Exploratory Data Analysis and Dimensionality Reduction on Graduate Admission Dataset.

Domain: Education

Problem Statement: What factors are affecting Graduate Admissions in America for Students?

Dataset: Graduate Admission dataset on Kaggle

Data Fitness:

This dataset is choosen because it contains several parameters which are considered important during the application for Masters Programs. The parameters included are:

1. GRE Scores ( out of 340 )
2. TOEFL Scores ( out of 120 )
3. University Rating ( out of 5 )
4. Statement of Purpose and Letter of Recommendation Strength ( out of 5 )
5. Undergraduate GPA ( out of 10 )
6. Research Experience ( either 0 or 1 )
7. Chance of Admit ( ranging from 0 to 1 ).

Although the dataset contains only 500 records, it is a very relevant dataset.

Analysis Code and Demo:

• Exploratory Data Visualization of Graduate Admission Data set:

  • Performed data analysis and visualizationon various factors and their effect on Chances of getting an admit.
  • This involves interactive graphing using Plotly and Cufflinks. Make sure you have that installed where you are trying to run the notebooks locally. pip install plotly pip install cufflinks
  • Demo: HTML Webage | Kaggle

• Principal Component Analysis (PCA) and Visualization:

  • Demo involves both numpy and sklearn implementation
  • This is mainly performed to visualize the 6-D features in 2-D.
  • Demo: HTML Webpage | Kaggle

Analysis Summary:

• CPGA plays the most important role in admissions followed by GRE score and TOEFL.
• Good SOPs and LORs are essential to get into the best universities.
• Research makes your SOP and LOR better. No worries if you don't have research experience, you can still get admission
• Studious students generally tend to do good at GRE and TOEFL.

The Analysis results are in accordance to the data given. To perform more analysis, it is essential that we have more records and features.

This repository is built for the Homework Assignment ( Data Visualization) of DataLit course.