anjulcodewiz Goto Github PK

500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

ai-smart-radio-dapp

android-ui

A curated list of awesome Android UI/UX libraries

cloud_data_lake

datalake_creation

Data lake project

deeplearningflappybird

Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).

excel-kickstarter-campaign-master

games

Games: Create interesting games in pure python.

hello-world

informatica-ucd

The UCD Informatica Plugin is an automation plugin that connects to specific repositories and directly access and merge data across remote systems within your organization.

javascript

movie-recommendation-system

Movie Recommendation Application This application takes in a movie title and returns 8 movie recommendations based on genre, movie overview, director and actors (Content based recommendation engine). Used the 5000 tmdb_movies dataset which includes movies up to 2017. Scraped 2018 to September 2021 movies from www.imdb.com. Engineered and cleaned the data so as to extract genre, director, movie overview and actors. Used Natural Language Processing (NLP) to tokenize and vectorize the data. Built a client facing Api using flask and hosted it on Heroku Resources Used Python Version: 3.8 Packages: Pandas, Numpy, Sklearn, Json, Flask, Pickle, Fetch API and Beautiful Soup For Web Framework Requirements: pip install -r requirements.txt APIs: TMDB API Web Scraping Used Beautiful Soup to scrape 1000 movies from www.imdb.com from year 2018 to September 2021. Scraped the following features: Movie Title Genre Movie Overview Director Actors Data Cleaning After scraping the data I needed to clean the data so as to feed it to the model so I made the following changes: Used the TMDB API to get the movie ids of the scraped movies Extracted only 3 actors from both the tmdb_dataset and scraped new movies to be used in the model Converted string data to integer data type Combined the director, genre, movie_overview and 3 actors columns Combined the tmdb dataset and the scraped new movies dataset to create one dataset of 6000 movies with 3 columns Title, Movie_id and Combined Model Building Tuned and used TfidfVectorizer from SKLearn package to tokenize and vectorize the combined text column. Transformed the Combined column to a matrix Used the Sigmoid_Kernel model from Sklearn metrics.pairwise to create a similarity matrix. Created a recommender function which links the similarity matrix to the Movie_id and Title columns Created the function to recommend 8 movies from a movie title input Productionization In this step, I built a flask API endpoint thats hosted on Heroku. I did the following: Used HTML,CSS and Javascript to build the frontend. Created a suggestion engine at the backend(Flask server) to suggest a movie to the user as they type each letter of a movie title, used javascript fetch API to move data between client and backend Used the fetch API to move movie title entered by the client to the backend to be processed in the recommendation engine then back to the frontend Utilized the TMDB API to fetch movie posters, budget, popularity, overview of the typed and recommended movies Created a javascript click event which also sends the clicked movie to the recommendation engine at the backend and return recommended movies with extra information from the TMDB API

movie-title-recommendation-system

moviesdb-analysis-using-python-sql-postgres

new-product_demand_prediction

news-bias-detector

Implements news bias detection from the All the News dataset from Kaggle. Classification is performed on publication labels obtained from mediabiasfactcheck.com. Classification is performed utilizing the following fully-configurable techniques: AdaBoost Ensemble Classification Gradient Boosting Ensemble Classification Extremely Random Trees Ensemble Classification PyTorch Neural Network Model Note that the PyTorch model found in mbd/model.py currently achieves approximately 52% accuracy with the current feature set after 100 training epochs. This model started of far more complex than the model currently found in the aforementioned module. After three iterations of reducing it's complexity, I was able to get the accuracy from ~30% to what you see now. It is clear to me that this model requires additional work to be considered a successful classification model. This project has shown that the feature set I have chosen does work to uniquely identify class instances. Further reifinement to the neural network model is to be considered future work. The data folder only currently contains generated features as the features file itself is ~88MB in size. This repository contains code for generating the feature set yourself, but it is space expensive to do so, requiring upwards of ~12GB of RAM. __init__.py contains some of the code needed to generate the features but it is incomplete. If you want to generate the features yourself, you will need to string together the functions I use to do so from the other modules in the repository. Documentation of all functions is forthcoming

nlp_pytorch_project

Embedding, NMT, Text_Classification, Text_Generation, NER etc.

pharmacy-management-system

INTRODUCTION: The main aim of the project is the management of the database of the pharmaceutical shop. This project is insight into the design and implementation of a Pharmacy Management System. This is done by creating a database of the available medicines in the shop. The primary aim of pharmacy management system is to improve accuracy and enhance safety and efficiency in the pharmaceutical store. The aim of this project is to develop software for the effective management of a pharmaceutical store. We have developed this software for ensuring effective policing by providing statistics of the drugs in stock. Description on the topic: This program can be used in any pharmaceutical shops having a database to maintain. The software used can generate reports, as per the user’s requirements. The software can print invoices, bills, receipts etc. It can also maintain the record of supplies sent in by the supplier. Here, the admin who are handling the organization will be responsible to manage the record of the employee. Each employee will be given with a separate username and password. Problem Definition: The aim of the project is to create an effective software to help the pharmacist to maintain the records of the medicines, handle user details, generate invoice, check and renew validity and provide a scope of communication between users by using inbuilt messaging system. Pharmacy management system deals with the maintenance of drugs and consumables in the pharmacy unit. This pharmacy management system is user friendly. Objectives -> Primary objective •To gain practical experience by modeling a software based on real world problem. •To understand how to work on Front-end (Java) and Back-end (MySQL) by using server(wamp). -> Secondary objective •To develop an application that deals with the day to day requirement of any pharmacy. •To develop the easy management of the medicines (drugs). •To handle the inventory details like sales details, purchase details and stock expiry and quantity. •To provide competitive advantage to the pharmacy. •To provide details information about the stock on details necessary and help locate it in shop easily. •To make the stock manageable and simplify the use of inventory in the pharmacy. Hardware and software tools: The system services and goals are established by consultation with system user. They are then defined in details and serve as a system specification. System requirement are those on which the system runs. ⚙️ Hardware Requirements: o Computer with either Intel Pentium processor or AMD processor. o 1GB+ DDR RAM o 40GB hard disk drive 💻 Software Requirements: o Windows/ MacOS/ Linux operating system. o JRE and JDK. o MySQL server (WAMP or XAMPP or any) Chapter 2 - DESIGN Database Design is a collection of processes that facilitate the designing, development, implementation and maintenance of enterprise data management systems. It helps produce database systems: o That meet the requirements of the users o Have high performance. Architecture Description The design of a DBMS depends on its architecture. It can be centralized or decentralized or hierarchical. The architecture of a DBMS can be seen as either single tier or multi-tier. ER Diagram image.png Fig 1: ER Diagram An entity–relationship model describes interrelated things of interest in a specific domain of knowledge (Refer Fig 1). It is composed of entity types and specifies relationships that can exist between instances of those entity types. Relational Schema Diagram image_1.png Fig 2: Relational Schema Relational schema is a collection of meta-data. Database schema describes the structure and constraints of data representing in a particular domain (Refer Fig 2). Chapter 3 - IMPLEMENTATION Description on Implementation The goal of this application is to manage the medicines and various function of the pharmacy. List of modules: o Login page o Home page o Company o Purchase o Drugs o Sales o User/Settings o Messaging Chapter 4 - Result and Discussion By using MySQL commands and its database this website Pharmacy management tends to store all the data received from the users including drugs sales details and the profit made by the owners are all in this data base. This website allows the user to generate invoices for sales, check expiry and quantity remaining of the drugs. It also provides user with options to renew validity and add more drugs into the store and update the database accordingly. By using xampp server these database commands are easily initiated into the database and the ER diagram with relational schema diagrams helps us to make the structure of the database faster and it was easier to make them understand the needs of the website. Login Information id :1 password: admin CONCLUSIONS AND FUTURE SCOPE o Detailed information gathering has to be done. Without that the purpose for using the software won’t be satisfied properly. o However, it can give good profits in the long run. o Implementing the software requires change in the business practices. o Efficient organization of all knowledge is the analysis company and easy analysis access and retrieval of information is possible. o In this project we can also include BAR CODE facility using the bar code reader, which will detect the expiry date and the other information about the related medicines. o Company using this software will always be able to plan in future and always be aware of their financial position in the market. o It leads to ease in functioning of business processes. o The project can be made more robust by including biometric verification. o There is also a scope to expand by implementing newer technologies like cloud etcetera.

port_scanner_network_security

powerbi-visuals-docs

Documentation for creating visuals for Power BI

prediction

PredictionIO, a machine learning server for developers and ML engineers.

real-time-data-warehouse

Real-time Data Warehouse with Apache Flink & Apache Kafka & Apache Hudi

risk-modeling-using-sas

self-driving-car-simulator

simple-linear-regression-car-price-prediction

social-network-dapp

state-wise-sentiment-analysis-of-caa-nrc-npr-tweets

State-Wise Sentiment Analysis Of CAA, NRC & NPR Tweets

task

Implement Continuous Integration/Continuous Deployment (CI/CD) for a NestJS application using TypeORM, PostgreSQL, and AWS.

task-manager

A simple task manager app

twitter-sentiment-analysis-

Here i collected data from twitter and applied many more supervised machine learning algorithms