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added bot prediction for assessments

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kowshik
2024-09-14 01:50:41 +00:00
parent 45bc62c745
commit cd8f499f97
14 changed files with 698 additions and 22 deletions
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notebooks/dummy_assessment_data.csv
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Assessment_ID,Open_Items,Red_Flags,Assessment_Frequency,Assessment_Start_Date,Assessment_End_Date,Assessment_Area,Assessment_Status,Assessment_Admin
1,3,1,Weekly,2023-01-01,2023-01-07,Deployment,Completed,Admin A
2,4,2,Bi-Weekly,2023-01-16,2023-01-22,Communication,Completed,Admin B
3,2,0,Weekly,2023-01-31,2023-02-06,Deployment,Completed,Admin A
4,5,1,Quarterly,2023-02-15,2023-02-21,Communication,In Progress,Admin B
5,1,0,Bi-Weekly,2023-03-02,2023-03-08,Deployment,Completed,Admin A
6,3,3,Weekly,2023-03-17,2023-03-23,Deployment,Completed,Admin A
7,2,2,Quarterly,2023-04-01,2023-04-07,Communication,Incomplete,Admin B
8,4,1,Bi-Weekly,2023-04-16,2023-04-22,Deployment,Completed,Admin A
9,5,1,Weekly,2023-05-01,2023-05-07,Communication,In Progress,Admin B
10,3,2,Quarterly,2023-05-16,2023-05-22,Deployment,Completed,Admin A
1 Assessment_ID Open_Items Red_Flags Assessment_Frequency Assessment_Start_Date Assessment_End_Date Assessment_Area Assessment_Status Assessment_Admin
2 1 3 1 Weekly 2023-01-01 2023-01-07 Deployment Completed Admin A
3 2 4 2 Bi-Weekly 2023-01-16 2023-01-22 Communication Completed Admin B
4 3 2 0 Weekly 2023-01-31 2023-02-06 Deployment Completed Admin A
5 4 5 1 Quarterly 2023-02-15 2023-02-21 Communication In Progress Admin B
6 5 1 0 Bi-Weekly 2023-03-02 2023-03-08 Deployment Completed Admin A
7 6 3 3 Weekly 2023-03-17 2023-03-23 Deployment Completed Admin A
8 7 2 2 Quarterly 2023-04-01 2023-04-07 Communication Incomplete Admin B
9 8 4 1 Bi-Weekly 2023-04-16 2023-04-22 Deployment Completed Admin A
10 9 5 1 Weekly 2023-05-01 2023-05-07 Communication In Progress Admin B
11 10 3 2 Quarterly 2023-05-16 2023-05-22 Deployment Completed Admin A
notebooks/test_prediction_pipeline.ipynb
+230 -9
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@@ -2,22 +2,204 @@
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Create a dummy dataset with past 5 assessments\n",
"import pandas as pd\n",
"\n",
"data_dummy = {\n",
" 'start_date': pd.date_range(start='2023-01-01', periods=5, freq='7D'),\n",
" 'end_date': pd.date_range(start='2023-01-02', periods=5, freq='7D'),\n",
" 'open_items': [10, 12, 11, 9, 13],\n",
" 'red_flags': [2, 1, 3, 1, 4],\n",
" 'num_employees': [30, 25, 28, 30, 27],\n",
" 'assessment_type': ['weekly', 'biweekly', 'quarterly', 'weekly', 'biweekly']\n",
" 'start_date': pd.date_range(start='2023-01-01', periods=12, freq='7D'),\n",
" 'end_date': pd.date_range(start='2023-01-02', periods=12, freq='7D'),\n",
" 'open_items': [10, 12, 11, 9, 13, 14, 15, 16, 12, 11, 10, 9],\n",
" 'red_flags': [2, 1, 3, 1, 4, 2, 1, 3, 2, 1, 4, 3],\n",
" 'num_employees': [30, 25, 28, 30, 27, 26, 31, 29, 25, 30, 27, 26],\n",
" 'assessment_type': ['weekly', 'biweekly', 'quarterly', 'weekly', 'biweekly', \n",
" 'weekly', 'quarterly', 'biweekly', 'weekly', 'quarterly', 'weekly', 'biweekly']\n",
"}\n",
"\n",
"df_dummy = pd.DataFrame(data_dummy)"
"df_dummy = pd.DataFrame(data_dummy)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>start_date</th>\n",
" <th>end_date</th>\n",
" <th>open_items</th>\n",
" <th>red_flags</th>\n",
" <th>num_employees</th>\n",
" <th>assessment_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2023-01-01</td>\n",
" <td>2023-01-02</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>30</td>\n",
" <td>weekly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2023-01-08</td>\n",
" <td>2023-01-09</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>25</td>\n",
" <td>biweekly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2023-01-15</td>\n",
" <td>2023-01-16</td>\n",
" <td>11</td>\n",
" <td>3</td>\n",
" <td>28</td>\n",
" <td>quarterly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2023-01-22</td>\n",
" <td>2023-01-23</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>30</td>\n",
" <td>weekly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2023-01-29</td>\n",
" <td>2023-01-30</td>\n",
" <td>13</td>\n",
" <td>4</td>\n",
" <td>27</td>\n",
" <td>biweekly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2023-02-05</td>\n",
" <td>2023-02-06</td>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>26</td>\n",
" <td>weekly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2023-02-12</td>\n",
" <td>2023-02-13</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>31</td>\n",
" <td>quarterly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2023-02-19</td>\n",
" <td>2023-02-20</td>\n",
" <td>16</td>\n",
" <td>3</td>\n",
" <td>29</td>\n",
" <td>biweekly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2023-02-26</td>\n",
" <td>2023-02-27</td>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" <td>25</td>\n",
" <td>weekly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2023-03-05</td>\n",
" <td>2023-03-06</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>30</td>\n",
" <td>quarterly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2023-03-12</td>\n",
" <td>2023-03-13</td>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>27</td>\n",
" <td>weekly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2023-03-19</td>\n",
" <td>2023-03-20</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>26</td>\n",
" <td>biweekly</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" start_date end_date open_items red_flags num_employees assessment_type\n",
"0 2023-01-01 2023-01-02 10 2 30 weekly\n",
"1 2023-01-08 2023-01-09 12 1 25 biweekly\n",
"2 2023-01-15 2023-01-16 11 3 28 quarterly\n",
"3 2023-01-22 2023-01-23 9 1 30 weekly\n",
"4 2023-01-29 2023-01-30 13 4 27 biweekly\n",
"5 2023-02-05 2023-02-06 14 2 26 weekly\n",
"6 2023-02-12 2023-02-13 15 1 31 quarterly\n",
"7 2023-02-19 2023-02-20 16 3 29 biweekly\n",
"8 2023-02-26 2023-02-27 12 2 25 weekly\n",
"9 2023-03-05 2023-03-06 11 1 30 quarterly\n",
"10 2023-03-12 2023-03-13 10 4 27 weekly\n",
"11 2023-03-19 2023-03-20 9 3 26 biweekly"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_dummy"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df_dummy.to_csv(\"test_data.csv\",index=False)"
]
},
{
@@ -1399,6 +1581,45 @@
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dummy assessment data has been saved as dummy_company_asseement_data.csv.\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Create dummy assessment data\n",
"data = {\n",
" 'Assessment_ID': range(1, 11),\n",
" 'Open_Items': [3, 4, 2, 5, 1, 3, 2, 4, 5, 3],\n",
" 'Red_Flags': [1, 2, 0, 1, 0, 3, 2, 1, 1, 2],\n",
" 'Assessment_Frequency': ['Weekly', 'Bi-Weekly', 'Weekly', 'Quarterly', 'Bi-Weekly', 'Weekly', 'Quarterly', 'Bi-Weekly', 'Weekly', 'Quarterly'],\n",
" 'Assessment_Start_Date': pd.date_range(start='2023-01-01', periods=10, freq='15D'),\n",
" 'Assessment_End_Date': pd.date_range(start='2023-01-07', periods=10, freq='15D'),\n",
" 'Assessment_Area': ['Deployment', 'Communication', 'Deployment', 'Communication', 'Deployment', 'Deployment', 'Communication', 'Deployment', 'Communication', 'Deployment'],\n",
" 'Assessment_Status': ['Completed', 'Completed', 'Completed', 'In Progress', 'Completed', 'Completed', 'Incomplete', 'Completed', 'In Progress', 'Completed'],\n",
" 'Assessment_Admin': ['Admin A', 'Admin B', 'Admin A', 'Admin B', 'Admin A', 'Admin A', 'Admin B', 'Admin A', 'Admin B', 'Admin A']\n",
"}\n",
"\n",
"# Create DataFrame\n",
"df = pd.DataFrame(data)\n",
"\n",
"# Save DataFrame to CSV\n",
"csv_file_path = 'dummy_company_asseement_data.csv'\n",
"df.to_csv(csv_file_path, index=False)\n",
"\n",
"print(f\"Dummy assessment data has been saved as {csv_file_path}.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,