This data-driven project provides valuable insights and predictions regarding graduate salaries, benefiting both recent graduates and employers in a competitive job market.
Graduates often struggle to negotiate fair salaries for their first jobs, while employers find it challenging to determine compensation based on factors like education and experience. This project aims to develop predictive models and uncover essential insights to estimate engineering graduate salaries effectively.
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Data Analysis:
- Conduct exploratory data analysis (EDA) to understand relationships between variables and graduate salaries.
- Identify patterns and trends within the dataset.
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Feature Engineering:
- Engineer relevant features impacting salary predictions.
- Handle categorical variables, missing data, and create new features.
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Machine Learning Models:
- Train and evaluate a variety of regression models to predict engineering graduate salaries.
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Hyperparameter Tuning:
- Fine-tune models by optimizing hyperparameters for improved accuracy.
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Model Evaluation:
- Assess model performance using metrics such as R2 score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).
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Interpretability:
- Provide insights into features with significant impacts on salary predictions, aiding graduates and employers in understanding salary determinants.
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- Notebook 1.
- Data preprocessing.
- Deriving insights from data visualizations.
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- Notebook 2.
- Machine learning model construction.
- Feature engineering.
- Trained and tested on major regression models.
- Various accuracy metrics.
- Hyperparameter tuning for enhanced accuracy.
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- Describes dataset features.
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Engineering_graduate_salary.csv:- Contains engineers' records and salary data.
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- Output of
Analysis.ipynb. - Used for model building.
- Output of
Made by Hrishikesh Reddy Papasani
Connect on LinkedIn: LinkedIn Profile
- Dataset Source: Kaggle
Made by Hrishikesh Reddy Papasani
Connect on LinkedIn: LinkedIn Profile
Contact at [email protected]