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We tried in this notebook to predict student admissions to graduate school at UCLA based on three pieces of data.
predicting-student-admissions-with-neural-networks-using-python's Introduction
Predicting Student Admissions
with Neural Networks using Python ๐ :
- We tried in this notebook to predict student admissions to graduate school at UCLA based on three pieces of data.
- GRE Scores (Test)
- GPA Scores (Grades)
- Class rank (1-4)
General overview
๐๏ธ :
- ๐ฃ Here are the steps we followed in this notebook :
- Loading the data.
- Plotting the data.
- One-hot encoding the input variable we are interested in.
- Scalling the data.
- Splitting the data into Training and Testing.
- Splitting the data into features and targets (labels).
- Training the 1-layer Neural Network.
- Calculating the Accuracy on the Test Data.
- ๐ The dataset used is provided in this repository.
- ๐ This notebook realised with the help of udacity courses.
- ๐ซ Feel free to contact me if anything is wrong or if anything needs to be changed ๐! [email protected]
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)
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