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Greatings Fellow Earthling, I am Yash Chauhan

Yash Chauhan's LinkedIn Yash Chauhan's Kaggle

Welcome to my Github Profile, This is the home of my curated projects, experiments and some lossely packed ideas.

About Me:

GIF

What I use on an average day(basically daily)

Python NumPy Pandas ScikitLearn Tableau TensorFlow

Yash Chauhan's Projects

a_classification_model_to_predict_blight_compliance icon a_classification_model_to_predict_blight_compliance

A Binary Classification Problem Optimized For AU-ROC Curve,. From Data Cleaning to Model Validation, Classifying whether a blight ticket will be paid in time or not, Trained 3 different Classifier on a Highly imbalanced Data provided by Detroit Open Data Portal with around 160000 Tickets.

advance_regression_model_stacking_kaggle icon advance_regression_model_stacking_kaggle

This Project is my Entry to a Kaggle Competition 'House Prices: Advanced Regression Techniques'. The aim was to rank in the Top 5% in the Leaderboard, I achieved this rank by Using Model Stacking with Meta-Modelling and my final score was RMSLE = 0.0726 and I Ranked 277th out of 5130 Submissions

basic-computer-games icon basic-computer-games

An updated version of the classic "Basic Computer Games" book, with well-written examples in a variety of common programming languages

churn_segmentation_modelling_ann icon churn_segmentation_modelling_ann

This is a complete Project that revolves around churn modeling and it contains every aspect from data cleaning down to model deployment. The data of a bank was used in this implementation. An Artificial Neural Network was trained and used to predict the probability that a given customer would leave the bank(With 87% Test accuracy) and for deployment, an API was developed which can be used for single prediction as well as batch prediction for a number of customers

exploratory_analysis_on_steam_game_statistics icon exploratory_analysis_on_steam_game_statistics

An Exploratory Analysis and data visualization project on Steam Gaming Statistics Datasets on Kaggle, To uncover trends in the data and answer the research question : " To Determine the relation if any between the rating of a game and its market Success (Measured as Hours the game was played) "

face-and-text-classifier-using-python icon face-and-text-classifier-using-python

A simple approach to computer vision for differentiating between text and faces in an image using Opencv, Tesseract-OCR with python Wrapper Pytesseract and Pillow.

indian_coin_classification_using_cnn icon indian_coin_classification_using_cnn

This repository is part of a research study in which, A convolution neural network(CNN) is developed from scratch for the classification of Indian coins by their denomination. The final train accuracy reached is 91.38% and the best validation accuracy is 90.62%

indian_coin_classification_web_application icon indian_coin_classification_web_application

This repository contains the front-end code, that revolves around deploying a Machine Learning Model Developed in one of my research study named "A Novel Convolutional Neural Network for Classifying Indian Coins by Denomination"

insaid_packages icon insaid_packages

A Repo for all the helping Packages I'll create to streamline my work at INSAID.

machine_learning_algorithms_from_scratch icon machine_learning_algorithms_from_scratch

Machine Learning Algorithms from Scratch. Explanation of the mathematics behind various ML Algorithms coupled with a Numpy only Implementation of the models. The Aim is to Demystify everything from Linear Regression to Deep Neural Networks

medium_data icon medium_data

A Repo for any External Resources I will mention in my Medium Stories

music_genre_classification_by_model_ensembling_approach icon music_genre_classification_by_model_ensembling_approach

This Project is my attempt at solving the music genre classification or MGR problem. I have used a model ensembling approach by bagging together the weighted prediction probabilities of three different classifiers. The algorithms used were XGBoost, CatBoost and Radnom Forest Classifer. My ensembled model was able to reach 97.8% Val. Accuracy

music_genre_classification_web_application icon music_genre_classification_web_application

This repository contains the front-end code, that revolves around deploying a Machine Learning Model Developed in one of my project named "Music Genre Classification Using Model Ensembling Approach"

od-weapondetection icon od-weapondetection

Datasets for weapon detection based on image classification and object detection tasks

sisodiya2421 icon sisodiya2421

This is a special repository which contains a README.md for my Github profile.

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