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About Me
About Me
I’m a self-taught Machine Learning Engineer with a natural curiosity for problem-solving and a dedication to creating meaningful, data-driven solutions. My journey into data science started with a fascination for how technology can reshape industries, and it quickly grew into a hands-on exploration of machine learning algorithms, predictive modeling, and deep learning.
Over the past few Month's, I’ve built a solid foundation in Python, TensorFlow, Scikit-learn, and Flask, among other essential tools, and have tackled projects ranging from car resale value prediction to celebrity image matching and book recommendations. I’m passionate about understanding data, building intelligent systems, and continuously learning the latest in AI to stay ahead in this dynamic field.
When I’m not coding or analyzing data, you’ll find me engaging with the data science community, exploring new trends in AI, or refining my skills on challenging new projects. I'm driven by the belief that AI and machine learning have the power to drive transformative change, and I’m excited to be part of that journey.
Projects Section
Projects Section
What it does:
A smart recommendation system that suggests books tailored to your taste using AI-powered collaborative filtering.
Highlights:
📖 Real-time recommendations based on user preferences.
🌟 Intuitive interface built with HTML and Bootstrap.
🧠 Powered by Python and Flask for lightning-fast insights
What it does:
An advanced machine learning system that identifies vehicles like Cars, Trains, Planes, and more using image processing.
Highlights:
🛠️ Feature extraction with Wavelet Transform.
🌐 Accurate classification using Random Forest algorithms.
📊 Perfect for smart traffic systems or AI exploration.
What it does:
CelebMatchAI is an advanced machine learning project that identifies and matches celebrity images using a custom-built Convolutional Neural Network (CNN) for exceptional accuracy.
Highlights:
🧠 Powerful Model: Trained on a dataset featuring 100 celebrity classes with cutting-edge techniques like data augmentation, Batch Normalization, and Global Average Pooling.
🔍 Feature Extraction: Performs similarity searches using cosine similarity to find visually similar celebrities.
🎨 Interactive Visualizations: Displays predictions and matches in an intuitive, user-friendly format.
🎉 Applications: Ideal for entertainment, personalized experiences, and AI exploration.
🚘💰 AutoValuate
🚘💰 AutoValuate
Intelligent Car Price Prediction Made Simple
Intelligent Car Price Prediction Made Simple
What it does:
AutoValuate is a machine learning-based system that predicts car prices using features like model, manufacturer, year, mileage, and fuel type, providing accurate market price estimates.
Highlights:
🧹 Data Cleaning: Ensures high-quality, reliable datasets.
📈 Predictive Power: Achieved an R² score of 89% using a robust Linear Regression model.
📊 Insights Made Visual: Uses Seaborn for interactive data visualization and trend analysis.
🛠️ Tech Stack: Built with Pandas, Scikit-Learn, and Python for seamless functionality.
Blog Section
Blog Section
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