# HR Analytics: Predicting Employee Kernelover

Here’s what I found: More than half of the employees with 2,6, and 7 projects left the company Majority of the employees who did not leave the company had 3,4, and 5 projects All of the employees with 7 projects left the company There is an increase in employee turnover rate as project count increases Stop and Think: Why are employees leaving at the lower/higher spectrum of project counts?.Does this means that employees with project counts 2 or less are not worked hard enough or are not highly valued, thus leaving the company?.Do employees with 6+ projects are getting overworked, thus leaving the company?.In[20]: ax = sns.barplot(x="projectCount", y="projectCount", hue="turnover", data=df, estimator=lambda x: len(x) / len(df) * 100) ax.set(ylabel="Percent") Out[20]: 3g..Turnover V.S..Evaluation Summary: There is a biomodal distribution for those that had a turnover..Employees with low performance tend to leave the company more Employees with high performance tend to leave the company more The sweet spot for employees that stayed is within 0.6-0.8 evaluation In[21]: # Kernel Density Plot fig = plt.figure(figsize=(15,4),) ax=sns.kdeplot(df.loc[(df[turnover] == 0),evaluation] , color=b,shade=True,label=no turnover) ax=sns.kdeplot(df.loc[(df[turnover] == 1),evaluation] , color=r,shade=True, label=turnover) ax.set(xlabel=Employee Evaluation, ylabel=Frequency) plt.title(Employee Evaluation Distribution – Turnover V.S. No Turnover) Out[21]: 3h..Turnover V.S..AverageMonthlyHours Summary: Another bi-modal distribution for employees that turnovered Employees who had less hours of work (~150 hours or less) left the company more Employees who had too many hours of work (~250 or more) left the company Employees who left generally were underworked or overworked In[24]: #ProjectCount VS AverageMonthlyHours [BOXPLOT] #Looks like the average employees who stayed worked about 200hours/month..Those that had a turnover worked about 250hours/month and 150hours/month import seaborn as sns sns.boxplot(x="projectCount", y="averageMonthlyHours", hue="turnover", data=df) Out[24]: 3k..ProjectCount VS Evaluation Summary: This graph looks very similar to the graph above..What I find strange with this graph is with the turnover group.. More details