streamlit-tushar2704 话题在 GitHub 上没有填写简介，这里按 Star 数汇总最相关的热门仓库。
This repository showcases my skills and experience in the field of data analysis. Here, you will find a collection of projects and analyses that demonstrate my ability to extract insights and make data-driven decisions.
欢迎来到我的个人Power BI投资组合仓库！这里你会找到一系列Power BI项目和仪表盘，展示了我在数据可视化、商业智能和使用Power BI分析方面的技能和专长。
Collection of personal SQL projects and queries I've worked on, showcasing my skills and expertise in database management, data analysis, and data manipulation using SQL.
Superstore Sales with Streamlit is a data visualization and analysis project that uses the Streamlit framework to create an interactive web application for exploring and analyzing sales data from a superstore. This project aims to provide an easy-to-use interface for users to gain insights into sales trends, Sales performance, product performance,
Streamlit Magic Cheat Sheets- All of Streamlit in one Streamlit App!(Available in English, Français & Deutsch.)
This repository contains valuable insights and visualizations derived from an extensive Pizza dataset with over 48,000 rows.
Welcome to Everyday Python Sheets – your go-to resource for everyday Python cheat sheets, pro tips, interview questions, Python one-liners, and Python data structures. Whether you're a beginner looking to learn Python or an experienced developer seeking quick reference materials, this Streamlit application has got you covered.
This repository showcases a collection of machine learning projects in various domains, demonstrating my skills and expertise as a data scientist and machine learning engineer. Each project provides step-by-step instructions, code, and visualizations to showcase the data analysis and modeling techniques employed.
101个超炫的应用程序——这个互动式网络应用集合展示了我的数据科学和机器学习项目成果。我对基于数据的洞察力有着浓厚的兴趣，同时擅长打造引人入胜的数据应用。我很高兴能够展示这个作品集，以此来证明我的技能和专业知识。
This repository is the Ultimate guide to exploring and mastering Python Libraries & frameworks, collection of code and guide by me, Tushar!
OllamaLit-Llama-3
Explore common ML algorithms, from scratch implementations to real-world use cases, Each algorithm is accompanied by clear explanations, code implementations, and real-world use cases, enabling you to grasp their underlying principles and apply them to different problem domains.
Welcome to Everyday SQL Sheets – your go-to resource for everyday SQL cheat sheets, pro tips, interview questions, and more. Whether you're a beginner looking to learn SQL or an experienced developer seeking quick reference materials, this application has got you covered.
The Global Statistics Dashboard is a comprehensive tool designed to provide insights into various global statistics. This project includes data processing scripts, a web application interface, and modular components for loading and visualizing data.
This project aims to predict the quality of wines using various machine learning algorithms. It utilizes the MLflow platform to manage the end-to-end machine learning lifecycle, including data preprocessing, model training, hyperparameter tuning, and deployment on AWS EC2.
GreenSight - Analyze and evaluate Environmental, Social, and Governance (ESG) factors for companies. The application provides insights into a company's ESG performance, trends, and impact, offering a comprehensive view of its sustainability practices.
📊 Build and manage a responsive business analytics dashboard using the MERN stack for effective data visualization and client interaction.
The New York Taxi Spending Analysis project is a data analysis and visualization project aimed at understanding and gaining insights into the spending patterns of taxi riders in New York City. This project utilizes publicly available data from New York City's Taxi and Limousine Commission to explore various aspects of taxi spending, such as fares
This project harnesses the power of cutting-edge technologies including H2O AutoML, MLflow, FastAPI, and Streamlit to enhance cross-selling campaigns and boost efficiency.
This project aims to revolutionize the way banks market to their customers by leveraging machine learning techniques to segment customers based on their account history, credit scores, and demographics.