100_days_of_coding_challenge

100_days_of_coding_challenge_part-2

ardiuno

Basic Ardiuno Programs

capgemini-tech-challenge-2021

car-price-prediction

covid-19_diagnosis

data-structures-and-algorithms-hacktoberfest18

List of data structures and algorithms. Feel free to contribute under Hacktoberfest '18!

datavisualisation

new

eda_amazon_top_50_bestselling_books

Exploratory Data Analysis on Amazon's Top 50 Bestselling Books from 2009 - 2019.

emotion-detector

A streamlit app based on a text dataset that predicts anger, disgust, fear, joy, neutral, sadness, shame, and surprise from the input text.

fake-news-classifier

file-ingestion-and-schema-validation

flexbox

Learning Flexbox & Grid

geospatial-data-analysis

gourmet-burgers

Learning Bootstrap

grid

insight-for-cab-investment-firm

After studying the historical data of the major cab services in major US cities, providing actionable advice to XYZ private firm for cab investment so that it can make an informed decision.

iris-ml

A Flask-based Machine Learning Web App deployed on Heroku that uses API to classify the type of Iris flower based on its sepal length, sepal width, petal length, and petal width.

javascript

Learning Javascript

machine-learning-coursera-stanford

ml-x-0

person_detection

Person Detection using Tensorflow pre-trained model.

predict-car-price

A Flask-based Machine Learning Web App that uses Heroku to forecast the price of an automobile based on numerous characteristics.

project-1-covid-19_tracker_notification

In a pop-up notice, get the most up-to-date information on the number of active cases, cured patients, and deaths in your preferred state.

resume_extraction_team_zeros

This capstone project was created as part of the Data Glacier internship, in which we (Team ZeRoS) produced a Resume Extractor using Named Entity Recognition with Spacy in Natural Language Processing. The dataset was in JSON format, which we transformed to a text file after exhaustively analyzing and cleaning it to train our model. This project is built with Flask, and it allows a user to upload a resume (in pdf or docx format) and receive entities categorized by our model, such as the person's name, college name, academic information, relevant experiences, skill set, and so on.

sign-lang

snapcartt

Python, Flask, Postgres-database, jQuery, and Bootstrap were used to create an E-Commerce Web App. The app provides a large number of jerseys to choose from, each with an image, a description, a price, and a simple form to add the item to your cart. The information about the shirt is kept in a Postgres database and shown with Bootstrap's card class. The application includes a set of filters that use Postgres queries to show just shirts that meet specific criteria, such as shirts by area, clubs vs. national teams, shirts on sale, IPL, and so on. If a user is not logged in and tries to add something to their shopping cart, a warning message (implemented with jQuery) will appear, requesting that they log in. The user can add shirts to their purchasing cart once they have registered and logged in. At the top right of the screen is a link to the shopping cart, which displays the number of products in the cart as well as the sub-total in rupees. The shopping cart link launches a Bootstrap modal window with further information about the shopping cart. You can click the Buy button to go to the full version of the shopping cart and make changes, such as adding one more item or removing a shirt. Because the payment method has not yet been deployed, the cart gets reset once you check out. Simply click the Purchased button to view your buy history, which will show you all of the shirts you've ever purchased. You'll also see a Buy Again option, which will take you to the product page if you wish to purchase it again. The project is complete and ready for production use, having been developed from the ground up and deployed on the Heroku cloud platform.

song-finder

start-here-guidelines

Lets Git started in the world of opensource, starting in the Zero To Mastery's opensource playground. Especially designed for education and practical experience purposes.

student-performance-dataset-machine-learning

This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).