This project aims to develop a model for predicting the project scores of applicants in a company's recruitment process. Applicants undergo a test covering various technical subjects and complete a project related to their field of employment. The goal is to estimate the project score based on test scores and other relevant information.

The challenge involves creating a predictive model that accurately estimates an applicant's project score using their test results and other variables. The project focuses on exploring different machine learning algorithms, including MLP (Multi-Layer Perceptron) and regression techniques, for this purpose.

The project involves several key steps:

1. Data Analysis and Pre-processing: Initial exploration and preparation of the data for modeling.
2. Model Development: Using MLP and regression algorithms to develop the predictive model.
3. Model Evaluation and Optimization: Each step in the data analysis and model development is explained, including the rationale behind the chosen methods.
4. Detailed Documentation: A PDF file containing a detailed report of the analysis, model development, and findings.

• HW2-1-v2.ipynb: Jupyter notebook containing the entire analysis and model development process.
• Q1.csv: The dataset used for the analysis.
• Report.pdf: A PDF file containing a detailed report of the analysis and findings.

• The comparison between LinearRegressionScore and MLPRegressorScore in terms of accuracy is depicted in the notebook.

  

• Insights on the best parameters and structure for MLP and other machine learning algorithms that yield the desired results are discussed.

• Clone the repository.
• Ensure you have Jupyter Notebook installed.
• Run HW2-1-v2.ipynb to view the analysis and results.