Tauqeer Ahmad Qureshi - Software Engineer

23/01/2025

The Localization Without GPS project aims to provide an alternative method for determining location in environments where GPS signals are weak or unavailable, such as indoors, underground, or in urban areas with tall buildings. This system uses image-based localization combined with Self-Organizing Maps (SOM) for clustering visual data to estimate the user’s position, without relying on traditional GPS methods. The project leverages Convolutional Neural Networks (CNN) for feature extraction from images and SOM for clustering similar visual patterns to determine the location.

Localization for checking your location without using gps, at night it is difficult to use gps that why developed localization project - tauqeer006/Localization_project

23/01/2025

The Mask Detection project leverages Convolutional Neural Networks (CNNs) to automatically identify whether individuals in images or videos are wearing masks. The primary goal is to promote health and safety in public spaces, ensuring compliance with mask mandates, particularly during the COVID-19 pandemic or other health crises. By employing CNNs for image classification, this system provides a real-time, efficient solution for monitoring and enforcing mask-wearing in various environments such as airports, offices, or public transportation.

This link will take you to a page that’s not on LinkedIn

23/01/2025

The Hand Gesture Disease Detection project combines advanced image processing and numerical health data to diagnose hand diseases, such as arthritis, Parkinson’s tremors, or carpal tunnel syndrome, based on hand gestures and additional patient health data. The system uses a Convolutional Neural Network (CNN) to process and analyze images of hand gestures, identifying patterns indicative of potential diseases. A Feedforward Neural Network (FNN) processes the patient's numerical health data (such as age, medical history, and pain levels) to enhance the prediction. By combining these two data sources, the system creates a multimodal model that offers a more accurate and comprehensive diagnosis.

Contribute to tauqeer006/Hand-Gesture-to-detect-hand-disease development by creating an account on GitHub.

19/10/2024

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07/08/2024

🚀 Exploring Image Recognition with PCA on the MNIST Dataset! 🧑‍💻📊
I’m excited to share that I've been diving into the fascinating world of image recognition using the MNIST dataset. Recently, I've been leveraging Principal Component Analysis (PCA) with scikit-learn to uncover the underlying patterns and features within this iconic dataset.
🔍 Key Highlights:
Dataset: MNIST, a benchmark for handwritten digit recognition.
Technique: PCA for dimensionality reduction, enhancing computational efficiency and model performance.
Tool: scikit-learn, renowned for its robust and user-friendly machine learning capabilities.
The link for code is given below , checkout for amazing visualization from 784d to 2d and 3d
https://lnkd.in/g97ikcib
Github : https://lnkd.in/g-jQdpx5

29/07/2024

Small Model training of data containing rich people in which prediction has been done on the basis of country name, total worth in billion and model will guess the Company name by using DecisionTreeClassifier. May be not a good approach but doing just for doing my practice.

For further details regarding model training , data cleaning free to contact.

17/07/2024

I successfully tackled a challenging task involving binning and discrimination techniques, demonstrating their powerful applications in data preprocessing and model development.
🔍 Task Overview: I applied advanced binning methods to preprocess data, enhancing feature relevance and model performance. Additionally, discrimination techniques were leveraged to identify key patterns and insights crucial for decision-making.
📊 Key Achievements:
Implemented robust binning strategies to effectively manage data granularity and improve feature interpretability.
Utilized discrimination techniques to uncover actionable insights, paving the way for precise model predictions and strategic business decisions.
🎯 Impact:
Achieved significant improvements in model accuracy and efficiency, translating complex data into actionable intelligence.
Empowered stakeholders with clear, data-driven recommendations, driving innovation and operational excellence.
To check complete processing click on link given below
https://lnkd.in/dbudrSpH

15/07/2024

Data based on the result on Whether the person will survive or not
based on their ages, their stops and how many persons travelling with them.
Project based on Machine Learning model (Decision tree)
1) Data cleaning
2) converting categorical data into numerical
3) Scaling the data by using minmaxScaler
4) Train the data using DecisionTreeClassifier

https://colab.research.google.com/drive/1-QSr6h0oyjo7cfKFhs7UsXkCUZkCewJ5 =YypWevggG-Bo

15/07/2024

Hello everyone! 👋 We're thrilled to introduce Tauqeer Ahmad Qureshi, our talented Software Engineer who brings expertise in Python development and machine learning to our team. Tauqeer is passionate about leveraging technology to solve complex challenges and drive innovation. Join us in welcoming Tauqeer aboard as we embark on exciting projects together! 🚀

15/07/2024

I scrape all the data from website using beautifulSoup and request in python and stored it into CSV file and after that, i have done data cleaning of it removing all the null values and find the alternative of duplicate values