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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.

Python-flask app to upload zip file, extract the csv file within and upload data to postgres DB

Local web app to search and catalog public Telegram channels & supergroups: SQLite DB, categories, CSV/XLSX export. By Road Soft. Локальный каталог публичных каналов и супергрупп Telegram: парсинг по ключевым словам, SQLite, веб-панель, экспорт CSV/Excel. Road Soft.

This software allows to insert clusters' points with specific location (longitude,latitude) from csv file into a DB. In this case these points belong to unsupervised analysis. From DB this data will be available to web app MediStormSeeker into leaflet map

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CSV to DB Loader app

Simple app to parse CSV into DB & get stats

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Report Generating App(CSV ) -data upload from CSV file to DB using spring batch; write data to CSV file using Angular2CSV

Command line app for executing a mysqldump importing csv files to db

Cardiovascular Disease Prediction using Machine Learning | Streamlit Web App for Heart Risk Diagnosis | Includes Model Training, Predictions, CSV & DB Storage

A single page dynamic react app that fetches/scrapes/processes data from a csv DB to a JSON and displays it.

Embeddings via InsightFace (ONNXRuntime CPU). First run downloads models. - DB: SQLite at `data/attendance.db`. - Privacy: consent checkbox at enrollment. See `app/config.py` for thresholds. - Export CSV from Admin > Logs.

Little Python app to track time spent on active windows in Windows. Outputs to CSV as well as connects to your MySQL DB

A python web app that features an interactive chart to query and explore data (obtained from CSV files and then uploads and queries back to a MySQL DB) for different human physical traits. Features: Python, flask, sql alchemy, JavaScript, D3, MySQL DB, Plotly, JSON.

This app uses Flask to create a web app for creating and updating journal entries. The app data is stored in a database created through the peewee module. User can navigate the web app in a browser to create, read, update, and delete entries. On the initial run of app, a journal.db file will be created with placeholder data from entry.csv file.

This project provides a lightweight admin interface to manage students: add, edit, delete, search, filter by semester, view paginated lists, and export student data to CSV. The app uses SQLite (file students.db) and includes a default admin user for quick testing.

Monitor your X app usage. Trackz makes use of Devilspie 2 to set up hooks for window focus and window name/title changes. These events are then inserted into an SQLite3 DB, allowing you to query it for time spent in a given app/tab/window. The data can then be visualised or exported to CSV, etc.

Website Title: Restaurant Finder NYC Tagline for the website: Your healthy neighborhood restaurant finder Brief description of the project: To create a web-based site that will provide information about all the restaurants in NYC (location and cuisine served) and the violations associated with it based on recent inspection result (2020 – 2021) from New York City Department of Health and Mental Hygiene List of Data sources: DOHMH New York City Restaurant Inspection Results Dataset - (https://dev.socrata.com/foundry/data.cityofnewyork.us/43nn-pn8j) Technologies: Python Jupyter Notebook, Pandas JavaScript Libraries (Leaflet, Mapbox, Plotly, D3) HTML/ CSS (Bootstrap) SQL/ Postgres DB APIs Quick DBD Excel Steps: GitHub repo creation Cleanup of dataset (remove duplicates, blank rows, extra columns etc.) (using CSV, Jupyter notebook, Python) ERD (optional) Creating and updating SQL database (Python SQL integration) Creating app routes to call our data from the SQL database and rendering Flask app (Python and JavaScript) Creating 3 HTML pages with Navbar using Bootstrap CSS oIndex page/homepage will contain restaurant info on NYC map (visualization 1) with their phone number and cuisine description displayed on popup. oThe second page will contain a graph describing number of restaurants per cuisines (visualization 2) oThe third page will contain info on violations and will flag restaurants that have many violations (visualization 3) Use JavaScript libraries to create all the three visualizations and interactive dashboard Presentation Readme Team Members: Jay Dhruv Meet K Kaur Sahni Kate Yayla Brian Johnson Saleha Ahmed Dennis Smith

A console app for reading csv files with guest house bookings info and then converting it to database entries.

An online data tool for create, edit, store, manage, interrogate, view data using human readable CSV format.

Batch app : csv file -> H2 DB

flask app loading csv to db

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Small CSV catching and db search app

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