- Dataset: INX Future Inc (IABAC). 1200 rows, 28 features.
- Key Insights: Department-wise performance, top factors affecting employee performance, trained model for prediction, and recommendations to improve performance.
1. Requirements:
- Data provided by IABAC™ based on INX Future Inc. The project was conducted in Jupyter using Python.
2. Analysis:
- Features: Numerical, categorical, and ordinal.
- Important Features: EmpNumber (not relevant), Gender, Education, MaritalStatus, JobRole, etc.
3. Data Analysis:
- Univariate Analysis: Distribution of categories and numerical data.
- Bivariate Analysis: Relationship with performance rating.
- Multivariate Analysis: Relationships across features.
4. Exploratory Data Analysis:
- Distribution and statistical checks for features.
- Skewness and Kurtosis analysis for normality.
- Visualizations for feature relationships.
5. Data Preprocessing:
- Missing value check, encoding categorical data, handling outliers, feature transformation, scaling.
6. Feature Selection:
- Dropped unique columns and performed PCA to reduce features from 28 to 25.
7. Machine Learning Model:
- Algorithms used: SVM, Random Forest, and Artificial Neural Networks (ANN).
- Best Model: ANN with 95.80% accuracy.
8. Model Saving:
- Saved the trained model using Pickle for future predictions.
Goal 1: Department-wise Performances:
- Violinplot and Countplot used for performance distribution across departments.
- Findings: Sales and Development departments have the highest performers. Female employees in HR perform well.
Goal 2: Top 3 Factors Affecting Performance:
- Important factors: Environment satisfaction, salary hike percentage, and experience in current role.
Goal 3: Trained Model for Prediction:
- Trained models: SVC (98.28%), Random Forest (95.61%), ANN (95.80%).
Goal 4: Recommendations to Improve Performance:
- Focus on environment satisfaction, salary hikes, and work-life balance.
- Promote employees regularly, focus on female candidates in HR, and improve job satisfaction and relationships.