#!/usr/bin/env python
# coding: utf-8

# **Business Problem Summary:**
# 
# As a data scientist at a growing startup, one of the challenges is establishing a fair and consistent pay scale across all roles while considering employees' years of experience. Currently, each staff member negotiates their salary independently, and the company lacks both a structured pay scale and defined job levels.
# 
# Currently, there's no structured pay system in place, and all employees negotiated their salaries individually, leading to lot of discrepancies and biases in offers to new hires.
# 
# The aim is to introduce a transparent pay scale that categorizes roles based on years of experience, ensuring that new hires are not unfairly compensated compared to existing employees. 
# 
# 
# **Business Problem Details:**
# 1. **Creating a fair salary framework::** The objective is to create a fair and transparent pay scale that categorizes roles based on years of experience, ensuring consistency and equality in compensation across the organization.
#   
# 2. **Introduction of Job Levels:** By categorizing roles into job levels based on years of experience (e.g., 0-2 years as Analyst, 2-4 years as Associate, etc.), the organization aims to provide a structured framework for salary determination.
#  
#  
#  **To arrive at a fair pay scale, salary data of current staff and their years of experiences are used**

# In[144]:


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


# In[145]:


df = pd.read_csv(r"C:\Users\Teni\Desktop\Datasets May-April\Salary_dataset.csv")


# In[158]:


df.head()


# In[160]:


df.describe()


# In[168]:


sns.regplot(data=df, x= 'YearsExperience', y = 'Salary')

# at experience 0, the data shows the salary is just below 40000 (start pay)


# **- Much as the pay has always been individually negotiated, there's a linear reationship between the years of experience and the salaries**

# In[169]:


X = df['YearsExperience']
y = df['Salary']


# **Because the dataset has only one Feature, the Simple Linear Regression will be used.**

# In[172]:


# y = mx+b

np.polyfit(X, y, deg=1)

# the starting salary an 0-year expereince is #24848


# ### Predict using the equation

# In[161]:


pro_exp = np.linspace(1,10, 30)

pro_exp


# In[162]:


pred_sal = 9449.96232146*pro_exp + 24848.20396652
pred_sal


# In[163]:


sns.scatterplot(data=df, x='YearsExperience', y='Salary')
plt.plot(pro_exp, pred_sal, color='red')


# **The HR team approached me, saying we have 2 new cabdidates whose years of experiences are 10 and 5; asking how much the offer should be**

# In[182]:


exp = 10
pred_sal = 9449.96232146*exp + 24848.20396652

print("For a 10-year work experience, the salary offer should be: $", round(pred_sal, 2))


# In[183]:


exp=5
pred_sal = 9449.96232146*exp + 24848.20396652

print("For a 5-year work experience, the salary offer should be: $", round(pred_sal, 2))


# ### Conclusion:
# 
# By aligning roles with these well-defined job levels, the organization provides a transparent roadmap for career advancement. By using the equation (y = m*(years of experience) + b), managers are able to negotiate fairly when hiring new talents- without ofshooting the payscale within the organization.
# 
# 
# 1. **Fair and Transparent Pay Scale:** The proposed salary framework introduces a fair and transparent pay scale by using a polyfit equation derived from the array ([9449.96232146, 24848.20396652]). This equation, represented as y = mx + b, determines salaries (y) based on years of experience (x). By relying on this mathematical approach, the organization eliminates potential biases and ensures consistency in compensation across all roles and levels.
# 
# 2. **Well-Defined Career Progression:** The framework establishes clear job levels, enabling structured career growth and progression within the organization. These job levels are:
# 
#    - a. Analyst: Covering professionals with 0-2 years of experience in their field.
#    - b. Associate: Encompassing individuals with 2-4 years of relevant experience.
#    - c. Senior Associate: For those with 4-6 years of demonstrated expertise.
#    - d. Lead: Recognizing professionals with 6-8 years of significant experience and leadership capabilities.
#    - e. Manager: Reserved for seasoned individuals with 8-10 years of extensive experience and managerial responsibilities.
# 

# In[ ]:





# In[ ]: