abhijit1990's starred repositories
instagram-helper
Instagram Scripts for Bulk Unsending Direct Messages
kite-helper
Download Indian NSE - BSE Stocks Historical Per minute Data
Heart-Failure-Prediction-and-Deployment-with-Flask-and-Heroku
Cardiovascular diseases are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. Early detection, and managment of cardiovascular diseases can be a great way to manage the fatality rate associated with cardiovascular diseases, and this is where a machine learning model comes in. For the purpose of predicting the risk of a heart failure in patients, I used the Support Vector Classifier to build a machine learning model, and deployed it using Flask and Heroku
nse-stocks-data-scrapper
Data Scrapper For NSE Stocks
ranking_algorithm_intro
Data and notebook for the ranking algorithm article
Market-Basket-Analysis-InstaCart-Orders
Can we use association mining and machine learning to understand groceries purchase? Can we predict products that a user will buy again, try for the first time or add to cart next during a session? Can we segment our customer base into several cohorts based on their preferred products and purchase behaviour?
South-African-COVID-19-Vulnerability-Map-by-ZindiWeekendz
The task is to predict the percentage of households that fall into a particularly vulnerable bracket - large households who must leave their homes to fetch water - using 2011 South African census data. Solving this challenge will show that with machine learning it is possible to use easy-to-measure stats to identify areas most at risk even in years when census data is not collected.
ipc_semantic_search
Creating a semantic search on Indian Penal Code
Machine-Learning-Competitions
A collection of codes submitted for machine learning competitions on various platforms
stock-market-india
API for Indian Stock Market's NSE and BSE.
Financial_Inclusion_Prediction_API
A Machine Learning System for predicting depression tendencies from socio-economic factors deployed as a RESTAPI using Python, regularized greedy forests & FastAPI..
Social-Media-Sentiment-Analysis-of-the-EPL
Natural Language Processing (NLP) on twitter data using roBERTa model with python for sentiment analysis.
air_quality_forecast
Time Series Analysis and Modeling: A case study of air pollution in Beijing
Exploratory-Data-Analysis
Using Pandas to answer a few questions about the Adult dataset.
Financial-Inclusion-in-Africa-Analysis
Utilizing Microsoft Power Bi to analyze, visualize, gain applicable insights and create a report on Financial Inclusion in four African Countries. Link to the Report: https://app.powerbi.com/view?r=eyJrIjoiNjAxYjZhZmEtYjE2Ny00NWE2LWExYTEtNTY4NGFmYjExODlhIiwidCI6ImI2YzRkMzlmLWMwODYtNDEyOC05NmE1LTA0NDZkNzVmMTdjYSJ9
Password-Generator
A program which generates a random password for a user. It ask the user how long they want their password to be, and how many letters and numbers they want in their password, with the password having a mix of upper and lowercase letters, as well as numbers and symbols. The password is also a minimum of 6 characters long.
Simple-Linear-Regression
Predicting the percentage of students based on the number of study hours/day using Simple linear regression.
Build-a-Fullstack-E-commerce-using-Next-js-react-js
Build a Fullstack E commerce using Next js react js, mongo, tailwind, styled components
Build-Projects-with-Nextjs-and-Reactjs-full-stack
Build a Small Project with Nextjs 13
Car-Showcase-Application-using-Next.js-13
Build and Deploy a Modern Next.js 13 Application | React, Next JS 13, TypeScript, Tailwind CSS
JavaScript-projects-Full-Stack-development
4 JavaScript projects Full Stack development
Nextjs-Ecommerce-Website
Building an Ecommerce Website: Core Features with Sanity, Drizzle, Postgres and Stripe
Account-Management-System
This is an Account Management System. The user can add multiple accounts and switch between them. The user can put a description and a date for every transaction he would like to record. The user can enter his/her profit/earnings and they will be highlighted in green. On the other hand, when the user puts his loss/spending using -the sign it will be highlighted in red. At the end there will be a total amount of money.