amanv1906 Goto Github PK

amanv1906.github.io

amanvaryani.github.io

Portfolio webpage to showcase description of word experiences, educations, projects with demo images and reference links, programming profiles across all platforms and mooc/award certificates.

amazon-apparel-recommendation

this project is about how items suggested on e-commerce website using unsupervised learning(item item similarity)

amazon-fashion-discovery-engine

Amazon Fashion Discovery is project to know how amazon recommend similar items. I take dataset through Amazon product advertising API and perform various NLP technique for recommendation.

api-fortest

testing api any change in code will now be reflected to django

bolt-python

A framework to build Slack apps using Python

cabbooking

chat-app

chat application for django

chatapp

clone-stackoverflow

coding-minutes-competitive-programming

coviddashboard

data-structures-algorithms-level-up-bootcamp

C++ Code Repository.

decision-tree-on-donor-choose

https://towardsdatascience.com/the-complete-guide-to-decision-trees-28a4e3c7be14

demo-api

used for serving api on frontend.

development-docker

docgpt

dockers

educative.io_courses

this is downloadings of all educative.io free student subscription courses as pdf from GitHub student pack

facebook-friend-recommendation-using-graph-mining

grokking-the-object-oriented-design-interview

human-activity-recognization

Human Activity recognization is a project which is used to detect the current activity of Human based on sensor reading.

interview-bit

Solutions to problems on Interview Bit

knn-on-donorchoose

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.[1] In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of k nearest neighbors. k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification

linear-regression-boston-housing-price

https://towardsdatascience.com/linear-regression-70cca59678ec

logistic-regression-on-donorchoose

In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc... Each object being detected in the image would be assigned a probability between 0 and 1 and the sum adding to one. Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression). Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail which is represented by an indicator variable, where the two values are labeled "0" and "1". In the logistic model, the log-odds (the logarithm of the odds) for the value labeled "1" is a linear combination of one or more independent variables ("predictors"); the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). The corresponding probability of the value labeled "1" can vary between 0 (certainly the value "0") and 1 (certainly the value "1"), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the log-odds scale is called a logit, from logistic unit, hence the alternative names. Analogous models with a different sigmoid function instead of the logistic function can also be used, such as the probit model; the defining characteristic of the logistic model is that increasing one of the independent variables multiplicatively scales the odds of the given outcome at a constant rate, with each independent variable having its own parameter; for a binary dependent variable this generalizes the odds ratio.

mouse-control

naive-bayes-on-donorchoose

netflix-prize

This project is about how netflix recommend tv series or movie to a particular person . Cold Start Problem is also covered in this project.

news-classification-using-bert-and-other-techniques