Facial recognition could soon jump from your smartphone to your workplace with employers using it to mark attendance and gauge the mood of the workforce.Every day, corporate offices and institutes are working to increase the productive working hours in a day. When the current system of clocking in daily using a fingerprint scanner is a time-consuming and inefficient use of time. I have planned to design a Voice Interactive Face Detection Based Smart Attendance management and behavior analysis to ensure a better work culture and environment,efficiency in a secure manner using Intel dev cloud. Currently, we have fingerprint and Smart-card Based entries in nearly all offices and a few schools and colleges. These system then automates the calculation of salary or attendance percentage.But fingerprint scanning and smart card barcode entries tend to take up time and prove to also be imperfect. In contrast, Face Recognition method provides a unique feature for every individual which is stored in a central database and can be retrieved during recognition and validation. The system includes an embedded application deployed in a SCB( Single Board Computer) which can interact with the users in real time. It will take down in and out time of every employee and monitor their working behavior(future scope) and notify the corresponding employee and the authority at times. We are aiming to analyze people's behavior,mood and emotions by monitoring and studying their actions in real time which in turn will help the organization know about the physical and mental status of the employees. This process of direct integration of physical world into computer vision based systems will indeed result in efficiency improvements, economic benefits and reduced human exertions. As of now I have developed a basic voice interactive attendance monitoring using Jupyter Notebook on Intel dev cloud. The in and out time (including mid in and out) will be monitored in Google spreadsheet and the system will calculate how many hours an employee has spent in office premises. The system won’t allow employees to step into the office after a certain time and won’t consider the attendance if the total hours spent is less than four hours. Everyday a mail will be sent to the admin containing the attendance details of the employees. In future, I would like to implement behavior and mood analysis of the employees and the staff on the office premises which in turn will help the concerned staff provide with solutions to get over the listless mood or erratic behavior.

Repository for my practice files and notes following CampusX "100 Days of ML" YouTube playlist. This repo includes Jupyter notebooks and datasets organized by day, documenting my learning journey in machine learning.

# Employee Database: A Mystery in Two Parts  ## Background It is a beautiful spring day, and it is two weeks since you have been hired as a new data engineer at Pewlett Hackard. Your first major task is a research project on employees of the corporation from the 1980s and 1990s. All that remain of the database of employees from that period are six CSV files. In this assignment, you will design the tables to hold data in the CSVs, import the CSVs into a SQL database, and answer questions about the data. In other words, you will perform: 1. Data Modeling 2. Data Engineering 3. Data Analysis ## Instructions #### Data Modeling Inspect the CSVs and sketch out an ERD of the tables. Feel free to use a tool like [http://www.quickdatabasediagrams.com](http://www.quickdatabasediagrams.com). #### Data Engineering * Use the information you have to create a table schema for each of the six CSV files. Remember to specify data types, primary keys, foreign keys, and other constraints. * Import each CSV file into the corresponding SQL table. #### Data Analysis Once you have a complete database, do the following: 1. List the following details of each employee: employee number, last name, first name, gender, and salary. 2. List employees who were hired in 1986. 3. List the manager of each department with the following information: department number, department name, the manager's employee number, last name, first name, and start and end employment dates. 4. List the department of each employee with the following information: employee number, last name, first name, and department name. 5. List all employees whose first name is "Hercules" and last names begin with "B." 6. List all employees in the Sales department, including their employee number, last name, first name, and department name. 7. List all employees in the Sales and Development departments, including their employee number, last name, first name, and department name. 8. In descending order, list the frequency count of employee last names, i.e., how many employees share each last name. ## Bonus (Optional) As you examine the data, you are overcome with a creeping suspicion that the dataset is fake. You surmise that your boss handed you spurious data in order to test the data engineering skills of a new employee. To confirm your hunch, you decide to take the following steps to generate a visualization of the data, with which you will confront your boss: 1. Import the SQL database into Pandas. (Yes, you could read the CSVs directly in Pandas, but you are, after all, trying to prove your technical mettle.) This step may require some research. Feel free to use the code below to get started. Be sure to make any necessary modifications for your username, password, host, port, and database name: ```sql from sqlalchemy import create_engine engine = create_engine('postgresql://localhost:5432/<your_db_name>') connection = engine.connect() ``` * Consult [SQLAlchemy documentation](https://docs.sqlalchemy.org/en/latest/core/engines.html#postgresql) for more information. * If using a password, do not upload your password to your GitHub repository. See [https://www.youtube.com/watch?v=2uaTPmNvH0I](https://www.youtube.com/watch?v=2uaTPmNvH0I) and [https://martin-thoma.com/configuration-files-in-python/](https://martin-thoma.com/configuration-files-in-python/) for more information. 2. Create a bar chart of average salary by title. 3. You may also include a technical report in markdown format, in which you outline the data engineering steps taken in the homework assignment. ## Epilogue Evidence in hand, you march into your boss's office and present the visualization. With a sly grin, your boss thanks you for your work. On your way out of the office, you hear the words, "Search your ID number." You look down at your badge to see that your employee ID number is 499942. ## Submission * Create an image file of your ERD. * Create a `.sql` file of your table schemata. * Create a `.sql` file of your queries. * (Optional) Create a Jupyter Notebook of the bonus analysis. * Create and upload a repository with the above files to GitHub and post a link on BootCamp Spot.

Learn Data Science By Doing Hands-on Projects, one-a-day, with Jupyter Notebooks, VS Code, GitHub Codespaces, GitHub Copilot - and AI!

The algorithm scrapes the Thomson Reuters archive between custom dates at an average rate of 200,000 news stories per day. In addition I include Jupyter Notebook running statistical analysis of scraped news such as number of atricles published by Reuters per weekday/month, distribution of news categories and others.