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easyforum

An open source forum written in Php/Mysql designed to be flexible with a one click installer-contributors Chris DeJarlais, Andrew Hoffman

ecommerce-data-analysis---exercise

My Solutions to the DataScience Bootcamp covenlabs 2019 - an analysis of user data in the dataset "ecommerce_purchase.csv".

ecommerce_website-online-shopping-

E-commerce(Online Shopping) website using php and MySQL.By this,any Client easily see products details with availability of products Choose products,Add to Chart products,Buy products by Cash on Delivery payment system and track his selling product.On the other hand,any admin dynamically Add product,Update product and Delete Product.ct details,Delete product

employee-management-system

A simple application for managing employee details of the company used by the admin.

employee-performance-system-projrct

"To analyses a current employee data and to dig out the employee performance" In this project, I have to analyze the employee data and to find out the cause of performance issues of employee. To understand a data properly it was important to visualize & understand the data using all EDA steps, Statistics, and plotting. Then to build a ML model dataset was divided into train test and I build a various model Logistic, Decision Tree, SVM, Random Forest, KNN & XGBoost then compare all the model with highest accuracy and to build a face recognition system to mark the attendance using libraries to track the performance of the employee

essential-cheat-sheets-for-deep-learning-and-machine-learning-researchers

Essential Cheat Sheets for deep learning and machine learning researchers

essential-statistics-for-non-stem-data-analysts

Hands-On Statistics for Data Science, published by Packt

ex-r-lecture

exercises

This repository contains all programming exercises for the Programming Skills for Data Science book. Solutions can be found in the solution branch.

face-recognition-and-drowsiness-detection

Global E-learning is estimated to witness an 8X over the next 5 years to reach USD 2B in 2021. India is expected to grow with a CAGR of 44% crossing the 10M users mark in 2021. Although the market is growing on a rapid scale, there are major challenges associated with digital learning when compared with brick and mortar classrooms. One of many challenges is how to ensure quality learning for students. Digital platforms might overpower physical classrooms in terms of content quality but when it comes to understanding whether students are able to grasp the content in a live class scenario is yet an open-end challenge. In a physical classroom during a lecturing teacher can see the faces and assess the emotion of the class and tune their lecture accordingly, whether he is going fast or slow. He can identify students who need special attention. Digital classrooms are conducted via video telephony software program (exZoom) where it’s not possible for medium scale class (25-50) to see all students and access the mood. Because of this drawback, students are not focusing on content due to lack of surveillance. While digital platforms have limitations in terms of physical surveillance but it comes with the power of data and machines which can work for you. It provides data in the form of video, audio, and texts which can be analysed using deep learning algorithms. Deep learning backed system not only solves the surveillance issue, but it also removes the human bias from the system, and all information is no longer in the teacher’s brain rather translated in numbers that can be analysed and tracked. We will solve the above-mentioned challenge by applying deep learning algorithms to live video data. The solution to this problem is by recognizing the face, mark the attendance, log the individual’s session time and put a drowsiness alert.

facial-emotion-recognition

A program to predict facial emotions using Deep Learning and CNNs

financely

Financely is an AI based financial adviser and user portfolio management system.

flask

flask-appbuilder-1

Simple and rapid application development framework, built on top of Flask. includes detailed security, auto CRUD generation for your models, google charts and much more. Demo (login with guest/welcome) - http://flaskappbuilder.pythonanywhere.com/

flask-cheat-sheet

A cheat-sheet for creating web apps with the Flask framework using the Python language.

flask-ml-azure-serverless

Deploy Flask Machine Learning Application on Azure App Services

formerly-wysiwyg-bootstrap-form-creator

This tool allows the creation and download of Bootstrap forms.

fullscalestockanalysis

Using Data Mining methods and ML models to predict future behavior of stocks.

getting-and-cleaning-data---peer-assessment-project

Getting and Cleaning Data - peer assessment project The data linked to from the course website represent data collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones The data for the project: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip In the run_analysis.R script, functions were created for each step. Function r_Data : This function takes two variables, the suffix for the filename, folder name in which the file exists. Since the data exists in two folders with file names as subject_train, X_train, y_train. The y_data file contains Activity_ID. The X_data file contains data for MeasureID and MeasureName. The subject_data file contains SubjectID. This function reads the three files from a folder and creates data.frame in R environment with appropriate column names. The grep function is used to match only those columns which has MeasureName for mean and standard deviation. Function read_test_data : This function is used to read the test data into the R environment Function read_train_data : This function is used to read the train data into the R environment Function mergeDataset : This function combines the two dataset read in the above functions, by rows.Further, proper name is given to the columns and dataset is returned. Function activityLabels: This function takes a dataset as argument and reads the activity labels from text files and merges it with the dataset. Function merge_label_data : This function runs the activityLabels function to get a activity labelled dataset. Function tidyData: This function creates a tidy data set with average of each variable for each activity and each subject. Function tidy_datafile: A new tidy independent dataset is generated and saved a text file to be submitted.

health_ai_training

Fine-tuning BERT models to classify and characterise all artificial intelligence publications relevant to human health outcomes

ibm-data-engineering-professional-certificate

ibm-ds-github-assessment

This repo is for the IBM Data Science Assessments and final Capstone Project

ibm-machine-learning-certificate-projects

Projects for ML courses on Supervised Learning, Unsupervised Learning, Deep Learning, Reinforcement Learning and specialized topics such as Time Series Analysis and Survival Analysis :robot:

ibm_data_science_professional_certificate

About this Professional Certificate Data science is one of the hottest professions of the decade and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate from IBM will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills and experience. The program consists of 10 online courses that will provide you with the latest job-ready tools and skills, including open source tools and libraries, Python, databases, SQL, data visualisation, data analysis, statistical analysis, predictive modelling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets. This Professional Certificate has a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs in the IBM Cloud that will give you practical skills with applicability to real jobs, including: Tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc. Projects: random album generator, predict housing prices, best classifier model, predicting successful rocket landing, dashboard and interactive mapping

ibmdataengineering

IBM Data Engineering Professional Certificate

ibmdataengineeringcoursera

IBM Data Engineering Courses from Coursera

identify-a-car-model-with-deep-learning

Explore how to practice real world Data Science by collecting data, curating it and apply advanced Deep Learning techniques to create high quality models which can be deployed in production. Use Keras and Pytorch libraries in python for applying advanced techniques like data augmentation, drop out, batch normalization and transfer learning

international-debt-statistics-project---datacamp

intro-to-machine-learning-udacity

This repository contains all mini projects completed during course

introdatasci-5000