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calculator-demo

classification_algorithms_miscellaneous

data-structures-using-python

data_cleaning_projects_and_eda

defaulter

dolly-testing

dvc_proj

dvc_proj_udacity

eda

Practicing different EDAs

employeesalaryprediction

example-code-2e

Example code for Fluent Python, 2nd edition (O'Reilly 2022)

fastapi

FastAPI framework, high performance, easy to learn, fast to code, ready for production

fastapi_blg

flower_pred

gender-classification

Context Gender is a social construct. The way males and females are treated differently since birth moulds their behaviour and personal preferences into what society expects for their gender. This small dataset is designed to provide an idea about whether a person's gender can be predicted with an accuracy significantly above 50% based on their personal preferences. Content The data was collected in Fall 2015 from university students of 21 nationalities studying various majors in various countries using this form: https://docs.google.com/forms/d/e/1FAIpQLSduEjDURjTh7-a1ZjjlIYx75ScVETLp_gmoFszypz2J7E0LtQ/viewform The responses were then pre-processed and grouped into categories in order to obtain the final, transformed dataset. Inspiration With the rise of feminism, the difference between males and females in terms of their personal preferences has decreased in recent years. For instance, historically in many cultures, warm colors such as red and pink were thought of as feminine colors while cool colors such as blue were considered masculine. Today, such ideas are considered outdated. Despite the decrease in the influence of gender on people’s personal preferences, can a decent gender classifier be built given a dataset with people’s personal preferences? What does this small dataset suggest?

genre_classification

Finale exercise of the udacity class on machine learning workflow

graduate-school-kaggle-cmp

This dataset is created by Mohan S Acharya to estimate chances of graduate admission from and indian perspective. Our analysis will help us in understand what factors are important in graduate admissions and how these factors are interrelated among themselves. It will also help predict one's chances of admission given the rest of the variables

great-expectations

great_expectations

higgsboson

kubernetes-learning-path

A roadmap to learn Kubernetes from scratch (Beginner to Advanced level)

lean-mlops

ml-ops-nov-27

ml_packaging

mle

To learning purpose also for keeping track of my work and practices

mlops_breast_cancer_fighter

mlops_test

mnist_cars24

nd0821-c2-build-model-workflow-starter

Starter Code for the Course 2 project of the Udacity ML DevOps Nanodegree Program

nd0821-c3-starter-code