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Name: Abhinav Navneet
Type: User
Bio: Applied ML Engineer
Location: Bengaluru, India
Name: Abhinav Navneet
Type: User
Bio: Applied ML Engineer
Location: Bengaluru, India
Implemented Classification and Clustering models using Spark MLlib on Airlines Data.
ALBERT and DistilBERT classification models for the DBpedia ontology dataset.
CNN Image classifier using InceptionV3 and ResNet for a t-shirt images.
Image segmentation model for checking apple quality using UNet model architecture on TensorFlow along with AWS SageMaker deployment.
This repository focuses on applied probability for ML using theory, abstract topics and APIs.
Attention mechanism based multiclass text classification on a dataset of customer complaints about consumer financial products.
Auto-encoders based Generative Models to generate new images of MNIST digits.
Abstractive text summarisation using BART model on articles data.
Text classification on AG News dataset using the state-of-the-art transformer model BERT.
Multi-class text classification model using the pre-trained BERT model.
Sales forecasting models for BigMart.
Brand Exposure Analysis using video processing in OpenCV and Tensorflow.
Implemented LSTM based forecast system for burger sales along with AWS Sagemaker deployment.
DNN based business license classifier using h2O base model and Flask deployment.
Ensemble multi-class classifiers on business license status data.
Customer Churn classification using various ensemble techniques.
Logistic regression using NumPy on churn data.
Skip-gram and FastText models to perform word embeddings for building a search engine for clinical trials dataset with a Streamlit user interface.
PyTorch implementation of CNN model for multi-class classification.
Model-Based and Memory-Based collaborative filtering recommendation system on retail data.
Content based filtering and similarity based recommender System and Streamlit deployment.
Credit delinquency analysis on borrower information and historical records using classical and advanced regression techniques along with LIME,SHAP.
Predictive model for loan defaulters using LightGBM, HyperOpt and SHAP model interpretation.
Implemented incremental ETL solution utilizing AWS CDK for analysing cryptocurrency data.
Classifier for predicting customers who can be converted from liability to asset.
Tuned Customer churn classification model by tuning key hyperparameters in PyTorch.
RNN and LSTM based text classification model on a dataset of customer complaints about consumer financial products.
Propensity Modelling and RFM Analysis to predict users' likelihood of making a purchase.
CycleGAN Model in PyTorch for translating animal image to other domain irrespective of the pairing between two images.
CNN classifier to identify MNIST handwritten digits.
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Bring data to life with SVG, Canvas and HTML. 📊📈🎉
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Open source projects and samples from Microsoft.
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Data-Driven Documents codes.
China tencent open source team.