Exploratory-Data-Analysis-on-Resume-data

Project Description:

Employers use resumes to get a deeper understanding of candidate skills, strengths and experience. Your resume should reflect achievements, awards, education, experience and any other outstanding accomplishments that align with your career path and goals.

About the Dataset:

Data description:

This is a cross-section data about resume, call-back and employer information for 4,870 fictitious resumes sent in response to employment advertisements in Chicago and Boston in 2001, in a randomized controlled experiment.

Data preprocessing:

• Firstly, the data set was imported using read.csv. The original dataset is read as Datafinal .
• To make better analysis, the categorical variables “yes and no” were changed to numerical values of 1 and 0 respectively in excel using find and replace. This data is imported and read as Datafinal1.
• In order to find if there are missing values, [which(is.na(Datafinal))] was used. But, it gave the output 0 indicating there aren’t any missing values.
• By using str(Datafinal) , a compact structure of the dataset was obtained, which makes the further analysis simple based on if the variable is numeric or categorical.
• Other commands like head(Datafinal) to obtain the initial rows, summary(Datafinal) to produce result summaries of various model fitting functions also were used.

Methodologies used:

• Filtering using dplyr library.
• Visualizations using bar graphs, histograms and box plot.
• Finding relationships using correlation matrix and chi-square test.

Result:

The results and inferences are mentioned in the the Report.pdf file.

Conclusion:

• Performing “Exploratory Data analysis using R” on the resume dataset using libraries like dplyr(), tidyr(), ggplot2(), corrplot(), Hmisc(), PerformanceAnalytic() , various statistical and visualization methods, we have drawn numerous conclusions and inferences.
• The baseline of all the analysis techniques is whether a candidate who has applied for a particular job gets a call back or not. Visualization techniques like bar plots, histogram, box plot, scatterplot and statistical tools like chi-square test and correlation were used, which helped in finding which variables are highly responsible for getting a call back for the interview.

Future Prospects:

The future prospect of this analysis can be to design a predictive model that helps predict if a candidate has to be called for the interview or not by considering all inferences drawn from Exploratory data analysis of the Resume names dataset