The idea behind this project is to predict the Chance of Admit during filing an application for Masters Degree, considering the features GRE Score, TOEFL Score, CGPA, University Rating, Research Experience and Statement of Purpose. Machine Learning models are used to predict the 'Chance of Admit'. We also visualise everything by plotting graphs.
- pandas - is a Python package providing fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive.
- numpy - NumPy is a python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices.
- scipy - The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation.
- joblib - Joblib is a set of tools to provide lightweight pipelining in Python.
- scikit-learn - scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.
- matplotlib - Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
- seaborn - Seaborn is a library for making statistical graphics in Python. It is built on top of matplotlib and closely integrated with pandas data structures.
You should have Python3 and Anaconda installed in your system. To install other required libraries, run the following command in the terminal.
pip install -r requirements.txt