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Hi πŸ‘‹, I'm Kunal Mehta

A passionate Data Scientist

Connect with me:

vishwasgowda217

Languages and Tools:

python pytorch scikit_learn pandas postgresql aws docker git java linux mysql seaborn langchain streamlit fastapi

kunal1406

kunal1406

kunal1406's Projects

clustering-to-improve-classification icon clustering-to-improve-classification

Devised a hierarchical clustering framework for feature extraction from scratch, applied to 90K+ rows data using cluster centroids. Transformed data enhanced KNN classifier performance from 90% to 97%, confirmed via K-fold cross-validation & base classification comparison.

decisiontrees-ensembleclassifiers icon decisiontrees-ensembleclassifiers

Decision Trees and Ensemble Classifiers - Develop and assess decision trees with varying depths, along with bagging and AdaBoost ensemble methods, for predicting gender using height, weight, and age data in order to analyze performance and overfitting.

generativeai-ats icon generativeai-ats

Used Gemini Pro for Resume ATS score and keywords to find the content to tailor your resume

kg2text icon kg2text

Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs (authors' implementation for the TACL20 paper)

leetcode icon leetcode

Collection of LeetCode questions to ace the coding interview! - Created using [LeetHub](https://github.com/QasimWani/LeetHub)

lstm icon lstm

Implementation of LSTM on a large dataset and anomaly detection

regression-linear-weighted-logistic icon regression-linear-weighted-logistic

Implementing and analyzing linear, locally weighted linear, and logistic regression techniques to fit and predict 1D data, trigonometric basis functions, and gender classification.

supervised-classification icon supervised-classification

Exploring Maximum Likelihood Estimation (MLE), Maximum a Posteriori (MAP) for Poisson processes, and implementing K-Nearest Neighbor and Gaussian NaΓ―ve Bayes classifiers for gender prediction.

unsupervised-semisupervised icon unsupervised-semisupervised

Explored unsupervised and semi-supervised learning techniques using hierarchical clustering and self-training with K-nearest neighbor classifier on height, weight, age, and gender data.

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