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:
- Data Analysis and Pre-processing: Initial exploration and preparation of the data for modeling.
- Model Development: Using MLP and regression algorithms to develop the predictive model.
- Model Evaluation and Optimization: Each step in the data analysis and model development is explained, including the rationale behind the chosen methods.
- 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.
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The comparison between LinearRegressionScore and MLPRegressorScore in terms of accuracy is depicted in the notebook.
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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.ipynbto view the analysis and results.