Automated memory optimization for Pandas DataFrames. Reduces memory by 50-80% and loads CSV 5-10x faster. Drop-in replacement that returns standard DataFrames - works with sklearn, matplotlib, and all your favorite libraries. Safe, fast, and zero refactoring required.

How to get started with your diet with code, fun, and PANDAS!

A Streamlit-based student performance dashboard that analyzes lifestyle factors like study habits, diet, and screen time using pandas, numpy, and matplotlib.

A Streamlit-based student performance dashboard that analyzes lifestyle factors like study habits, diet, and screen time using pandas, numpy, and matplotlib.

Having user friendly, appealing interface made by using- CSS, JavaScript, HTML Backend - Flask, MySQL, Python (Pandas, NumPy, Werkzeug), Pickle. AI Model for Diet Planner - Pandas, Scikit-learn (Random Forest Classifier, One Hot Encoder), Chardet, and Pickle to train, test and save the machine learning model.

Analyze how lifestyle habits like sleep, diet, stress, and physical activity influence BMI and health risks using Python, EDA, and risk scoring logic.python • data-analysis • health-data • pandas • eda • risk-scoring

🧑‍🍳 Rasoiyo — Discover, Filter & Cook with Delight Rasoiyo is a beautifully crafted recipe discovery platform that blends intuitive design with powerful filtering. Built using Flask and Pandas, it allows users to explore dishes based on cuisine, course, diet preferences, and preparation time

This project helps users track their diet progress by logging meals into the database. Calories, carbs, protein, fat and weight are all logged so the user can track progress. We use MySQL connector to connect to database and use Pandas to explore our data.

Technologies Used: Python, Flask, HTML, CSS, JavaScript, Bootstrap, SQLite, Machine Learning (Scikit-learn, NumPy, Pandas) Description: A complete healthcare support system that provides disease prediction, symptoms analysis, precautions, description, suggested workouts, and diet plans. The system also includes a feedback form and a blog section.

"Disappointment Panda would be the hero that none of us would want but all of us would need. He’d be the proverbial vegetables to our mental diet of junk food. He’d make our lives better despite making us feel worse. He’d make us stronger by tearing us down, brighten our future by showing us the darkness. Listening to him would be like watching a movie where the hero dies in the end: you love it even more despite making you feel horrible, because it feels real."

Pandas based standard diet problem as Tidy Tested Safe repository

FitGenAI is an AI-based personalized fitness planner that generates diet and workout plans using python, Flask , and Pandas

Personalized Diet Recommendation System using Flask, Pandas & Chart.js. Calculates needs, suggests foods based on goals/preferences, visualizes nutrition.

The AI Recipe Generator finds recipes by ingredients, suggests random recipes, and filters by diet using pandas and spaCy NLP.

A simple Python command-line app to record and analyze your daily diet, exercise, sleep, and mood using Pandas and Matplotlib.

Healthify is a Streamlit app that provides personalized health, fitness, and diet advice using Google Gemini AI. Users input their details and lifestyle habits to receive actionable insights, diet plans, and recommendations. Built with Python, Streamlit, Pandas, and NLP.

A machine-learning powered diet recommendation system that predicts daily needed calories, protein, carbs and fats based on user health inputs. Built using Python, Pandas, and Scikit-Learn.

Automated pipeline downloading GEO data (GSE115342) to study low-carb ketogenic diet effects on mouse liver gene expression. Features pandas analysis, bar/volcano plots, and fold change visualization.

AI model to generate a diet chart based on health condition of the user or fitness goal of the user using only python libraries like matplotlib, pandas, numpy etc

A Flask-based web app that uses a scikit-learn ML model to predict conditions and recommend diets/treatments based on user input. Built with Flask, Pandas, NumPy, and HTML templates.

AI Diet Planner 🥗 A Streamlit web app that generates a personalized 7-day meal plan based on your height, weight, country, goals, and health concerns. Built with Python, Streamlit, and Pandas.

The Diet Plan Prediction System is a Python-based mini project using pandas that provides personalized diet recommendations. Users input details like age, gender, weight, height, food preference, and fitness goals (weight loss, gain, or maintenance). The system calculates nutritional needs and filters food items from a dataset accordingly.

This repository contains the numerical source data for the graphs and charts presented in the article "Bamboo Diet-Driven Shifts in Gut Microbial Ecology and Pathogen Interactions in Giant Pandas (Ailuropoda melanoleuca)."

Interactive Streamlit dashboard analyzing how student habits impact academic performance. Includes data cleaning, filters, KPIs, and visualizations on study time, sleep, social media, diet, attendance, and extracurriculars using Pandas, Plotly, Seaborn, and Matplotlib.

A Python project for analyzing and visualizing food-related datasets. This repository demonstrates data cleaning, filtering, sorting, grouping, and aggregation using pandas, along with exploratory data analysis (EDA) on cuisines, diets, and nutritional information.

A comprehensive analysis of various animal species, focusing on their habitat, diet, speed, and interaction with humans. Leveraging Pandas, NumPy, and Matplotlib for data analysis and visualization. Includes insights on conservation status and population trends.

Decile-based analysis of diet and sleeping patterns for over 12,000 participants logging food and sleep activity for Panda Lab circadian-rhymth related studies.

Personalized Nutrition & Diet Suggester – calculates daily calorie needs (BMR/TDEE) and recommends meal plans based on age, gender, activity, and dietary preferences. Built with Flask, scikit‑learn, pandas, and a clean frontend (HTML/CSS/JS + Chart.js)

- Built a Personalized Medical Recommendation System using machine learning models for disease prediction. - Implemented Gradio interface for user-friendly health recommendations (medication, diet, workouts). - Used Python, Scikit-Learn, Pandas, and NumPy for data processing and model accuracy.

This is an ETL(Extract, Transform, Load) Data Pipeline project using Keto Diet API from Rapid API. In this we extracted the data from the Keto Diet API through requests and stored it on AWS S3 bucket using boto3 library, then transformed the data to desired format using Pandas library, and then finally load it on target S3 folder.