There are 2 repositories under plotly-express topic.
Resources for teaching & learning practical data visualization with python.
This project is about predicting stock prices with more accuracy using LSTM algorithm. For this project we have fetched real-time data from yfinance library.
Data Visualizations is emerging as one of the most essential skills in almost all of the IT and Non IT Background Sectors and Jobs. Using Data Visualizations to make wiser decisions which could land the Business to make bigger profits and understand the root cause and behavioral analysis of people and customers associated to it. In this Repository I have deeply discussed about Line Plots, Bar plots, Scatter Plots, and Pie Charts, Apart from that I have Discussed scientific plots, 3d plots, animated plots, interactive plots to visualize any kind of business problem and that too of any complexity.
Sentiment Analysis Project using Natural Language Processing (NLP) Techniques
使用 plotly 绘制 Choropleth 地图。
Learning Data Science
A simple Data science project on Predicting Student's dropout using Machine Learning classification models
Data Visualization in Python using Matplotlib, Seaborn and Plotly Express
OpenCharts is a free and open source data visualization tool created so that people can create beautiful charts without code. OpenCharts is built on top of Streamlit, Plotly and Pandas.
Provides a comprehensive solution for detecting plagiarism and finding similarities between text documents
from 2D Visualization to 3D and even more dimensions.
Predicting user movements from temporal streams of RSS (Radio Signal Strength) measured between the nodes of a WSN (Wireless Sensor Network WSN)
This is a Jupyter Notebook( iPython Notebook) with Data Analysis (EDA) on FIFA WC Qatar 2022 match data.
This is a dashboard which fetches data from a linked csv file and visualizes it on the dashboard in realtime
This is a streamlit dash board for financial data sets analysis and visualization.
Plotly Express - Simple syntax for complex charts. Now integrated into plotly.py!
This project utilizes Python visualizations packages, such as Plotly Express, HVPlot and PyPlot/Matplotlib to create an interactive dashboard exploring the San Francisco real estate housing market. Uses MapBox API.
A HR dashboard built on Streamlit that analyzes raw data to gain knowledge about how well organizational policies affect staff promotions and layoffs as well as employee behaviors like attrition and job satisfaction.
VisuVerse is an innovative and user-friendly Data Analysis and Data Visualization WebApp developed using Streamlit.
Covid.info web app is built to track and analyze the Coronavirus global pandemic. It displays live stats of Covid-19 cases from all countries on a Globe Projection. Bar graphs and Pie charts are used for easier understanding and studying the trend of the pandemic.
This repository contains the data analysis I made on the 'US E-commerce records 2020' dataset on Kaggle as a Capstone project of the Up School Data Analysis education program.
Exploring F1 through Python and Pandas
Power Outage Data Analysis in USA
Explanatory data Analysis with plotly Express
Using sentiment scores to visualize character arcs in Avatar: The Last Airbender. End-to-end data science project in Python: 1) data scraping, 2) sentiment analysis, 3) and data visualization.
Apply Plotly Express for rapid data visualization and analysis. Customize charts, animations, and demonstrate the various plotly.express features.
This is an extension of FINAL YEAR PROJECT in which further features such as news feed, stock analysis have been added.
Real estate analysis dashboard with interactive visualizations to explore investment opportunities
A dashboard builded with streamlit, plotly and pandas.
Simple python script that provides a summary of specific data from ILMT
Real-Time Stock Predictor offers a streamlined way to analyze stock market trends using live data. 📈 With powerful features and efficient algorithms, it empowers users to make informed trading decisions. 🐱💻
Automates Medical Test Reports
Predicting transaction fraud using classification problems such as Guardian Boosting as well as user interfaces using Streamlite, Accuracy: 98% AUC-ROC