I have developed a trading app using Flutter Flow, incorporating Supa base as the database and Python for backend connectivity. within this setup, I have created a Python script to seamlessly populate data in Supa base by interfacing with the Stock developer API. The data, once populated, is then displayed within the app.
I have independently created a website using Django, Python, and Bootstrap that encompasses both backend and frontend development. This website is a dedicated project within our company and is primarily designed for trading purposes. Users can easily access, delete, manage, and review their accounts, making it a versatile tool for maintaining multiple accounts seamlessly.
I have developed a project that automates the login process for two websites. This project stores login credentials in private INI files and allows the websites to automatically open and log in without manual intervention. This automation significantly reduces daily login times and streamlines work processes by leveraging Selenium.
I have successfully completed a solo project where I implemented automated Telegram messaging using Python. This project involves fetching trading information from a website through an API, parsing the relevant data, and then sending automated messages to a specific Telegram group. To achieve this, I created a custom Telegram bot that facilitates the seamless distribution of the parsed information.
In our project, we conducted testing on various deep learning imaging algorithms, including VGG16, AlexNet, CNN, SegNet, and ResNet. We utilized MRI images obtained from the Kaggle site to evaluate the algorithms' effectiveness in detecting various brain diseases such as Alzheimer's, cerebrovascular issues, neoplastic conditions, degenerative diseases, and inflammatory disorders. By assessing the accuracy scores of each algorithm, we were able to draw conclusions about which algorithms provided the most accurate results in image classification for the specific set of brain diseases under consideration.
Created a web page for predicting failure of aircraft engine before take-off, with the help of machine learning we can able to make this prediction model with the trained data set of previous engine records.
Developed a prediction algorithm for calculating the demand period in advance, so that we can be ready or stock up the product in prior time period with this help of algorithm.