https://github.com/shreyansh26/ai-assignment-3
Logistic Regression
https://github.com/shreyansh26/ai-assignment-3
Last synced: 3 months ago
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Logistic Regression
- Host: GitHub
- URL: https://github.com/shreyansh26/ai-assignment-3
- Owner: shreyansh26
- Created: 2020-01-25T14:14:00.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-01-26T15:09:24.000Z (over 5 years ago)
- Last Synced: 2025-01-14T02:12:47.898Z (9 months ago)
- Language: Jupyter Notebook
- Size: 162 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
Assignment 3 - Logistic Regression
================================
In this assignment, you'll be implementing a basic logistic regression model to solve a binary classification problem. This model will be used to fit the given 2D data that comprises of marks of some students. Aim here is to predict whether the student will get admission in a college based upon these marks or not. The fits would be visualized. So the goal here is to find the decision boundary that fits the data best.
Working files:
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You will be restricted to work with 'model.py' and 'Assignment3-Logistic Regression.ipynb'. On running and working through the 'Assignment3-Linear Regression.ipynb', a file named 'hyper_param.json' would be generated.
Data:
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Data is already stored in a csv file and provided to you. You are not allowed to change the CSV file in any manner.
Actual Work:
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- Complete some functions related to logistic regression (in models.py)
- computing sigmoid - *sigmoid*
- loss and gradient - *loss*
- predicting values - *assign_predictions*
- calculating accuracy - *accuracy*
- training the model - *train*
- Tuning various parameters of the models in order to achieve the best results (in Assignment3-Logistic Regression.ipynb)
Steps:
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0. Open 'Assignment3-Logistic Regression.ipynb' via Jupyter Notebook.
1. Work through the 'Assignment3-Logistic Regression.ipynb' and follow the instructions therein.
2. You need to submit the files: 'models.py', 'Assignment3-Logistic Regression.ipynb' and 'hyper_param.json'.
NOTE: We will be testing your results with the help of 'model.py' and the hyperparameters in 'hyper_param.json'.
Hint File:
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There is also a hint.pdf, that contains the mathematical workout for logistic
regression's. Try to complete the assignment without looking at it, but do
consult it if you get stuck.
All the best.