└── README.md


/README.md:
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 1 | # HR ANALYTICS(AICTE INTERN)
 2 | 
 3 | ### Project Overview
 4 | The dataset contains information about employees, including attributes such as age, years of experience in the company, department, gender, job role, education level, salary, and attrition status (whether they left the company or not).
 5 | 
 6 | ![image](https://github.com/nishantsingha13/Data-Analysis/assets/103675762/3394b72d-3422-4869-98b9-c75a58814a28)
 7 | 
 8 | 
 9 | ### Data Sources
10 | Data: The primary dataset used for this analysis is the "employee_data.csv" file, containing detailed information about each employer of  the company.
11 | 
12 | ### Tools
13 | Excel - Data Cleaning ,
14 | PowerBI - Data Analysis, Creating reports
15 | 
16 | ### Data Cleaning/Preparation
17 | In the initial data preparation phase, we performed the following tasks:
18 | 1. Data loading and inspection.
19 | 2. Handling missing values.
20 | 3. Data cleaning and formatting.
21 | 
22 | ### Exploratory Data Analysis
23 | EDA involved exploring the sales data to answer key questions, such as:
24 | 1. What is the trend in employee attrition (leaving the company) over time?
25 | 2. Which departments have the highest employee satisfaction scores?
26 | 3. How is the Salary Benchmark?
27 | 
28 | 
29 | ### Visualization
30 | 
31 | ![Screenshot (178)](https://github.com/nishantsingha13/Data-Analysis/assets/103675762/c4a8fb34-09ef-4507-95b9-3db2c88e7652)
32 | you can also access the dashboard according to the gender, department, etc.
33 | 
34 | ### Results/Findings
35 | The analysis results are summarized as follows:
36 | 
37 | Employee Attrition:
38 | A total of 1412 employees left the company.
39 | Among them:
40 | 716 employees had a salary under 5k.
41 | 424 employees had a salary between 5k and 10k
42 | 
43 | Experience-Based Attrition:
44 | Employees tend to leave when they have either 1 year or 5 years of experience.
45 | 
46 | Job Role-Specific Attrition:
47 | The following job roles are the most experiencing attrition:
48 | 1. Sales Executives
49 | 2. Laboratory Technicians
50 | 3. Research Scientists
51 | 
52 |    
53 | ### Recommendations
54 | Based on the analysis, we recommend the following actions:
55 | 1. Regularly review and adjust salaries based on market trends and employee performance.
56 | 2. Implement performance-based bonuses or incentives.
57 | 3. Provide growth opportunities, training, and mentorship programs.
58 | 4. Address any concerns related to workload, team dynamics, or job roles.
59 | 
60 | 


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