A product based recommendation system which uses Machine learning algorithm such as KNN and cosine similarity

A product based recommendation system which uses Machine learning algorithm such as KNN and cosine similarity and also uses MongoDB as a database which stores the user data for a semi-collaborative filtering. Accuracy : Calculated accuracy using nDCG. Some randomly selected product efficiency: Batman killer croc takedown figures: nDCG=0.917 Star Wars Movie Heroes Yoda: nDCG=0.942 Harry Potter Hogwarts Bookmarks: nDCG=0.9406 Technology Used in this project: Pandas Numpy Sklearn MongoDB as Databases Streamlit for UI Demo: Home UI: Database structure: Result […]

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Az Residential Real Estate Analysis

-Decided on libraries to import. Includes pandas, json, requests, plotly express, hvplot, geopandas, numpy and url. -Read in a nested json and cvs files of residential properties for sale in PHX and surrounding areas, and sorted through the atrributes to get our dataframes in a clean format. -Using urlopen, we read other json files into a sperate dataframe to plot and compare with our other data. -Analyzed and plotted the data using plotly, hvplot, and geopandas. -Plots included a map […]

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PIRDS – Prescription Image Recognition and Digitalizing System is a OCR make with Tensorflow

PIRDS – Prescription Image Recognition and Digitalizing System is a OCR make with Tensorflow that digitalises images of Prescription of Handwritten Texts by Doctors. Abstract PIRDS does the Digital transformation of hand-written prescription text using advance image processing techniques and deep learning methods. Image processing techniques helps to create images which are less noisy, and easily understandable for neural networks. Once image with required configuration are obtained, they are fed to neural network model for training. The neural network model […]

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Udacity api reporting pipeline

In this exercise, you’ll use portions of each of the skills you’ve learned in this lesson. First, you’ll configure a new API. This API will have a new app configuration file that you can call appfinal.py. Second, you’ll write a new endpoint for the API. This new endpoint will read a pickle file for a deployed model, and it will also read a dataset. It will use the deployed pickle file to make predictions for the dataset, and it will […]

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SQL Injection Vulnerability on PhpIPAM v1.4.4

PhpIPAM v1.4.4 allows an authenticated admin user to inject SQL sentences in the “subnet” parameter while searching a subnet via app/admin/routing/edit-bgp-mapping-search.php. Build git clone https://github.com/dnr6419/CVE-2022-23046.git cd CVE-2022-23046 && docker-compose up -d pip3(or pip) install -r requirements.txt python3(or python) CVE-2022-23046.py -h Setup2-1. Go to the http://[YOUR_IP] and Choose [New phpipam installation]. 2-2. Choose [Automatic database installation]. 2-3. MySQL username & Password is “root”/”my_secret_mysql_root_pass”. 2-4. Setting the Password and Login to check the installation is complete.

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Providing Multiple Constructors in Your Python Classes

Sometimes you need to write a Python class that provides multiple ways to construct objects. In other words, you want a class that implements multiple constructors. This kind of class comes in handy when you need to create instances using different types or numbers of arguments. Having the tools to provide multiple constructors will help you write flexible classes that can adapt to changing needs. In Python, there are several techniques and tools that you can use to construct classes, […]

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Auto Rollnumber sender with python

The above code fits better on my system but it can vary system to system. To set up this code perfectly in your system,Perform the following steps: Take the Screenshot of the google meet after opening the comment section/message section as shown below – Take this screenshot to `Paint` and note the coordinates of `writting box` and `sending button`– Now change the corrdinates in the code by your coordinate values and save the changes– Run the code Keep your meet […]

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1D Burgers equation simulated by PINN

This is an implementation of PINN(s) on TensorFlow 2 to solve Burgers equation (1D Navier-Stokes eq. with no pressure gradient / external force) under Dirichlet boundary condition w/o training data (data to fit initial & boundary conditions need to be provided). This is keras-utilized version unlike other two of my repos (PINN_wave / PINN_von_Karman). Usage Simply type python main.py to run the entire code. Basic parameters (e.g., network architecture, batch size, initializer, etc.) are found in params.py and could be […]

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WANDB Predictions With Python

CI/CD Below we capture the CI/CD scenarios that we would expect with our model endpoints. In the automated build scenario, we capture any changes in the source code for the model server, build the new resultant docker image, push the image to the container registry, and then deploy via cloud run. This captures the CI component. Automated builds based on changes in the master branch In the scheduled build scenario, to ensure that we pull the latest model from wandb […]

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