The Balanced dashboard.

Balanced status dashboard.

Sentiment Analysis with Insights using NLP and Dash This project show the sentiment analysis of text data using NLP and Dash. I used Amazon reviews dataset to train the model and further scrap the reviews from Etsy.com in order to test my model. Prerequisites: Python3 Amazon Dataset (3.6GB) Anaconda How this project was made? This project has been built using Python3 to help predict the sentiments with the help of Machine Learning and an interactive dashboard to test reviews. To start, I downloaded the dataset and extracted the JSON file. Next, I took out a portion of 7,92,000 reviews equally distributed into chunks of 24000 reviews using pandas. The chunks were then combined into a single CSV file called balanced_reviews.csv. This balanced_reviews.csv served as the base for training my model which was filtered on the basis of review greater than 3 and less than 3. Further, this filtered data was vectorized using TF_IDF vectorizer. After training the model to a 90% accuracy, the reviews were scrapped from Etsy.com in order to test our model. Finally, I built a dashboard in which we can check the sentiments based on input given by the user or can check the sentiments of reviews scrapped from the website. What is CountVectorizer? CountVectorizer is a great tool provided by the scikit-learn library in Python. It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. This is helpful when we have multiple such texts, and we wish to convert each word in each text into vectors (for using in further text analysis). CountVectorizer creates a matrix in which each unique word is represented by a column of the matrix, and each text sample from the document is a row in the matrix. The value of each cell is nothing but the count of the word in that particular text sample. What is TF-IDF Vectorizer? TF-IDF stands for Term Frequency - Inverse Document Frequency and is a statistic that aims to better define how important a word is for a document, while also taking into account the relation to other documents from the same corpus. This is performed by looking at how many times a word appears into a document while also paying attention to how many times the same word appears in other documents in the corpus. The rationale behind this is the following: a word that frequently appears in a document has more relevancy for that document, meaning that there is higher probability that the document is about or in relation to that specific word a word that frequently appears in more documents may prevent us from finding the right document in a collection; the word is relevant either for all documents or for none. Either way, it will not help us filter out a single document or a small subset of documents from the whole set. So then TF-IDF is a score which is applied to every word in every document in our dataset. And for every word, the TF-IDF value increases with every appearance of the word in a document, but is gradually decreased with every appearance in other documents. What is Plotly Dash? Dash is a productive Python framework for building web analytic applications. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python. Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready. Dash is an open source library, released under the permissive MIT license. Plotly develops Dash and offers a platform for managing Dash apps in an enterprise environment. What is Web Scrapping? Web scraping is a term used to describe the use of a program or algorithm to extract and process large amounts of data from the web. Running the project Step 1: Download the dataset and extract the JSON data in your project folder. Make a folder filtered_chunks and run the data_extraction.py file. This will extract data from the JSON file into equal sized chunks and then combine them into a single CSV file called balanced_reviews.csv. Step 2: Run the data_cleaning_preprocessing_and_vectorizing.py file. This will clean and filter out the data. Next the filtered data will be fed to the TF-IDF Vectorizer and then the model will be pickled in a trained_model.pkl file and the Vocabulary of the trained model will be stored as vocab.pkl. Keep these two files in a folder named model_files. Step 3: Now run the etsy_review_scrapper.py file. Adjust the range of pages and product to be scrapped as it might take a long long time to process. A small sized data is sufficient to check the accuracy of our model. The scrapped data will be stored in csv as well as db file. Step 4: Finally, run the app.py file that will start up the Dash server and we can check the working of our model either by typing or either by selecting the preloaded scrapped reviews.

Balanced Scorecard Dashboard for E-commerce Businesses built with MERN Full-stack: React.js, MongoDB, Node.js & Express. Project 3 for UPenn LPS Bootcamp. https://bscd.herokuapp.com/

A web-based delivery routing and clustering dashboard that optimizes last-mile delivery operations by grouping orders using pincode and GPS distance-based algorithms, while ensuring priority-aware and balanced driver assignment.

Introduction This project looks at the mergers and acquisitions of 30 publicly traded companies and attempts to determine the stock price at closing. M&As are incredibly difficult to assess, and while the company's instrinsic value and fundamentals play a significant role in predicting whether a merger will be "successful", public sentiment from Wall Street investors is another commonly referenced topic. Brainstorming for this project prompted two notable observations; data on M&As are often incomplete and highly inconsistent given the confidentiality behind these deals, and determining an appropriate dependent variable y for analysis presents a significant challenge (would most likely require an additional project on its own). The success of a merger could be measured various ways, but often times the unpredictability of management makes all the more challenging. Culture, reorganization, and leadership shake-up are all attributes that play an important role in the success of an M&A but are difficult to quantify. Although I do build and run a model in this proejct, the complexity around this subject urged me to focus primarily on data gathering and manipulation. Since one would most likely need to compose a dataframe with the attributes necessary to run an a useful analysis on Mergers and Acquisition, I believe this is a valuable first step. For a more balanced notebook between EDA, data manipulation, and models, I have a project that focuses on COVID19's impact on Post-Secondary Education below titled COVID19 Effects on Post-Secondary Education https://github.com/clozgil The Process My objective was to build a dataframe with useful attribtues from scratch. I found that three reports per company would have sufficient information to get started. Acquistion data (any and all information on the company's M&A) Financial ratios (data to determine the company's fundamentals) Stock information (data to gain insight into Wall Street sentiment) Since downloading, importanting, and cleaning each one of those files for each of the 30 companies would be cumbersome, I looped on all the data files using the OS module, simulteanously cleaning and merging each one of the files. However, for the purpose of this presentation, I will feature each one of my data cleaning techniques for one company - Apple. Data Sources For reference only. All necessary data for this project can be found in the data dictionary Acquisition data: https://www.capitaliq.com/CIQDotNet/my/dashboard.aspx * Financial ratios: https://www-mergentonline-com.pitt.idm.oclc.org/companyfinancials.php?pagetype=ratios&compnumber=46247&period=Quarters&range=50&Submit=Refresh&csrf_token_mol=3680683535 * Stock info: https://www-mergentonline-com.pitt.idm.oclc.org/equitypricing.php?pagetype=report&compnumber=46247 * (*) = Account required. University of Pittsburgh account used for access

A quick Splunk dashboard to view the health and response of any application being load balanced by the F5.

Designed a Power BI dashboard using the Balanced Scorecard framework to track sales, profit, regional performance, and customer insights.

Designed a dashboard and balanced scorecard for Google’s Play App Store so that actionable insights can be drawn for developers to work on and capture the Android market

A secure, web-based accounting system designed to simplify financial tracking and reporting. LedgerLogic ensures accuracy with balanced entries, ratio alerts, and role-based dashboards for admins, managers, and employees.

A machine learning pipeline and Streamlit dashboard for classifying Near-Earth Objects using NASA's NeoWs API. Features a Random Forest Classifier with balanced class weighting to identify potentially hazardous asteroids based on orbital and physical metrics.

Electron desktop application for automating teacher assignments to exam invigilation. Uses CP-SAT optimization algorithm (OR-Tools) to generate balanced schedules, with automatic sending of PDF invitations by email, real-time statistics dashboard, and complete management of availability constraints.

A modern, grid-based dashboard component inspired by the "Bento Box" design trend. This project demonstrates advanced layout techniques using CSS Grid, focusing on creating a visually balanced structure that adapts seamlessly from a single-column mobile view to a complex multi-column desktop interface.

A high-precision Machine Learning dashboard designed to predict song success by analyzing 13 acoustic dimensions across 125 genres. Built with a robust Multi-Vote Ensemble model trained on 52,616 balanced tracks, featuring real-time spectral DNA visualization and SHAP-based explainable AI.

The Python Time Table Generator is a Django app for creating conflict-free school timetables. It lets users manage subjects, teachers, classes, and sections, and automatically generates balanced schedules. Features include real-time conflict detection, a user-friendly admin dashboard, and flexible deployment options.

Spring Boot app that lets college students build optimal multi-semester course plans. Add semesters (with credit caps) and courses (credits, offerings, prerequisites), then auto-generate & save multiple schedules—balanced load, front-loaded, back-loaded, or custom constraint-solver mixes—all in a clean personal dashboard.

WellBeing360 is a full-stack web application designed to help users take control of their health and daily productivity through a unified and personalized dashboard. The platform allows individuals to log, track, and analyze both wellness metrics and task progress, promoting a balanced and productive lifestyle.

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Balanced Scorecard Dashboard for E-commerce Businesses built with MERN Full-stack: React.js, MongoDB, Node.js & Express

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This dashboard prototype serves as a foundation for future development. By integrating diverse metrics into a compact dashboard, aimed to empower executives to make informed decisions while efficiently managing their time across various responsibilities.

A sleek, balanced sales dashboard

Developed Dashboard of a company based on KPIs and Data Mockup given by clients using Microsoft Power BI. The company and dataset given was random and dummy. Built using the IT Balanced Scorecard mapping method, this dashboard features four (4) KPIs perspectives: Financial, Customer, Internal Business Process, and Learning & Growth.

Power BI dashboard project to analyze health and nutrition data, including BMI, health score, and diet patterns, using Python and Power BI.

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created a balanced scorecard dashboard that enables management to track the performance of the company using Tableau

Base project for a Balanced Dashboard Addon

Balanced Scorecard Builder - Create multi-perspective performance dashboards - https://keeprule.com