Data Science and Analytics Tools - Employee Performance Analytics Dashboard

 Notes:

• What problem does it solve?
  This script helps HR or managers track employee performance based on various metrics (e.g., goals achieved, feedback ratings, etc.).

• How can businesses or users benefit from customizing the code?
  Users can modify the performance metrics, adjust the time range for performance analysis, or add different weightings to performance criteria.

• How can businesses or users adopt the solution further, if needed?
  This can be integrated into HR systems to monitor performance in real time and trigger alerts based on predefined thresholds.

Actual Python Code:

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

# Load employee performance data (e.g., goals, feedback, KPIs)

data = pd.read_csv('employee_performance.csv')

# Calculate performance score (example: weighted average of multiple metrics)

data['Performance_Score'] = (data['Goals_Achieved'] * 0.5 + data['Feedback_Rating'] * 0.3 + data['KPI_Score'] * 0.2)

# Visualize the distribution of performance scores

plt.figure(figsize=(10, 6))

sns.histplot(data['Performance_Score'], kde=True)

plt.title('Distribution of Employee Performance Scores')

plt.show()

# Visualize performance by department

plt.figure(figsize=(10, 6))

sns.boxplot(x='Department', y='Performance_Score', data=data)

plt.title('Employee Performance by Department')

plt.xticks(rotation=45)

plt.show()