An Open source repository that was active during the term 2020-2021 at DSC-VIT,Bhopal. This repository consists of multiple ML and DL projects for people to learn from. Students could also contribute to it during the 2020-2021 season of DSC.
Currently, the repository is not maintained and all the contributors are requested to contribute to the new repositories that are there for the term 2021-2022
Here's a list of the showcased projects in this repo:
This project deals with the age old problem in optical character recognition— Dealing w/ noise in the image data that can lead up to misrepresentation and inaccuracies during inference. Here, we implement an AutoEncoder model using TensorFlow and Keras that eliminates noise/distortions within the image data for better OCR operation.
A Machine Learning project that makes the use of the KNN algorithm to recommend books to the users based off of the average ratings of the books in the data, the language they are written in, and the rating for that book.
A Flask-based web application that uses Machine Learning to predict the selling price of a car.
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A model that aims to prevent road accidents caused due to sleep depriviation, by alerting drowsy drivers. In this project, a convolutional neural network has been trained to determine whether the eyes of the driver are closed or not. Further, eye-patches are extracted from the face image to make all predictions.
The Deep Space comprises of innumerable celetial bodies— planets, stars, galaxies, asteroids, etc. As a result, it is not possible to label each of these celetial bodies via a more traditional manual method. This is where machine learning shines, which allows Scientists to label a celestial body based on a variety of features like its gradient and standard deviation in a 2 dimensional space, etc. In this project, some models have been implemented, based on the same principles for classification of celestial bodies based on their features.
Each year, hundreds of thousands of women lose their lives to cervical cancer, especially in developing countries where people often neglect/can't afford regular checkups and pap tests. This beginner-level project aims at an early detection of the risk of cervical cancer using different machine algorithms.
The objective of this project was to predict the amount of followers gained by a streamer on Twitch based on the streaming data. Different visualization and data analysis techniques were used for understanding the data as well as deriving various insights from it.
A detailed data analysis for the Kaggle ML & DS survey.
A beginner-level project that uses different machine algorithms to predict whether a student will get placed into a job via campus recruitment or not.
The "Titanic-Machine Learning from Disaster" competition is an introductory kaggle competition for getting started with machine learning.
This Machine Learning/Data Analysis project uses a relatively small dataset that exemplifies many of the practical problems that one deals with while doing machine learning projects. A great tutorial for beginners in ML.
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| Aman Sharma |
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| "Let The Dataset change your Mindset" |
Need help? Feel free to contact me @ [email protected]
Aman Sharma, ML Lead @ DSC VIT © 2020
Contributions by amansharma2910: Noise Removal and OCR Using CNNs and Autoencoders || Cervical Cancer Risk Prediction
Contributions by AM1CODES: Book Recommender System || Campus Recruitment Analysis || Kaggle 2020 Suvey Analysis
Contributions by kritikashah20: Celestial Bodies' Classification
Contributions by Ani0202: Added KNN, Logistic Regression and SVM Classifiers; improved Decision Tree Classifier in Cervical Cancer Detection project
Contributions by AndroAvi: Celestial Bodies' Classification
Contributions by Jackson-hub: Used Price Predictor
Contributions by mayureshagashe2105: Drowsiness Detection Web App