- You can access the complete Case python file here - Python
- You can access the complete Casestudy in pdf format here - Report
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Jamboreeis a renowned educational institution that has successfully assisted numerous students in gaining admission to top colleges abroad. With their proven problem-solving methods, they have helped students achieve exceptional scores on exams like GMAT, GRE, and SAT with minimal effort. -
To further support students, Jamboree has recently introduced a new feature on their website. This feature enables students to assess their probability of admission to Ivy League colleges, considering the unique perspective of Indian applicants.
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By conducting a thorough analysis, we can assist Jamboree in understanding the crucial factors impacting graduate admissions and their interrelationships. Additionally, we can provide predictive insights to determine an individual's admission chances based on various variables.
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Solving this business case holds immense importance for aspiring data scientists and ML engineers.
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Building predictive models using machine learning is widely popular among the data scientists/ML engineers. By working through this case study, individuals gain hands-on experience and practical skills in the field.
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Additionally, it will enhance one's ability to communicate with the stakeholders involved in data-related projects and help the organization take better, data-driven decisions.
As a data scientist/ML engineer hired by Jamboree, your primary objective is toanalyze the given dataset and derive valuable insights from it. Additionally, utilize the dataset to construct a predictive model capable of estimating an applicant's likelihood of admission based on the available features.
Column Profiling:
| Feature | Description |
|---|---|
| Serial No. | This column represents the unique row identifier for each applicant in the dataset. |
| GRE Scores | This column contains the GRE (Graduate Record Examination) scores of the applicants, which are measured on a scale of 0 to 340. |
| TOEFL Scores | This column includes the TOEFL (Test of English as a Foreign Language) scores of the applicants, which are measured on a scale of 0 to 120. |
| University Rating | This column indicates the rating or reputation of the university that the applicants are associated with , & The rating is based on a scale of 0 to 5, with 5 representing the highest rating. |
| SOP | This column represents the strength of the applicant's statement of purpose, rated on a scale of 0 to 5, with 5 indicating a strong and compelling SOP. |
| LOR | This column represents the strength of the applicant's letter of recommendation, rated on a scale of 0 to 5, with 5 indicating a strong and compelling LOR. |
| CGPA | This column contains the undergraduate Grade Point Average (GPA) of the applicants, which is measured on a scale of 0 to 10. |
| Research | This column indicates whether the applicant has research experience (1) or not (0). |
| Chance of Admit | This column represents the estimated probability or chance of admission for each applicant, ranging from 0 to 1. |
These columns provide relevant information about the applicants' academic qualifications, testscores, university ratings, and other factors that may influence their chances of admission.
Jamboree, a leading educational institution, aims to enhance student admissions to Ivy League colleges by predicting admission probabilities for Indian applicants using a newly introduced website feature.
Conduct a comprehensive regression analysis to identify key predictors influencing admission chances and provide actionable insights for optimizing the admissions process.
Conducted thorough data preprocessing, including handling missing values, treating duplicates, and checking for outliers to ensure data integrity. Implemented univariate and bivariate analyses using statistical and graphical methods to understand variable distributions and relationships, highlighting the significance of factors like GRE score, TOEFL score, and CGPA. Built and evaluated Linear Regression, Ridge Regression, and ElasticNet models to predict admission probabilities, assessing model performance metrics such as MAE, RMSE, R-squared, and adjusted R-squared. Investigated multicollinearity using VIF scores, confirming robust model predictors with minimal collinearity issues despite high correlations among variables. Analyzed residual plots for normality and heteroscedasticity, informing model refinement strategies and suggesting potential data augmentation opportunities.
Our regression analysis revealed that CGPA, GRE score, and TOEFL score are critical predictors of admission chances. Despite minor issues with residual normality and heteroscedasticity, both Linear Regression and regularized models demonstrated strong performance, capturing up to 82% of the variance in admission probabilities. Recommendations include enhancing feature diversity in applicant profiles beyond academic metrics and focusing on improving key predictors to optimize admission outcomes.