Sejal Shrivastava's starred repositories
ravithemore
Hello pep's Welcome to my profile repo. You can also build yours with my profile. Don't just star it, fork it too.
LibraHub
📚✨ Step into the Future of Library Management with LibraHub! 🌐🚀 Embrace streamlined, secure, and data-driven library operations. Say goodbye to manual chaos and hello to a library experience that's truly elevated. Join the future of library administration at LibraHub and discover the transformation where libraries thrive! 📖💻
House-Price-Prediction
Creating a Machine Learning model to predict the home prices in Bangalore, India. We are going to use the dataset from Kaggle.com. We are also going to create a single page website which will provide the front end to access our model for predictions. Below data science concepts are used in this project Data loading and cleaning Outlier detection and removal Feature engineering Dimensionality reduction Gridsearchcv for hyperparameter tunning K fold cross validation Technology and tools used in this project Python Numpy and Pandas for data cleaning Matplotlib for data visualization Sklearn for model building Google Colaboratory Notebook Python flask for http server HTML/CSS/Javascript for UI Steps We will first build a model using sklearn and linear regression using banglore home prices dataset from kaggle.com. Second step would be to write a python flask server that uses the saved model to serve http requests. Third component is the website built in html, css and javascript that allows user to enter home square ft area, bedrooms etc and it will call python flask server to retrieve the predicted price. Step#1: Import the required libraries Step#2: Load the data Step#3: Understand the data -drop unnecessary columns Step#4: Data Cleaning - Check for na values - Verify unique values of each column - Make sure values are correct (eg. 23 BHK home with 2000 Sqrft size is worng) - Feature Engineering - Dimesionality Reduction - Outlier removal using domain knowledge (2bhk price < 3bhk price, size per bhk >= 300 sqft) - Outlier removal using standard eviation and mean - One Hot encoding Step#5: Build Machine Learning Model Step#6: Testing The model Step#7: Export the model Step#8: Export any other important info
TheNatureFresh-web
An online e-commerce website for Mushrooms complete with admin dashboard and shop with order tracking.
Awesome-Twitter-Threads
Awesome Twitter threads by Various Twitter Content Creators on Web Development, Freelancing, Investments, Progarmming, Tech, Devops and Guidance.
VITCabShare_old
VIT Cab Share is an easy way to share your cab from Chennai or Bengaluru Airport to VIT Vellore.
portfolio-html
🌍 Open source portfolio template built with plain Html, CSS and JavaScript for developers to create their portfolio website
Everybody-dance-now
Implementation of paper everybody dance now for Deep learning course project
Build-An-AI-Virtual-Mouse-With-OpenCV
In our continuing deep dive into practical real-time computer vision, we’ll show you how to code a hands-free webcam-based controller for your computer mouse using the OpenCV library on Python. This will allow you to control your computer without any physical peripheral required—Iron Man style! In this session, we’ll first obtain our live camera feed using OpenCV and then estimate hand poses using MediaPipe Hands, an open-source framework that employs machine learning to infer 3D landmarks of the hand from single frames in real-time without any fancy hardware acceleration, working even on mobile phones. Following this, we’ll set up our simulated mouse movement in response to the poses using the AutoPy automation module. Prerequisites: ✅ Python (latest release: https://www.python.org/downloads/release/python-395/) ✅ PyCharm (https://www.jetbrains.com/pycharm/download/) or any other Python code editor ✅ pip install: OpenCV (https://pypi.org/project/opencv-python/), MediaPipe, AutoPy ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings
developer-portfolio
Software Developer Portfolio Template built with react.js and next.js bootstrap that helps you showcase your work and skills as a software developer.
awesome-github-profile-readme
A collection of awesome readme templates to display on your profile
Linkedin_Scraper_2022
LinkedIn scraping usually comes in as the second step of a lead generation strategy. Not only can you automate the profile scraping itself, but you can also automate the process of finding the right profiles to scrape data from.
Awesome-Twitter-Threads
Awesome Twitter threads by Various Twitter Content Creators on Web Development, Freelancing, Investments, Progarmming, Tech, Devops and Guidance.
Lets-learn-Git
Learning git
DSA-Bootcamp-Java
This repository consists of the code samples, assignments, and the curriculum for the Community Classroom complete Data Structures & Algorithms Java bootcamp.