Graduate Admission Prediction

Background

Admission of postgraduate students into various universities plays a significant role in the education sector of different countries in the world. India is not an exception as it is the universities that act as the center for research and innovation. This project will use the dataset from India to predict the chance of admission of graduate students into the university to pursue further studies while considering various factors such as letters of recommendation from the previous institution or previous undergraduate program in India.

Problem Statement

While admitting graduate students for different postgraduate academic programs, there are factors that affect the chances of admission. This project is interested in analyzing factors that come into consideration while admitting graduate students and how they affect the chance of admission. The project uses a classification technique for the prediction of Graduate Admissions from an Indian perspective.

Objectives

The main objectives of this project are:

1. Download the project dataset from Kaggle.
2. Perform exploratory data analysis to derive insights from the dataset and use visuals such as boxplots, line charts, and histograms for a clear understanding and precise presentation.
3. Model building and evaluation of the complexity using the K-Nearest Neighbors algorithm and Python libraries like Sklearn.
4. Take screenshots of the visuals for the final presentation o summarize the key findings of the analysis, along with clear and concise explanations.

Final Presentations

Data

This dataset is inspired by the UCLA Graduate Dataset. The test scores and GPA are in the older format. The dataset is owned by Mohan S Acharya.

Inspiration This dataset was built with the purpose of helping students in shortlisting universities with their profiles. The predicted output gives them a fair idea about their chances at a particular university.

Resources

1. Download the Graduate Admission dataset from Kaggle.

2. Data set link:https://www.kaggle.com/datasets/mohansacharya/graduate-admissions

3. Python libraries such as Pandas, Numpy, Seaborn, Matplotlib, and Sklearn for analysis and model building.

   Methodology

The following steps will be taken to complete this project:

1. Data acquisition and preparation: Download the dataset from Kaggle.com and prepare the data for analysis using Python libraries such as Pandas and NumPy.

2. Data analysis: Perform data analysis on the cleaned data using techniques such as exploratory data analysis to uncover insights to use in model building and evaluation.

3. Visualize model complexity to check how it works.

4. Results and Recommendation: Create a PowerPoint slide with visualizations and metrics to summarize the key findings of the analysis, along with clear and concise explanations.