Jupyter Notebook

AI Object Detection is used to build computer vision-based applications. It helps in face & vehicle detection, pedestrian counting, and security systems. 

Using this 1-click install kandi kit you can build an application that can (a) localize and classify objects, (b) detect objects in a video stream. You can download this pre-trained model and run it on any device. It is fast and very effective at identifying objects in images with high accuracy (99%). It also provides many advanced features like face detection, smile detection, etc. without any extra effort from your side!

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

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.

Image Preparation and Processing

These libraries help in preparing data by annotating and labelling images. Also processes images for running machine learning algorithm. We use opencv library for capturing frames from live streaming videocam.

Machine Learning

There are libraries and model collections that help to create machine learning models for the core recognition use cases in our solution. We will use pytorch to load pre-trained models of Object detection.

Support

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

You can build predictive analytic based applications with this ready to deploy template application. Fully modifiable source code modifies your needs. 

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.

PyCaret is a Python library. It helps build and deploy ML models. The interface helps data scientists and analysts with different tasks. Minimal code relates to machine learning.   

Here are some key functionalities and features of PyCaret:  

• AutoML: It offers a simple and efficient way to perform AutoML. Automating repetitive tasks, such as model selection, tuning, and evaluation, accomplishes this goal.  
• End-to-End Workflow: It supports the entire machine learning workflow. This includes data preprocessing, feature selection, model training, hyperparameter optimization, and model evaluation.  
• Wide Range of Algorithms: PyCaret supports many machine learning algorithms. It makes it easy to experiment with different models. The user can use it to find the best-performing one for a given problem.  
• Model Comparison: It provides tools to compare the performance of many models. By helping users identify the top-performing model, we achieve this.  
• Interactive Visualizations: PyCaret generates various visualizations to understand data distributions. I also used to understand the model performance and feature importance. These aid in the decision-making process.  
• Model Deployment: After model selection and training, PyCaret offers options for model deployment. Making it easier to put your models into production is what we do.  
• Compatibility: It is compatible with popular machine-learning libraries. They are like scikit-learn, XGBoost, LightGBM, and more. Users can leverage these libraries to do it.  
• Ease of Use: PyCaret's simple and intuitive API reduces the need for extensive coding. This makes it accessible to both beginners and experienced data scientists.  

Pycaret is applicable across various types of installations. Small businesses and large corporations obtain it.   

PyCaret offers several ways to use it: 

Small Businesses:  

• Local Environment: Small businesses with limited resources can install PyCaret. You can do it on a local machine or a small server.   

Medium-Sized Enterprises:  

• On-Premises Servers: Medium-sized companies may have more data and computational needs.   

Large Corporations:  

• Cloud Infrastructure: You can install PyCaret on cloud-based virtual machines. Enabling scalable and distributed machine learning across various departments does this.   
• Big Data Ecosystems: PyCaret can integrate with big data processing frameworks. The frameworks are like Apache Spark or Hadoop.   

Multi-Tenant SaaS:  

• PyCaret as a Service: Many users or teams can access PyCaret's capabilities. The web interface simplifies collaboration and model deployment.   

Hybrid Environments:  

• Combining On-Premises and Cloud: This ensures data privacy while utilizing cloud resources. We use those resources for model training and deployment.   

Custom Deployments:  

• Custom Containers: You can containerize PyCaret using Docker or Kubernetes.   

Integration with Existing Systems:  

• API Integration: You can integrate it into existing business apps and systems via API.   

Pycaret automates and simplifies machine learning but lacks remote monitoring and real-time alerts. Instead, PyCaret excels in the following areas:  

• AutoML: PyCaret makes ML tasks easier by automating data preparation and model training. It also handles feature selection, hyperparameter tuning, and evaluation. This accelerates the model development process.  
• Model Selection and Comparison: PyCaret provides tools to compare many machine learning models. It helps select the best-performing one based on various evaluation metrics.  
• Data Preprocessing: It offers a wide range of preprocessing functions. We use those functions for handling missing values, categorical encoding, and feature scaling.   
• Hyperparameter Tuning: PyCaret allows you to fine-tune model hyperparameters. Using techniques like grid search and randomized search accomplishes it.  
• Model Interpretation: You can interpret and explain your models with PyCaret. That is essential for understanding model predictions.  
• Model Deployment: PyCaret can deploy trained models but doesn't handle remote monitoring. Using other frameworks creates web applications. The monitoring and alerting features do it.  

In conclusion, leveraging Pycaret can enhance productivity and efficiency. From tracking employee hours to monitoring inventory, pycaret performs some tasks. Pycaret streamlines data analysis automates tasks, and provides valuable insights. Pycaret saves time and resources. Embracing Pycaret is a smart move for businesses looking to stay competitive. It helps make data-driven decisions that lead to growth and success.   

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

Code

In this solution we are using pycaret library of Python.

Instructions

Follow the steps carefully to get the output easily.

1. Download and Install the PyCharm Community Edition on your computer.
2. Open the terminal and install the required libraries with the following commands.
3. Install pycaret - pip install pycaret.
4. Copy the snippet using the 'copy' button and paste it into terminal.
5. After pasting the first command " pip install numpy" on terminal press enter.
6. After that past the second command " pip install pycaret" on terminal press enter.
7. Wait till installation process to be completed.

I hope you found this useful.

I found this code snippet by searching for 'How to install Pycaret' 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. PyCharm Community Edition 2022.3.1
2. The solution is created in Python 3.11.1 Version
3. pycaret 3.0.2 Version

Using this solution, we can able to install pycaret in Python using pip 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 install pycaret in Python using pip.

Dependent library

You can search for any dependent library on kandi like 'pycaret' and 'pip'

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

FAQ:  

1.What does the PyCaret library include, and which packages does it include? 

PyCaret is an open-source Python library that simplifies the end-to-end machine-learning workflow. PyCaret includes various pre-built functionalities. It is also consisting of packages to streamline these tasks, including but not limited to:  

• Data Preprocessing  
• Feature Engineering  
• Model Selection  
• Hyperparameter Tuning  
• Model Evaluation  
• Model Deployment  

Some of the popular machine learning packages integrated with PyCaret include:  

scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, NLTK, and others.   

2. How do I install PyCaret as a module within Python?   

To install PyCaret as a Python module, you can use pip. First, open a terminal or command prompt. Then, run the command: pip install pycaret. This will download and install PyCaret and its dependencies on your Python environment. After installation, you can import. Also, use PyCaret in your Python scripts or Jupyter notebooks.  

3. What workflows does PyCaret offer to help with low-code machine learning projects?   

PyCaret is a Python library. It helps to simplify building machine learning models by offering different workflows. Some of the key workflows it provides include:  

• Setup: PyCaret's setup function helps automate common data preprocessing tasks.   
• Compare Models: This allows you to compare the performance of many models on your dataset.  
• Create Models: You can create and tune ML models with a single line of code.   
• Test Models: PyCaret provides comprehensive model evaluation tools.   
• Interpret Models: It offers model interpretation capabilities. The capabilities include feature important plots, SHAP values, and confusion matrices. These tools help us understand how models make predictions.  
• Predict Models: PyCaret makes it easy to generate predictions on new data.   
• Deploy Models: PyCaret also supports deployment with a few lines of code.   
• Automate Workflows: PyCaret allows you to automate the end-to-end machine learning workflow.  

4. Is it possible to use Docker to run PyCaret for machine learning models?  

It is possible to use Docker to run PyCaret for machine learning models. Docker allows you to create containerized environments. That can encapsulate all the dependencies and configurations. It would help to have these for your machine learning project, including PyCaret. This helps ensure consistency and reproducibility across different environments.  

Meta releases new details on GenAI Infrastructure