183 skills found · Page 3 of 7
klarEDA / Klar EDAA python library for automated exploratory data analysis
naga251602 / AiStoraAistora is an end-to-end AI analytics platform built with Flask and Docker. It features a custom in-memory data engine for SQL-like operations on CSVs, integrated with Gemini AI to enable natural language querying, automatic schema detection, and instant data visualizations without relying on Pandas.
sandy9999 / Sih Rk312 WinterfellCyber Offenders are related CDR/IPDRs are very important for LEAs, the given CDR/IPDR data is in a Spreadsheet/Excel/CSV/Notepad (Rows & Column Structure) file format. Desired Solution: The solution should take different input file formats like .XLSX, .CSV, TXT. and it shall convert into Info Graphical and Data Visualizer forms with connected Roots, Nodes and Edges Relationships.
cid-harvard / Visualization Notebook TemplatesA set of iPython notebooks that can be used to generate some of the most common atlas-style visualizations with datasets in stata or csv
ShuaiBOnlyLoveEating / Nomoto PID Trajectory TrackingBased on the first-order Nomoto model and an improved PID controller, the project realizes real-time tracking control and visualization of a given trajectory (CSV historical point column), and exports key timing data (position, heading angle, rudder angle) to Excel for subsequent analysis.
easonlai / Chat With Csv Streamlit With ChartIn this repository, you will find an example code for creating an interactive chat experience that allows you to ask questions about your CSV data with chart visualization capabilities.
srijitseal / HermessmilesA tool to compare two CSV files of SMILES strings, find overlapping compounds by InChIKey prefix, and generate an HTML visualization of matching structures.
Rishi-Sudhakar / UniparserA parser that allows you to parse CSV and visualize it as image.
dareneiri / Unleash FoursquareGet your Foursquare Swarm check-in history in JSON or CSV and visualize a summary of your data in a micro web framework
kanitvural / Data Analyzer App With Llm AgentsData Analyzer with LLM Agents is an application that utilizes advanced language models to analyze CSV files. It offers automatic descriptive statistics, data visualization, and the ability to ask questions about the dataset, with options to choose from models like Gemini, Claude, or GPT.
unnohwn / Osint Industries CanvasA specialized visualization tool for OSINT Industries CSV exports, transforming digital footprint results into an organized Obsidian Canvas format.
mirzayasirabdullahbaig07 / StockMarket Trend Prediction ModelPredict future stock prices using a pre-trained LSTM deep learning model. Upload a CSV file with historical stock data or use a sample to visualize trends and forecast closing prices.
data-spice / Matplotlib Cleaned And VisualThis is a messy csv file that was sourced from github containing job data like Name ,Age, Salary etc. I then took this data inspected and cleaned it and made visualizations using matplotlib.pyplot which explains the relation between age and salary in this dataset.
brainband-vizard / VizardCSV-like Markdown extensions for rendering charts and lightweight data visualizations
ftomassetti / Civs BrowserA web application to visualize the history files produced by csv
hesbonangwenyi606 / Realtime AnalyticsReal-Time Analytics Dashboard – An interactive dashboard built with React, TypeScript, and D3.js for real-time data visualization. Features live metrics, animated charts, KPI cards, sortable tables, CSV export, and responsive dark-themed UI with smooth transitions.
edumunozsala / GDELT Events Data Eng ProjectData Orquestration and Visualization of GDELT Project Events from csv files to datalake to datawarehouse
hita03 / Sales Report GeneratorThis is a project about building a sales data report generator web application using Django and JavaScript, to visualize the sales data, and upload sales records from csv files & generate pdfs.
roboflow / Inference Dashboard ExampleRoboflow's inference server to analyze video streams. This project extracts insights from video frames at defined intervals and generates informative visualizations and CSV outputs.
Jai-Agarwal-04 / Sentiment Analysis With InsightsSentiment 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.
