Salary Prediction of Data Professions

[Harshit-code-tech]

Salary Prediction

Description

We are looking to enhance our Salary Prediction model to improve its accuracy and robustness. The goal of this issue is to implement, evaluate, and document enhancements to the existing Salary Prediction model. This issue involves several key tasks:

Tasks

Data Preprocessing Enhancements

• Identify and handle outliers in the dataset.
• Implement advanced feature engineering techniques to create more predictive features.
• Explore and document different methods for handling missing values.

Model Development

• Experiment with different machine learning algorithms and ensemble methods.
• Perform hyperparameter tuning to optimize model performance.
• Implement cross-validation to ensure the model's robustness.

Model Evaluation

• Evaluate the models using relevant performance metrics (e.g., MAE, MSE, RMSE, R2).
• Compare the performance of different models and document the results.
• Generate and include visualization plots to illustrate model performance.

Documentation

• Update the project documentation to reflect the new enhancements.
• Include a detailed README file explaining the changes made, how to run the enhanced model, and the results obtained.
• Ensure the code is well-commented for better understanding and maintenance.

Web Application Integration

• Integrate the best-performing model into the existing Flask web application.
• Update the web application's front-end to accommodate any new input features or changes in the model.
• Test the web application to ensure it functions correctly with the new model.

Requirements

• Strong knowledge of Python and machine learning libraries (pandas, scikit-learn, joblib, etc.).
• Experience with data preprocessing, feature engineering, and model evaluation.
• Familiarity with Flask and web application development.
• Good documentation practices.

Expected Outcome

• An enhanced Salary Prediction model with improved accuracy and robustness.
• Comprehensive documentation detailing the enhancements and their impact on model performance.
• A fully integrated and tested web application showcasing the new model.

[github-actions[bot]]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[Harshit-code-tech]

I am a contributor on Social Summer Of Code Season 3 please assign me this issue

[abhisheks008]

What are the models you are planning to implement?

[Harshit-code-tech]

Models to Implement

1. Linear Regression

   • Reason for Selection: Provides a simple baseline to compare against more complex models. It is easy to interpret and implement.

2. Decision Tree Regressor

   • Reason for Selection: Captures non-linear relationships and provides easy interpretation through tree visualization.

3. Random Forest Regressor

   • Reason for Selection: Reduces overfitting and improves accuracy by averaging multiple decision trees.

4. Gradient Boosting Regressor

   • Reason for Selection: Often outperforms other models by focusing on correcting errors iteratively, providing high accuracy.

[abhisheks008]

Can you share the dataset URL, as this type of Salary Prediction model is already present in this repository.

[Harshit-code-tech]

https://www.kaggle.com/datasets/mohamedeldakrory8/salary-prediction-data

[abhisheks008]

Assigned this issue to you @Harshit-code-tech

[github-actions[bot]]

Hello @Harshit-code-tech! Your issue #670 has been closed. Thank you for your contribution!