• Build a dataset of job postings with the field of data science & analytics.
• Create labels for each job posting. For example: data scientist, data engineer, etc.
• Develop a machine learning model to classify a job based on description text
• Identify the most common words and phrases for each label

As a Data Scientist seeking a job in-field, I thought it would be fun and useful to practice my skills using NLP to classify jobs based on the posting descriptions from a popular job hosting site like Indeed.

Many candidates starting their search may have their resume's sidestepped by recruiters or a company's ATS system based on lack of key skills, buzzwords, and phrases that 'fit' the job.

I want to build a robust list of common words and phrases that each job title uses to hopefully increase candidate's chances by tailoring their individual skills & experience to better match the job they're looking for.

• A script that runs the classification model.
• A CSV of the most common words and phrases for each job title.
• A Jupyter Notebook walkthrough of my process, thoughts, and takeaways.

• Data scrapped from Indeed
• BeautifulSoup Documentation
• Project inspired by Codeup's NLP Project
• Getting past CAPTCHA's
• Inspiration for building scrapper: here

• I'll need to make sure my data is representative by including a variety of industries, balanced data from each label, and locations.
• I need to create the labels for each job posting using regex, manually, or some form of normalization.
• Each observation is a single job posting
• I'll use the BeatifulSoup Library to webscrape and collect my data
• Find n-grams, verbs, and phrases within the exploratory phase
• Normalize, tokenize, stem or lemmatize words, and remove stop words
• Features to include:
  • Job Title (original)
  • Job label - target
  • Company
  • Location
  • Remote - binary category
  • Seniority level
  • Industry
  • Job description - word vectors
  • Top skills - word vectors
  • Benefits - word vectors

• Data scientist jobs are more generalized, where they can focus on any part of the pipeline from data acquistition to delivery. So a data scientist job will involve a variety of technical tools like: Python, SQL, visualization tools like Tableau, distributed and cloud environments like Spark and AWS, various Machine Learning techniques and possbily deep learning.
• Data Analyst jobs involve more about drawing insights from data with dashboards and data visualation tools. A big part of this job and the others as well will be soft skills like: communication among various other teams in a company, making insights and technical concepts understandable in non-technical language, and having some domain knowledge depending on the industry.
• Data Enginering roles would be centered on creating data pipelines and warehouses. This positions is more heavily programming oriented and will require skills like: advanced python, SQL, and even cloud-computing. ETL is a also very common in these roles.

Created a series of functions to extract the data from indeed using the Beautiful Soup library. The idea was to parse the html of each job posting and reference different elements to obtain each variable.

1. make_soup: takes in a url and uses the requests module to parse HTML from the page returning a soup object. We can then use the soup object to call various methods to get the parts of the page that we need like, job title, and job description. I had to implement a random string generator as a user agent to get past CAPTCHA when scrapping (see acknowledgements). This is because a website detects strange activity like scrapping too frequently.
2. get_search_urls: scrapes the Indeed search results pages for each of 4 job titles (data scientist, data analyst, data engineer, and machine learning engineer) for the first 10 pages of each and returns a list of all the urls. I ended up with 60 urls (15 pages for each job title).
3. get_all_cards: This function scrapes the url from each job card within each page of the search result urls and returns a complete list of urls for each job. I had to implement random number generator to use a delay timer to get past CAPTCHA (see acknowledgements). I ended up with 904 unique job postings. I also saved the list of urls locally as a text file so that I have them saved for later use/reproducibility.
4. get_job_content: takes in a list of job urls and a parameter with default cached == False which scrapes the job_title, company, location, remote, salary, post_date, access_date, and job_description for each url, creates a list of dictionaries with the features mentioned for each job, converts list to df, and returns df. If cached == True, the function returns a df from a json file. Try and except statements are in place in case a variable isn't indicated. We will replace it with an empty string
5. Takeaways: I ran the get_job_content function in 5 separate parts to essentially save my progress in case I happened to run into an error with the url's content while scrapping. Finally I used pandas to concatenate them into a single dataframe for preparation and preprocessing.

The preparation phase involved finding a way to create labels for the data I acquired and making sure I was keeping the data integrity by removing any jobs that weren't part of the targets I was looking for. Creating labels involved manually using the index to label sets of data with the corresponding label. Since my web scaper acquired a set of data for one label before the other, I found the cut-offs for each label arounf 224 rows, more or less. Other rows that weren't correctly labeled were filtered using regex.

1. prep_create_labels:

• removes rows without a description
• creates labels based on index
• added these labels as a column to the dataframe from a list
• handled nulls by filtering titles that weren't explicit based on job title.
• used regex to further filter relevant rows
• combined data back to the original dataframe with all labels created.

2. basic_clean: This function takes in a string and returns the string normalized.
3. tokenize: This function takes in a string and returns a tokenized string.
4. stem: This function takes in a string and returns a string with words stemmed.
5. lemmatize: This function takes in string for and returns a string with words lemmatized.
6. remove_stopwords: This function takes in a string, optional extra_words and exclude_words parameters with default empty lists and returns a string.
7. prep_job_data: This function take in a df and the string name for a text column with option to pass lists for extra_words and exclude_words and returns a df with the text article title, original text, stemmed text, lemmatized text, cleaned, tokenized, & lemmatized text with stopwords removed.

1. What are the most frequently occurring words?
2. Are there any words that uniquely identify each label?
3. What is the proportion of DS vs DA vs DE vs MLE for the 20 most common words?

Python | Pandas | Requests | Bs4 | Time | Datetime | Sci-Kit Learn

Standard License

Brandon Martinez