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.

Streamlit Magic Cheat Sheets- All of Streamlit in one Streamlit App!(Available in English, Français & Deutsch.)

This repository is the Ultimate guide to exploring and mastering Python Libraries & frameworks, collection of code and guide by me, Tushar!

Welcome to my personal Power BI portfolio repository! Here you will find a collection of Power BI projects and dashboards that demonstrate my skills and expertise in data visualization, business intelligence, and analytics using Power BI.

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.

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.

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.

101 Super Streamlit Applications-This interactive web application collection serves as a showcase of my data science and machine learning projects. With a passion for data-driven insights and a knack for creating engaging data applications, I am excited to present this portfolio as a demonstration of my skills and expertise.

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.

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,

Stats-Mosaic-Streamlit is a comprehensive GitHub repository that aims to provide a growing collection of curated content and projects centered around statistics and its intersection with data science, machine learning, and artificial intelligence.

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.

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.

All links in one place Streamlit Application

This project harnesses the power of cutting-edge technologies including H2O AutoML, MLflow, FastAPI, and Streamlit to enhance cross-selling campaigns and boost efficiency.

SummarizeIt is a powerful yet user-friendly Streamlit application designed to quickly generate concise summaries of text content. Whether you're dealing with lengthy articles, research papers, or any other form of textual data, SummarizeIt simplifies the process, providing a convenient way to distill information.

TradeAnalytica

Welcome to the Consumable Sales Dashboard, a powerful and intuitive data visualization tool built using Power BI. This dashboard offers a comprehensive view of sales data for consumable products, allowing you to quickly and easily analyze performance and identify trends.

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.