Webscraper adapted from https://towardsdatascience.com/selenium-tutorial-scraping-glassdoor-com-in-10-minutes-3d0915c6d905

Inspiration for the project was based on Ken Jee's youtube series 'data science project from scratch' Major changes include:

• Unique model building approach based on sklearn ensemble module
• The model was deployed to production via streamlit on heroku url: https://glassdoor-fin-analyst.herokuapp.com/
• Updated webcrawler was in need of overall due to glassdoor's updated website
• Unique field objective

• Adapt web scraper for data for model
• Clean data for analysis
• Analyze data
• Submit findings
• Scale and Build Machine Learning Model
• Host product on heroku

The objective of this project is to further understand what it takes to be a financial analyst in London. This exercise will serve as a gateway to those seeking to become analyst themselves as well as create an entry point adapting a machine learning model in predicting what role may be expected in relation to the different variables.

• Inferential Statistics
• Machine Learning
• Data Visualization
• Predictive Modeling

• Python
• Pandas
• Numpy
• Matplotlib
• Nltk
• Wordcloud
• Seaborn
• Sklean
• Selenium
• Sklearn

As we move closer to the full cycle of graduates moving into the work force, the question has been posed is what does it take/what is it like to be a financial analyst? Some questions we plan on answering include:

• What kind of salary should be expected?

• What positions are the most popular?

• Types of companies Hiring?

• What industries are the most popular?

• Similarities between different roles?

• Other questions we might want answered as we explore the data some more?

  things to note:

• The data was gathered from Glassdoor job postings on 6/7/2020 via web scraper with the use of the Selenium Python library. As such, COVID-19 has remained a constant factor in our lives and should be taken into consideration.
• -1 represents data that wasn't specified in the job posting
• The sample size for this data set was 1,000 entries.
• We ran the web scraper multiple times to get a wider pool of data due to the number of missing data

• Some of the most common words mentioned in the analysis include: 'Problem Solving','Bachelor Degree','team' and 'attention to detail'
• Average salary came out to around 30K depending on the seniority level
• Most big corporations are doing the hiring at the moment

• With more data and better feature selection, users could calculate their exact salary