Pinned Repositories
100daysofcode-with-python-course
Course materials and handouts for #100DaysOfCode in Python course
500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
500 AI Machine learning Deep learning Computer vision NLP Projects with code
Abhiroyq1.github.io
Description of my personal projects
airport
check data
Awesome-Data-Science-Resoruces
A curated list of data science educational resources for essential data science skills
awesome-interview-questions
:octocat: A curated awesome list of lists of interview questions. Feel free to contribute! :mortar_board:
eBooks-PDFs-necessary-for-data-analysis-by-Python-R-
Repository for all eBooks/PDFs for data science in Python/R
industry-machine-learning
A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)
Machine-Learning-Week-4-solutions
Coursera Machine Learning Week 4 assignments
swirl_courses
:mortar_board: A collection of interactive courses for the swirl R package.
Abhiroyq1's Repositories
Abhiroyq1/industry-machine-learning
A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)
Abhiroyq1/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
500 AI Machine learning Deep learning Computer vision NLP Projects with code
Abhiroyq1/Abhiroyq1.github.io
Description of my personal projects
Abhiroyq1/Artificial-Intelligence-And-Data-Science-Pro
Regularly Updated | Collection of the best Data Science and AI Material from the Web | Covering Everything from Books, Courses along with Material, Research Papers, and Interview Prep to Cheatsheet
Abhiroyq1/Awesome-Data-Science-Resoruces
A curated list of data science educational resources for essential data science skills
Abhiroyq1/awesome-interview-questions
:octocat: A curated awesome list of lists of interview questions. Feel free to contribute! :mortar_board:
Abhiroyq1/awesome-mlops
:sunglasses: A curated list of awesome MLOps tools
Abhiroyq1/awesome-mlops-visenger
A curated list of references for MLOps
Abhiroyq1/careerhub-data
Certification Data
Abhiroyq1/awesome-remote-job
A curated list of awesome remote jobs and resources. Inspired by https://github.com/vinta/awesome-python
Abhiroyq1/build-your-own-x
Master programming by recreating your favorite technologies from scratch.
Abhiroyq1/cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep
Abhiroyq1/Data-Science-EBooks
Data Science E-books, Interview Resources and Cheat-sheets
Abhiroyq1/Data-Science-End-to-End-Projects
Abhiroyq1/Data-Science-Interview-Preperation-Resources
Resoruce to help you to prepare for your comming data science interviews
Abhiroyq1/Data-Science-Interview-Questions-Answers
Curated list of data science interview questions and answers
Abhiroyq1/Efficient-Python-for-Data-Scientists
Writing clean and optimized Python code
Abhiroyq1/FLASK-End-to-end-Zomato-Restaurant-Price-Prediction-and-Deployment
# **ABSTRACT** Main Objective: The main agenda of this project is: Perform extensive Exploratory Data Analysis(EDA) on the Zomato Dataset. Build an appropriate Machine Learning Model that will help various Zomato Restaurants to predict their respective Ratings based on certain features DEPLOY the Machine learning model via Flask that can be used to make live predictions of restaurants ratings A step by step guide is attached to this documnet as well as a video explanation of each concpet. Zomato is one of the best online food delivery apps which gives the users the ratings and the reviews on restaurants all over india.These ratings and the Reviews are considered as one of the most important deciding factors which determine how good a restaurant is. We will therefore use the real time Data set with variuos features a user would look into regarding a restaurant. We will be considering Banglore City in this analysis. Content The basic idea of analyzing the Zomato dataset is to get a fair idea about the factors affecting the establishment of different types of restaurant at different places in Bengaluru, aggregate rating of each restaurant, Bengaluru being one such city has more than 12,000 restaurants with restaurants serving dishes from all over the world. With each day new restaurants opening the industry has’nt been saturated yet and the demand is increasing day by day. Inspite of increasing demand it however has become difficult for new restaurants to compete with established restaurants. Most of them serving the same food. Bengaluru being an IT capital of India. Most of the people here are dependent mainly on the restaurant food as they don’t have time to cook for themselves. With such an overwhelming demand of restaurants it has therefore become important to study the demography of a location. What kind of a food is more popular in a locality. Do the entire locality loves vegetarian food. If yes then is that locality populated by a particular sect of people for eg. Jain, Marwaris, Gujaratis who are mostly vegetarian. These kind of analysis can be done using the data, by studying the factors such as • Location of the restaurant • Approx Price of food • Theme based restaurant or not • Which locality of that city serves that cuisines with maximum number of restaurants • The needs of people who are striving to get the best cuisine of the neighborhood • Is a particular neighborhood famous for its own kind of food. “Just so that you have a good meal the next time you step out” The data is accurate to that available on the zomato website until 15 March 2019. The data was scraped from Zomato in two phase. After going through the structure of the website I found that for each neighborhood there are 6-7 category of restaurants viz. Buffet, Cafes, Delivery, Desserts, Dine-out, Drinks & nightlife, Pubs and bars. Phase I, In Phase I of extraction only the URL, name and address of the restaurant were extracted which were visible on the front page. The URl's for each of the restaurants on the zomato were recorded in the csv file so that later the data can be extracted individually for each restaurant. This made the extraction process easier and reduced the extra load on my machine. The data for each neighborhood and each category can be found here Phase II, In Phase II the recorded data for each restaurant and each category was read and data for each restaurant was scraped individually. 15 variables were scraped in this phase. For each of the neighborhood and for each category their onlineorder, booktable, rate, votes, phone, location, resttype, dishliked, cuisines, approxcost(for two people), reviewslist, menu_item was extracted. See section 5 for more details about the variables. Acknowledgements The data scraped was entirely for educational purposes only. Note that I don’t claim any copyright for the data. All copyrights for the data is owned by Zomato Media Pvt. Ltd.. Source: Kaggle
Abhiroyq1/Flight-Price-Predict-Deployment-Heroku
Abhiroyq1/Free-Certifications
A curated list of free courses & certifications.
Abhiroyq1/generative-ai
All codes related to Generative AI
Abhiroyq1/LeetCode-Questions-CompanyWise
Contains Company Wise Questions sorted based on Frequency and all time
Abhiroyq1/MLOps-Basics
Abhiroyq1/Practical-Computer-Vision-In-Python
Abhiroyq1/Practical-Machine-Learning
Practical machine learning notebook & articles covers the machine learning end to end life cycle.
Abhiroyq1/Practical-Time-Series-In-Python
Practical guidance for time series analysis in Python
Abhiroyq1/Roadmap-To-Learn-Generative-AI-In-2024
Abhiroyq1/Stable-Diffusion-Crash-Course
Abhiroyq1/tensorflow-deep-learning
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
Abhiroyq1/test_repo_2023
sample test repo