Project submission by Team Byte Me for #BuildwithAI 2021. MANASTHAIRYA, a Sanskrit word for ‘Strength of Mind’, is a mental health monitor for students. This monitor has the following modules: ● MOOD TRACKER AFTER CLASSES ● ADDICTION ANALYSIS ● QUESTIONNAIRE ● SCORE CHECKER Download this kit on GitHub and run the four files present in the repository to get output for each function.

GitHub Repository

Use this kandi 1-Click Solution kit to build your own AI-based Breast Cancer Detection Engine in minutes.

✅ Using this application you can do early stage detection for breast cancer and help in identifying it as malignant(cancerous) or benign(non-cancerous).

✅ You can build predictive analytic based applications with this ready to deploy template application.

✅ Fully modifiable source code is provided to enable you to modify for your requirements.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Simple and efficient tools for predictive data analysis.

Scikit-learn is a free software machine learning library which features various classification, regression and clustering algorithms including support-vector machines,etc. Similar libraries for ML support in Java, Scala and R programming language

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

Use this kandi 1-Click Solution kit to build your own AI-based Breast Cancer Detection Engine in minutes. ✅ Using this application you can do early stage detection for breast cancer and help in identifying it as malignant(cancerous) or benign(non-cancerous). ✅ You can build predictive analytic based applications with this ready to deploy template application. ✅ Fully modifiable source code is provided to enable you to modify for your requirements. Click on the button below to download the solution and follow the deployment instructions to begin set-up. This 1-click kit has all the required dependencies and resources you may need to build your own Breast Cancer Predictive Analysis App. ⬇️ Download 1-Click Installer

Training and Certification - Breast Cancer Prediction

Watch this self-guided tutorial on how you can use Dataset to train the model, Exploratory Data Analysis, and Vector Classification to build your own AI Powered Breast Cancer Detection Engine. Completed the training? Apply for your Participation Certificate and Achievement Certificate now! Apply for Certification Tag us on social media with a screenshot or video of your working application for a chance to be featured as an Open Source Champion and get a verified badge.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Machine Learning

Simple and efficient tools for predictive data analysis. Scikit-learn is a free software machine learning library which features various classification, regression and clustering algorithms including support-vector machines,etc. Similar libraries for ML support in Java, Scala and R programming language

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

A weight converter in Python is a program that allows users to convert between different units of weight or mass. This can be useful in a variety of scenarios, such as when dealing with different measurement systems or when working with data that uses different units.

In Python, a weight converter can be implemented using a simple script that takes user input in one unit of weight and converts it to another unit using conversion factors. The program may offer options for converting between units such as kilograms & pounds, etc.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Kit Solution Source

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

John Venn found the Venn diagram in the year 1880. A Venn diagram is a graphical model of data or a logical representation. It illustrates two or three groups' relationships. We can use it to highlight similarities and differences. It helps compare the characteristics of various data sets. Circles represent each data set in the Venn diagram. 

Creating a simple Venn diagram

To create a Venn diagram, the following libraries are important Matplotlib and Matplotlib-Venn. It is a Python library for creating 2D graphs and plots using Python scripts. It has a module called pyplot that makes things easier for plotting. It will provide a feature to control line styles, formatting axes, and font properties. There are three types of Venn diagrams, two-circle, three-circle, and four-circle. In Python, to plot a two-circle Venn diagram, there are many ways to implement it. The venn2() function is the simplest way to supply two sets of values. Then it will plot the Venn diagrams for us. Customize Venn diagrams with the default colors of red and green circles. But the great thing is that we can customize the colors. 

Types of Venn diagram

There are three different types of Venn diagrams are there, 

1.Two-circle diagram(venn2). 

2.Three-circle diagram(venn3). 

3.Four-circle diagram(venn4). 

Generally, most charts we generate in data visualization belong to one or more types. The types can be bar charts, pie charts, line charts, and time series. But a Venn diagram is not used. It is another type of data visualization that needs to be more underrated. It allows us to examine the relationship between two different data sets. 

Advantages: 

• A great thing for visual learners. 
• A swift way to illustrate a complicated issue. 
• Required high-order thinking. 

Disadvantages: 

• Difficult to implement new ideas. 
• Requires a knowledge base. 
• May limit responses.

What is a Venn diagram? 

A set or a logical diagram shows operations, like the intersection and the union of sets. Then a Venn diagram is the difference between sets and is also used to depict subsets of a set. Example: A set of natural numbers is s subset of whole numbers, a subset of integers. 

To create a Venn diagram, we need two important libraries. The libraries can be Matplolib and Matplotlib-venn. There are three types of Venn diagrams, two-circle, three-circle, and four-circle. In Python, to plot a two-circle Venn diagram, there are two ways to do it. The simplest way is to supply two sets of values to the venn2() function, and then it will plot the Venn diagram for us. 

Matplotlib

• It is a Python library to create 2D graphs and plots using Python scripts.
• It has a module named pyplot that makes things easier for plotting.
• It will provide a feature to control line styles, formatting axes, font properties, etc. 

Matplotlib-venn

• We can use the functions for plotting area-proportional two and three-way Venn diagram.
• This package provides four main functions such as venn2, venn2_circles, venn3, and venn3_circles.
• venn2 and venn2_circles functions accept a 3-element list. The elements can be (Ab, aB, AB) of subset sizes as their required argument.

Here is an example of creating a Venn diagram using Matplotlib in Python:

Fig: Preview of the output that you will get on running this code from your IDE.

Code

In this solution we're using Matplotlib and Matplotlib-venn libraries.

Instructions

Follow the steps carefully to get the output easily.

1. Install PyCharm Community Edition on your computer.
2. Open terminal and install the required libraries with following commands.
3. Install Matplotlib - pip install matplotlib.
4. Install Matplotlib-venn - pip install matplotlib-venn.
5. Create a new Python file(eg: test.py).
6. Copy the snippet using the 'copy' button and paste it into that file.
7. Run the file using run button.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for 'venn diagram using matplotlib' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. The solution is created in PyCharm 2022.3.3.
2. The solution is tested on Python 3.9.7.
3. Matplotlib version 3.7.1.
4. Matplotlib-venn version 0.11.9.

Using this solution, we are able to create venn diagram using Matplotlib in Python with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to create venn diagram using Matplotlib in Python.

Dependent Libraries

FAQ:  

1. How can I customize Venn Diagrams in Python? 

By default, red and green are the colors of the circles, but the great thing is that we can customize the colors as we wish. 

2. What matplotlib import pyplot command can we use when creating a Venn diagram? 

Matplotlib is a Python library to create 2D graphs and plots using Python scripts. It has a module named pyplot that makes things easier for plotting. 

3. Can you provide an example plot of a 3-circle Venn diagram created with Python? 

Begin with entering the number of items in common to all three data sets. Then, enter the remaining items in each pair of data sets' overlapping regions. And finally, use any known totals to find missing numbers. 

4. What are some ways to compare three groups of data using a Matplotlib-Venn diagram? 

The typical method for comparing 3 or more group means is the One-way analysis of variance. Its usual goal is to determine if at least one group's mean or median differs. Another way to compare is follow-up comparisons. It helps determine if we can encounter the difference. 

5. How do I create a Venn diagram from a given list in Python? 

The best way is to supply two given sets of values to the venn2() function, and then it will plot the Venn diagram for us. 

You can also search for any dependent libraries on kandi like 'Matplotlib' and 'Matplotlib-venn'.

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

The function scipy.linalg.eig used to take a complex or a real matrix M whose eigenvalues and eigenvectors are to be evaluated. It is used to find the eigenvalues and the right or left eigenvectors of the matrix. It returns the scalar set of eigenvalues for the matrix. 

Syntax: scipy.linalg.eig(a , b , left , right , overwrite_a , overwrite_b , check_finite , homogeneous_eigvals) 

Please check the below code to know how to Compute the eigenvalues and eigenvectors of a matrix using SciPy's linalg.eig function. 

Fig: Preview of the output that you will get on running this code from your IDE

Code

In this solution we're using numpy and scipy.linalg Library

Instructions

Follow the steps carefully to get the output easily.

1. Install numpy,Scipy.linalg on your IDE , using the following command(Any of your favorite IDE).
2. To install numpy use ' pip install numpy '
3. To install Scipy.linalg use ' python -m pip install scipy '
4. Copy the snippet using the 'copy' and paste it in your IDE.
5. Run the file to generate the output.

NOTE: Do not copy the output given in the code snippet

I hope you found this useful. I have added the link to dependent library, version information in the following sections.

I found this code snippet by searching for ' eigen value eigen vector computations' in kandi. You can try any such use case!

Environment tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. The solution is created in Pycharm 2022.3.3 (Community Edition)
2. The solution is tested on Python 3.11.1.

Using this solution, we are able to know how to Compute the eigenvalues and eigenvectors of a matrix using SciPy's linalg.eig function with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to know how to Compute the eigenvalues and eigenvectors of a matrix using SciPy's linalg.eig function

Dependent Libraries

You can also search for any dependent libraries on kandi like 'numpy' and 'scipy.linalg'

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

Nirodhah

(Transform Stress Into Serenity)

PROJECT THEME:

To celebrate India’s G20 Presidency, the theme of the project is based upon the third sustainable development goal which is GOOD HEALTH AND WELL BEING. निरोधः is designed to help users identify and manage the factors that contribute to their stress levels, with the ultimate goal of achieving a state of calm and mental stillness.

PROJECT DESCRIPTION:

The project leverages machine learning algorithms to analyze user data and generate accurate stress level predictions. It predicts the stress levels of the user and provides actionable insights The project has features for tracking progress and setting goals. The ML model continuously learns and adapts to the user's data, making the stress level predictions more accurate over time.

DATASET USED:

The dataset used in building the model is "Wellbeing and Lifestyle" dataset available on Kaggle.com

TARGET VARIABLE:

DAILY_STRESS is the target variable. It contains 6 classes from range 0 to 5 indicating the level and severity of stress of the user.

DATA VISUALISATIONS::

ACCURACY OF THE MODEL:

92.76%

MODEL DEPLOYED:

Using Flask

UI DESIGNING:

To showcase our idea ,we have designed UI of this website

TEAM DETAILS:

Team Name: Ode to Code

Team Members:

1. Advika Thakur- 1st Year, B.Tech AI-ML, IGDTUW
2. Ayushi Dubey- 1st Year, B.Tech AI-ML, IGDTUW

I have developed my project using regression, NumPy, pandas, and basic ML concepts. I had completed my internships in data science and the ML domain a long months ago.

Topics/Domain

Libraries

BOOK RECOMMENDAR SYSTEM USING COLLABORATIVE FILTERING

simple front end model below:

Datasets :https://www.kaggle.com/datasets/arashnic/book-recommendation-dataset

A book recommendation system is a type of recommender system that suggests books to users based on their preferences, interests, and past reading history. The system uses algorithms and data analysis techniques to determine which books a user is likely to enjoy based on a variety of factors, such as the user's rating of previous books, publishers and other user behavior patterns.

This book recommendation systems build on collaborative filtering approach, while collaborative filtering systems rely on user data to identify books that similar users have enjoyed.

Book recommendation systems are often used by online retailers, such as Amazon and Barnes & Noble, to suggest books to customers. They can also be integrated into library systems and other book-related services to help users discover new titles and authors.

By using these systems, users can discover new books and authors they might not have otherwise found and continue to explore the vast world of literature.

In this solution we have tried to detect fake news from a set news feeds. The feeds, in the form of csv files contains the headers as news_text and label. In myproj-fakenews-test.ipynb file we have tested the news feed from myproj-fakenews-test.csv data file and myproj-fakenews-train.csv train file. We have done it details in myproj-fakenews-analysis.ipynb project. Again we collected data from myproj-Articles_scraper.ipynb project. Added that data to the myproj-fakenews-train.csv file. again tested the data through myproj-fakenews-test.ipynb.

Group Name 1

In this solution we have tried to detect fake news from a set news feeds. The feeds, in the form of csv files contains the headers as news_text and label. In myproj-fakenews-test.ipynb file we have tested the news feed from myproj-fakenews-test.csv data file and myproj-fakenews-train.csv train file. We have done it details in myproj-fakenews-analysis.ipynb project. Again we collected data from myproj-Articles_scraper.ipynb project. Added that data to the myproj-fakenews-train.csv file. again tested the data through myproj-fakenews-test.ipynb.

Group Name 2

We had made a mobile app named SoWhat. The app name indicates a question "Sow What?" which is prevalent among farmers regarding what to sow on a particular land.  This app takes input of Nitrogen content, Phosphorous content, Potassium content, pH value of soil and Humidity, Rainfall, and average Temperature. After getting input of all values from user, it predicts the best crop that can be grown in that particular area with maximum profit. It aware the farmers by providing some additional knowledge regarding some new sustainable methods that they can practice on their land to promote sustainability. In addition to prediction this app will suggest some new sustainable agriculture practices to farmers. Our team had chosen a problem statement related to Crop Guidance and Farmers Friend - (Bio-Tech). We had majorly utilized Machine Learning to make predictions and used Flutter ,Tenserflow and Tenserflow lite to integrate our ML model into a mobile app. This app aims to ease the decision making of farmers. GitHub link for the following project is attached below : https://github.com/Debasmita-mondal/SoWhat-Kya-Boyein-

Category 1

Simply run the kit by adding some rows with the news article of your choice to the 'fakenewstest.csv' file to showcase the model prediction

Category 2

Test the model with the different categories of Fake News in different domains like climate, politics, entertainment, healthcare, and education using kaggle etc

Category 3

Create a web scrapper application that has a labeled training dataset (similar to 'fakenews.csv') file. Use Factly to validate and use similar platforms to prepare the input and check models response accordingly

Category 4

Create a Dashboard which classifies the news article as Fake or Real along with stats on with the model's probablity score of how well it determines a certain news into a fake or real class

Category 5

Showcase a use case that can work with fake news post on Whatsapp, Twitter & Facebook. Making a chrome extension for the same

Game recommender system

Ever thought how apps like steam, epic game launcher, play store and x-box recommends games based on games you have played recently . Me and my team build a small project that use similar way to recommend you some games

we have used python and its library such as panda and NumPy to build a small project that recommend you games based on your recent activities.

# we have collected some csv of about 16600 games with their ranks, rating, genre, producers, and ids from websites like IGN and IGDB.

# we checked the recent games that you have been playing

# according to that games we recommend you games that have similar rating and genre.

1.The Passive-Aggressive algorithms are a family of Machine learning algorithms that are not very well known by beginners and even intermediate Machine Learning enthusiasts. However, they can be very useful and efficient for certain applications. 2.How Passive-Aggressive Algorithms Work: Passive-Aggressive algorithms are called so because : Passive: If the prediction is correct, keep the model and do not make any changes. i.e., the data in the example is not enough to cause any changes in the model. Aggressive: If the prediction is incorrect, make changes to the model. i.e., some change to the model may correct it. 3.The model I've chosen to use is the Passive-Aggressive (PA) Classifier (see original paper here). In essence, the PA classifier is an algorithm that only updates its weights ("aggressive" action) when it encounters examples for which its predictions are wrong, but otherwise remains unchanged ("passive" action). 4.The PA classifier is an online algorithm, meaning it uses one example at a time to update its weights and moves on, never seeing the same example again. This is in contrast to a batch algorithm, which would use the same set of multiple examples and updates weights in each iteration of training. Because of this, the PA classifier is particularly useful when dealing with a dataset containing a large or rapidly increasing number of examples, like news articles or Tweets! Of course, the data I'm using in this notebook are toy static data, but you can imagine its advantages in real-life applications. Other Kagglers, like Ayushi Mishra have shown that the PA classifier outperforms several other types of models as well, so I can be confident that it is a good choice. If you'd like to learn more about the mathematics behind the PA classifier algorithm, check out this video by Dr. Victor Lavrenko that explains the steps in very clear steps!