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<!DOCTYPE HTML>
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<head>
<title>Rohith Teja - Projects</title>
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<header id="header">
<a href="index.html" class="logo">Make the data shine!</a>
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<ul class="links">
<li><a href="index.html">About Me</a></li>
<li><a href="generic.html">CV</a></li>
<li class="active"><a href="elements.html">Projects</a></li>
</ul>
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<!-- Main -->
<div id="main">
<section class="posts">
<article>
<a href="https://github.com/rohithteja/DEFI-IA-2021-Kaggle-Challenge"><h2>NLP Kaggle Competition (TOP 8 Rank)</h2></a>
<p>2021 NLP challenge of a multi-class classification task with 28 classes, where the task is to assign the correct job category to a job description. In this competition, our team secured 8th position in the private leaderboard. </p>
</article>
<article>
<a href="https://github.com/rohithteja/mlflow-twitter-social-spam"><h2>MLflow Pipeline of Twitter Spam Detection Problem
</h2></a>
<p>Integrated MLFlow project on twitter social spam
detection problem in conda and docker env to perform
experiments and access metadata using MLFlow UI</p>
</article>
<article>
<a href="https://github.com/rohithteja/Twitter-Sentiment-Analysis-and-Tweet-Extraction"><h2>Twitter Sentiment Analysis and Tweet Extraction</h2></a>
<p>Detailed analysis of 9 different neural network
algorithms including Transformer models on sentiment
analysis and tweet extraction to understand and report
performance of each method on classification task</p>
</article>
<article>
<a href="https://github.com/rohithteja/Deep-AutoEncoders"><h2>Image Denoising using Autoencoders</h2></a>
<p>Generated artificial images using Deep Convolution
GANS and denoised images with deep autoencoder for
MNIST and CIFAR10 images</p>
</article>
<article>
<a href="https://github.com/rohithteja/Social-Spam-Detection"><h2>Twitter Social Spam Detection</h2></a>
<p>Built an ML model for spam detection by applying
different case studies for feature selection (sklearn, PCA) and performed analysis to identify outliers and
clusters in the twitter spam dataset</p>
</article>
<article>
<a href="https://github.com/rohithteja/K-Nearest-Neighbours"><h2>Effects of Artifical Imbalancy on KNN Performance</h2></a>
<p>Analyzed Waveform dataset by using K-NN algorithm
and experimented by generating artificial imbalancy in
the labels to test robustness of KNN</p>
</article>
<article>
<a href="https://github.com/rohithteja/pytorch-geometric"><h2>Graph Machine Learning using Pytorch Geometric</h2></a>
<p>Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) implementation using pytorch geometric on the cora dataset</p>
</article>
<article>
<a href="https://github.com/rohithteja/DGL-Graph-Neural-Network"><h2>Graph Machine Learning using DGL</h2></a>
<p>Graph Neural Network implementation using DGL and benchmark datasets like Cora, Citeseer and Pubmed.</p>
</article>
<article>
<a href="https://github.com/rohithteja/Data-Mining-Project"><h2>Data mining on Mice Protein Expression Dataset</h2></a>
<p>This project involves data cleaning, feature extraction, model building, hyperparameter tuning with cross validation and PCA in R.</p>
</article>
<article>
<a href="https://github.com/rohithteja/Hidden-Markov-Model"><h2>Probalilistic Modeling with Hidden Markov Models</h2></a>
<p>Application of Hidden markov models to calculate the probability of future observations using Forward, Backward and Viterbi algorithms.</p>
</article>
<article>
<a href="https://github.com/rohithteja/Generative-Adversarial-Networks"><h2>DC-GANS using Keras for Image Generation</h2></a>
<p>Generative Adversarial Neural Networks used to generate new images for MNIST and CIFAR10 datasets.</p>
</article>
<article>
<a href="https://github.com/rohithteja/Reinforcement-Learning"><h2>Reinforcement Learning and Deep Q Learning</h2></a>
<p>Reinforcement techniques like policy iteration, value iteration, Q-learning are studied and experimented on different OpenAI environments (using the gym python library).</p>
</article>
<article>
<a href="https://github.com/rohithteja/Kernel-Methods"><h2>Kernel Trick on Machine Learning Algorithms</h2></a>
<p> Machine Learning algorithms were built from scratch with and without using Kernel trick. Algorithms like PCA, KMeans, LASVM, OCSVM,
Passive aggressive online algorithm were covered.</p>
</article>
<article>
<h2>Digital AD Marketing Data Analysis</h2>
<p>Deployed ML model using FastAPI to predict optimal
keyword bid for digital ad marketing data and
performed analysis on how to improve KPIs, identified
best performing markets, campaigns and keywords</p>
</article>
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