Data Parsing, Cleansing and Analysis Instructions & Brief Nowadays there are many job hunting websites including seek.com, Azuna.com, etc. These job hunting sites all manage a job search system, where job hunters could search for relevant jobs based on keywords, salary and categories, etc. Job advertisement data analysis is becoming increasingly important and beneficial for job hunting sites, as they can be use to make improvements in the experience of users searching for jobs.
Examine and load the data into a Pandas DataFrame. After the data is parsed and loaded into Pandas, you should have a DataFrame where each row is a job advertisement record and each column is one of the attributes listed in the table above. All the columns are be parsed with attribute names . In the final output (after cleansing), each column will need will be format as listed in the above table.
In this task, I have inspected and audit the data to identify the data problems, and then fix the problems. Different generic and major data problems were found in the data : Typos and spelling mistakes Irregularities, e.g., abnormal data values and data formats Violations of the Integrity constraint. Outliers Duplications Missing values Inconsistency, e.g., inhomogeneity in values and types in representing the same data
In this task, I have build 3 predictive machine learning models to predict Job salaries. Have also analysed, compared and evaluated the different models built, and provided an ultimate judgement of the final trained model that I would use in a real-world setting.