🤖 HealthCare ChatBot Major -1 (4th year - 7th semester) Health Care Chat-Bot is a Healthcare Domain Chatbot to simulate the predictions of a General Physician. ChatBot can be described as software that can chat with people using artificial intelligence. These software are used to perform tasks such as quickly responding to users, informing them, helping to purchase products and providing better service to customers. We have made a healthcare based chatbot. The three main areas where chatbots can be used are diagnostics, patient engagement outside medical facilities, and mental health. In our major we are working on diagnostic. 📃 Brief A chatbot is an artificially intelligent creature which can converse with humans. This could be text-based, or a spoken conversation. In our project we will be using Python as it is currently the most popular language for creating an AI chatbot. In the middle of AI chatbot, architecture is the Natural Language Processing (NLP) layer. This project aims to build an user-friendly healthcare chatbot which facilitates the job of a healthcare provider and helps improve their performance by interacting with users in a human-like way. Through chatbots one can communicate with text or voice interface and get reply through artificial intelligence Typically, a chat bot will communicate with a real person. Chat bots are used in applications such as E-commerce customer service, Call centres, Internet gaming,etc. Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for. 📜 Problem Statement During the pandemic, it is more important than ever to get your regular check-ups and to continue to take prescription medications. The healthier you are, the more likely you are to recover quickly from an illness. In this time patients or health care workers within their practice, providers are deferring elective and preventive visits, such as annual physicals. For some, it is not possible to consult online. In this case, to avoid false information, our project can be of help. 📇 Features Register Screen. Sign-in Screen. Generates database for user login system. Offers you a GUI Based Chatbot for patients for diagnosing. [A pragmatic Approach for Diagnosis] Reccomends an appropriate doctor to you for the following symptom. 📜 Modules Used Our program uses a number of python modules to work properly: tkinter os webbrowser numpy pandas matplotlib 📃 Algorithm We have used Decision tree for our health care based chat bot. Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.It usually mimic human thinking ability while making a decision, so it is easy to understand. :suspect: Project Members Anushka Bansal - 500067844 - R164218014 Shreya Sharma - 500068573 - R164218070 Silvi - 500069092 - R164218072 Ishika Agrawal - 500071154 - R164218097

Data cleaning, Tokenization, Regular Expressions and Pandas guide.

A brief guide and tutorial on how to clean data using pandas and Jupyter notebook

A guide to useful pandas features for intermediate-level data mungers

A complete guide to Panda 3DS custom firmware, from stock to boot9strap.

Detailed notes and codes on learning pandas quickly for machine learning.

Teaching guide for a one-hour hands-on session at an IRE/NICAR conference on using pandas to analyze data.

Pandas data analysis guide examples

Pandas style guide and best practices. Opinionated guide on how to write Pandas code which is more consistent, reliable, maintainable and readable.

My Pandas Guide for Beginner

This repository contains code for the book "Mastering Data Analysis with Python: A Comprehensive Guide to NumPy, Pandas, and Matplotlib".

🦠 Python project for visualizing COVID-19 vaccination data by ZIP code. Includes data cleaning and plots like bar chart, pie chart, heatmap, scatter, and pair plot using Pandas, Matplotlib, and Seaborn. Guided by Baljinder Kaur.

## Step 1 - Scraping Complete your initial scraping using Jupyter Notebook, BeautifulSoup, Pandas, and Requests/Splinter. * Create a Jupyter Notebook file called `mission_to_mars.ipynb` and use this to complete all of your scraping and analysis tasks. The following outlines what you need to scrape. ### NASA Mars News * Scrape the [Mars News Site](https://redplanetscience.com/) and collect the latest News Title and Paragraph Text. Assign the text to variables that you can reference later. ```python # Example: news_title = "NASA's Next Mars Mission to Investigate Interior of Red Planet" news_p = "Preparation of NASA's next spacecraft to Mars, InSight, has ramped up this summer, on course for launch next May from Vandenberg Air Force Base in central California -- the first interplanetary launch in history from America's West Coast." ``` ### JPL Mars Space Images - Featured Image * Visit the url for the Featured Space Image site [here](https://spaceimages-mars.com). * Use splinter to navigate the site and find the image url for the current Featured Mars Image and assign the url string to a variable called `featured_image_url`. * Make sure to find the image url to the full size `.jpg` image. * Make sure to save a complete url string for this image. ```python # Example: featured_image_url = 'https://spaceimages-mars.com/image/featured/mars2.jpg' ``` ### Mars Facts * Visit the Mars Facts webpage [here](https://galaxyfacts-mars.com) and use Pandas to scrape the table containing facts about the planet including Diameter, Mass, etc. * Use Pandas to convert the data to a HTML table string. ### Mars Hemispheres * Visit the astrogeology site [here](https://marshemispheres.com/) to obtain high resolution images for each of Mar's hemispheres. * You will need to click each of the links to the hemispheres in order to find the image url to the full resolution image. * Save both the image url string for the full resolution hemisphere image, and the Hemisphere title containing the hemisphere name. Use a Python dictionary to store the data using the keys `img_url` and `title`. * Append the dictionary with the image url string and the hemisphere title to a list. This list will contain one dictionary for each hemisphere. ```python # Example: hemisphere_image_urls = [ {"title": "Valles Marineris Hemisphere", "img_url": "..."}, {"title": "Cerberus Hemisphere", "img_url": "..."}, {"title": "Schiaparelli Hemisphere", "img_url": "..."}, {"title": "Syrtis Major Hemisphere", "img_url": "..."}, ] ``` - - - ## Step 2 - MongoDB and Flask Application Use MongoDB with Flask templating to create a new HTML page that displays all of the information that was scraped from the URLs above. * Start by converting your Jupyter notebook into a Python script called `scrape_mars.py` with a function called `scrape` that will execute all of your scraping code from above and return one Python dictionary containing all of the scraped data. * Next, create a route called `/scrape` that will import your `scrape_mars.py` script and call your `scrape` function. * Store the return value in Mongo as a Python dictionary. * Create a root route `/` that will query your Mongo database and pass the mars data into an HTML template to display the data. * Create a template HTML file called `index.html` that will take the mars data dictionary and display all of the data in the appropriate HTML elements. Use the following as a guide for what the final product should look like, but feel free to create your own design.  - - - ## Step 3 - Submission To submit your work to BootCampSpot, create a new GitHub repository and upload the following: 1. The Jupyter Notebook containing the scraping code used. 2. Screenshots of your final application. 3. Submit the link to your new repository to BootCampSpot. 4. Ensure your repository has regular commits and a thorough README.md file ## Hints * Use Splinter to navigate the sites when needed and BeautifulSoup to help find and parse out the necessary data. * Use Pymongo for CRUD applications for your database. For this homework, you can simply overwrite the existing document each time the `/scrape` url is visited and new data is obtained. * Use Bootstrap to structure your HTML template.

A comprehensive guide to common commands and techniques in Pandas, the powerful data analysis and manipulation library in Python. This cheat sheet provides quick access to essential Pandas functions.

A Collection of Cheatsheets,Tips and Reference Guide for Data Analysis Tools - Pandas, Pypolars,Pyspark,etc

FooDine Concept: The app is created to serve the food ordering market and can be a direct competitor to companies like Food Panda. The final version of the app (which we will present at the end of the session) intends to fill the following market gaps. Allow the user to choose from a list of restaurants Show the list of food categories in any given restaurant (drop down) Show the list of food items in any given category Allow the person to place order in quantity of his choice See the order cart before confirming the order Shoot a mail or SMS to a given phone number of email ID as an order confirmation Each of the above intents will have sub objectives and mile stones. Since the project is not being imported into Github which is a version control platform for all open source projects (like the one we are building), We will add all the further tas ks and milestones on the same. Request all of you to install Github for Windows/Linux based on the preference of your OS and your IDE. In case you are not comfortable doing it alone, we can do it first thing we meet. Available Resources: 3 Amateur developer guided by a mentor Mr. Shakti 3 laptops (One with Zaheed and two with me) Office space for collaborative working Complete blue print of the project Sample source codes and android tutorials on all computers Timelines: Give a dhinchak name to the project The first milestone should be complete on or before 6 PM Monday 16 Feb 2015. The complete project plan to be ready and ratified before end of day 16 Feb 2015

This repository is full of practical guide of python libraries NumPy, Pandas, Matplotlib, simple program to create QRcode

This repository provides a guide to preprocess and analyze the network intrusion data set using NumPy, Pandas, and matplotlib, and implement a random forest classifier machine learning model using Scikit-learn.

Titanic-Machine-Learning-from-Disaster This repository contains a machine learning project for predicting survival of passengers who travelled on Titanic Ship in 1912. Problem Description- This project highlights my approach to the introductory machine learning competition on Kaggle website- Titanic: Machine Learning from Disaster [1]. The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. This project analyses which people were likely to survive. In particular, tools of machine learning have been used to predict which passengers survived the tragedy. Project Description This project has been made in Python v3.4. It uses various data processing, visualisation and machine learning packages such as numpy, pandas, matplotlib, scikit-learn etc. which should be installed if the code is run on a local machine. The project uses a 5 step process (general procedure) for it's predicting task which is as follows [2]: Perform a statistical analysis of the data and look over it's characteristics such as data type of columns, number of instances, correlation of each attribute with the output variable, finding mean and other information about data, correlation matrix etc. After performing statistical analysis, do a visual analysis by plotting the data. Do analyse the scatter_matrix, plot box plots etc. so as to know which attributes are relevant and which are not. Remove irrelevant attributes from the dataset for further analysis. Make a list of all machine learning algorithms that can give good prediction results and spot check each one of them (apply each one of them on the dataset) to find which one is better for prediction. Use k-fold cross validation to calculate performance characteristics of each of the learners (accuracy, precision, recall, area under ROC curve etc.). Take some of the good performing algorithms and perform a grid search/ randomised search over it's hyperparameters to find the optimal hyperparameters for the prediction task. Ensure that the optimal hyperparameters do not overfit the data, by performing k-fold cross validations on learners using these tuned hyperparametes as well. Use an ensemble or Voting Classifier on the above selected algorithms to achieve better performance or use any one of the above algorithm directly to perform predictions. Keep iterating over the above steps again and again and tune them according to the need so as to achieve better performance. File Description titanic_predictor - contains python code for predicting survival. my_solution.csv - contains sample output file generated from algorithm. train.csv- contains training data test.csv - contains testing data for making predictions readme.md - for guide to this project.

Intro to Pandas Python library. The guide is based on Pandas User Guide documentation.

A crash course to get you started using Python Pandas library. The course is designed to be a practical guide with real-life examples of the most common data manipulation tasks.

The complete guide to Pandas for beginners

This AI Python Jupyter Notebook project guides users through setting up, data manipulation with numpy/pandas, and upcoming advanced AI phases.

Build real Pandas projects with 3 beginner-friendly challenges. Learn by doing with guided coding exercises and practical applications.

The Python and Pandas Field Guide: An Introduction to Computer and Data Science

The notebook from my video "Pandas TA: A complete Guide"

This repository serves as a practical guide to mastering Pandas, a powerful Python library for data manipulation and analysis. It includes examples and code snippets that demonstrate how to effectively use Pandas for data wrangling tasks in various scenarios.

A Complete Guide for Pandas Learners

This is a guide how to get started with pandas from zero

A guided project implemented using libraries like numpy, pandas, matplotlib, seaborn and plotly