7v/RBOHNryAzXPg7173GDVXdL375cvtmK3fOoGibYWRp/pHhC9/6Zb5rWXDnofk9DyzGRm8jRITHQJohWLses/dAkqgP6D8N1yD+Tk7N+e+lps/u/A53PJq2Z9T8BceT32pWew//MIdMvCTJy8/3N8fkhOxXfjHXiP3gt1tZiRp2POmojdvhnflddjuvfC9M6NrOO/+Z62gyMigpJKInHPhoKR1iM2HHZPtwA+Wo73yYdgwVz7c3xnnIf9cht2zQrM5GmHVp6aKlj2r38kvHyxGzfo8w0u+ZHSBepq3FSydbXuaV//QTBgiBsv6JlHYPNGGPqV2U0Au3cP9tnHsYD5zvehpga7eYN70giQ3cP1i//+tQdVRL13/+Zm8ggF8f7rdmhsbBoPAAgE8E2/A7v+Q+yrL4I/ABdNcc20/X6stW5/bPgI+9zjMGQ4ZsJkCDZidxTgPfFrzKSp0FiPGX8pJqfnwdtcXwt7Ckk45wJC354AoRA2/wk3O0pqGiY55Yjj5TvnQjx/APs/j2BOGummxh1xGvbDpXDSKPh03cExaZoxxgaDmAQ3noEtLYaKskiTdFtbg331BczE7x5Slo5i33sDGuqwr72EPefCg8bXEJHYZK2FokK8uQ9A4Q5sSiq+W/8TM2BI+9b3wrC7EHJ6YpJaGC9G4o4NBgn/9lew6WNo7g4+aBh8uR3vkdkwdATmtLMwp5wGyaluqvbDjEVojMF37c/x8p/Avvy8G/No7NmYcyZhAtHfZthQCMIhKNqFfWshtqYKinZB8S6X+LnkB5i0Q7s5mfQMGPUtIo+XLv4+du0H7uHMmG+7a+y+cuzrf8J78E63zrhJ2GXv4D1wB3RJh25NraIbG6CkyNVBfD63n/x+1yJq6EjY8mmkVRLgWvn8n9tdS6o+/TGjv41d55JNdtX7Ta2kW9l/Pj/m4u+77d2yCe/R2a7F2NhzsPPnYl98msap12D37MJu2YRdvWx//emkUfgu+YEbU+ryazBd0rDDT4XPP4GBQ1psgWWyursHdYXboWdfTFIy/pl3Rx0nEZEDGduZUze0Ye3atcybNw/P85g4cSKXX375YZfftWvXcdqy2JCTk0NJSUlnb4ZEoaNjZtetxPvvhyAxCd+MOyEpGe++WzBTr8UuyHfNvbN7uIE2u2WDF4bKCsy0n2HOOBeKdmNLizHDRrqKTF0tZvBw7BcFUFaC+cFP3Sxl1VXY+lp8516EXb4Yc8U/YTIy3TY0NOD9+z9BcjLm4itg9xeQlOIG0szpgX3zFffkLncg7NzmZufIzMFcPNUlmDasgfUfusRPz76YUd9yTbn/dy5mwmQ3IOjSNyGtK/Tqixk4FHr0wSQludZUT89xlTafD5JTICML9u52P4fDkNUd3+0PRJIgtrIC76n/cpUucF338k5xibmr/hmT3hXv3dex/zuXzPt/T2V2LwDCj9/ntnNgHv47Hzr62AUbXRIsEMCu/gfe3Afw3f4A3gO3R5YxV16P74LvuYrmQ3dhrp6O75wLCd/7cyjcgbnsaszFU7H5T7pBOEeMxXfl9djNGzBnTTzqp8U2FMIufs09kV27wjWp370TM2Eyvh/ecLS74JjpyO9Z8xNpaiqh/6DIzYy1FvvmAmzBJnwXXobJO7lDPu+bStezjmNDQdeSo74W75mHYcNHrjXHNTe6JHzzFOe9c13rhMOMl+LNn4td/DoEAtA7FxKTMEPcsZ42YBA1p5x+SLLJ1tXiPTjLtYK4eCp2/WrXSvMYDPJr62vdNrXWwsSYqLrrWi8M5aWY7B5tL3zQdtRBQmKHTJV+rHX5+AMqf/NrzFkT4YRBmD79YegI+PhD15Xq03WwrwwSE6F3f9hRgG/GnW4cpcOInBPf/ZtLyPTOxQw+CVtTHRnA2paXuO7i2T3c35OSsI2NrqXP9gLssrddQifsufpLSirk9ISkFHyXXIEZMfaoy283b3STa5w8Gt+4SW4Skg1rYcNqbJ3r+mL8Afe5oaDr4tf3BCgphrJi7AdLYPgo9zDs5DFQW+UeNrXSgtlaC9Zr8RhtcfnaakhOwfj8ePlPYpf8ff8fjQ8GDMZ30RSs52GGj8Z0absluhxfup7FJ8WtbX36tNy7BGI4qeR5Hrfccgt333032dnZzJo1i1tuuYV+/fq1uo6SShLrOjJm3qql2GfmuC5U1ZWuUpaZDVs24fv1XOyqpS55c/o4189+41qXSOnZxyV3DpScAvV1mMt+hG/yVS7hUV8XGTeoLXZHAd7vH3JPEpOSoflJYxNz/iX4rp7uKmxJyQclOyIV0RXvuYGwm2dKAfdEvY2xmqznuTEEigpd8qu22rXWqa7C7tyK74Z/x/Q+9LxhKyugah/eH592M9jU1UJCIuQOgN1fQlZ3ejzyHKWlpW75YNCNHdE7t80xlKJlrYUvt2NyB+K9/yZs+RS7fjX0G4Dv+tvw8n8LH61wC4883Y0Z0cwfcPu6d657etn8+ymnYfoPgr79MX0HQM8+LoHV1Iqrxe1oavVGeSls+wzvrYWum2JGpmuGf9U/uzGh3nnVPRFOSnE3Ht17uW6M2T2axoUybmDXtAx3XHbLAmvbleSyNU0V6sPcnFlroaIMunY7aDnrhcHzyOnZi9LSUrdcfZ07vhsbIRxstSuo9cIQDLmbZ4P7ua4a+3J+pLtnczcLM2wkVJS65GRSMvj8mLFnu5Z7J+ZBWgZ240euzCeNcjfVA/OgW6br6piY1KHjUllr9z9Vz8h0+7+h3m1bE2NMq7G3XtjNQtQl/aAWaDYcBp+vzW211rrZg/yBdi3fkqM9N1rPcy0PY2S6685gPc+NI/NKvjvewSWuv3cV5tQzMf0GYPfuwXvmYUz3XtjNG905t98Ad0z2HYD9cps7boKNUPgF1NVgzjwfumZid++E+lrXKtX43E1/tyzocwKmawb07At+P/az9S6RBe5hQHWlS1qEgu44PWGwu1Ykp7pzVEY3qChz5+60rtjmGbHCYdc6pLrS/ZyYiMnq7gZDrqt114ovd7j1c3pBUpIbf6+xwSWbtm12D1KsdQ8dwHV7ttYNZhxshB693eDFqV1cmepr3bl40hRISnLnmcwcqKzAfrnddVNubIRgA2bUtyC9m+v+9MUW990+aaRrwZKRCcGgSyZ0ScdWlEFKF0xKKrauxiXYklPcv6Rkt+7OrW5/ZWRC1T5s8W5ISMAkJWPr6qB6n0uSZWS51jz7yiAYdOec5FRISXHTv/t87lywrwyMwRbucJ9RtMuVub4WevVzs5q2dD4Ih6GkyE0qUbwLwmF8N86KPEhq8zi0FtZ9gPdyvktgdklvShSFISvH7dPyUnf8fIU57WyXzElw+8eMm9Su7vvHk62vO6rWylF9Vk01dvkiMoYMo7KhEXJPVCvhOKD7s/ikuLUtLpNKn3/+OX/605+46667AFiwYAEAU6ZMaXWdr0NSyfvvOdiSPe1aNiEQIBgKtb2gxIwOi1ko5Cqxg0/Cd/Ns2PMl3u/uh/IS6HsCvtmPt37z2Njgxhqor8P07AuBAN5Lz0DVPnw3z8b0P/GINsl6nktq9Ojjbh6+2ILdtdNVDM88v103mrahAbt6KSa7J1RVwGlnH7dBoe32zdglb2CLCt04TpdOo/u4iZ12gfH+9v+wLz8X+d2cPdHNZPPRCkhKdrPGFGzCbloHA/MwY87Cvvs6FO6AlFQ3llRNlXvKCu6GPyXFNcNP6eJiFA65GxDjd4mU+rr9sgdj2AAAE9BJREFUN4vgbuKuuwXf6fsHaLWNDdiF87EfuVkAaah3UzsfTiDgjtmERHezEAq5mxtXMvAZ979h/3Je0w1lQlMrCOu51hdYt92NjW45n++A98KV1+d3yS6fz5X3gDGrSEx0+8Lvd/8bn/tbXSuDMxof5jtXYIaNwHvzFfhk9f6/9c7FN/MuvGcfd8d+dTsnizjomG4qtzFNrzf933xj2FYizlp389pcvkDArVdf17S+cQmyFJc4Jr2be//G+qZEW8i9R/O66d3czzVV7kbQmKb3aTpGDtp+4/ajtftbv0DTvvW74yoccrELJDS95ttfVl/zzz58AT+eZw96zX2OdduRkLS/1UDTR0f2FbjuuXW1brwzz3NlCQbdceQ7YFrpUMj9nJLqti0cdv98vkhSzL3WtF+sdceZMU3HoHXvb63bNq9pmeZdEgq5G/jEJLe8d+iNc6RsvgP/P+Dn9mip6lZX65LrI8a61kc+P2bEGEy/lsdQsrU1rnvR1k1u3+7c7pJExrix+PoNdAnj8ZdGut6C69JMSioZ1RWU//l59/3fVx6ZHRN/ADPpcuiSjt30MaZ7T/fQICvHJbZ2bIGu3Vxs9hS6pFF6hkuw1Na4xINtOo6s55ISCYkuGVJe6vZvcopL0vTp7xIgNVWu/JUVLhmUnOoS3SmpLv7WQl0N9tOP3WuZ2e6YLC9xrXgb6ly5a2vc75+sdr+ndXVJouQU19o2NW3/+ezTte47lDsQM+bbUFbiWt5m5rjBpJOS3XtUV7r16mrcsZWS6tZvqHPfyYZ6t33pGa4MoaA7fnr0cfuood6dN7tlu+XK9rr90Zzk+WKrOwc2Nrjzm+e590/PcK/3GwAN9S6eSUkkp3ah4dQzXaK7k9hQyJ0zw6H9+yk5FdMtq+2Vv4F0sxtfFK/4pLi1LS6TSitWrGDt2rVMnz4dgCVLlrB582Z+9rOfRZZ5++23efvttwG4//77aWxsbPG94knl7x4gXLy7Xcs2P3mW+NGRMUsYNpIuU67e36zcWvekNzUt5mfniheBQIBQJyZuGz7+kPAXW7HWkjL+O/jSuhIuL8U21BPo1bfN9W2wkdCXOwjt2ELoy+3Y6io35kJtDaY5ueJ5kZtl0yUdgo2Y9AwSTxmNP6cnvnY8nfbqagjvKSS8Zxf+7O5gDOHi3Xj7yrGhEF5ZCSY52SWkGhvczDeJScD+G3XbdAPvS8/Aqyh1N3zhsGuVYEwkiWCabsT9OT0JlxYDHNytJikZXyhIuK4WGwziz+5OuKIUf7ds8Afw9pVDOBQZt8N6Hr6UVPe9SUpyr1vrxgIJJJA05tsEcge4/WktXmkxwc824FWWk3TGufibujxYawkX7iC0vQCvuhJ/996YpGSCWz8j4cShhL7Y4loN1NW6rjI0JUyayu3yZXZ/8swLu9ZxLSUlDmTcALD+PrmAIVy8C9vQgD8rB6+6CrwwJjEJr7YGk5SMt6/MJcqSk92shIEE8AfwZWQSLt6FV7nPxaF5UNpQyLVcDH/le9B0HnNdVzz8Pfu4pHXIJWRsc2KmOTEWbMSGQ01l8/aX1fOw1uIDvHB4//EQSdy42RNtY4Mb6LY5iYVtSjISWcbXNYNw0a7IWGwEEjCBBLddTe9tEhLdoNV1dRAIuJZu/oDb383b7Pc3HVMuaWabxp0xX014NSWBmiLpjpuEBGx9nVvH78dEkmP795uNJKaakk5N+8D93vR/i+fwFl47cDkDyWeeT/LEycflGvDV86Otr3PfpygnF7Ch0BGNvXMshSvKMD4/vq4Z7vj3Bw5padl8vDRfg4+UtdYljhKbEqfGtDqo82HfJxh037XmVlmtHAOdfV2T6Clm8UXxik+KW9sSE1u/NsV1Uumrvg4tlaKhjGr8Uczii+IVnxS3+KOYxR/FLD4pbvFHMYsvild8UtzadriWSh0752cHysrKioxlAlBaWkpWlprFioiIiIiIiIjEgphNKg0aNIjdu3dTXFxMKBRi2bJljB179LM+iIiIiIiIiIjI0YutTuwH8Pv9/PSnP+XXv/41nucxfvx4cnNzO3uzRERERERERESEGE4qAYwZM4YxY8Z09maIiIiIiIiIiMhXxGz3NxERERERERERiV1KKomIiIiIiIiISNSUVBIRERERERERkagpqSQiIiIiIiIiIlFTUklERERERERERKKmpJKIiIiIiIiIiERNSSUREREREREREYmakkoiIiIiIiIiIhI1JZVERERERERERCRqxlprO3sjREREREREREQkvqilUhy74447OnsTJEqKWXxRvOKT4hZ/FLP4o5jFJ8Ut/ihm8UXxik+K29FRUklERERERERERKKmpJKIiIiIiIiIiETNf++9997b2RshR+7EE0/s7E2QKClm8UXxik+KW/xRzOKPYhafFLf4o5jFF8UrPiluR04DdYuIiIiIiIiISNTU/U1ERERERERERKIW6OwN+CYpKSnhiSeeoKKiAmMMF1xwAZdccgnV1dU88sgj7N27l+7du/Ov//qvpKWlUVhYyJNPPsm2bdu46qqr+N73vhd5r7/+9a8sWrQIYwy5ubnMmDGDxMTEQz7z3Xff5eWXXwZg6tSpnH/++QAsW7aMl19+Gc/zGDNmDNdcc81x2QfxpiNj9vrrr/POO+9grWXixIlceumlLX7m2rVrmTdvHp7nMXHiRC6//HIA/v73v/Paa69RVFTE008/TdeuXY/LPognsRSvJ554go0bN5KamgrATTfdxIABA475PohHsRS3Tz75hPz8fEKhEAMHDuTGG2/E7/cfl/0QT6KN2fvvv8/ChQux1pKSksL1118f+T60Fouvau169sILL7BkyRKqq6vJz88/HsWPS7EUs3vvvZfy8vJIveXuu+8mIyPjmO+DeBRLcVPdsX06MmZPPvkka9asISMjgzlz5rT6mao7HrlYipfqju0XS3FT3RGwctyUlZXZLVu2WGutra2ttTfffLPduXOnzc/PtwsWLLDWWrtgwQKbn59vrbW2oqLCbt682c6fP98uXLgw8j6lpaV2xowZtqGhwVpr7Zw5c+zixYsP+byqqip700032aqqqoN+rqystNOnT7f79u2z1lr7m9/8xn788cfHsuhxq6NitmPHDnvrrbfa+vp6GwqF7H333Wd37959yOeFw2E7c+ZMu2fPHhsMBu1tt91md+7caa21duvWrbaoqMjOmDEjEjs5WCzF67e//a1dvnz5sS7y10KsxC0cDtvp06fbwsJCa621L774on3nnXeOdfHjUrQx27Rpk62qqrLWWrtmzRo7a9Ysa+3hv0MHau16Zq21n332mS0rK7PXXHPNMS93PIulmM2ePdsWFBQc8zJ/HcRK3FR3bL+Oipm11m7YsMFu2bLF3nrrra1+nuqORyeW4qW6Y/vFStxUd3TU/e04yszMjAwAlpKSQt++fSkrK2PVqlWcd955AJx33nmsWrUKgIyMDAYPHtxiptPzPBobGwmHwzQ2NpKZmXnIMmvXrmXkyJGkpaWRlpbGyJEjWbt2LUVFRfTu3TvytGLkyJF88MEHx6rYca2jYlZYWMjgwYNJSkrC7/dz0kkntbjPCwoK6NWrFz179iQQCHDWWWdF3nvgwIH06NHjWBY37sVSvKT9YiVu1dXVBAIB+vTpA+jceDjRxmzo0KGkpaUBMGTIEEpLS4H2f4dau54B5OXltXgNlIPFUsyk/WIlbqo7tl9HxQxg+PDhkb+1RnXHoxNL8ZL2i5W4qe7oKKnUSYqLi9m2bRuDBw9m3759kQpxt27d2Ldv32HXzcrK4rvf/S433ngjN9xwA6mpqYwaNeqQ5crKysjOzj5ovbKyMnr16sWuXbsoLi4mHA6zcuVKSkpKOraAX0NHE7Pc3Fw2bdpEVVUVDQ0NfPTRRwedzJp9NWbZ2dmUlZV1bEG+IWIhXi+88AK33XYbzz77LMFgsINK9vXWmXFLT08nHA6zZcsWAFasWKFzYztEG7NFixYxevRooP3nvNauZ3JkYiFmTz75JL/4xS/485//jNWcMe3SmXFT3fHIHE3M2kt1x44TC/FS3TF6nRk31R0djanUCerr65kzZw7XXXddpM9sM2MMxpjDrl9dXc2qVat44oknSE1N5eGHH2bJkiWce+657fr8tLQ0rr/+eh599FGMMQwdOpQ9e/YccXm+CY42Zv369eOyyy7jV7/6FcnJyQwYMACfTzndYyUW4vWjH/2Ibt26EQqFeOqpp1i4cCFXXHFF1GX5JunsuBlj+Jd/+Reee+45gsEgo0aN0ve0DdHG7JNPPmHx4sXcd999x3Mz5QCxELObb76ZrKws6urqmDNnDkuWLIk8WZaWdXbcVHeMXmfHTKITC/FS3TF6nR031R0dJZWOs1AoxJw5cxg3bhxnnHEG4LpylJeXk5mZSXl5eZuD6K1fv54ePXpEljvjjDP4/PPP6d27N7///e8BuPLKK8nKymLjxo2R9crKyhg+fDgAY8eOZezYsQC8/fbb38iDv706ImYAEyZMYMKECQDMnz+f7OxsSkpKeOCBBwC48MILGTBgwEEtK0pLS8nKyjoGpfr6ipV4NT8lSUhIYPz48bz66qsdWs6vm1iJW15eXqSisW7dOnbt2tWh5fw6iTZmO3bs4KmnnmLWrFmkp6cDrhVES7HYvHlzu69n0n6xErPm71tKSgrnnHMOBQUFSiodRqzETXXH9uuImLVGdceOFyvxUt0xOrESN9Ud1f3tuLLWMnfuXPr27cvkyZMjr48dO5b33nsPgPfee4/TTz/9sO+Tk5PD5s2baWhowFrL+vXr6du3L0OGDOHBBx/kwQcfZOzYsZx66qmsW7eO6upqqqurWbduHaeeeipApClgdXU1b7zxRuQmTA7WUTGD/fu8pKSElStXcs4555CTkxOJ2aRJkxg0aBC7d++muLiYUCjEsmXLIhU4aVssxau8vDyyTatWrSI3N7eji/u1EUtxa14/GAyycOFCJk2a1NHF/VqINmYlJSU89NBDzJw5MzLuANBqLKK5nkn7xErMwuEwlZWVgLshWL16tc6PhxErcQPVHduro2LWGtUdO1YsxUt1x/aLpbip7gjGqiP7cbNp0ybuuece+vfvH2mK98Mf/pAhQ4bwyCOPUFJSctDUhxUVFdxxxx3U1dVhjCE5OZmHH36Y1NRUXnrpJZYtW4bf72fAgAFMnz6dhISEQz5z0aJFLFiwAHDTwo4fPx6ARx99lB07dgBwxRVXcPbZZx+nvRBfOjJm99xzD1VVVQQCAX7yk58wYsSIFj9zzZo1PPfcc3iex/jx45k6dSrgpkr/y1/+QkVFBRkZGYwePZrp06cft30RD2IpXr/85S8jN04nnHACN9xwA8nJycdnR8SZWIpbfn4+a9aswfM8Jk2axKWXXnrc9kM8iTZmc+fO5YMPPiAnJwcAv9/P/fffD7Qei69q7Xr2hz/8gaVLl0aeTE6YMIFp06Yd610Qd2IlZvX19cyePZtwOIzneYwYMYJrr71WrV5aEStxA9Ud26sjY/boo4+yceNGqqqqyMjIYNq0aS0m81R3PHKxFC/VHdsvluKmuqOSSiIiIiIiIiIicgT0WEhERERERERERKKmpJKIiIiIiIiIiERNSSUREREREREREYmakkoiIiIiIiIiIhI1JZVERERERERERCRqSiqJiIiIiIiIiEjUAp29ASIiIiLx7KabbqKiogK/34/P56Nfv36ce+65XHDBBfh8h39+V1xczMyZM3nhhRfw+/3HaYtFREREOoaSSiIiIiJH6fbbb2fkyJHU1tayceNG5s2bR0FBATNmzOjsTRMRERE5ZpRUEhEREekgqampjB07lm7dunHXXXcxefJkSkpKePHFFykqKiI1NZXx48czbdo0AGbPng3AddddB8B//Md/kJeXx6JFi3j11VepqKhg8ODB3HDDDXTv3r2ziiUiIiLSIo2pJCIiItLBBg8eTFZWFps2bSIpKYmZM2cyb9487rjjDt566y1WrlwJwC9/+UsAnn32WfLz88nLy2PVqlUsWLCAf/u3f+Ppp59m2LBhPPbYY51ZHBEREZEWKakkIiIicgxkZWVRXV3NySefTP/+/fH5fJxwwgmcffbZbNy4sdX13nrrLaZMmUK/fv3w+/1MmTKF7du3s3fv3uO49SIiIiJtU/c3ERERkWOgrKyMtLQ0Nm/ezPz58/niiy8IhUKEQiHOPPPMVtfbu3cv8+bN4/nnn4+8Zq2lrKxMXeBEREQkpiipJCIiItLBCgoKKCsrY9iwYTz44INcdNFFzJo1i8TERJ599lkqKysBMMYcsm5OTg5Tp05l3Lhxx3uzRURERKKi7m8iIiIiHaS2tpbVq1fz2GOPMW7cOPr3709dXR1paWkkJiZSUFDA0qVLI8t37doVYwxFRUWR1y688EJeeeUVdu7cGXnP5cuXH/eyiIiIiLTFWGttZ2+EiIiISLy66aabqKiowO/3Y4yhX79+jBs3jkmTJuHz+VixYgXPP/881dXVDB8+nO7du1NTU8PNN98MwB//+EfefPNNwuEwd955J3l5eSxZsoSFCxdSUlJCamoqI0aMYMaMGZ1cUhEREZGDKakkIiIiIiIiIiJRU/c3ERERERERERGJmpJKIiIiIiIiIiISNSWVREREREREREQkakoqiYiIiIiIiIhI1JRUEhERERERERGRqCmpJCIiIiIiIiIiUVNSSUREREREREREoqakkoiIiIiIiIiIRE1JJRERERERERERidr/B0VVLXaNnu2VAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"code","source":["df.corr()['Revenue'].sort_values(ascending=False).reset_index()[1:].style.bar(align='mid')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":457},"id":"4tW-JMcYTcYT","executionInfo":{"status":"ok","timestamp":1668958931212,"user_tz":-330,"elapsed":467,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"5ae7c458-8ff5-46c7-cecf-53fe5ac350fa"},"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7fc0cc72c210>"],"text/html":["<style type=\"text/css\">\n","#T_3adbc_row0_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg, transparent 24.7%, #d65f5f 24.7%, #d65f5f 100.0%, transparent 100.0%);\n","}\n","#T_3adbc_row1_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg, transparent 24.7%, #d65f5f 24.7%, #d65f5f 99.6%, transparent 99.6%);\n","}\n","#T_3adbc_row2_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg, transparent 24.7%, #d65f5f 24.7%, #d65f5f 99.4%, transparent 99.4%);\n","}\n","#T_3adbc_row3_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg, transparent 24.7%, #d65f5f 24.7%, #d65f5f 98.5%, transparent 98.5%);\n","}\n","#T_3adbc_row4_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg, transparent 24.7%, #d65f5f 24.7%, #d65f5f 98.4%, transparent 98.4%);\n","}\n","#T_3adbc_row5_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg, transparent 24.7%, #d65f5f 24.7%, #d65f5f 96.7%, transparent 96.7%);\n","}\n","#T_3adbc_row6_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg, transparent 24.7%, #d65f5f 24.7%, #d65f5f 94.1%, transparent 94.1%);\n","}\n","#T_3adbc_row7_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg, transparent 24.7%, #d65f5f 24.7%, #d65f5f 86.9%, transparent 86.9%);\n","}\n","#T_3adbc_row8_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg, transparent 24.7%, #d65f5f 24.7%, #d65f5f 74.2%, transparent 74.2%);\n","}\n","#T_3adbc_row9_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg, transparent 24.7%, #d65f5f 24.7%, #d65f5f 69.3%, transparent 69.3%);\n","}\n","#T_3adbc_row10_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg, transparent 24.7%, #d65f5f 24.7%, #d65f5f 65.3%, transparent 65.3%);\n","}\n","#T_3adbc_row11_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg, transparent 24.7%, #d65f5f 24.7%, #d65f5f 41.1%, transparent 41.1%);\n","}\n","#T_3adbc_row12_col1 {\n","  width: 10em;\n","  height: 80%;\n","  background: linear-gradient(90deg,#d65f5f 24.7%, transparent 24.7%);\n","}\n","</style>\n","<table id=\"T_3adbc_\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th class=\"col_heading level0 col0\" >index</th>\n","      <th class=\"col_heading level0 col1\" >Revenue</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n","      <td id=\"T_3adbc_row0_col0\" class=\"data row0 col0\" >Watch_hours</td>\n","      <td id=\"T_3adbc_row0_col1\" class=\"data row0 col1\" >0.984514</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n","      <td id=\"T_3adbc_row1_col0\" class=\"data row1 col0\" >Unique_viewers</td>\n","      <td id=\"T_3adbc_row1_col1\" class=\"data row1 col1\" >0.978712</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n","      <td id=\"T_3adbc_row2_col0\" class=\"data row2 col0\" >Views</td>\n","      <td id=\"T_3adbc_row2_col1\" class=\"data row2 col1\" >0.976545</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n","      <td id=\"T_3adbc_row3_col0\" class=\"data row3 col0\" >Likes</td>\n","      <td id=\"T_3adbc_row3_col1\" class=\"data row3 col1\" >0.964728</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n","      <td id=\"T_3adbc_row4_col0\" class=\"data row4 col0\" >Dislikes</td>\n","      <td id=\"T_3adbc_row4_col1\" class=\"data row4 col1\" >0.963711</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n","      <td id=\"T_3adbc_row5_col0\" class=\"data row5 col0\" >Impressions</td>\n","      <td id=\"T_3adbc_row5_col1\" class=\"data row5 col1\" >0.941682</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n","      <td id=\"T_3adbc_row6_col0\" class=\"data row6 col0\" >Subscribers</td>\n","      <td id=\"T_3adbc_row6_col1\" class=\"data row6 col1\" >0.907303</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n","      <td id=\"T_3adbc_row7_col0\" class=\"data row7 col0\" >Shares</td>\n","      <td id=\"T_3adbc_row7_col1\" class=\"data row7 col1\" >0.812967</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n","      <td id=\"T_3adbc_row8_col0\" class=\"data row8 col0\" >Average_view_sec</td>\n","      <td id=\"T_3adbc_row8_col1\" class=\"data row8 col1\" >0.646830</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n","      <td id=\"T_3adbc_row9_col0\" class=\"data row9 col0\" >Average_views_per_viewer</td>\n","      <td id=\"T_3adbc_row9_col1\" class=\"data row9 col1\" >0.583606</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n","      <td id=\"T_3adbc_row10_col0\" class=\"data row10 col0\" >Average_viewed</td>\n","      <td id=\"T_3adbc_row10_col1\" class=\"data row10 col1\" >0.530312</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n","      <td id=\"T_3adbc_row11_col0\" class=\"data row11 col0\" >Videos_published</td>\n","      <td id=\"T_3adbc_row11_col1\" class=\"data row11 col1\" >0.214147</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_3adbc_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n","      <td id=\"T_3adbc_row12_col0\" class=\"data row12 col0\" >Click_rate</td>\n","      <td id=\"T_3adbc_row12_col1\" class=\"data row12 col1\" >-0.322937</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["plt.figure(figsize=(10,6))\n","sns.regplot(data=df, x='Watch_hours', y='Revenue')\n","plt.title('Revenue by Watch_hours', fontsize=20)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":412},"id":"C9KVrNKqTezF","executionInfo":{"status":"ok","timestamp":1668958935836,"user_tz":-330,"elapsed":767,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"e03b73e7-a0cf-48ab-fb5c-c665b5229cf6"},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x432 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.figure(figsize=(7,5))\n","sns.histplot(df['Revenue'], kde=True)\n","plt.title('YouTube Revenue per day', fontsize=15)\n","plt.xlabel('Revenue (USD, $)')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"gyTBWTCxThOt","executionInfo":{"status":"ok","timestamp":1668958939908,"user_tz":-330,"elapsed":1089,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"300082a7-6bfc-4a23-9c47-a4120f07203a"},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 504x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["df = df[(df['Revenue'] <= 150)].reset_index(drop=True)\n","plt.figure(figsize=(7,5))\n","sns.histplot(df['Revenue'], kde=True)\n","plt.title('YouTube Revenue per day', fontsize=15)\n","plt.xlabel('Revenue (USD, $)')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"RJvCtBegTjmQ","executionInfo":{"status":"ok","timestamp":1668958942839,"user_tz":-330,"elapsed":833,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"fead275e-329e-4966-ebbc-b4e7ec2298ca"},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 504x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.figure(figsize=(7,5))\n","sns.histplot(np.log(df['Revenue']), kde=True)\n","plt.title('YouTube Revenue per day', fontsize=15)\n","plt.xlabel('Revenue (log transformation)')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":390},"id":"t_goFQVDTl1j","executionInfo":{"status":"ok","timestamp":1668958945554,"user_tz":-330,"elapsed":767,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"12f8c064-5535-4c13-bd1e-68ae121b57df"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n","  result = getattr(ufunc, method)(*inputs, **kwargs)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 504x360 with 1 Axes>"],"image/png":"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'std', 'min', '50%', 'max']]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":270},"id":"MJoPX6dhTp8i","executionInfo":{"status":"ok","timestamp":1668958947343,"user_tz":-330,"elapsed":27,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"34f9040a-07b5-455a-9b1d-1a30b0dfc172"},"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Average_views_per_viewer  Unique_viewers  Click_rate   Impressions  \\\n","mean                  1.614731    18141.957234    6.616151  6.165458e+05   \n","std                   0.321479    33306.404588    2.259077  1.345969e+06   \n","min                   1.000000        8.000000    3.520000  4.400000e+01   \n","50%                   1.545200     3652.000000    6.140000  6.588600e+04   \n","max                   2.615800   182161.000000   23.140000  6.615916e+06   \n","\n","           Shares    Dislikes         Likes  Average_viewed  Videos_published  \\\n","mean    87.180164   29.328480   1760.494995       28.441228          0.138308   \n","std    192.646676   54.560777   3747.027020        5.768121          0.429955   \n","min      0.000000   -1.000000     -1.000000       10.950000          0.000000   \n","50%     20.000000    5.000000    244.000000       27.800000          0.000000   \n","max   2233.000000  284.000000  28326.000000       62.190000          4.000000   \n","\n","      Subscribers          Views   Watch_hours  Average_view_sec     Revenue  \n","mean   380.376706   37299.474067   2710.063589        179.736124   13.229895  \n","std    683.353242   74876.404616   5858.248179         56.878049   27.814934  \n","min     -8.000000       8.000000      0.115200         51.000000    0.000000  \n","50%    111.000000    5792.000000    273.117200        174.000000    1.438000  \n","max   3665.000000  403655.000000  37234.927900        367.000000  147.525000  "],"text/html":["\n","  <div id=\"df-ae9cd319-8eb5-4216-91d0-25e1185311de\">\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>Average_views_per_viewer</th>\n","      <th>Unique_viewers</th>\n","      <th>Click_rate</th>\n","      <th>Impressions</th>\n","      <th>Shares</th>\n","      <th>Dislikes</th>\n","      <th>Likes</th>\n","      <th>Average_viewed</th>\n","      <th>Videos_published</th>\n","      <th>Subscribers</th>\n","      <th>Views</th>\n","      <th>Watch_hours</th>\n","      <th>Average_view_sec</th>\n","      <th>Revenue</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>mean</th>\n","      <td>1.614731</td>\n","      <td>18141.957234</td>\n","      <td>6.616151</td>\n","      <td>6.165458e+05</td>\n","      <td>87.180164</td>\n","      <td>29.328480</td>\n","      <td>1760.494995</td>\n","      <td>28.441228</td>\n","      <td>0.138308</td>\n","      <td>380.376706</td>\n","      <td>37299.474067</td>\n","      <td>2710.063589</td>\n","      <td>179.736124</td>\n","      <td>13.229895</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.321479</td>\n","      <td>33306.404588</td>\n","      <td>2.259077</td>\n","      <td>1.345969e+06</td>\n","      <td>192.646676</td>\n","      <td>54.560777</td>\n","      <td>3747.027020</td>\n","      <td>5.768121</td>\n","      <td>0.429955</td>\n","      <td>683.353242</td>\n","      <td>74876.404616</td>\n","      <td>5858.248179</td>\n","      <td>56.878049</td>\n","      <td>27.814934</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>8.000000</td>\n","      <td>3.520000</td>\n","      <td>4.400000e+01</td>\n","      <td>0.000000</td>\n","      <td>-1.000000</td>\n","      <td>-1.000000</td>\n","      <td>10.950000</td>\n","      <td>0.000000</td>\n","      <td>-8.000000</td>\n","      <td>8.000000</td>\n","      <td>0.115200</td>\n","      <td>51.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>1.545200</td>\n","      <td>3652.000000</td>\n","      <td>6.140000</td>\n","      <td>6.588600e+04</td>\n","      <td>20.000000</td>\n","      <td>5.000000</td>\n","      <td>244.000000</td>\n","      <td>27.800000</td>\n","      <td>0.000000</td>\n","      <td>111.000000</td>\n","      <td>5792.000000</td>\n","      <td>273.117200</td>\n","      <td>174.000000</td>\n","      <td>1.438000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>2.615800</td>\n","      <td>182161.000000</td>\n","      <td>23.140000</td>\n","      <td>6.615916e+06</td>\n","      <td>2233.000000</td>\n","      <td>284.000000</td>\n","      <td>28326.000000</td>\n","      <td>62.190000</td>\n","      <td>4.000000</td>\n","      <td>3665.000000</td>\n","      <td>403655.000000</td>\n","      <td>37234.927900</td>\n","      <td>367.000000</td>\n","      <td>147.525000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae9cd319-8eb5-4216-91d0-25e1185311de')\"\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 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<td>13.0</td>\n","      <td>7.38</td>\n","      <td>122.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>5.0</td>\n","      <td>53.59</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>15.0</td>\n","      <td>0.4666</td>\n","      <td>111</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2018-08-22</td>\n","      <td>1.1935</td>\n","      <td>31.0</td>\n","      <td>23.14</td>\n","      <td>121.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>44.58</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>37.0</td>\n","      <td>0.9575</td>\n","      <td>93</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c88cfa14-9c34-43c6-8b93-85505f31f276')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              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Subs_accumulated\n","df.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":211},"id":"tZy7dQZWZeFu","executionInfo":{"status":"ok","timestamp":1668960163452,"user_tz":-330,"elapsed":14,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"8def04d7-8e1b-4ce3-cd4c-9d7e1e851206"},"execution_count":29,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Date  Average_views_per_viewer  Unique_viewers  Click_rate  \\\n","0 2018-08-21                    1.1538            13.0        7.38   \n","1 2018-08-22                    1.1935            31.0       23.14   \n","\n","   Impressions  Shares  Dislikes  Likes  Average_viewed  Videos_published  \\\n","0        122.0     1.0       0.0    5.0           53.59               1.0   \n","1        121.0     0.0       0.0    4.0           44.58               0.0   \n","\n","   Subscribers  Views  Watch_hours  Average_view_sec  Revenue  \\\n","0          5.0   15.0       0.4666               111      0.0   \n","1          4.0   37.0       0.9575                93      0.0   \n","\n","   Subs_accumulated  \n","0               5.0  \n","1               9.0  "],"text/html":["\n","  <div id=\"df-cea8143f-24e3-4cfb-8140-68e7b86aeff5\">\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>Date</th>\n","      <th>Average_views_per_viewer</th>\n","      <th>Unique_viewers</th>\n","      <th>Click_rate</th>\n","      <th>Impressions</th>\n","      <th>Shares</th>\n","      <th>Dislikes</th>\n","      <th>Likes</th>\n","      <th>Average_viewed</th>\n","      <th>Videos_published</th>\n","      <th>Subscribers</th>\n","      <th>Views</th>\n","      <th>Watch_hours</th>\n","      <th>Average_view_sec</th>\n","      <th>Revenue</th>\n","      <th>Subs_accumulated</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2018-08-21</td>\n","      <td>1.1538</td>\n","      <td>13.0</td>\n","      <td>7.38</td>\n","      <td>122.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>5.0</td>\n","      <td>53.59</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>15.0</td>\n","      <td>0.4666</td>\n","      <td>111</td>\n","      <td>0.0</td>\n","      <td>5.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2018-08-22</td>\n","      <td>1.1935</td>\n","      <td>31.0</td>\n","      <td>23.14</td>\n","      <td>121.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>44.58</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>37.0</td>\n","      <td>0.9575</td>\n","      <td>93</td>\n","      <td>0.0</td>\n","      <td>9.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cea8143f-24e3-4cfb-8140-68e7b86aeff5')\"\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 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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":29}]},{"cell_type":"code","source":["plt.figure(figsize=(10,6))\n","sns.regplot(data=df, x='Subs_accumulated', y='Revenue')\n","plt.title('Revenue by Total Subscribers', fontsize=20)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":412},"id":"OXjlOpbvZkKZ","executionInfo":{"status":"ok","timestamp":1668960166283,"user_tz":-330,"elapsed":1103,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"2ce9d05b-487c-4a07-eaa1-178d6c5921ac"},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x432 with 1 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= df.Videos_published.values.tolist()\n","Videos = []\n","count = 0\n","for v in Video_pub:\n","    count += v\n","    Videos.append(count)\n","\n","df['Videos'] = Videos\n","df.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":211},"id":"3hhmdFEAZnGk","executionInfo":{"status":"ok","timestamp":1668960168124,"user_tz":-330,"elapsed":21,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"b0b4b697-8d68-4952-b159-128171f481d9"},"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Date  Average_views_per_viewer  Unique_viewers  Click_rate  \\\n","0 2018-08-21                    1.1538            13.0        7.38   \n","1 2018-08-22                    1.1935            31.0       23.14   \n","\n","   Impressions  Shares  Dislikes  Likes  Average_viewed  Videos_published  \\\n","0        122.0     1.0       0.0    5.0           53.59               1.0   \n","1        121.0     0.0       0.0    4.0           44.58               0.0   \n","\n","   Subscribers  Views  Watch_hours  Average_view_sec  Revenue  \\\n","0          5.0   15.0       0.4666               111      0.0   \n","1          4.0   37.0       0.9575                93      0.0   \n","\n","   Subs_accumulated  Videos  \n","0               5.0     1.0  \n","1               9.0     1.0  "],"text/html":["\n","  <div id=\"df-9933f5de-a37e-4bbb-8f7e-bf8e7825fb0d\">\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>Date</th>\n","      <th>Average_views_per_viewer</th>\n","      <th>Unique_viewers</th>\n","      <th>Click_rate</th>\n","      <th>Impressions</th>\n","      <th>Shares</th>\n","      <th>Dislikes</th>\n","      <th>Likes</th>\n","      <th>Average_viewed</th>\n","      <th>Videos_published</th>\n","      <th>Subscribers</th>\n","      <th>Views</th>\n","      <th>Watch_hours</th>\n","      <th>Average_view_sec</th>\n","      <th>Revenue</th>\n","      <th>Subs_accumulated</th>\n","      <th>Videos</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2018-08-21</td>\n","      <td>1.1538</td>\n","      <td>13.0</td>\n","      <td>7.38</td>\n","      <td>122.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>5.0</td>\n","      <td>53.59</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>15.0</td>\n","      <td>0.4666</td>\n","      <td>111</td>\n","      <td>0.0</td>\n","      <td>5.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2018-08-22</td>\n","      <td>1.1935</td>\n","      <td>31.0</td>\n","      <td>23.14</td>\n","      <td>121.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>44.58</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>37.0</td>\n","      <td>0.9575</td>\n","      <td>93</td>\n","      <td>0.0</td>\n","      <td>9.0</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9933f5de-a37e-4bbb-8f7e-bf8e7825fb0d')\"\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 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  4.04   \n","356 2021-09-26                    2.3480        132573.0        4.24   \n","\n","     Impressions  Shares  Dislikes    Likes  Average_viewed  Videos_published  \\\n","0        82419.0    32.0       3.0    344.0           23.68                 0   \n","1       162769.0    40.0      11.0   1602.0           30.79                 1   \n","2       182250.0    55.0      13.0    997.0           28.86                 0   \n","3       231165.0    62.0      29.0   1115.0           28.39                 0   \n","4       264697.0    62.0      27.0   1322.0           28.87                 0   \n","..           ...     ...       ...      ...             ...               ...   \n","352    4995859.0   539.0     197.0  12117.0           38.75                 0   \n","353    5140093.0   441.0     178.0  11353.0           37.68                 0   \n","354    4509271.0   453.0     163.0   9536.0           37.28                 0   \n","355    4376635.0   399.0     195.0  10594.0           25.75                 0   \n","356    5375782.0   555.0     191.0  14857.0           39.27                 0   \n","\n","     Subscribers     Views  Watch_hours  Average_view_sec  Revenue  \\\n","0          195.0    6166.0     305.4844               178    1.532   \n","1          268.0   15698.0     910.9613               208    4.096   \n","2          277.0   15979.0     854.6027               192    4.233   \n","3          342.0   19987.0    1038.3357               187    4.248   \n","4          453.0   22674.0    1177.9374               187    4.607   \n","..           ...       ...          ...               ...      ...   \n","352       2173.0  266218.0   20999.7588               283  112.856   \n","353       2222.0  261821.0   20159.2703               277  110.829   \n","354       1997.0  232530.0   17900.4225               277  107.846   \n","355       1809.0  240554.0   19512.3516               292  126.600   \n","356       2152.0  311277.0   22117.2441               255  135.841   \n","\n","     Subs_accumulated  Videos  \n","0             53582.0    60.0  \n","1             53850.0    61.0  \n","2             54127.0    61.0  \n","3             54469.0    61.0  \n","4             54922.0    61.0  \n","..                ...     ...  \n","352          409854.0   152.0  \n","353          412076.0   152.0  \n","354          414073.0   152.0  \n","355          415882.0   152.0  \n","356          418034.0   152.0  \n","\n","[357 rows x 17 columns]"],"text/html":["\n","  <div id=\"df-9720470d-57de-4f82-8ba5-7a5d2315c3b0\">\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: 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<td>1.532</td>\n","      <td>53582.0</td>\n","      <td>60.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2020-09-03</td>\n","      <td>1.6349</td>\n","      <td>9602.0</td>\n","      <td>6.96</td>\n","      <td>162769.0</td>\n","      <td>40.0</td>\n","      <td>11.0</td>\n","      <td>1602.0</td>\n","      <td>30.79</td>\n","      <td>1</td>\n","      <td>268.0</td>\n","      <td>15698.0</td>\n","      <td>910.9613</td>\n","      <td>208</td>\n","      <td>4.096</td>\n","      <td>53850.0</td>\n","      <td>61.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2020-09-04</td>\n","      <td>1.6092</td>\n","      <td>9930.0</td>\n","      <td>6.89</td>\n","      <td>182250.0</td>\n","      <td>55.0</td>\n","      <td>13.0</td>\n","      <td>997.0</td>\n","      <td>28.86</td>\n","      <td>0</td>\n","      <td>277.0</td>\n","      <td>15979.0</td>\n","      <td>854.6027</td>\n","      <td>192</td>\n","      <td>4.233</td>\n","      <td>54127.0</td>\n","      <td>61.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2020-09-05</td>\n","      <td>1.6086</td>\n","      <td>12425.0</td>\n","      <td>6.86</td>\n","      <td>231165.0</td>\n","      <td>62.0</td>\n","      <td>29.0</td>\n","      <td>1115.0</td>\n","      <td>28.39</td>\n","      <td>0</td>\n","      <td>342.0</td>\n","      <td>19987.0</td>\n","      <td>1038.3357</td>\n","      <td>187</td>\n","      <td>4.248</td>\n","      <td>54469.0</td>\n","      <td>61.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2020-09-06</td>\n","      <td>1.5435</td>\n","      <td>14690.0</td>\n","      <td>6.96</td>\n","      <td>264697.0</td>\n","      <td>62.0</td>\n","      <td>27.0</td>\n","      <td>1322.0</td>\n","      <td>28.87</td>\n","      <td>0</td>\n","      <td>453.0</td>\n","      <td>22674.0</td>\n","      <td>1177.9374</td>\n","      <td>187</td>\n","      <td>4.607</td>\n","      <td>54922.0</td>\n","      <td>61.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>352</th>\n","      <td>2021-09-18</td>\n","      <td>2.3484</td>\n","      <td>113363.0</td>\n","      <td>3.90</td>\n","      <td>4995859.0</td>\n","      <td>539.0</td>\n","      <td>197.0</td>\n","      <td>12117.0</td>\n","      <td>38.75</td>\n","      <td>0</td>\n","      <td>2173.0</td>\n","      <td>266218.0</td>\n","      <td>20999.7588</td>\n","      <td>283</td>\n","      <td>112.856</td>\n","      <td>409854.0</td>\n","      <td>152.0</td>\n","    </tr>\n","    <tr>\n","      <th>353</th>\n","      <td>2021-09-19</td>\n","      <td>2.3207</td>\n","      <td>112822.0</td>\n","      <td>3.72</td>\n","      <td>5140093.0</td>\n","      <td>441.0</td>\n","      <td>178.0</td>\n","      <td>11353.0</td>\n","      <td>37.68</td>\n","      <td>0</td>\n","      <td>2222.0</td>\n","      <td>261821.0</td>\n","      <td>20159.2703</td>\n","      <td>277</td>\n","      <td>110.829</td>\n","      <td>412076.0</td>\n","      <td>152.0</td>\n","    </tr>\n","    <tr>\n","      <th>354</th>\n","      <td>2021-09-20</td>\n","      <td>2.3670</td>\n","      <td>98239.0</td>\n","      <td>3.78</td>\n","      <td>4509271.0</td>\n","      <td>453.0</td>\n","      <td>163.0</td>\n","      <td>9536.0</td>\n","      <td>37.28</td>\n","      <td>0</td>\n","      <td>1997.0</td>\n","      <td>232530.0</td>\n","      <td>17900.4225</td>\n","      <td>277</td>\n","      <td>107.846</td>\n","      <td>414073.0</td>\n","      <td>152.0</td>\n","    </tr>\n","    <tr>\n","      <th>355</th>\n","      <td>2021-09-22</td>\n","      <td>2.2721</td>\n","      <td>105872.0</td>\n","      <td>4.04</td>\n","      <td>4376635.0</td>\n","      <td>399.0</td>\n","      <td>195.0</td>\n","      <td>10594.0</td>\n","      <td>25.75</td>\n","      <td>0</td>\n","      <td>1809.0</td>\n","      <td>240554.0</td>\n","      <td>19512.3516</td>\n","      <td>292</td>\n","      <td>126.600</td>\n","      <td>415882.0</td>\n","      <td>152.0</td>\n","    </tr>\n","    <tr>\n","      <th>356</th>\n","      <td>2021-09-26</td>\n","      <td>2.3480</td>\n","      <td>132573.0</td>\n","      <td>4.24</td>\n","      <td>5375782.0</td>\n","      <td>555.0</td>\n","      <td>191.0</td>\n","      <td>14857.0</td>\n","      <td>39.27</td>\n","      <td>0</td>\n","      <td>2152.0</td>\n","      <td>311277.0</td>\n","      <td>22117.2441</td>\n","      <td>255</td>\n","      <td>135.841</td>\n","      <td>418034.0</td>\n","      <td>152.0</td>\n","    </tr>\n","  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 'Watch_hours', \n","                #  'Unique_viewers', \n","                #  'Views', \n","                #  'Likes', \n","                #  'Dislikes',\n","                #  'Impressions', \n","                 'Subs_accumulated',\n","                #  'Subscribers', \n","                  \n","                 'Shares',\n","                #  'Videos',\n","                 'Average_view_sec', \n","                #  'Average_views_per_viewer', \n","                 'Average_viewed'\n","                 ]\n"],"metadata":{"id":"6AIUm8-U5O-5","executionInfo":{"status":"ok","timestamp":1668960181454,"user_tz":-330,"elapsed":3,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":36,"outputs":[]},{"cell_type":"code","source":["cols_remove = [\n","                    'Date',\n","                # 'Watch_hours', \n","                #  'Unique_viewers', \n","                #  'Views', \n","                 'Dislikes',\n","                #  'Impressions', \n","                 'Subscribers',  \n","                 'Shares',\n","                 'Videos',\n","                 'Average_view_sec', \n","                 'Average_views_per_viewer', \n","                 'Average_viewed',\n","                 'Videos_published',\n","                 'Click_rate', \n","            #    'Subs_accumulated',\n","                 ]\n","\n","\n","df_new = df.copy()\n","df_new = df_new.drop(cols_remove, axis=1)\n","print('New Data Shape: ', df_new.shape)\n","df_new.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":129},"id":"8hpMnKCt5oCS","executionInfo":{"status":"ok","timestamp":1668960182963,"user_tz":-330,"elapsed":5,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"1ab4834c-ed02-4c36-b7e2-883db4ff895d"},"execution_count":37,"outputs":[{"output_type":"stream","name":"stdout","text":["New Data Shape:  (357, 7)\n"]},{"output_type":"execute_result","data":{"text/plain":["   Unique_viewers  Impressions   Likes    Views  Watch_hours  Revenue  \\\n","0          3565.0      82419.0   344.0   6166.0     305.4844    1.532   \n","1          9602.0     162769.0  1602.0  15698.0     910.9613    4.096   \n","\n","   Subs_accumulated  \n","0           53582.0  \n","1           53850.0  "],"text/html":["\n","  <div id=\"df-59a184c7-8d99-422f-b2b7-234e51f2be92\">\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>Unique_viewers</th>\n","      <th>Impressions</th>\n","      <th>Likes</th>\n","      <th>Views</th>\n","      <th>Watch_hours</th>\n","      <th>Revenue</th>\n","      <th>Subs_accumulated</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3565.0</td>\n","      <td>82419.0</td>\n","      <td>344.0</td>\n","      <td>6166.0</td>\n","      <td>305.4844</td>\n","      <td>1.532</td>\n","      <td>53582.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>9602.0</td>\n","      <td>162769.0</td>\n","      <td>1602.0</td>\n","      <td>15698.0</td>\n","      <td>910.9613</td>\n","      <td>4.096</td>\n","      <td>53850.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59a184c7-8d99-422f-b2b7-234e51f2be92')\"\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","  <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-59a184c7-8d99-422f-b2b7-234e51f2be92 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Kaur","userId":"00197144500844905503"}},"outputId":"3c12f8d3-e38f-4312-c293-ae88422b1630"},"execution_count":38,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Unique_viewers      0.977431\n","Impressions         0.962151\n","Likes               0.944926\n","Views               0.975850\n","Watch_hours         0.972490\n","Revenue             1.000000\n","Subs_accumulated    0.896805\n","Name: Revenue, dtype: float64"]},"metadata":{},"execution_count":38}]},{"cell_type":"code","source":["sns.pairplot(df_new, x_vars=df_new.columns.drop('Revenue'), y_vars=['Revenue'])\n","plt.title('Correlations with Target', fontsize=15)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":215},"id":"5zZEZ4sc5_O7","executionInfo":{"status":"ok","timestamp":1668960188963,"user_tz":-330,"elapsed":2333,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"97b3a2aa-edc5-4cbb-f01c-121f43bee143"},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x180 with 6 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## **Data Split**"],"metadata":{"id":"CdX7iGh-W1cc"}},{"cell_type":"code","source":["x = df_new.drop(['Revenue'], axis=1)\n","y=df_new.drop(['Unique_viewers','Impressions','Likes','Views','Watch_hours','Subs_accumulated'],axis=1)"],"metadata":{"id":"cZ_G2tSO-Umu","executionInfo":{"status":"ok","timestamp":1668960188964,"user_tz":-330,"elapsed":5,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":40,"outputs":[]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import StandardScaler\n","# split training and testing data\n","xtrain,xtest,ytrain,ytest= train_test_split(\n","                                            x,y,\n","                                            test_size=0.2,\n","                                            random_state=33\n","                                           )\n","stand= StandardScaler()\n","Fit_Transform = stand.fit_transform(xtrain)\n","Fit_Transform"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n7KpNAeD_5tT","executionInfo":{"status":"ok","timestamp":1668960189587,"user_tz":-330,"elapsed":7,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"b6b1c525-f0aa-4e14-bcda-7a625ae1ec07"},"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.71990072, -0.71205653, -0.776954  , -0.74758685, -0.77006595,\n","        -0.52134099],\n","       [ 0.24647642,  0.41671581,  0.06206084,  0.34185226,  0.27720613,\n","         1.08191012],\n","       [ 0.38906949,  0.5146008 ,  0.24965384,  0.51602836,  0.53873249,\n","         0.35913851],\n","       ...,\n","       [-0.82423234, -0.7554832 , -0.71923308, -0.80886469, -0.78936502,\n","        -0.83546108],\n","       [ 0.79858701,  0.45136255,  1.03981204,  0.68740068,  0.81259529,\n","        -0.05618827],\n","       [-0.71833403, -0.74114064, -0.66068757, -0.73547874, -0.74135863,\n","        -0.94369543]])"]},"metadata":{},"execution_count":41}]},{"cell_type":"code","source":["print(\"X_train:\",xtrain.shape)\n","print(\"X_test:\",xtest.shape)\n","print(\"Y_train:\",ytrain.shape)\n","print(\"Y_test:\",ytest.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"46nAG43692Uy","executionInfo":{"status":"ok","timestamp":1668960191892,"user_tz":-330,"elapsed":8,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"ec2e408e-1d21-4c43-9db7-f3e28c55785d"},"execution_count":42,"outputs":[{"output_type":"stream","name":"stdout","text":["X_train: (285, 6)\n","X_test: (72, 6)\n","Y_train: (285, 1)\n","Y_test: (72, 1)\n"]}]},{"cell_type":"markdown","source":["\n","##**Linear Regression**"],"metadata":{"id":"qz0fSfrlYhpk"}},{"cell_type":"code","source":["from sklearn.linear_model import LinearRegression\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.pipeline import make_pipeline\n","from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"],"metadata":{"id":"Qw5DGQNf7JVa","executionInfo":{"status":"ok","timestamp":1668960193821,"user_tz":-330,"elapsed":10,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":43,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","from sklearn import linear_model\n","import statsmodels.api as sm"],"metadata":{"id":"wvpqE7FX8DFL","executionInfo":{"status":"ok","timestamp":1668960195782,"user_tz":-330,"elapsed":2,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":44,"outputs":[]},{"cell_type":"code","source":["regr = linear_model.LinearRegression()\n","regr.fit(xtrain, ytrain)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BtEylWuP7gU-","executionInfo":{"status":"ok","timestamp":1668960196409,"user_tz":-330,"elapsed":3,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"febbf37f-61ea-421e-e625-f2ee485720f1"},"execution_count":45,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LinearRegression()"]},"metadata":{},"execution_count":45}]},{"cell_type":"code","source":["print('Intercept: \\n', regr.intercept_)\n","print('Coefficients: \\n', regr.coef_)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7t48ppJ18mSu","executionInfo":{"status":"ok","timestamp":1668960199592,"user_tz":-330,"elapsed":715,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"ba300ed0-221e-4778-fac5-af4be27ebd13"},"execution_count":46,"outputs":[{"output_type":"stream","name":"stdout","text":["Intercept: \n"," [-4.9147311]\n","Coefficients: \n"," [[ 6.65090963e-04  8.23820584e-06  7.62606163e-04 -3.74410474e-04\n","   3.07715524e-03  3.63198127e-05]]\n"]}]},{"cell_type":"code","source":["ypred=regr.predict(xtest)"],"metadata":{"id":"BHH4qVhu87Qu","executionInfo":{"status":"ok","timestamp":1668960200277,"user_tz":-330,"elapsed":3,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":47,"outputs":[]},{"cell_type":"code","source":["Accuracy=r2_score(ytest,ypred)*100\n","print(\" Accuracy of the model is %.2f\" %Accuracy)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8cWPNkQ2R-Gs","executionInfo":{"status":"ok","timestamp":1668960202460,"user_tz":-330,"elapsed":4,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"b3293dd0-65c4-4705-e236-2b7a458a4301"},"execution_count":48,"outputs":[{"output_type":"stream","name":"stdout","text":[" Accuracy of the model is 96.19\n"]}]},{"cell_type":"code","source":["plt.scatter(ytest,ypred);\n","plt.xlabel('Actual');\n","plt.ylabel('Predicted');"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":284},"id":"r-oKc_90SYHn","executionInfo":{"status":"ok","timestamp":1668960204925,"user_tz":-330,"elapsed":9,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"6f29097d-dd18-4d97-8e6e-1a7888d216f7"},"execution_count":49,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["sns.regplot(x=ytest,y=ypred,ci=None,color ='darkred');"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":284},"id":"PMK-8VGKSckX","executionInfo":{"status":"ok","timestamp":1668960206185,"user_tz":-330,"elapsed":7,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"9255a128-9af1-4a3b-e590-0c8f98e7ecd2"},"execution_count":50,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["##**Support Vector Regression**"],"metadata":{"id":"PWm8llStSMaO"}},{"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd"],"metadata":{"id":"Pill_arXS8Aa","executionInfo":{"status":"ok","timestamp":1668960207923,"user_tz":-330,"elapsed":3,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":51,"outputs":[]},{"cell_type":"code","source":["x = df_new.drop(['Revenue'], axis=1)\n","y=df_new.drop(['Unique_viewers','Impressions','Likes','Views','Watch_hours','Subs_accumulated'],axis=1)"],"metadata":{"id":"csPqXGSQTE3H","executionInfo":{"status":"ok","timestamp":1668960209011,"user_tz":-330,"elapsed":7,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":52,"outputs":[]},{"cell_type":"code","source":["from sklearn.preprocessing import StandardScaler\n","sc_X = StandardScaler()\n","sc_y = StandardScaler()\n","x = sc_X.fit_transform(x)\n","y = sc_y.fit_transform(y)\n","y=np.ravel(y)"],"metadata":{"id":"4zpV0GenTQwg","executionInfo":{"status":"ok","timestamp":1668960210910,"user_tz":-330,"elapsed":3,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":53,"outputs":[]},{"cell_type":"code","source":["from sklearn.svm import SVR"],"metadata":{"id":"e2mpHcJ7T9N9","executionInfo":{"status":"ok","timestamp":1668960212559,"user_tz":-330,"elapsed":7,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":54,"outputs":[]},{"cell_type":"code","source":["regressor = SVR(kernel='rbf')\n","regressor.fit(x,y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fvNQO8kyUSd5","executionInfo":{"status":"ok","timestamp":1668960214031,"user_tz":-330,"elapsed":7,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"52c22752-6f23-4ecb-976a-4618e045387c"},"execution_count":55,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVR()"]},"metadata":{},"execution_count":55}]},{"cell_type":"code","source":["ypred = regressor.predict(x)"],"metadata":{"id":"D-ZbTZZWYVIZ","executionInfo":{"status":"ok","timestamp":1668960215841,"user_tz":-330,"elapsed":3,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}}},"execution_count":56,"outputs":[]},{"cell_type":"code","source":["Accuracy=r2_score(y,ypred)*100\n","print(\" Accuracy of the model is %.2f\" %Accuracy)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xq-lIktaas7f","executionInfo":{"status":"ok","timestamp":1668960217670,"user_tz":-330,"elapsed":5,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"0a908d10-2c04-4abe-f05c-4e449945e4d3"},"execution_count":57,"outputs":[{"output_type":"stream","name":"stdout","text":[" Accuracy of the model is 97.24\n"]}]},{"cell_type":"markdown","source":["##**ANN**"],"metadata":{"id":"wL0JOuM9bEwr"}},{"cell_type":"code","source":["# Installing required libraries\n","!pip install tensorflow\n","!pip install keras"],"metadata":{"id":"m0zMIuR9bSIi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668960229052,"user_tz":-330,"elapsed":7347,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"6c11e72d-289a-40a0-beb4-16272dcb12af"},"execution_count":58,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: tensorflow in /usr/local/lib/python3.7/dist-packages (2.9.2)\n","Requirement already satisfied: keras<2.10.0,>=2.9.0rc0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.9.0)\n","Requirement already satisfied: flatbuffers<2,>=1.12 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.12)\n","Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.4.0)\n","Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.50.0)\n","Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (4.1.1)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from tensorflow) (57.4.0)\n","Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.15.0)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from tensorflow) (21.3)\n","Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.1.0)\n","Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.2.0)\n","Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.27.0)\n","Requirement already satisfied: protobuf<3.20,>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.19.6)\n","Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.6.3)\n","Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.14.1)\n","Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.3.0)\n","Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.3.0)\n","Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.1.2)\n","Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.21.6)\n","Requirement already satisfied: tensorflow-estimator<2.10.0,>=2.9.0rc0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.9.0)\n","Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (14.0.6)\n","Requirement already satisfied: tensorboard<2.10,>=2.9 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.9.1)\n","Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.1.0)\n","Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.7/dist-packages (from astunparse>=1.6.0->tensorflow) (0.38.3)\n","Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py>=2.9.0->tensorflow) (1.5.2)\n","Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.10,>=2.9->tensorflow) (0.6.1)\n","Requirement already 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Generating the standardized values of X and y\n","X=PredictorScalerFit.transform(X)\n","y=TargetVarScalerFit.transform(y)\n"," \n","# Split the data into training and testing set\n","from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n"," \n","# Quick sanity check with the shapes of Training and testing datasets\n","print(X_train.shape)\n","print(y_train.shape)\n","print(X_test.shape)\n","print(y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"81ZThNovfktT","executionInfo":{"status":"ok","timestamp":1668960232142,"user_tz":-330,"elapsed":6,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"75d3c95e-7b9d-43e9-adf3-7e4a382dfef6"},"execution_count":59,"outputs":[{"output_type":"stream","name":"stdout","text":["(249, 6)\n","(249, 1)\n","(108, 6)\n","(108, 1)\n"]}]},{"cell_type":"code","source":["# importing the libraries\n","from keras.models import Sequential\n","from keras.layers import Dense\n","\n","# create ANN model\n","model = Sequential()\n","\n","# Defining the Input layer and FIRST hidden layer, both are same!\n","model.add(Dense(units=6, input_dim=6, kernel_initializer='normal', activation='relu'))\n","\n","# Defining the Second layer of the model\n","# after the first layer we don't have to specify input_dim as keras configure it automatically\n","model.add(Dense(units=5, kernel_initializer='normal', activation='tanh'))\n","\n","# The output neuron is a single fully connected node \n","# Since we will be predicting a single number\n","model.add(Dense(1, kernel_initializer='normal'))\n","\n","# Compiling the model\n","model.compile(loss='mean_squared_error', optimizer='adam')\n","\n","# Fitting the ANN to the Training set\n","model.fit(X_train, y_train ,batch_size = 20, epochs = 500, verbose=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lmdZqwHagB2n","executionInfo":{"status":"ok","timestamp":1668960437465,"user_tz":-330,"elapsed":23752,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"00085174-6911-4992-82c7-afc960052ad5"},"execution_count":66,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/500\n","13/13 [==============================] - 1s 4ms/step - loss: 1.0192\n","Epoch 2/500\n","13/13 [==============================] - 0s 3ms/step - loss: 1.0111\n","Epoch 3/500\n","13/13 [==============================] - 0s 5ms/step - loss: 0.9953\n","Epoch 4/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.9627\n","Epoch 5/500\n","13/13 [==============================] - 0s 4ms/step - loss: 0.9095\n","Epoch 6/500\n","13/13 [==============================] - 0s 5ms/step - loss: 0.8213\n","Epoch 7/500\n","13/13 [==============================] - 0s 4ms/step - loss: 0.7100\n","Epoch 8/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.5808\n","Epoch 9/500\n","13/13 [==============================] - 0s 7ms/step - loss: 0.4500\n","Epoch 10/500\n","13/13 [==============================] - 0s 5ms/step - loss: 0.3417\n","Epoch 11/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.2549\n","Epoch 12/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.1976\n","Epoch 13/500\n","13/13 [==============================] - 0s 4ms/step - loss: 0.1626\n","Epoch 14/500\n","13/13 [==============================] - 0s 5ms/step - loss: 0.1425\n","Epoch 15/500\n","13/13 [==============================] - 0s 4ms/step - loss: 0.1308\n","Epoch 16/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.1227\n","Epoch 17/500\n","13/13 [==============================] - 0s 6ms/step - loss: 0.1175\n","Epoch 18/500\n","13/13 [==============================] - 0s 5ms/step - loss: 0.1136\n","Epoch 19/500\n","13/13 [==============================] - 0s 5ms/step - loss: 0.1103\n","Epoch 20/500\n","13/13 [==============================] - 0s 4ms/step - loss: 0.1068\n","Epoch 21/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.1041\n","Epoch 22/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.1010\n","Epoch 23/500\n","13/13 [==============================] - 0s 7ms/step - loss: 0.0984\n","Epoch 24/500\n","13/13 [==============================] - 0s 6ms/step - loss: 0.0960\n","Epoch 25/500\n","13/13 [==============================] - 0s 5ms/step - loss: 0.0934\n","Epoch 26/500\n","13/13 [==============================] - 0s 4ms/step - loss: 0.0910\n","Epoch 27/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0886\n","Epoch 28/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0863\n","Epoch 29/500\n","13/13 [==============================] - 0s 5ms/step - loss: 0.0839\n","Epoch 30/500\n","13/13 [==============================] - 0s 6ms/step - loss: 0.0819\n","Epoch 31/500\n","13/13 [==============================] - 0s 9ms/step - loss: 0.0799\n","Epoch 32/500\n","13/13 [==============================] - 0s 5ms/step - loss: 0.0779\n","Epoch 33/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0760\n","Epoch 34/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0744\n","Epoch 35/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0727\n","Epoch 36/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0711\n","Epoch 37/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0696\n","Epoch 38/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0683\n","Epoch 39/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0667\n","Epoch 40/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0656\n","Epoch 41/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0642\n","Epoch 42/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0632\n","Epoch 43/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0621\n","Epoch 44/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0610\n","Epoch 45/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0600\n","Epoch 46/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0589\n","Epoch 47/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0579\n","Epoch 48/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0570\n","Epoch 49/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0563\n","Epoch 50/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0554\n","Epoch 51/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0548\n","Epoch 52/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0538\n","Epoch 53/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0532\n","Epoch 54/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0528\n","Epoch 55/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0516\n","Epoch 56/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0510\n","Epoch 57/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0503\n","Epoch 58/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0499\n","Epoch 59/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0492\n","Epoch 60/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0487\n","Epoch 61/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0482\n","Epoch 62/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0478\n","Epoch 63/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0472\n","Epoch 64/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0468\n","Epoch 65/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0464\n","Epoch 66/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0460\n","Epoch 67/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0459\n","Epoch 68/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0452\n","Epoch 69/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0450\n","Epoch 70/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0448\n","Epoch 71/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0445\n","Epoch 72/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0442\n","Epoch 73/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0438\n","Epoch 74/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0435\n","Epoch 75/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0432\n","Epoch 76/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0430\n","Epoch 77/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0427\n","Epoch 78/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0425\n","Epoch 79/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0422\n","Epoch 80/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0420\n","Epoch 81/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0426\n","Epoch 82/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0416\n","Epoch 83/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0418\n","Epoch 84/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0413\n","Epoch 85/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0410\n","Epoch 86/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0414\n","Epoch 87/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0407\n","Epoch 88/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0407\n","Epoch 89/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0404\n","Epoch 90/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0404\n","Epoch 91/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0405\n","Epoch 92/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0402\n","Epoch 93/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0399\n","Epoch 94/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0399\n","Epoch 95/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0397\n","Epoch 96/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0396\n","Epoch 97/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0396\n","Epoch 98/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0393\n","Epoch 99/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0390\n","Epoch 100/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0392\n","Epoch 101/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0387\n","Epoch 102/500\n","13/13 [==============================] - 0s 4ms/step - loss: 0.0388\n","Epoch 103/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0388\n","Epoch 104/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0384\n","Epoch 105/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0382\n","Epoch 106/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0382\n","Epoch 107/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0381\n","Epoch 108/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0383\n","Epoch 109/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0379\n","Epoch 110/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0379\n","Epoch 111/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0381\n","Epoch 112/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0379\n","Epoch 113/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0377\n","Epoch 114/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0374\n","Epoch 115/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0372\n","Epoch 116/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0372\n","Epoch 117/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0371\n","Epoch 118/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0369\n","Epoch 119/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0370\n","Epoch 120/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0371\n","Epoch 121/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0366\n","Epoch 122/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0371\n","Epoch 123/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0369\n","Epoch 124/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0365\n","Epoch 125/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0362\n","Epoch 126/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0363\n","Epoch 127/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0363\n","Epoch 128/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0360\n","Epoch 129/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0361\n","Epoch 130/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0359\n","Epoch 131/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0358\n","Epoch 132/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0357\n","Epoch 133/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0355\n","Epoch 134/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0361\n","Epoch 135/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0354\n","Epoch 136/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0353\n","Epoch 137/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0370\n","Epoch 138/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0358\n","Epoch 139/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0357\n","Epoch 140/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0353\n","Epoch 141/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0349\n","Epoch 142/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0349\n","Epoch 143/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0346\n","Epoch 144/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0347\n","Epoch 145/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0346\n","Epoch 146/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0349\n","Epoch 147/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0348\n","Epoch 148/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0346\n","Epoch 149/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0351\n","Epoch 150/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0342\n","Epoch 151/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0347\n","Epoch 152/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0346\n","Epoch 153/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0340\n","Epoch 154/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0341\n","Epoch 155/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0341\n","Epoch 156/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0356\n","Epoch 157/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0339\n","Epoch 158/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0342\n","Epoch 159/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0336\n","Epoch 160/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0336\n","Epoch 161/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0335\n","Epoch 162/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0335\n","Epoch 163/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0335\n","Epoch 164/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0334\n","Epoch 165/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0334\n","Epoch 166/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0335\n","Epoch 167/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0335\n","Epoch 168/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0334\n","Epoch 169/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0336\n","Epoch 170/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0333\n","Epoch 171/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0333\n","Epoch 172/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0331\n","Epoch 173/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0339\n","Epoch 174/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0330\n","Epoch 175/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0329\n","Epoch 176/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0330\n","Epoch 177/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0328\n","Epoch 178/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0327\n","Epoch 179/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0329\n","Epoch 180/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0326\n","Epoch 181/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0329\n","Epoch 182/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0335\n","Epoch 183/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0324\n","Epoch 184/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0334\n","Epoch 185/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0328\n","Epoch 186/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0324\n","Epoch 187/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0324\n","Epoch 188/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0324\n","Epoch 189/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0329\n","Epoch 190/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0322\n","Epoch 191/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0325\n","Epoch 192/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0321\n","Epoch 193/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0320\n","Epoch 194/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0322\n","Epoch 195/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0322\n","Epoch 196/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0320\n","Epoch 197/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0318\n","Epoch 198/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0325\n","Epoch 199/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0318\n","Epoch 200/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0319\n","Epoch 201/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0319\n","Epoch 202/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0316\n","Epoch 203/500\n","13/13 [==============================] - 0s 4ms/step - loss: 0.0319\n","Epoch 204/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0327\n","Epoch 205/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0318\n","Epoch 206/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0318\n","Epoch 207/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0320\n","Epoch 208/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0316\n","Epoch 209/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0314\n","Epoch 210/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0316\n","Epoch 211/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0313\n","Epoch 212/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0312\n","Epoch 213/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0316\n","Epoch 214/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0313\n","Epoch 215/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0316\n","Epoch 216/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0318\n","Epoch 217/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0313\n","Epoch 218/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0317\n","Epoch 219/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0312\n","Epoch 220/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0314\n","Epoch 221/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0310\n","Epoch 222/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0312\n","Epoch 223/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0311\n","Epoch 224/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0308\n","Epoch 225/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0310\n","Epoch 226/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0310\n","Epoch 227/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0307\n","Epoch 228/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0307\n","Epoch 229/500\n","13/13 [==============================] - 0s 4ms/step - loss: 0.0318\n","Epoch 230/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0307\n","Epoch 231/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0310\n","Epoch 232/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0312\n","Epoch 233/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0313\n","Epoch 234/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0304\n","Epoch 235/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0307\n","Epoch 236/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0302\n","Epoch 237/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0306\n","Epoch 238/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0302\n","Epoch 239/500\n","13/13 [==============================] - 0s 4ms/step - loss: 0.0305\n","Epoch 240/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0302\n","Epoch 241/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0303\n","Epoch 242/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0299\n","Epoch 243/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0302\n","Epoch 244/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0302\n","Epoch 245/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0303\n","Epoch 246/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0300\n","Epoch 247/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0302\n","Epoch 248/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0305\n","Epoch 249/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0305\n","Epoch 250/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0311\n","Epoch 251/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0298\n","Epoch 252/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0301\n","Epoch 253/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0299\n","Epoch 254/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0299\n","Epoch 255/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0297\n","Epoch 256/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0301\n","Epoch 257/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0295\n","Epoch 258/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0296\n","Epoch 259/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0298\n","Epoch 260/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0299\n","Epoch 261/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0295\n","Epoch 262/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0296\n","Epoch 263/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0297\n","Epoch 264/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0298\n","Epoch 265/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0291\n","Epoch 266/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0297\n","Epoch 267/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0296\n","Epoch 268/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0292\n","Epoch 269/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0299\n","Epoch 270/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0292\n","Epoch 271/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0291\n","Epoch 272/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0298\n","Epoch 273/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0291\n","Epoch 274/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0290\n","Epoch 275/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0291\n","Epoch 276/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0290\n","Epoch 277/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0290\n","Epoch 278/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0295\n","Epoch 279/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0290\n","Epoch 280/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0294\n","Epoch 281/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0289\n","Epoch 282/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0294\n","Epoch 283/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0287\n","Epoch 284/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0288\n","Epoch 285/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0289\n","Epoch 286/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0290\n","Epoch 287/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0285\n","Epoch 288/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0287\n","Epoch 289/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0293\n","Epoch 290/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0298\n","Epoch 291/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0284\n","Epoch 292/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0284\n","Epoch 293/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0288\n","Epoch 294/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0285\n","Epoch 295/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0287\n","Epoch 296/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0281\n","Epoch 297/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0291\n","Epoch 298/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0292\n","Epoch 299/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0285\n","Epoch 300/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0285\n","Epoch 301/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0286\n","Epoch 302/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0285\n","Epoch 303/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0283\n","Epoch 304/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0283\n","Epoch 305/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0286\n","Epoch 306/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0282\n","Epoch 307/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0284\n","Epoch 308/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0282\n","Epoch 309/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0296\n","Epoch 310/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0281\n","Epoch 311/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0280\n","Epoch 312/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0296\n","Epoch 313/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0281\n","Epoch 314/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0282\n","Epoch 315/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0289\n","Epoch 316/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0279\n","Epoch 317/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0282\n","Epoch 318/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0283\n","Epoch 319/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0279\n","Epoch 320/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0281\n","Epoch 321/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0278\n","Epoch 322/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0278\n","Epoch 323/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0280\n","Epoch 324/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0279\n","Epoch 325/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0284\n","Epoch 326/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0281\n","Epoch 327/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0277\n","Epoch 328/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0280\n","Epoch 329/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0276\n","Epoch 330/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0275\n","Epoch 331/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0276\n","Epoch 332/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0276\n","Epoch 333/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0276\n","Epoch 334/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0278\n","Epoch 335/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0284\n","Epoch 336/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0274\n","Epoch 337/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0279\n","Epoch 338/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0274\n","Epoch 339/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0275\n","Epoch 340/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0275\n","Epoch 341/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0276\n","Epoch 342/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0272\n","Epoch 343/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0274\n","Epoch 344/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0272\n","Epoch 345/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0273\n","Epoch 346/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0271\n","Epoch 347/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0275\n","Epoch 348/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0271\n","Epoch 349/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0272\n","Epoch 350/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0271\n","Epoch 351/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0273\n","Epoch 352/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0272\n","Epoch 353/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0274\n","Epoch 354/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0270\n","Epoch 355/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0273\n","Epoch 356/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0268\n","Epoch 357/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0274\n","Epoch 358/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0280\n","Epoch 359/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0279\n","Epoch 360/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0268\n","Epoch 361/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0283\n","Epoch 362/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0272\n","Epoch 363/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0272\n","Epoch 364/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0279\n","Epoch 365/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0268\n","Epoch 366/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0272\n","Epoch 367/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0269\n","Epoch 368/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0273\n","Epoch 369/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0265\n","Epoch 370/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0268\n","Epoch 371/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0268\n","Epoch 372/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0273\n","Epoch 373/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0266\n","Epoch 374/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0265\n","Epoch 375/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0268\n","Epoch 376/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0268\n","Epoch 377/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0267\n","Epoch 378/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0270\n","Epoch 379/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0268\n","Epoch 380/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0273\n","Epoch 381/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0266\n","Epoch 382/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0263\n","Epoch 383/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0274\n","Epoch 384/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0267\n","Epoch 385/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0268\n","Epoch 386/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0265\n","Epoch 387/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0263\n","Epoch 388/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0264\n","Epoch 389/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0262\n","Epoch 390/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0269\n","Epoch 391/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0263\n","Epoch 392/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0264\n","Epoch 393/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0263\n","Epoch 394/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0264\n","Epoch 395/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0267\n","Epoch 396/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0266\n","Epoch 397/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0261\n","Epoch 398/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0263\n","Epoch 399/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0267\n","Epoch 400/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0267\n","Epoch 401/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0260\n","Epoch 402/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0270\n","Epoch 403/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0268\n","Epoch 404/500\n","13/13 [==============================] - 0s 5ms/step - loss: 0.0264\n","Epoch 405/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0264\n","Epoch 406/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0260\n","Epoch 407/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0260\n","Epoch 408/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0260\n","Epoch 409/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0261\n","Epoch 410/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0260\n","Epoch 411/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0263\n","Epoch 412/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0259\n","Epoch 413/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0264\n","Epoch 414/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0263\n","Epoch 415/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0268\n","Epoch 416/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0273\n","Epoch 417/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0259\n","Epoch 418/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0261\n","Epoch 419/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0258\n","Epoch 420/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0257\n","Epoch 421/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0258\n","Epoch 422/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0259\n","Epoch 423/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0259\n","Epoch 424/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0258\n","Epoch 425/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0265\n","Epoch 426/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0257\n","Epoch 427/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0256\n","Epoch 428/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0260\n","Epoch 429/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0258\n","Epoch 430/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0260\n","Epoch 431/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0257\n","Epoch 432/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0265\n","Epoch 433/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0256\n","Epoch 434/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0264\n","Epoch 435/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0256\n","Epoch 436/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0266\n","Epoch 437/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0255\n","Epoch 438/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0257\n","Epoch 439/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0256\n","Epoch 440/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0255\n","Epoch 441/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0257\n","Epoch 442/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0257\n","Epoch 443/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0261\n","Epoch 444/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0253\n","Epoch 445/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0256\n","Epoch 446/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0255\n","Epoch 447/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0258\n","Epoch 448/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0255\n","Epoch 449/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0256\n","Epoch 450/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0255\n","Epoch 451/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0256\n","Epoch 452/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0256\n","Epoch 453/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0257\n","Epoch 454/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0255\n","Epoch 455/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0256\n","Epoch 456/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0254\n","Epoch 457/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0253\n","Epoch 458/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0253\n","Epoch 459/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0255\n","Epoch 460/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0254\n","Epoch 461/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0261\n","Epoch 462/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0253\n","Epoch 463/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0263\n","Epoch 464/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0253\n","Epoch 465/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0254\n","Epoch 466/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0261\n","Epoch 467/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0253\n","Epoch 468/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0260\n","Epoch 469/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0255\n","Epoch 470/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0253\n","Epoch 471/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0262\n","Epoch 472/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0261\n","Epoch 473/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0273\n","Epoch 474/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0252\n","Epoch 475/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0254\n","Epoch 476/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0279\n","Epoch 477/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0263\n","Epoch 478/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0258\n","Epoch 479/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0251\n","Epoch 480/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0251\n","Epoch 481/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0254\n","Epoch 482/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0251\n","Epoch 483/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0252\n","Epoch 484/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0256\n","Epoch 485/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0251\n","Epoch 486/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0266\n","Epoch 487/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0257\n","Epoch 488/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0251\n","Epoch 489/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0254\n","Epoch 490/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0252\n","Epoch 491/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0250\n","Epoch 492/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0250\n","Epoch 493/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0265\n","Epoch 494/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0250\n","Epoch 495/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0254\n","Epoch 496/500\n","13/13 [==============================] - 0s 2ms/step - loss: 0.0250\n","Epoch 497/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0254\n","Epoch 498/500\n","13/13 [==============================] - 0s 4ms/step - loss: 0.0252\n","Epoch 499/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0250\n","Epoch 500/500\n","13/13 [==============================] - 0s 3ms/step - loss: 0.0252\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7fc0cc545490>"]},"metadata":{},"execution_count":66}]},{"cell_type":"code","source":["# Generating Predictions on testing data\n","Predictions=model.predict(X_test)\n","\n","# Scaling the predicted Price data back to original price scale\n","Predictions=TargetVarScalerFit.inverse_transform(Predictions)\n","\n","# Scaling the y_test Price data back to original price scale\n","y_test_orig=TargetVarScalerFit.inverse_transform(y_test)\n","\n","# Scaling the test data back to original scale\n","Test_Data=PredictorScalerFit.inverse_transform(X_test)\n","\n","TestingData=pd.DataFrame(data=Test_Data, columns=Predictors)\n","TestingData['Revenue']=y_test_orig\n","TestingData['PredictedRevenue']=Predictions\n","TestingData.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":223},"id":"KYz7qkodj-dy","executionInfo":{"status":"ok","timestamp":1668960442267,"user_tz":-330,"elapsed":582,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"f39d76c6-5199-4801-e3ab-e1719dcaa0a5"},"execution_count":67,"outputs":[{"output_type":"stream","name":"stdout","text":["4/4 [==============================] - 0s 3ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["   Unique_viewers  Impressions   Likes     Views  Watch_hours  \\\n","0         36782.0    1330067.0  2938.0   78420.0    5553.7608   \n","1          4459.0      97966.0   326.0    7254.0     400.2379   \n","2         77404.0    3266651.0  6918.0  177361.0   13798.6015   \n","3         76444.0    3647678.0  7099.0  190863.0   14897.1865   \n","4         14817.0     337315.0  1794.0   22794.0    1671.2070   \n","\n","   Subs_accumulated  Revenue  PredictedRevenue  \n","0          153841.0   32.466         27.111526  \n","1           66697.0    2.134          1.219571  \n","2          261543.0   73.799         63.730762  \n","3          277495.0   67.963         67.119827  \n","4           69641.0   11.915          8.242512  "],"text/html":["\n","  <div id=\"df-853bd9d2-2482-4163-8bf1-8041439ce497\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","  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   <td>97966.0</td>\n","      <td>326.0</td>\n","      <td>7254.0</td>\n","      <td>400.2379</td>\n","      <td>66697.0</td>\n","      <td>2.134</td>\n","      <td>1.219571</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>77404.0</td>\n","      <td>3266651.0</td>\n","      <td>6918.0</td>\n","      <td>177361.0</td>\n","      <td>13798.6015</td>\n","      <td>261543.0</td>\n","      <td>73.799</td>\n","      <td>63.730762</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>76444.0</td>\n","      <td>3647678.0</td>\n","      <td>7099.0</td>\n","      <td>190863.0</td>\n","      <td>14897.1865</td>\n","      <td>277495.0</td>\n","      <td>67.963</td>\n","      <td>67.119827</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>14817.0</td>\n","      <td>337315.0</td>\n","      <td>1794.0</td>\n","      <td>22794.0</td>\n","      <td>1671.2070</td>\n","      <td>69641.0</td>\n","      <td>11.915</td>\n","      <td>8.242512</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-853bd9d2-2482-4163-8bf1-8041439ce497')\"\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","      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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":67}]},{"cell_type":"code","source":["# Computing the absolute percent error\n","APE=100*(abs(TestingData['Revenue']-TestingData['PredictedRevenue'])/TestingData['Revenue'])\n","TestingData['APE']=APE\n","\n","print('The Accuracy of ANN model is:', 100-np.mean(APE))\n","TestingData.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":223},"id":"PjdE9Gt-hMvI","executionInfo":{"status":"ok","timestamp":1668960443939,"user_tz":-330,"elapsed":26,"user":{"displayName":"Noordeep Kaur","userId":"00197144500844905503"}},"outputId":"cb5265db-3892-4214-d6d2-2a8158ba7550"},"execution_count":68,"outputs":[{"output_type":"stream","name":"stdout","text":["The Accuracy of ANN model is: 83.28596899614118\n"]},{"output_type":"execute_result","data":{"text/plain":["   Unique_viewers  Impressions   Likes     Views  Watch_hours  \\\n","0         36782.0    1330067.0  2938.0   78420.0    5553.7608   \n","1          4459.0      97966.0   326.0    7254.0     400.2379   \n","2         77404.0    3266651.0  6918.0  177361.0   13798.6015   \n","3         76444.0    3647678.0  7099.0  190863.0   14897.1865   \n","4         14817.0     337315.0  1794.0   22794.0    1671.2070   \n","\n","   Subs_accumulated  Revenue  PredictedRevenue        APE  \n","0          153841.0   32.466         27.111526  16.492557  \n","1           66697.0    2.134          1.219571  42.850491  \n","2          261543.0   73.799         63.730762  13.642783  \n","3          277495.0   67.963         67.119827   1.240635  \n","4           69641.0   11.915          8.242512  30.822394  "],"text/html":["\n","  <div id=\"df-7144bb3a-ab01-4773-b9d5-ece0dd1c99de\">\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>Unique_viewers</th>\n","      <th>Impressions</th>\n","      <th>Likes</th>\n","      <th>Views</th>\n","      <th>Watch_hours</th>\n","      <th>Subs_accumulated</th>\n","      <th>Revenue</th>\n","      <th>PredictedRevenue</th>\n","      <th>APE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>36782.0</td>\n","      <td>1330067.0</td>\n","      <td>2938.0</td>\n","      <td>78420.0</td>\n","      <td>5553.7608</td>\n","      <td>153841.0</td>\n","      <td>32.466</td>\n","      <td>27.111526</td>\n","      <td>16.492557</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4459.0</td>\n","      <td>97966.0</td>\n","      <td>326.0</td>\n","      <td>7254.0</td>\n","      <td>400.2379</td>\n","      <td>66697.0</td>\n","      <td>2.134</td>\n","      <td>1.219571</td>\n","      <td>42.850491</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>77404.0</td>\n","      <td>3266651.0</td>\n","      <td>6918.0</td>\n","      <td>177361.0</td>\n","      <td>13798.6015</td>\n","      <td>261543.0</td>\n","      <td>73.799</td>\n","      <td>63.730762</td>\n","      <td>13.642783</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>76444.0</td>\n","      <td>3647678.0</td>\n","      <td>7099.0</td>\n","      <td>190863.0</td>\n","      <td>14897.1865</td>\n","      <td>277495.0</td>\n","      <td>67.963</td>\n","      <td>67.119827</td>\n","      <td>1.240635</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>14817.0</td>\n","      <td>337315.0</td>\n","      <td>1794.0</td>\n","      <td>22794.0</td>\n","      <td>1671.2070</td>\n","      <td>69641.0</td>\n","      <td>11.915</td>\n","      <td>8.242512</td>\n","      <td>30.822394</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7144bb3a-ab01-4773-b9d5-ece0dd1c99de')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg 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